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Supplemental Material - The Impact of Postoperative Intensive Care Unit Admission on Postoperative Hospital Length of Stay – A Pre-Specified Propensity Matched Cohort Study.Tharusan Thevathasan cand. med.*, Dr. Curtis C. Copeland M.D.*, Dr. Dustin Long M.D.*, Maria D. Patrocínio M.D., Sabine Friedrich cand. med., Stephanie D. Grabitz cand. med., Dr. George Kasotakis M.D. M.P.H., Dr. John Benjamin M.D., Dr. Karim Ladha M.D. M.Sc., Dr. Todd Sarge M.D., Prof. Matthias Eikermann M.D. Ph.D.* The authors made equal contributions to the manuscript. The Supplemental Material contains 3 text paragraphs, 7 tables and 3 figures:eText 1. Intensive care model and ICU bed allocation process at the Massachusetts General HospitaleText 2. Methods: Study population and sensitivity analyseseText 3. Results: Sensitivity analyseseTable 1 (detailed). Characteristics of propensity score-matched study population by postoperative ICU or ward admission.eTable 2. Characteristics of unmatched study population by postoperative ICU or ward admission.eTable 3. Characteristics of propensity score-matched study population by tertile of propensity score.eTable 4. Subgroup Analyses: Association between postoperative ICU vs. ward admission and postoperative hospital length of stay (primary outcome) in patient sub-cohorts.eTable 5. Sensitivity Analyses: Association between postoperative ICU vs. ward admission and postoperative hospital length of stay (primary outcome).eTable 6. Summary description of surgical ICUs at Massachusetts General HospitaleTable 7. Predictors for calculating the propensity score of postoperative ICU or ward admissioneFigure 1. Incidence rate ratio for postoperative length of stay stratified by propensity score (including mean, SD) in the propensity-matched cohorteFigure 2. Incidence rate ratio for postoperative length of stay stratified by propensity score (including mean, SD) in the unmatched study cohorteFigure 3. Propensity score (A), formula for calculation of the propensity score (B), receiver operating characteristic (ROC) curve (C) and reliability plot (D) for postoperative ICU admissionReferenceseText 1. Intensive care model and ICU bed allocation process at the Massachusetts General HospitalAt the Massachusetts General Hospital, enough ICU beds are available to ensure that a surgical procedure almost never needs to be canceled because of a lack in ICU bed availability. Very rarely, a patient needs to be “boarded” in the PACU until an ICU bed becomes available later that day. Both surgeon and anesthesiologist can preoperatively request an ICU bed for their surgical patient scheduled for elective surgery the next day. On the day of surgery, the OR manager (“floor walker”) processes the ICU bed requests at the “ICU-bed meeting”, which takes place in the morning; participants are the OR floor-manager, the ICU nursing managers and the ICU bed “charge” nurse, as well as the ICU fellow. The purpose of that meeting is to make sure enough ICU beds become available to cover the OR requests on file, and to create a strategy for opening up additional beds as needed during the rest of the day. The ICU teams may then prioritize discharge decisions in the morning, typically to the locations home, rehabilitation facility, or to a surgical/medical floor. Operating room physicians responsible for a case (both surgeon and anesthesiologist) can modify the patients’ initial OR discharge plan: The OR team may decide not to utilize an ICU bed if the patient does not need one (for instance if a patient scheduled for extended cancer surgery turns out to be inoperable). In turn, they may also decide to request an ICU bed later on during the surgical procedure if the team believes this would be in the patients’ best interest, for instance, based on a patient’s complicated OR course. Those late requests are communicated with the OR floor manager who will process the request. eText 2. MethodsStudy population: Diagnosis and surgical procedure codesTo investigate the effects of decision making by health care providers on cases that might be admitted postoperatively to either level of care, we excluded patients with clear indications for postoperative ICU care, i.e., need for ongoing mechanical ventilation, cardiac or major vascular surgery, lung or liver transplantation. Diagnoses and surgical procedures are coded based on the International Statistical Classification of Diseases and Related Health Problems, ninth revision (ICD-9), Volume 1 and 3. Diagnosis or surgical proceduresICD-9 codeCardiac surgeryVolume 3: 35-37Complete pneumonectomyVolume 3: 32.5Diagnosis or repair of thoracic or abdominal aortic aneurysmVolume 1: 441.1-441.9Volume 3: 38.34, 38.44, 38.64 Sensitivity AnalysesIn our primary analysis, we aimed to account for confounding in the association of postoperative ICU vs. ward admission and postoperative length of stay (PLOS) due to a variety of patient-, procedure- and healthcare-related factors by performing propensity matching for these factors and performing negative binomial regression analyses in the propensity-matched study cohort. To account for residual confounding, we performed a variety of sensitivity analyses, pre-specified as well as suggested by peer-reviewers. Patient comorbidity statusWe adjusted the analysis by matching for the patient’s Charlson Comorbidity Index (CCI), an index for the patient’s chronic underlying level of comorbidities (including chronic obstructive pulmonary disease [COPD]), as well as the American Society of Anesthesiologists (ASA) physical status, as a surrogate for the patient’s acute physical status prior to the surgery. Sensitivity analyses were performed in subgroups of patients with low (CCI <3) and high (CCI ≥3) baseline comorbidity status, low (<3) and high ASA scores (≥3), as well as with non-severe (BMI <35) and severe obesity (BMI ≥35).Further, we adjusted the propensity score model for the score for preoperative prediction of obstructive sleep apnea (SPOSA)ADDIN CSL_CITATION { "citationItems" : [ { "id" : "ITEM-1", "itemData" : { "DOI" : "10.1186/s12871-017-0361-z", "ISSN" : "1471-2253", "PMID" : "28558716", "abstract" : "BACKGROUND Postoperative respiratory complications (PRCs) are associated with significant morbidity, mortality, and hospital costs. Obstructive sleep apnea (OSA), often undiagnosed in the surgical population, may be a contributing factor. Thus, we aimed to develop and validate a score for preoperative prediction of OSA (SPOSA) based on data available in electronic medical records preoperatively. METHODS OSA was defined as the occurrence of an OSA diagnostic code preceded by a polysomnography procedure. A priori defined variables were analyzed by multivariable logistic regression analysis to develop our score. Score validity was assessed by investigating the score's ability to predict non-invasive ventilation. We then assessed the effect of high OSA risk, as defined by SPOSA, on PRCs within seven postoperative days and in-hospital mortality. RESULTS A total of 108,781 surgical patients at Partners HealthCare hospitals (2007-2014) were studied. Predictors of OSA included BMI >25\u00a0kg*m(-2) and comorbidities, including pulmonary hypertension, hypertension, and diabetes. The score yielded an area under the curve of 0.82. Non-invasive ventilation was significantly associated with high OSA risk (OR 1.44, 95% CI 1.22-1.69). Using a dichotomized endpoint, 26,968 (24.8%) patients were identified as high risk for OSA and 7.9% of these patients experienced PRCs. OSA risk was significantly associated with PRCs (OR 1.30, 95% CI 1.19-1.43). CONCLUSION SPOSA identifies patients at high risk for OSA using electronic medical record-derived data. High risk of OSA is associated with the occurrence of PRCs.", "author" : [ { "dropping-particle" : "", "family" : "Shin", "given" : "Christina H", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Grabitz", "given" : "Stephanie D", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Timm", "given" : "Fanny P", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Mueller", "given" : "Noomi", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Chhangani", "given" : "Khushi", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Ladha", "given" : "Karim", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Devine", "given" : "Scott", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Kurth", "given" : "Tobias", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Eikermann", "given" : "Matthias", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" } ], "container-title" : "BMC anesthesiology", "id" : "ITEM-1", "issue" : "1", "issued" : { "date-parts" : [ [ "2017", "5", "30" ] ] }, "page" : "71", "title" : "Development and validation of a Score for Preoperative Prediction of Obstructive Sleep Apnea (SPOSA) and its perioperative outcomes.", "type" : "article-journal", "volume" : "17" }, "uris" : [ "" ] } ], "mendeley" : { "formattedCitation" : "¹", "plainTextFormattedCitation" : "1", "previouslyFormattedCitation" : "¹" }, "properties" : { }, "schema" : "" }1. In order to account for specific severe comorbidities, we identified indicators of severe renal, cardiac and respiratory comorbidities (i.e., baseline need for dialysis [ICD-9: V45.11], implanted cardiac defibrillator [ICD-9: V45.00, V45.01, V45.02, V45.09, V53.31, V53.39], acute myocardial infarction [ICD-9: 410.X, within one month prior to surgery] or oxygen home requirement [ICD-9: V46.1, V46.2], and added these separately as confounders to the negative binomial regression model in the propensity-matched cohort. Procedure-related factorsTo account for different surgical procedures, we matched for the principal surgical procedures, the procedural risk (i.e., the “Sessler” score, a measure of procedural risk quantificationADDIN CSL_CITATION { "citationItems" : [ { "id" : "ITEM-1", "itemData" : { "DOI" : "10.1097/ALN.0b013e318219d5f9", "ISBN" : "1528-1175 (Electronic)\r0003-3022 (Linking)", "PMID" : "21519230", "abstract" : "BACKGROUND: Optimal risk adjustment is a requisite precondition for monitoring quality of care and interpreting public reports of hospital outcomes. Current risk-adjustment measures have been criticized for including baseline variables that are difficult to obtain and inadequately adjusting for high-risk patients. The authors sought to develop highly predictive risk-adjustment models for 30-day mortality and morbidity based only on a small number of preoperative baseline characteristics. They included the Current Procedural Terminology code corresponding to the patient's primary procedure (American Medical Association), American Society of Anesthesiologists Physical Status, and age (for mortality) or hospitalization (inpatient vs. outpatient, for morbidity). METHODS: Data from 635,265 noncardiac surgical patients participating in the American College of Surgeons National Surgical Quality Improvement Program between 2005 and 2008 were analyzed. The authors developed a novel algorithm to aggregate sparsely represented Current Procedural Terminology codes into logical groups and estimated univariable Procedural Severity Scores-one for mortality and morbidity, respectively-for each aggregated group. These scores were then used as predictors in developing respective risk quantification models. Models were validated with c-statistics, and calibration was assessed using observed-to-expected ratios of event frequencies for clinically relevant strata of risk. RESULTS: The risk quantification models demonstrated excellent predictive accuracy for 30-day postoperative mortality (c-statistic [95% CI] 0.915 [0.906-0.924]) and morbidity (0.867 [0.858-0.876]). Even in high-risk patients, observed rates calibrated well with estimated probabilities for mortality (observed-to-expected ratio: 0.93 [0.81-1.06]) and morbidity (0.99 [0.93-1.05]). CONCLUSION: The authors developed simple risk-adjustment models, each based on three easily obtained variables, that allow for objective quality-of-care monitoring among hospitals.", "author" : [ { "dropping-particle" : "", "family" : "Dalton", "given" : "J E", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Kurz", "given" : "A", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Turan", "given" : "A", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Mascha", "given" : "E J", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Sessler", "given" : "D I", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Saager", "given" : "L", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" } ], "container-title" : "Anesthesiology", "edition" : "2011/04/27", "id" : "ITEM-1", "issue" : "6", "issued" : { "date-parts" : [ [ "2011" ] ] }, "language" : "eng", "note" : "Dalton, Jarrod E\nKurz, Andrea\nTuran, Alparslan\nMascha, Edward J\nSessler, Daniel I\nSaager, Leif\nRandomized Controlled Trial\nValidation Studies\nUnited States\nAnesthesiology\nAnesthesiology. 2011 Jun;114(6):1336-44. doi: 10.1097/ALN.0b013e318219d5f9.", "page" : "1336-1344", "title" : "Development and validation of a risk quantification index for 30-day postoperative mortality and morbidity in noncardiac surgical patients", "type" : "article-journal", "volume" : "114" }, "uris" : [ "" ] } ], "mendeley" : { "formattedCitation" : "²", "plainTextFormattedCitation" : "2", "previouslyFormattedCitation" : "²" }, "properties" : { }, "schema" : "" }2), as well as intraoperative variables reflecting procedural complexity, i.e., work relative value units (RVUs), duration of surgery, transfused red blood cell (RBC) units, administered fluid volume, high riskADDIN CSL_CITATION { "citationItems" : [ { "id" : "ITEM-1", "itemData" : { "DOI" : "10.1097/ALN.0b013e31829ce8fd", "ISBN" : "1528-1175", "ISSN" : "1528-1175", "PMID" : "23756454", "abstract" : "BACKGROUND The allocation of intensive care unit (ICU) beds for postoperative patients is a challenging daily task that could be assisted by the real-time detection of ICU needs. The goal of this study was to develop and validate an intraoperative predictive model for unplanned postoperative ICU use. METHODS With the use of anesthesia information management system, postanesthesia care unit, and scheduling data, a data set was derived from adult in-patient noncardiac surgeries. Unplanned ICU admissions were identified (4,847 of 71,996; 6.7%), and a logistic regression model was developed for predicting unplanned ICU admission. The model performance was tested using bootstrap validation and compared with the Surgical Apgar Score using area under the curve for the receiver operating characteristic. RESULTS The logistic regression model included 16 variables: age, American Society of Anesthesiologists physical status, emergency case, surgical service, and 12 intraoperative variables. The area under the curve was 0.905 (95% CI, 0.900-0.909). The bootstrap validation model area under the curves were 0.513 at booking, 0.688 at 3 h before case end, 0.738 at 2 h, 0.791 at 1 h, and 0.809 at case end. The Surgical Apgar Score area under the curve was 0.692. Unplanned ICU admissions had more ICU-free days than planned ICU admissions (5 vs. 4; P < 0.001) and similar mortality (5.6 vs. 6.0%; P = 0.248). CONCLUSIONS The authors have developed and internally validated an intraoperative predictive model for unplanned postoperative ICU use. Incorporation of this model into a real-time data sniffer may improve the process of allocating ICU beds for postoperative patients.", "author" : [ { "dropping-particle" : "", "family" : "Wanderer", "given" : "J P", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Anderson-Dam", "given" : "J", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Levine", "given" : "W", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Bittner", "given" : "E A", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" } ], "container-title" : "Anesthesiology", "id" : "ITEM-1", "issue" : "3", "issued" : { "date-parts" : [ [ "2013" ] ] }, "page" : "516-24", "title" : "Development and validation of an intraoperative predictive model for unplanned postoperative intensive care.", "type" : "article-journal", "volume" : "119" }, "uris" : [ "" ] } ], "mendeley" : { "formattedCitation" : "³", "plainTextFormattedCitation" : "3", "previouslyFormattedCitation" : "³" }, "properties" : { }, "schema" : "" }3 or emergent surgical procedures and duration of intraoperative hypotension.Furthermore, we performed multiple sensitivity analyses in surgical sub-groups (musculoskeletal, thoracic, abdominal, and male or female genital organ surgery), as well as in sub-groups with varying procedural severity (high risk and non-high risk surgery, procedural duration ≥120 minutes, non-emergent surgical procedures) to analyze the differential effects of different surgical procedures on postoperative length of stay. Competing risk of deathWe assigned patients who died prior to hospital discharge a PLOS of 28 days to account for effect variation caused by in-hospital mortality, as previously described.ADDIN CSL_CITATION { "citationItems" : [ { "id" : "ITEM-1", "itemData" : { "DOI" : "10.1016/S0140-6736(16)31637-3", "ISSN" : "01406736", "PMID" : "27707496", "abstract" : "BACKGROUND Immobilisation predicts adverse outcomes in patients in the surgical intensive care unit (SICU). Attempts to mobilise critically ill patients early after surgery are frequently restricted, but we tested whether early mobilisation leads to improved mobility, decreased SICU length of stay, and increased functional independence of patients at hospital discharge. METHODS We did a multicentre, international, parallel-group, assessor-blinded, randomised controlled trial in SICUs of five university hospitals in Austria (n=1), Germany (n=1), and the USA (n=3). Eligible patients (aged 18 years or older, who had been mechanically ventilated for <48 h, and were expected to require mechanical ventilation for \u226524 h) were randomly assigned (1:1) by use of a stratified block randomisation via restricted web platform to standard of care (control) or early, goal-directed mobilisation using an inter-professional approach of closed-loop communication and the SICU optimal mobilisation score (SOMS) algorithm (intervention), which describes patients' mobilisation capacity on a numerical rating scale ranging from 0 (no mobilisation) to 4 (ambulation). We had three main outcomes hierarchically tested in a prespecified order: the mean SOMS level patients achieved during their SICU stay (primary outcome), and patient's length of stay on SICU and the mini-modified functional independence measure score (mmFIM) at hospital discharge (both secondary outcomes). This trial is registered with (NCT01363102). FINDINGS Between July 1, 2011, and Nov 4, 2015, we randomly assigned 200 patients to receive standard treatment (control; n=96) or intervention (n=104). Intention-to-treat analysis showed that the intervention improved the mobilisation level (mean achieved SOMS 2\u00b72 [SD 1\u00b70] in intervention group vs 1\u00b75 [0\u00b78] in control group, p<0\u00b70001), decreased SICU length of stay (mean 7 days [SD 5-12] in intervention group vs 10 days [6-15] in control group, p=0\u00b70054), and improved functional mobility at hospital discharge (mmFIM score 8 [4-8] in intervention group vs 5 [2-8] in control group, p=0\u00b70002). More adverse events were reported in the intervention group (25 cases [2\u00b78%]) than in the control group (ten cases [0\u00b78%]); no serious adverse events were observed. Before hospital discharge 25 patients died (17 [16%] in the intervention group, eight [8%] in the control group). 3 months after hospital discharge 36 patients died (21 [22%] in the intervention group, 1\u2026", "author" : [ { "dropping-particle" : "", "family" : "Schaller", "given" : "Stefan J", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Anstey", "given" : "Matthew", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Blobner", "given" : "Manfred", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Edrich", "given" : "Thomas", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Grabitz", "given" : "Stephanie D", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Gradwohl-Matis", "given" : "Ilse", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Heim", "given" : "Markus", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Houle", "given" : "Timothy", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Kurth", "given" : "Tobias", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Latronico", "given" : "Nicola", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Lee", "given" : "Jarone", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Meyer", "given" : "Matthew J", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Peponis", "given" : "Thomas", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Talmor", "given" : "Daniel", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Velmahos", "given" : "George C", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Waak", "given" : "Karen", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Walz", "given" : "J Matthias", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Zafonte", "given" : "Ross", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Eikermann", "given" : "Matthias", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "International Early SOMS-guided Mobilization Research Initiative", "given" : "", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" } ], "container-title" : "The Lancet", "id" : "ITEM-1", "issue" : "10052", "issued" : { "date-parts" : [ [ "2016", "10", "1" ] ] }, "page" : "1377-1388", "title" : "Early, goal-directed mobilisation in the surgical intensive care unit: a randomised controlled trial", "type" : "article-journal", "volume" : "388" }, "uris" : [ "" ] } ], "mendeley" : { "formattedCitation" : "⁴", "plainTextFormattedCitation" : "4", "previouslyFormattedCitation" : "⁴" }, "properties" : { }, "schema" : "" }4In an additional sensitivity analysis, we performed Cox regression analysis accounting for the competing risk of death. Patients were censored after the day they died in case of death on or before the day of hospital discharge. The proportional hazards assumptions were tested prior to performing Cox regression. Healthcare-related factorsTo account for any changes of staffing or care across the 7-year study period, we added the year of surgery as a confounder to the negative binomial regression analysis in a sensitivity analysis. Similarly, we performed sensitivity analyses accounting for attending-to-attending handover in anesthesia care as a confounder. Further, we have included mixed-effects negative binomial regression analyses accounting for individual anesthesia or surgical provider preferences set as random effects in the matched-cohort.Postoperative length of stay stratified by propensity scoreWe plotted the IRR [95% CI] of PLOS as a function of quintiles (in the propensity-matched study cohort) and deciles (in the unmatched study cohort; deciles to display higher data granularity due to larger cohort size) of the propensity score to display the decreasing trend in PLOS as well as a “flipping point” at IRR of 1, below which ICU admission was associated with a shorter PLOS than ward admission. Evaluation of propensity score model for postoperative ICU admissionTo determine the quality of the confounder control model for ICU admission, we have performed c-statistics to evaluate the model discrimination and calculated a reliability plot to assess model calibration of the propensity score to predict postoperative ICU admission.eText 3. ResultsSensitivity AnalysesPatient comorbidity statusThe results remained robust in sub-group analyses stratified by low and high patient baseline comorbidity based on the Charlson Comorbidity Index and the ASA score (eTable 4). In the 1st and 3rd tertiles, particularly ICU-admitted patients with severe obesity showed relatively large differences of PLOS compared to severely obese ward-admitted patients: IRR 1.85 [95% CI 1.58-2.15], p<0.001 in the 1st tertile and 0.83 [0.71-0.96], p=0.012 in the 3rd tertile (eTable 4). Additionally, the results remained robust after adjusting the propensity score for SPOSA (eTable 5).31 (0.44%) of patients in the matched cohort were dependent on home oxygen therapy. Adding this variable as a confounder to the negative binomial regression model in a sensitivity analysis confirmed our primary findings in all tertiles of the propensity-matched cohort (e Table 5). Similarly, adding baseline need for dialysis (34 patients, 0.44%), internal cardiac defibrillator (134 patients, 1.90%) or acute myocardial infarction (68 patients, 0.96%) as confounders also reiterated the results of our primary analysis in the matched cohort (eTable 5). Procedure-related factorsThe results also remained robust after performing sub-group analyses stratified by procedural severity and surgical duration (eTable 4). ICU-admitted patients after non-emergent surgical procedures had an IRR 1.71 [1.61-1.81], p<0.001 in tertile 1 and IRR 0.92 [0.87-0.97], p=0.003 in tertile 3, compared to ward-admitted patients. ICU-admitted patients undergoing high risk surgical procedures showed a PLOS of IRR 1.67 [1.56-1.78], p<0.001 in the 1st tertile and IRR 0.95 [0.89-1.01], p=0.086 in the 3rd tertile, when comparing to patients admitted to the ward. A similar pattern applied to long duration of surgeries: IRR 1.63 [1.54-1.73], p<0.001 in the 1st tertile and IRR 0.95 [0.90-1.0], p=0.071 in the 3rd peting risk of deathIn the Cox regression analysis accounting for the competing risk of in-hospital mortality, our main findings were confirmed: In the first and second tertile patients that went to the ICU postoperatively were more likely to stay longer in the hospital than patients who went to the floor (tertile 1: hazard ratio (HR) 1.87 [95% CI 1.72-2.04], p<0.001; tertile 2: HR 1.23 [95% CI 1.13-1.34], p<0.001), while in the third tertile of the propensity score ICU patients had a shorter length of stay (tertile 3: HR 0.87 [95% CI 0.81-0.95], p=0.001) (see eTable 5).Healthcare-related factorsOur findings remained robust in all tertiles of the propensity-matched cohort after adjusting for the year of surgery (eTable 5). At least one handover in care between anesthesiology attendings occurred in 1 289 (18.28%) cases of the propensity matched cohort: 307 (13.15%) in the 1st tertile, 440 (18.75%) in the 2nd tertile and 542 (23.10%) in the 3rd tertile. Addition of this confounder did not change the results of the negative binomial regression in tertile 1 (IRR 1.81 [95% CI 1.70-1.93], p<0.001), tertile 2 (IRR 1.14 [95% CI 1.09-1.21], p<0.001) or tertile 3 (IRR 0.89 [95% CI 0.84-0.94], p<0.001, eTable 5).Even when accounting for the individual anesthesia or surgical preferences of clinicians, the primary findings remained robust (eTable 5). Postoperative length of stay stratified by propensity scoreIn both the propensity-matched (eFigure 1) and unmatched (eFigure 2) study cohort, the IRR for PLOS between ICU vs. ward admitted patients decreased with increasing propensity score. In both cohorts, a “flipping point” could be observed where the IRR decreased to less than 1. In the propensity-matched cohort, the “flipping point, was between the 3rd and the 4th quintile of the propensity score; hereafter, ICU admission was more preventive than ward admission, i.e., ICU patients had shorter PLOS than ward patients.Evaluation of propensity score model for postoperative ICU admissionWith an AUC of 0.94 (almost perfect prediction) and good model calibration (eFigure 3), we conclude that the built propensity score for ICU admission captures a wide variety of factors underlying the allocation process which we accounted for by performing matching based on this propensity score.eTable 1 (detailed). Characteristics of propensity score-matched study population by postoperative ICU or ward admission.CharacteristicsLevelAdmission to surgical ward(N =3 530)Admission to ICU(N =3 530)p valueTotal(N =7 060)Men 1 771 (50.2%)1 801 (51.0%)0.4803 572 (50.6%)American Society of Anesthesiologists physical status classification198 (2.8%)121 (3.4%)0.020219 (3.1%)21 559 (44.2%)1 667 (47.2%)3 226 (45.7%)31 727 (48.9%)1 600 (45.3%)3 327 (47.1%)4144 (4.1%)138 (3.9%)282 (4.0%)52 (0.1%)4 (0.1%)6 (0.1%)Age (years) 58 (16)58 (16)0.12058 (16)Body mass index (kg m-2) 27.9 (6.6)27.9 (6.4)0.6027.9 (6.5)Charlson Comorbidity IndexADDIN CSL_CITATION { "citationItems" : [ { "id" : "ITEM-1", "itemData" : { "DOI" : "10.1136/bmjopen-2015-008990", "ISSN" : "2044-6055", "PMID" : "26351192", "abstract" : "OBJECTIVES Our primary objective was to compare the utility of the Deyo-Charlson Comorbidity Index (DCCI) and Elixhauser-van Walraven Comorbidity Index (EVCI) to predict mortality in intensive care unit (ICU) patients. SETTING Observational study of 2 tertiary academic centres located in Boston, Massachusetts. PARTICIPANTS The study cohort consisted of 59,816 patients from admitted to 12 ICUs between January 2007 and December 2012. PRIMARY AND SECONDARY OUTCOME For the primary analysis, receiver operator characteristic curves were constructed for mortality at 30, 90, 180, and 365\u2005days using the DCCI as well as EVCI, and the areas under the curve (AUCs) were compared. Subgroup analyses were performed within different types of ICUs. Logistic regression was used to add age, race and sex into the model to determine if there was any improvement in discrimination. RESULTS At 30\u2005days, the AUC for DCCI versus EVCI was 0.65 (95% CI 0.65 to 0.67) vs 0.66 (95% CI 0.65 to 0.66), p=0.02. Discrimination improved at 365\u2005days for both indices (AUC for DCCI 0.72 (95% CI 0.71 to 0.72) vs AUC for EVCI 0.72 (95% CI 0.72 to 0.72), p=0.46). The DCCI and EVCI performed similarly across ICUs at all time points, with the exception of the neurosciences ICU, where the DCCI was superior to EVCI at all time points (1-year mortality: AUC 0.73 (95% CI 0.72 to 0.74) vs 0.68 (95% CI 0.67 to 0.70), p=0.005). The addition of basic demographic information did not change the results at any of the assessed time points. CONCLUSIONS The DCCI and EVCI were comparable at predicting mortality in critically ill patients. The predictive ability of both indices increased when assessing long-term outcomes. Addition of demographic data to both indices did not affect the predictive utility of these indices. Further studies are needed to validate our findings and to determine the utility of these indices in clinical practice.", "author" : [ { "dropping-particle" : "", "family" : "Ladha", "given" : "Karim S", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Zhao", "given" : "Kevin", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Quraishi", "given" : "Sadeq A", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Kurth", "given" : "Tobias", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Eikermann", "given" : "Matthias", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Kaafarani", "given" : "Haytham M A", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Klein", "given" : "Eric N", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Seethala", "given" : "Raghu", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Lee", "given" : "Jarone", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" } ], "container-title" : "BMJ open", "id" : "ITEM-1", "issue" : "9", "issued" : { "date-parts" : [ [ "2015", "9", "8" ] ] }, "page" : "e008990", "publisher" : "British Medical Journal Publishing Group", "title" : "The Deyo-Charlson and Elixhauser-van Walraven Comorbidity Indices as predictors of mortality in critically ill patients.", "type" : "article-journal", "volume" : "5" }, "uris" : [ "" ] } ], "mendeley" : { "formattedCitation" : "⁵", "plainTextFormattedCitation" : "5", "previouslyFormattedCitation" : "⁵" }, "properties" : { }, "schema" : "" }5 2 [1-8]2 [1-6]0.7302 [1-7]Emergency case 300 (8.5%)260 (7.4%)0.078560 (7.9%)Duration of surgery (hours) 4.6 [3.1-6.4]4.6 [3.4-6.3]0.0594.6 [3.3-6.4]Work relative value unit 4.19 (1.10)4.27 (1.07)0.0024.23 (1.08)Duration of intraoperative hypotension (minutes) 0 [0]0 [0-5]<0.0010 [0] eTable 1 (detailed). Characteristics of propensity score-matched study population by postoperative ICU or ward admission (continued).Intraoperative colloid volume (ml)ADDIN CSL_CITATION { "citationItems" : [ { "id" : "ITEM-1", "itemData" : { "DOI" : "10.1097/SLA.0000000000002220", "ISSN" : "0003-4932", "author" : [ { "dropping-particle" : "", "family" : "Shin", "given" : "Christina H.", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Long", "given" : "Dustin R.", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "McLean", "given" : "Duncan", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Grabitz", "given" : "Stephanie D.", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Ladha", "given" : "Karim", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Timm", "given" : "Fanny P.", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Thevathasan", "given" : "Tharusan", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Pieretti", "given" : "Alberto", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Ferrone", "given" : "Cristina", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Hoeft", "given" : "Andreas", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Scheeren", "given" : "Thomas W. L.", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Thompson", "given" : "Boyd Taylor", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Kurth", "given" : "Tobias", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Eikermann", "given" : "Matthias", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" } ], "container-title" : "Annals of Surgery", "id" : "ITEM-1", "issued" : { "date-parts" : [ [ "2017", "3" ] ] }, "page" : "1", "title" : "Effects of Intraoperative Fluid Management on Postoperative Outcomes", "type" : "article-journal" }, "uris" : [ "" ] } ], "mendeley" : { "formattedCitation" : "⁶", "plainTextFormattedCitation" : "6", "previouslyFormattedCitation" : "⁶" }, "properties" : { }, "schema" : "" }6 155 (355)139 (356)0.072147 (356)Intraoperative crystalloid volume (ml)ADDIN CSL_CITATION { "citationItems" : [ { "id" : "ITEM-1", "itemData" : { "DOI" : "10.1097/SLA.0000000000002220", "ISSN" : "0003-4932", "author" : [ { "dropping-particle" : "", "family" : "Shin", "given" : "Christina H.", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Long", "given" : "Dustin R.", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "McLean", "given" : "Duncan", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Grabitz", "given" : "Stephanie D.", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Ladha", "given" : "Karim", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Timm", "given" : "Fanny P.", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Thevathasan", "given" : "Tharusan", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Pieretti", "given" : "Alberto", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Ferrone", "given" : "Cristina", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Hoeft", "given" : "Andreas", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Scheeren", "given" : "Thomas W. L.", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Thompson", "given" : "Boyd Taylor", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Kurth", "given" : "Tobias", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Eikermann", "given" : "Matthias", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" } ], "container-title" : "Annals of Surgery", "id" : "ITEM-1", "issued" : { "date-parts" : [ [ "2017", "3" ] ] }, "page" : "1", "title" : "Effects of Intraoperative Fluid Management on Postoperative Outcomes", "type" : "article-journal" }, "uris" : [ "" ] } ], "mendeley" : { "formattedCitation" : "⁶", "plainTextFormattedCitation" : "6", "previouslyFormattedCitation" : "⁶" }, "properties" : { }, "schema" : "" }6 2 688 (3 343)2 712 (2 781)0.7502 700 (3075)Packed red blood cells (units) 0.33 (0.96)0.28 (0.88)0.0120.31 (0.92)Intraoperative heart rateBradycardia (<60)804 (22.8%)910 (25.8%)0.0041 714 (24.3%)Normocardia (60-100)2 626 (74.4%)2 544 (72.1%)5 170 (73.2%)Tachycardia (≥100)100 (2.8%)76 (2.2%)176 (2.5%)Intraoperative PEEP (cm H2O)ADDIN CSL_CITATION { "citationItems" : [ { "id" : "ITEM-1", "itemData" : { "DOI" : "10.1136/bmj.h3646", "ISSN" : "1756-1833", "PMID" : "26174419", "abstract" : "OBJECTIVE To evaluate the effects of intraoperative protective ventilation on major postoperative respiratory complications and to define safe intraoperative mechanical ventilator settings that do not translate into an increased risk of postoperative respiratory complications. DESIGN Hospital based registry study. SETTING Academic tertiary care hospital and two affiliated community hospitals in Massachusetts, United States. PARTICIPANTS 69 265 consecutively enrolled patients over the age of 18 who underwent a non-cardiac surgical procedure between January 2007 and August 2014 and required general anesthesia with endotracheal intubation. INTERVENTIONS Protective ventilation, defined as a median positive end expiratory pressure (PEEP) of 5 cmH2O or more, a median tidal volume of less than 10 mL/kg of predicted body weight, and a median plateau pressure of less than 30 cmH2O. MAIN OUTCOME MEASURE Composite outcome of major respiratory complications, including pulmonary edema, respiratory failure, pneumonia, and re-intubation. RESULTS Of the 69 265 enrolled patients 34 800 (50.2%) received protective ventilation and 34 465 (49.8%) received non-protective ventilation intraoperatively. Protective ventilation was associated with a decreased risk of postoperative respiratory complications in multivariable regression (adjusted odds ratio 0.90, 95% confidence interval 0.82 to 0.98, P=0.013). The results were similar in the propensity score matched cohort (odds ratio 0.89, 95% confidence interval 0.83 to 0.97, P=0.004). A PEEP of 5 cmH2O and median plateau pressures of 16 cmH2O or less were associated with the lowest risk of postoperative respiratory complications. CONCLUSIONS Intraoperative protective ventilation was associated with a decreased risk of postoperative respiratory complications. A PEEP of 5 cmH2O and a plateau pressure of 16 cmH2O or less were identified as protective mechanical ventilator settings. These findings suggest that protective thresholds differ for intraoperative ventilation in patients with normal lungs compared with those used for patients with acute lung injury.", "author" : [ { "dropping-particle" : "", "family" : "Ladha", "given" : "Karim", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Vidal Melo", "given" : "Marcos F", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "McLean", "given" : "Duncan J", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Wanderer", "given" : "Jonathan P", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Grabitz", "given" : "Stephanie D", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Kurth", "given" : "Tobias", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Eikermann", "given" : "Matthias", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" } ], "container-title" : "Bmj", "id" : "ITEM-1", "issued" : { "date-parts" : [ [ "2015" ] ] }, "page" : "h3646", "title" : "Intraoperative protective mechanical ventilation and risk of postoperative respiratory complications: hospital based registry study.", "type" : "article-journal", "volume" : "351" }, "uris" : [ "" ] } ], "mendeley" : { "formattedCitation" : "⁷", "plainTextFormattedCitation" : "7", "previouslyFormattedCitation" : "⁷" }, "properties" : { }, "schema" : "" }70-51 437 (40.7%)1 419 (40.2%)0.5502 856 (40.5%)51 612 (45.7%)1 653 (46.8%)3 265 (46.2%)>5481 (13.6%)458 (13.0%)939 (13.3%) eTable 1 (detailed). Characteristics of propensity score-matched study population by postoperative ICU or ward admission (continued).Intraoperative plateau pressure (cm H2O)ADDIN CSL_CITATION { "citationItems" : [ { "id" : "ITEM-1", "itemData" : { "DOI" : "10.1136/bmj.h3646", "ISSN" : "1756-1833", "PMID" : "26174419", "abstract" : "OBJECTIVE To evaluate the effects of intraoperative protective ventilation on major postoperative respiratory complications and to define safe intraoperative mechanical ventilator settings that do not translate into an increased risk of postoperative respiratory complications. DESIGN Hospital based registry study. SETTING Academic tertiary care hospital and two affiliated community hospitals in Massachusetts, United States. PARTICIPANTS 69 265 consecutively enrolled patients over the age of 18 who underwent a non-cardiac surgical procedure between January 2007 and August 2014 and required general anesthesia with endotracheal intubation. INTERVENTIONS Protective ventilation, defined as a median positive end expiratory pressure (PEEP) of 5 cmH2O or more, a median tidal volume of less than 10 mL/kg of predicted body weight, and a median plateau pressure of less than 30 cmH2O. MAIN OUTCOME MEASURE Composite outcome of major respiratory complications, including pulmonary edema, respiratory failure, pneumonia, and re-intubation. RESULTS Of the 69 265 enrolled patients 34 800 (50.2%) received protective ventilation and 34 465 (49.8%) received non-protective ventilation intraoperatively. Protective ventilation was associated with a decreased risk of postoperative respiratory complications in multivariable regression (adjusted odds ratio 0.90, 95% confidence interval 0.82 to 0.98, P=0.013). The results were similar in the propensity score matched cohort (odds ratio 0.89, 95% confidence interval 0.83 to 0.97, P=0.004). A PEEP of 5 cmH2O and median plateau pressures of 16 cmH2O or less were associated with the lowest risk of postoperative respiratory complications. CONCLUSIONS Intraoperative protective ventilation was associated with a decreased risk of postoperative respiratory complications. A PEEP of 5 cmH2O and a plateau pressure of 16 cmH2O or less were identified as protective mechanical ventilator settings. These findings suggest that protective thresholds differ for intraoperative ventilation in patients with normal lungs compared with those used for patients with acute lung injury.", "author" : [ { "dropping-particle" : "", "family" : "Ladha", "given" : "Karim", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Vidal Melo", "given" : "Marcos F", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "McLean", "given" : "Duncan J", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Wanderer", "given" : "Jonathan P", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Grabitz", "given" : "Stephanie D", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Kurth", "given" : "Tobias", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Eikermann", "given" : "Matthias", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" } ], "container-title" : "Bmj", "id" : "ITEM-1", "issued" : { "date-parts" : [ [ "2015" ] ] }, "page" : "h3646", "title" : "Intraoperative protective mechanical ventilation and risk of postoperative respiratory complications: hospital based registry study.", "type" : "article-journal", "volume" : "351" }, "uris" : [ "" ] } ], "mendeley" : { "formattedCitation" : "⁷", "plainTextFormattedCitation" : "7", "previouslyFormattedCitation" : "⁷" }, "properties" : { }, "schema" : "" }70-16706 (20.0%)745 (21.1%)0.3201 451 (20.6%)16325 (9.2%)344 (9.7%)669(9.5%)>162 499 (70.8%)2 441 (69.2%)4 940 (70.0%)Intraoperative vasopressor useNo466 (13.2%)491 (13.9%)0.380957 (13.6%)Yes3 064 (86.8%)3 039 (86.1%)6 103 (86.4%)Intraoperative neuromuscular blocking agents (multiples of ED95)81st quintile442 (12.5%)394 (11.2%)0.200836 (11.8%)2nd quintile346 (9.8%)318 (9.0%)664 (9.4%)3rd quintile442 (12.5%)468 (13.3%)910 (12.9%)4th quintile736 (20.8%)780 (22.1%)1 516 (21.5%)5th quintile1 564 (44.3%)1 570 (44.5%)3 134 (44.4%)Median intraoperative SaO2/FiO2-ratio0-150891 (25.2%)837 (23.7%)<0.0011 728 (24.5%)150-2001 082 (30.7%)1 021 (28.9%)2 103 (29.8%)200-300956 (27.1%)918 (26.0%)1 874 (26.5%)>300601 (17.0%)754 (21.4%)1 355 (19.2%) eTable 1 (detailed). Characteristics of propensity score-matched study population by postoperative ICU or ward admission (continued).Principal Surgical ServiceVascular system 485 (13.7%)524 (14.8%) 1 009 (14.3%)Digestive system 848 (24.0%)776 (22.0%) 1 624 (23.0%)Endocrine system 81 (2.3%)78 (2.2%) 159 (2.3%)Hematologic-lymphatic system 39 (1.1%)30 (0.8%) 69 (1.0%)Miscellaneous 1 (<1%)2 (0.1%) 3 (<1%)Nervous system 908 (25.7%)1 085 (30.7%) 1 993 (28.2%)Thoracic Surgery 516 (14.6%)471 (13.3%) 987 (14.0%)High risk surgeryADDIN CSL_CITATION { "citationItems" : [ { "id" : "ITEM-1", "itemData" : { "DOI" : "10.1097/ALN.0b013e31829ce8fd", "ISBN" : "1528-1175", "ISSN" : "1528-1175", "PMID" : "23756454", "abstract" : "BACKGROUND The allocation of intensive care unit (ICU) beds for postoperative patients is a challenging daily task that could be assisted by the real-time detection of ICU needs. The goal of this study was to develop and validate an intraoperative predictive model for unplanned postoperative ICU use. METHODS With the use of anesthesia information management system, postanesthesia care unit, and scheduling data, a data set was derived from adult in-patient noncardiac surgeries. Unplanned ICU admissions were identified (4,847 of 71,996; 6.7%), and a logistic regression model was developed for predicting unplanned ICU admission. The model performance was tested using bootstrap validation and compared with the Surgical Apgar Score using area under the curve for the receiver operating characteristic. RESULTS The logistic regression model included 16 variables: age, American Society of Anesthesiologists physical status, emergency case, surgical service, and 12 intraoperative variables. The area under the curve was 0.905 (95% CI, 0.900-0.909). The bootstrap validation model area under the curves were 0.513 at booking, 0.688 at 3 h before case end, 0.738 at 2 h, 0.791 at 1 h, and 0.809 at case end. The Surgical Apgar Score area under the curve was 0.692. Unplanned ICU admissions had more ICU-free days than planned ICU admissions (5 vs. 4; P < 0.001) and similar mortality (5.6 vs. 6.0%; P = 0.248). CONCLUSIONS The authors have developed and internally validated an intraoperative predictive model for unplanned postoperative ICU use. Incorporation of this model into a real-time data sniffer may improve the process of allocating ICU beds for postoperative patients.", "author" : [ { "dropping-particle" : "", "family" : "Wanderer", "given" : "J P", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Anderson-Dam", "given" : "J", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Levine", "given" : "W", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Bittner", "given" : "E A", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" } ], "container-title" : "Anesthesiology", "id" : "ITEM-1", "issue" : "3", "issued" : { "date-parts" : [ [ "2013" ] ] }, "page" : "516-24", "title" : "Development and validation of an intraoperative predictive model for unplanned postoperative intensive care.", "type" : "article-journal", "volume" : "119" }, "uris" : [ "" ] } ], "mendeley" : { "formattedCitation" : "³", "plainTextFormattedCitation" : "3", "previouslyFormattedCitation" : "³" }, "properties" : { }, "schema" : "" }32 820 (79.9%)2 906 (82.3%)0.0095 726 (81.1%)Procedural complexityADDIN CSL_CITATION { "citationItems" : [ { "id" : "ITEM-1", "itemData" : { "DOI" : "10.1097/ALN.0b013e318219d5f9", "ISBN" : "1528-1175 (Electronic)\r0003-3022 (Linking)", "PMID" : "21519230", "abstract" : "BACKGROUND: Optimal risk adjustment is a requisite precondition for monitoring quality of care and interpreting public reports of hospital outcomes. Current risk-adjustment measures have been criticized for including baseline variables that are difficult to obtain and inadequately adjusting for high-risk patients. The authors sought to develop highly predictive risk-adjustment models for 30-day mortality and morbidity based only on a small number of preoperative baseline characteristics. They included the Current Procedural Terminology code corresponding to the patient's primary procedure (American Medical Association), American Society of Anesthesiologists Physical Status, and age (for mortality) or hospitalization (inpatient vs. outpatient, for morbidity). METHODS: Data from 635,265 noncardiac surgical patients participating in the American College of Surgeons National Surgical Quality Improvement Program between 2005 and 2008 were analyzed. The authors developed a novel algorithm to aggregate sparsely represented Current Procedural Terminology codes into logical groups and estimated univariable Procedural Severity Scores-one for mortality and morbidity, respectively-for each aggregated group. These scores were then used as predictors in developing respective risk quantification models. Models were validated with c-statistics, and calibration was assessed using observed-to-expected ratios of event frequencies for clinically relevant strata of risk. RESULTS: The risk quantification models demonstrated excellent predictive accuracy for 30-day postoperative mortality (c-statistic [95% CI] 0.915 [0.906-0.924]) and morbidity (0.867 [0.858-0.876]). Even in high-risk patients, observed rates calibrated well with estimated probabilities for mortality (observed-to-expected ratio: 0.93 [0.81-1.06]) and morbidity (0.99 [0.93-1.05]). CONCLUSION: The authors developed simple risk-adjustment models, each based on three easily obtained variables, that allow for objective quality-of-care monitoring among hospitals.", "author" : [ { "dropping-particle" : "", "family" : "Dalton", "given" : "J E", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Kurz", "given" : "A", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Turan", "given" : "A", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Mascha", "given" : "E J", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Sessler", "given" : "D I", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Saager", "given" : "L", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" } ], "container-title" : "Anesthesiology", "edition" : "2011/04/27", "id" : "ITEM-1", "issue" : "6", "issued" : { "date-parts" : [ [ "2011" ] ] }, "language" : "eng", "note" : "Dalton, Jarrod E\nKurz, Andrea\nTuran, Alparslan\nMascha, Edward J\nSessler, Daniel I\nSaager, Leif\nRandomized Controlled Trial\nValidation Studies\nUnited States\nAnesthesiology\nAnesthesiology. 2011 Jun;114(6):1336-44. doi: 10.1097/ALN.0b013e318219d5f9.", "page" : "1336-1344", "title" : "Development and validation of a risk quantification index for 30-day postoperative mortality and morbidity in noncardiac surgical patients", "type" : "article-journal", "volume" : "114" }, "uris" : [ "" ] } ], "mendeley" : { "formattedCitation" : "²", "plainTextFormattedCitation" : "2", "previouslyFormattedCitation" : "²" }, "properties" : { }, "schema" : "" }255.7 (14.4)56.5 (14.1)0.01456.1 (14.3)Values are frequencies (percentages), mean (standard deviation) or median [interquartile range]. eTable 2. Characteristics of unmatched study population by postoperative ICU or ward admission.CharacteristicsLevelAdmission to surgical ward(N =58 516)Admission to ICU(N =4 950)Standardized mean differencesTotal(N =63 466)Men 33 460 (57.2%)2 602 (52.6%)0.09336 062 (56.8%)American Society of Anesthesiologists physical status classification15 865 (10.0%)164 (3.3%)0.5226 029 (9.5%)237 380 (63.9%)2 400 (48.5%)39 780 (62.7%)314 798 (25.3%)2 186 (44.2%)16 984 (26.8%)4466 (0.8%)193 (3.9%)659 (1.0%)57 (<1%)7 (0.1%)14 (<1%)Age (years) 55 (16)57 (16)0.11555 (16)Body mass index (kg m-2) 28.6 (6.9)27.8 (6.3)0.11928.6 (6.9)Charlson Comorbidity IndexADDIN CSL_CITATION { "citationItems" : [ { "id" : "ITEM-1", "itemData" : { "DOI" : "10.1136/bmjopen-2015-008990", "ISSN" : "2044-6055", "PMID" : "26351192", "abstract" : "OBJECTIVES Our primary objective was to compare the utility of the Deyo-Charlson Comorbidity Index (DCCI) and Elixhauser-van Walraven Comorbidity Index (EVCI) to predict mortality in intensive care unit (ICU) patients. SETTING Observational study of 2 tertiary academic centres located in Boston, Massachusetts. PARTICIPANTS The study cohort consisted of 59,816 patients from admitted to 12 ICUs between January 2007 and December 2012. PRIMARY AND SECONDARY OUTCOME For the primary analysis, receiver operator characteristic curves were constructed for mortality at 30, 90, 180, and 365\u2005days using the DCCI as well as EVCI, and the areas under the curve (AUCs) were compared. Subgroup analyses were performed within different types of ICUs. Logistic regression was used to add age, race and sex into the model to determine if there was any improvement in discrimination. RESULTS At 30\u2005days, the AUC for DCCI versus EVCI was 0.65 (95% CI 0.65 to 0.67) vs 0.66 (95% CI 0.65 to 0.66), p=0.02. Discrimination improved at 365\u2005days for both indices (AUC for DCCI 0.72 (95% CI 0.71 to 0.72) vs AUC for EVCI 0.72 (95% CI 0.72 to 0.72), p=0.46). The DCCI and EVCI performed similarly across ICUs at all time points, with the exception of the neurosciences ICU, where the DCCI was superior to EVCI at all time points (1-year mortality: AUC 0.73 (95% CI 0.72 to 0.74) vs 0.68 (95% CI 0.67 to 0.70), p=0.005). The addition of basic demographic information did not change the results at any of the assessed time points. CONCLUSIONS The DCCI and EVCI were comparable at predicting mortality in critically ill patients. The predictive ability of both indices increased when assessing long-term outcomes. Addition of demographic data to both indices did not affect the predictive utility of these indices. Further studies are needed to validate our findings and to determine the utility of these indices in clinical practice.", "author" : [ { "dropping-particle" : "", "family" : "Ladha", "given" : "Karim S", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Zhao", "given" : "Kevin", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Quraishi", "given" : "Sadeq A", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Kurth", "given" : "Tobias", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Eikermann", "given" : "Matthias", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Kaafarani", "given" : "Haytham M A", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Klein", "given" : "Eric N", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Seethala", "given" : "Raghu", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Lee", "given" : "Jarone", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" } ], "container-title" : "BMJ open", "id" : "ITEM-1", "issue" : "9", "issued" : { "date-parts" : [ [ "2015", "9", "8" ] ] }, "page" : "e008990", "publisher" : "British Medical Journal Publishing Group", "title" : "The Deyo-Charlson and Elixhauser-van Walraven Comorbidity Indices as predictors of mortality in critically ill patients.", "type" : "article-journal", "volume" : "5" }, "uris" : [ "" ] } ], "mendeley" : { "formattedCitation" : "⁵", "plainTextFormattedCitation" : "5", "previouslyFormattedCitation" : "⁵" }, "properties" : { }, "schema" : "" }5 1 [0-3]2 [1-5]0.4561 [0-3] Emergency case 2 330 (4.0%)337 (6.8%)0.1252 667 (4.2%)Duration of surgery (hours) 2.7 [1.8-3.9]5.0 [3.7-7.1]1.1482.8 [1.9-4.2] eTable 2. Characteristics of unmatched study population by postoperative ICU or ward admission (continued).Work relative value unit 2.85 (1.37)4.47 (0.96)1.3442.98 (1.42)Duration of intraoperative hypotension (minutes) 0 [0]0 [0-5]0.3070 [0-0]Intraoperative colloid volume (ml)ADDIN CSL_CITATION { "citationItems" : [ { "id" : "ITEM-1", "itemData" : { "DOI" : "10.1097/SLA.0000000000002220", "ISSN" : "0003-4932", "author" : [ { "dropping-particle" : "", "family" : "Shin", "given" : "Christina H.", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Long", "given" : "Dustin R.", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "McLean", "given" : "Duncan", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Grabitz", "given" : "Stephanie D.", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Ladha", "given" : "Karim", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Timm", "given" : "Fanny P.", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Thevathasan", "given" : "Tharusan", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Pieretti", "given" : "Alberto", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Ferrone", "given" : "Cristina", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Hoeft", "given" : "Andreas", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Scheeren", "given" : "Thomas W. L.", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Thompson", "given" : "Boyd Taylor", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Kurth", "given" : "Tobias", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Eikermann", "given" : "Matthias", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" } ], "container-title" : "Annals of Surgery", "id" : "ITEM-1", "issued" : { "date-parts" : [ [ "2017", "3" ] ] }, "page" : "1", "title" : "Effects of Intraoperative Fluid Management on Postoperative Outcomes", "type" : "article-journal" }, "uris" : [ "" ] } ], "mendeley" : { "formattedCitation" : "⁶", "plainTextFormattedCitation" : "6", "previouslyFormattedCitation" : "⁶" }, "properties" : { }, "schema" : "" }6 40 (80)125 (335.7)0.23147 (197.9)Intraoperative crystalloid volume (ml)ADDIN CSL_CITATION { "citationItems" : [ { "id" : "ITEM-1", "itemData" : { "DOI" : "10.1097/SLA.0000000000002220", "ISSN" : "0003-4932", "author" : [ { "dropping-particle" : "", "family" : "Shin", "given" : "Christina H.", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Long", "given" : "Dustin R.", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "McLean", "given" : "Duncan", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Grabitz", "given" : "Stephanie D.", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Ladha", "given" : "Karim", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Timm", "given" : "Fanny P.", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Thevathasan", "given" : "Tharusan", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Pieretti", "given" : "Alberto", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Ferrone", "given" : "Cristina", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Hoeft", "given" : "Andreas", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Scheeren", "given" : "Thomas W. L.", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Thompson", "given" : "Boyd Taylor", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Kurth", "given" : "Tobias", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Eikermann", "given" : "Matthias", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" } ], "container-title" : "Annals of Surgery", "id" : "ITEM-1", "issued" : { "date-parts" : [ [ "2017", "3" ] ] }, "page" : "1", "title" : "Effects of Intraoperative Fluid Management on Postoperative Outcomes", "type" : "article-journal" }, "uris" : [ "" ] } ], "mendeley" : { "formattedCitation" : "⁶", "plainTextFormattedCitation" : "6", "previouslyFormattedCitation" : "⁶" }, "properties" : { }, "schema" : "" }6 1 723 (1671.4)2 959 (2 551)0.0241 819 (1 787)Intraoperative heart rateBradycardia (<60)13 226 (22.6%)1 392 (28.1%)0.05114 618 (23.0%)Normocardia (60-100)44 635 (76.3%)3 472 (70.1%)48 107 (75.8%)Tachycardia (≥100)655 (1.1%)86 (1.7%)741 (1.2%)Packed red blood cells (units) 0.06 (0.38)0.26 (0.86)0.2930.08 (0.44)Intraoperative PEEP(cm H2O)ADDIN CSL_CITATION { "citationItems" : [ { "id" : "ITEM-1", "itemData" : { "DOI" : "10.1136/bmj.h3646", "ISSN" : "1756-1833", "PMID" : "26174419", "abstract" : "OBJECTIVE To evaluate the effects of intraoperative protective ventilation on major postoperative respiratory complications and to define safe intraoperative mechanical ventilator settings that do not translate into an increased risk of postoperative respiratory complications. DESIGN Hospital based registry study. SETTING Academic tertiary care hospital and two affiliated community hospitals in Massachusetts, United States. PARTICIPANTS 69 265 consecutively enrolled patients over the age of 18 who underwent a non-cardiac surgical procedure between January 2007 and August 2014 and required general anesthesia with endotracheal intubation. INTERVENTIONS Protective ventilation, defined as a median positive end expiratory pressure (PEEP) of 5 cmH2O or more, a median tidal volume of less than 10 mL/kg of predicted body weight, and a median plateau pressure of less than 30 cmH2O. MAIN OUTCOME MEASURE Composite outcome of major respiratory complications, including pulmonary edema, respiratory failure, pneumonia, and re-intubation. RESULTS Of the 69 265 enrolled patients 34 800 (50.2%) received protective ventilation and 34 465 (49.8%) received non-protective ventilation intraoperatively. Protective ventilation was associated with a decreased risk of postoperative respiratory complications in multivariable regression (adjusted odds ratio 0.90, 95% confidence interval 0.82 to 0.98, P=0.013). The results were similar in the propensity score matched cohort (odds ratio 0.89, 95% confidence interval 0.83 to 0.97, P=0.004). A PEEP of 5 cmH2O and median plateau pressures of 16 cmH2O or less were associated with the lowest risk of postoperative respiratory complications. CONCLUSIONS Intraoperative protective ventilation was associated with a decreased risk of postoperative respiratory complications. A PEEP of 5 cmH2O and a plateau pressure of 16 cmH2O or less were identified as protective mechanical ventilator settings. These findings suggest that protective thresholds differ for intraoperative ventilation in patients with normal lungs compared with those used for patients with acute lung injury.", "author" : [ { "dropping-particle" : "", "family" : "Ladha", "given" : "Karim", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Vidal Melo", "given" : "Marcos F", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "McLean", "given" : "Duncan J", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Wanderer", "given" : "Jonathan P", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Grabitz", "given" : "Stephanie D", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Kurth", "given" : "Tobias", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Eikermann", "given" : "Matthias", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" } ], "container-title" : "Bmj", "id" : "ITEM-1", "issued" : { "date-parts" : [ [ "2015" ] ] }, "page" : "h3646", "title" : "Intraoperative protective mechanical ventilation and risk of postoperative respiratory complications: hospital based registry study.", "type" : "article-journal", "volume" : "351" }, "uris" : [ "" ] } ], "mendeley" : { "formattedCitation" : "⁷", "plainTextFormattedCitation" : "7", "previouslyFormattedCitation" : "⁷" }, "properties" : { }, "schema" : "" }70-521 223 (36.3%)2 024 (40.9%)0.04723 247 (36.6%)529 266 (50.0%)2 284 (46.1%)31 550 (49.7%)>58 027 (13.7%)642 (13.0%)8 669 (13.7%) eTable 2. Characteristics of unmatched study population by postoperative ICU or ward admission (continued).Intraoperative plateau pressure (cm H2O)ADDIN CSL_CITATION { "citationItems" : [ { "id" : "ITEM-1", "itemData" : { "DOI" : "10.1136/bmj.h3646", "ISSN" : "1756-1833", "PMID" : "26174419", "abstract" : "OBJECTIVE To evaluate the effects of intraoperative protective ventilation on major postoperative respiratory complications and to define safe intraoperative mechanical ventilator settings that do not translate into an increased risk of postoperative respiratory complications. DESIGN Hospital based registry study. SETTING Academic tertiary care hospital and two affiliated community hospitals in Massachusetts, United States. PARTICIPANTS 69 265 consecutively enrolled patients over the age of 18 who underwent a non-cardiac surgical procedure between January 2007 and August 2014 and required general anesthesia with endotracheal intubation. INTERVENTIONS Protective ventilation, defined as a median positive end expiratory pressure (PEEP) of 5 cmH2O or more, a median tidal volume of less than 10 mL/kg of predicted body weight, and a median plateau pressure of less than 30 cmH2O. MAIN OUTCOME MEASURE Composite outcome of major respiratory complications, including pulmonary edema, respiratory failure, pneumonia, and re-intubation. RESULTS Of the 69 265 enrolled patients 34 800 (50.2%) received protective ventilation and 34 465 (49.8%) received non-protective ventilation intraoperatively. Protective ventilation was associated with a decreased risk of postoperative respiratory complications in multivariable regression (adjusted odds ratio 0.90, 95% confidence interval 0.82 to 0.98, P=0.013). The results were similar in the propensity score matched cohort (odds ratio 0.89, 95% confidence interval 0.83 to 0.97, P=0.004). A PEEP of 5 cmH2O and median plateau pressures of 16 cmH2O or less were associated with the lowest risk of postoperative respiratory complications. CONCLUSIONS Intraoperative protective ventilation was associated with a decreased risk of postoperative respiratory complications. A PEEP of 5 cmH2O and a plateau pressure of 16 cmH2O or less were identified as protective mechanical ventilator settings. These findings suggest that protective thresholds differ for intraoperative ventilation in patients with normal lungs compared with those used for patients with acute lung injury.", "author" : [ { "dropping-particle" : "", "family" : "Ladha", "given" : "Karim", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Vidal Melo", "given" : "Marcos F", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "McLean", "given" : "Duncan J", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Wanderer", "given" : "Jonathan P", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Grabitz", "given" : "Stephanie D", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Kurth", "given" : "Tobias", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Eikermann", "given" : "Matthias", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" } ], "container-title" : "Bmj", "id" : "ITEM-1", "issued" : { "date-parts" : [ [ "2015" ] ] }, "page" : "h3646", "title" : "Intraoperative protective mechanical ventilation and risk of postoperative respiratory complications: hospital based registry study.", "type" : "article-journal", "volume" : "351" }, "uris" : [ "" ] } ], "mendeley" : { "formattedCitation" : "⁷", "plainTextFormattedCitation" : "7", "previouslyFormattedCitation" : "⁷" }, "properties" : { }, "schema" : "" }70-1613 082 (22.4%)1 051 (21.2%)0.11514 133 (22.3%)165 225 (8.9%)507 (10.2%)5 732 (9.0%)>1640 209 (68.7%)3 392 (68.5%)43 601 (68.7%)Intraoperative neuromuscular blocking agents (multiples of ED95)8 1st quintile12 174 (20.8%)542 (10.9%)0.71112 716 (20.0%)2nd quintile12 266 (21.0%)410 (8.3%)12 676 (20.0%)3rd quintile12 093 (20.7%)595 (12.0%)12 688 (20.0%)4th quintile11 685 (20.0%)1 008 (20.4%)12 693 (20.0%)5th quintile10 298 (17.6%)2 395 (48.4%)12 693 (20.0%)Median intraoperative SaO2/FiO2-ratio0-1507 543 (12.9%)958 (19.4%)0.1428 501 (13.4%)150-20021 357 (36.5%)1 315 (26.6%)22 672 (35.7%)200-30019 238 (32.9%)1 318 (26.6%)20 556 (32.4%)>30010 378 (17.7%)1 359 (27.5%)11 737 (18.5%)Intraoperative vasopressor useNo21 705 (37.1%)684 (13.8%)0.34822 389 (35.3%)Yes36 811 (62.9%)4 266 (86.2%)41 077 (64.7%) eTable 2. Characteristics of unmatched study population by postoperative ICU or ward admission (continued).Principal surgical service 0.249 Vascular system2 314 (4.0%)704 (14.2%)3018 (4.8%)Diagnostic radiology 18 (<1%)1 (<1%)19 (<1%)Digestive system 14 816 (25.3%)821 (16.6%)15 637 (24.6%)Ear 12 (<1%)5 (0.1%)17 (<1%)Endocrine system 4 531 (7.7%)84 (1.7%)4 615 (7.3%)Eye 37 (0.1%)9 (0.2%)46 (0.1%)Female genital organs 4 643 (7.9%)66 (1.3%)4709 (7.4%)Hematologic-lymphatic system 1 205 (2.1%)31 (0.6%)1 236 (1.9%)Integumentary 5 020 (8.6%)75 (1.5%)5 095 (8.0%)Male genital organs 2 361 (4.0%)9 (0.2%)2 370 (3.7%)Miscellaneous 28 (<1%)2 (<1%)30 (<1%)Musculoskeletal system 15 379 (26.3%)341 (6.9%)15 720 (24.8%)Nervous system 2 834 (4.8%)2 205 (44.5%)5 039 (7.9%)Nose 409 (0.7%)9 (0.2%)418 (0.7%)Obstetrics 9 (<1%)1 (<1%)10 (<1%)Thoracic surgery 2 648 (4.5%)522 (10.5%)3 170 (5.0%)Urinary system 2 252 (3.8%)65 (1.3%)2 317 (3.7%)High risk surgeryADDIN CSL_CITATION { "citationItems" : [ { "id" : "ITEM-1", "itemData" : { "DOI" : "10.1097/ALN.0b013e31829ce8fd", "ISBN" : "1528-1175", "ISSN" : "1528-1175", "PMID" : "23756454", "abstract" : "BACKGROUND The allocation of intensive care unit (ICU) beds for postoperative patients is a challenging daily task that could be assisted by the real-time detection of ICU needs. The goal of this study was to develop and validate an intraoperative predictive model for unplanned postoperative ICU use. METHODS With the use of anesthesia information management system, postanesthesia care unit, and scheduling data, a data set was derived from adult in-patient noncardiac surgeries. Unplanned ICU admissions were identified (4,847 of 71,996; 6.7%), and a logistic regression model was developed for predicting unplanned ICU admission. The model performance was tested using bootstrap validation and compared with the Surgical Apgar Score using area under the curve for the receiver operating characteristic. RESULTS The logistic regression model included 16 variables: age, American Society of Anesthesiologists physical status, emergency case, surgical service, and 12 intraoperative variables. The area under the curve was 0.905 (95% CI, 0.900-0.909). The bootstrap validation model area under the curves were 0.513 at booking, 0.688 at 3 h before case end, 0.738 at 2 h, 0.791 at 1 h, and 0.809 at case end. The Surgical Apgar Score area under the curve was 0.692. Unplanned ICU admissions had more ICU-free days than planned ICU admissions (5 vs. 4; P < 0.001) and similar mortality (5.6 vs. 6.0%; P = 0.248). CONCLUSIONS The authors have developed and internally validated an intraoperative predictive model for unplanned postoperative ICU use. Incorporation of this model into a real-time data sniffer may improve the process of allocating ICU beds for postoperative patients.", "author" : [ { "dropping-particle" : "", "family" : "Wanderer", "given" : "J P", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Anderson-Dam", "given" : "J", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Levine", "given" : "W", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Bittner", "given" : "E A", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" } ], "container-title" : "Anesthesiology", "id" : "ITEM-1", "issue" : "3", "issued" : { "date-parts" : [ [ "2013" ] ] }, "page" : "516-24", "title" : "Development and validation of an intraoperative predictive model for unplanned postoperative intensive care.", "type" : "article-journal", "volume" : "119" }, "uris" : [ "" ] } ], "mendeley" : { "formattedCitation" : "³", "plainTextFormattedCitation" : "3", "previouslyFormattedCitation" : "³" }, "properties" : { }, "schema" : "" }3 36.12 (17.1)58.27 (13.3)0.94437.85 (17.9)Procedural complexityADDIN CSL_CITATION { "citationItems" : [ { "id" : "ITEM-1", "itemData" : { "DOI" : "10.1097/ALN.0b013e318219d5f9", "ISBN" : "1528-1175 (Electronic)\r0003-3022 (Linking)", "PMID" : "21519230", "abstract" : "BACKGROUND: Optimal risk adjustment is a requisite precondition for monitoring quality of care and interpreting public reports of hospital outcomes. Current risk-adjustment measures have been criticized for including baseline variables that are difficult to obtain and inadequately adjusting for high-risk patients. The authors sought to develop highly predictive risk-adjustment models for 30-day mortality and morbidity based only on a small number of preoperative baseline characteristics. They included the Current Procedural Terminology code corresponding to the patient's primary procedure (American Medical Association), American Society of Anesthesiologists Physical Status, and age (for mortality) or hospitalization (inpatient vs. outpatient, for morbidity). METHODS: Data from 635,265 noncardiac surgical patients participating in the American College of Surgeons National Surgical Quality Improvement Program between 2005 and 2008 were analyzed. The authors developed a novel algorithm to aggregate sparsely represented Current Procedural Terminology codes into logical groups and estimated univariable Procedural Severity Scores-one for mortality and morbidity, respectively-for each aggregated group. These scores were then used as predictors in developing respective risk quantification models. Models were validated with c-statistics, and calibration was assessed using observed-to-expected ratios of event frequencies for clinically relevant strata of risk. RESULTS: The risk quantification models demonstrated excellent predictive accuracy for 30-day postoperative mortality (c-statistic [95% CI] 0.915 [0.906-0.924]) and morbidity (0.867 [0.858-0.876]). Even in high-risk patients, observed rates calibrated well with estimated probabilities for mortality (observed-to-expected ratio: 0.93 [0.81-1.06]) and morbidity (0.99 [0.93-1.05]). CONCLUSION: The authors developed simple risk-adjustment models, each based on three easily obtained variables, that allow for objective quality-of-care monitoring among hospitals.", "author" : [ { "dropping-particle" : "", "family" : "Dalton", "given" : "J E", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Kurz", "given" : "A", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Turan", "given" : "A", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Mascha", "given" : "E J", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Sessler", "given" : "D I", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Saager", "given" : "L", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" } ], "container-title" : "Anesthesiology", "edition" : "2011/04/27", "id" : "ITEM-1", "issue" : "6", "issued" : { "date-parts" : [ [ "2011" ] ] }, "language" : "eng", "note" : "Dalton, Jarrod E\nKurz, Andrea\nTuran, Alparslan\nMascha, Edward J\nSessler, Daniel I\nSaager, Leif\nRandomized Controlled Trial\nValidation Studies\nUnited States\nAnesthesiology\nAnesthesiology. 2011 Jun;114(6):1336-44. doi: 10.1097/ALN.0b013e318219d5f9.", "page" : "1336-1344", "title" : "Development and validation of a risk quantification index for 30-day postoperative mortality and morbidity in noncardiac surgical patients", "type" : "article-journal", "volume" : "114" }, "uris" : [ "" ] } ], "mendeley" : { "formattedCitation" : "²", "plainTextFormattedCitation" : "2", "previouslyFormattedCitation" : "²" }, "properties" : { }, "schema" : "" }2 36.12 (17.1)58.27 (13.3)1.44637.85 (17.9)Values are frequencies (percentages), mean (standard deviation) or median [interquartile range]. eTable 3. Characteristics of propensity score-matched study population by tertile of propensity score.CharacteristicsLevel1st Tertile(N =2 354)2nd Tertile(N = 2 353)3rd Tertile(N = 2 353)p valuePostoperative admission to ICU 1 177 (50%)952 (40.5%)1 401 (59.5%)Postoperative admission to floor 1 177(50%)1 401 (59.5%)952 (40.5%)Men 1 201 (51.0%)1 168 (49.6%)1 203 (51.1%)0.520American Society of Anesthesiologists physical status classification1102 (4.3%)56 (2.4%)61 (2.6%)<0.00121 104 (46.9%)1 029 (43.7%)1 093 (46.5%)31 068 (45.4%)1 195 (50.8%)1 064 (45.2%)480 (3.4%)72 (3.1%)130 (5.5%)50 (0.0%)1 (<1%)5 (0.2%)Age (years) 59.3 (16.8)59.3 (15.3)56.1 (15.6)<0.001Body mass index (kg m-2) 28.4 (7.3)27.7 (6.1)27.6 (5.9)<0.001Charlson Comorbidity IndexADDIN CSL_CITATION { "citationItems" : [ { "id" : "ITEM-1", "itemData" : { "DOI" : "10.1136/bmjopen-2015-008990", "ISSN" : "2044-6055", "PMID" : "26351192", "abstract" : "OBJECTIVES Our primary objective was to compare the utility of the Deyo-Charlson Comorbidity Index (DCCI) and Elixhauser-van Walraven Comorbidity Index (EVCI) to predict mortality in intensive care unit (ICU) patients. SETTING Observational study of 2 tertiary academic centres located in Boston, Massachusetts. PARTICIPANTS The study cohort consisted of 59,816 patients from admitted to 12 ICUs between January 2007 and December 2012. PRIMARY AND SECONDARY OUTCOME For the primary analysis, receiver operator characteristic curves were constructed for mortality at 30, 90, 180, and 365\u2005days using the DCCI as well as EVCI, and the areas under the curve (AUCs) were compared. Subgroup analyses were performed within different types of ICUs. Logistic regression was used to add age, race and sex into the model to determine if there was any improvement in discrimination. RESULTS At 30\u2005days, the AUC for DCCI versus EVCI was 0.65 (95% CI 0.65 to 0.67) vs 0.66 (95% CI 0.65 to 0.66), p=0.02. Discrimination improved at 365\u2005days for both indices (AUC for DCCI 0.72 (95% CI 0.71 to 0.72) vs AUC for EVCI 0.72 (95% CI 0.72 to 0.72), p=0.46). The DCCI and EVCI performed similarly across ICUs at all time points, with the exception of the neurosciences ICU, where the DCCI was superior to EVCI at all time points (1-year mortality: AUC 0.73 (95% CI 0.72 to 0.74) vs 0.68 (95% CI 0.67 to 0.70), p=0.005). The addition of basic demographic information did not change the results at any of the assessed time points. CONCLUSIONS The DCCI and EVCI were comparable at predicting mortality in critically ill patients. The predictive ability of both indices increased when assessing long-term outcomes. Addition of demographic data to both indices did not affect the predictive utility of these indices. Further studies are needed to validate our findings and to determine the utility of these indices in clinical practice.", "author" : [ { "dropping-particle" : "", "family" : "Ladha", "given" : "Karim S", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Zhao", "given" : "Kevin", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Quraishi", "given" : "Sadeq A", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Kurth", "given" : "Tobias", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Eikermann", "given" : "Matthias", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Kaafarani", "given" : "Haytham M A", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Klein", "given" : "Eric N", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Seethala", "given" : "Raghu", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Lee", "given" : "Jarone", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" } ], "container-title" : "BMJ open", "id" : "ITEM-1", "issue" : "9", "issued" : { "date-parts" : [ [ "2015", "9", "8" ] ] }, "page" : "e008990", "publisher" : "British Medical Journal Publishing Group", "title" : "The Deyo-Charlson and Elixhauser-van Walraven Comorbidity Indices as predictors of mortality in critically ill patients.", "type" : "article-journal", "volume" : "5" }, "uris" : [ "" ] } ], "mendeley" : { "formattedCitation" : "⁵", "plainTextFormattedCitation" : "5", "previouslyFormattedCitation" : "⁵" }, "properties" : { }, "schema" : "" }5 2 [0-5]3 [1-8]2 [1-6]<0.001Emergency case 194 (8.2%)169 (7.2%)197 (8.4%)0.250Duration of surgery (hours) 3.5 [2.4-4.8]5.0 [3.6-6.6]5.4 [4.0-7.5]<0.001Work relative value unit 3.47 (1.23)4.40 (0.84)4.83 (0.56)<0.001Duration of intraoperative hypotension (minutes) 0 [0]0 [0-5]0 [0-5]<0.001Intraoperative colloid volume (ml)ADDIN CSL_CITATION { "citationItems" : [ { "id" : "ITEM-1", "itemData" : { "DOI" : "10.1097/SLA.0000000000002220", "ISSN" : "0003-4932", "author" : [ { "dropping-particle" : "", "family" : "Shin", "given" : "Christina H.", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Long", "given" : "Dustin R.", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "McLean", "given" : "Duncan", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Grabitz", "given" : "Stephanie D.", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Ladha", "given" : "Karim", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Timm", "given" : "Fanny P.", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Thevathasan", "given" : "Tharusan", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Pieretti", "given" : "Alberto", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Ferrone", "given" : "Cristina", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Hoeft", "given" : "Andreas", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Scheeren", "given" : "Thomas W. L.", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Thompson", "given" : "Boyd Taylor", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Kurth", "given" : "Tobias", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Eikermann", "given" : "Matthias", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" } ], "container-title" : "Annals of Surgery", "id" : "ITEM-1", "issued" : { "date-parts" : [ [ "2017", "3" ] ] }, "page" : "1", "title" : "Effects of Intraoperative Fluid Management on Postoperative Outcomes", "type" : "article-journal" }, "uris" : [ "" ] } ], "mendeley" : { "formattedCitation" : "⁶", "plainTextFormattedCitation" : "6", "previouslyFormattedCitation" : "⁶" }, "properties" : { }, "schema" : "" }6 113 (300)200 (415)128 (336)<0.001 eTable 3. Characteristics of propensity score-matched study population by tertile of propensity score (continued).Intraoperative crystalloid volume (ml)ADDIN CSL_CITATION { "citationItems" : [ { "id" : "ITEM-1", "itemData" : { "DOI" : "10.1097/SLA.0000000000002220", "ISSN" : "0003-4932", "author" : [ { "dropping-particle" : "", "family" : "Shin", "given" : "Christina H.", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Long", "given" : "Dustin R.", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "McLean", "given" : "Duncan", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Grabitz", "given" : "Stephanie D.", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Ladha", "given" : "Karim", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Timm", "given" : "Fanny P.", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Thevathasan", "given" : "Tharusan", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Pieretti", "given" : "Alberto", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Ferrone", "given" : "Cristina", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Hoeft", "given" : "Andreas", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Scheeren", "given" : "Thomas W. L.", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Thompson", "given" : "Boyd Taylor", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Kurth", "given" : "Tobias", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Eikermann", "given" : "Matthias", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" } ], "container-title" : "Annals of Surgery", "id" : "ITEM-1", "issued" : { "date-parts" : [ [ "2017", "3" ] ] }, "page" : "1", "title" : "Effects of Intraoperative Fluid Management on Postoperative Outcomes", "type" : "article-journal" }, "uris" : [ "" ] } ], "mendeley" : { "formattedCitation" : "⁶", "plainTextFormattedCitation" : "6", "previouslyFormattedCitation" : "⁶" }, "properties" : { }, "schema" : "" }6 2 185 (2 281)2 714 (2 932)3 202 (3 749)<0.001Intraoperative heart rateBradycardia (<60)443 (18.8%)523 (22.2%)748 (31.8%)<0.001Normocardia (60-100)1 849 (78.5%)1 766 (75.1%)1 555 (66.1%)Tachycardia (≥100)62 (2.6%)64 (2.7%)50 (2.1%)Packed red blood cells (units) 0.21 (0.73)0.37 (0.99)0.33 (1.01)<0.001Intraoperative PEEP(cm H2O)ADDIN CSL_CITATION { "citationItems" : [ { "id" : "ITEM-1", "itemData" : { "DOI" : "10.1136/bmj.h3646", "ISSN" : "1756-1833", "PMID" : "26174419", "abstract" : "OBJECTIVE To evaluate the effects of intraoperative protective ventilation on major postoperative respiratory complications and to define safe intraoperative mechanical ventilator settings that do not translate into an increased risk of postoperative respiratory complications. DESIGN Hospital based registry study. SETTING Academic tertiary care hospital and two affiliated community hospitals in Massachusetts, United States. PARTICIPANTS 69 265 consecutively enrolled patients over the age of 18 who underwent a non-cardiac surgical procedure between January 2007 and August 2014 and required general anesthesia with endotracheal intubation. INTERVENTIONS Protective ventilation, defined as a median positive end expiratory pressure (PEEP) of 5 cmH2O or more, a median tidal volume of less than 10 mL/kg of predicted body weight, and a median plateau pressure of less than 30 cmH2O. MAIN OUTCOME MEASURE Composite outcome of major respiratory complications, including pulmonary edema, respiratory failure, pneumonia, and re-intubation. RESULTS Of the 69 265 enrolled patients 34 800 (50.2%) received protective ventilation and 34 465 (49.8%) received non-protective ventilation intraoperatively. Protective ventilation was associated with a decreased risk of postoperative respiratory complications in multivariable regression (adjusted odds ratio 0.90, 95% confidence interval 0.82 to 0.98, P=0.013). The results were similar in the propensity score matched cohort (odds ratio 0.89, 95% confidence interval 0.83 to 0.97, P=0.004). A PEEP of 5 cmH2O and median plateau pressures of 16 cmH2O or less were associated with the lowest risk of postoperative respiratory complications. CONCLUSIONS Intraoperative protective ventilation was associated with a decreased risk of postoperative respiratory complications. A PEEP of 5 cmH2O and a plateau pressure of 16 cmH2O or less were identified as protective mechanical ventilator settings. These findings suggest that protective thresholds differ for intraoperative ventilation in patients with normal lungs compared with those used for patients with acute lung injury.", "author" : [ { "dropping-particle" : "", "family" : "Ladha", "given" : "Karim", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Vidal Melo", "given" : "Marcos F", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "McLean", "given" : "Duncan J", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Wanderer", "given" : "Jonathan P", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Grabitz", "given" : "Stephanie D", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Kurth", "given" : "Tobias", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Eikermann", "given" : "Matthias", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" } ], "container-title" : "Bmj", "id" : "ITEM-1", "issued" : { "date-parts" : [ [ "2015" ] ] }, "page" : "h3646", "title" : "Intraoperative protective mechanical ventilation and risk of postoperative respiratory complications: hospital based registry study.", "type" : "article-journal", "volume" : "351" }, "uris" : [ "" ] } ], "mendeley" : { "formattedCitation" : "⁷", "plainTextFormattedCitation" : "7", "previouslyFormattedCitation" : "⁷" }, "properties" : { }, "schema" : "" }70-5868 (36.9%)1 001 (42.5%)987 (41.9%)<0.00151 117 (47.5%)1 078 (45.8%)1 070 (45.5%)>5369 (15.7%)274 (11.6%)296 (12.6%)Intraoperative plateau pressure (cm H2O)ADDIN CSL_CITATION { "citationItems" : [ { "id" : "ITEM-1", "itemData" : { "DOI" : "10.1136/bmj.h3646", "ISSN" : "1756-1833", "PMID" : "26174419", "abstract" : "OBJECTIVE To evaluate the effects of intraoperative protective ventilation on major postoperative respiratory complications and to define safe intraoperative mechanical ventilator settings that do not translate into an increased risk of postoperative respiratory complications. DESIGN Hospital based registry study. SETTING Academic tertiary care hospital and two affiliated community hospitals in Massachusetts, United States. PARTICIPANTS 69 265 consecutively enrolled patients over the age of 18 who underwent a non-cardiac surgical procedure between January 2007 and August 2014 and required general anesthesia with endotracheal intubation. INTERVENTIONS Protective ventilation, defined as a median positive end expiratory pressure (PEEP) of 5 cmH2O or more, a median tidal volume of less than 10 mL/kg of predicted body weight, and a median plateau pressure of less than 30 cmH2O. MAIN OUTCOME MEASURE Composite outcome of major respiratory complications, including pulmonary edema, respiratory failure, pneumonia, and re-intubation. RESULTS Of the 69 265 enrolled patients 34 800 (50.2%) received protective ventilation and 34 465 (49.8%) received non-protective ventilation intraoperatively. Protective ventilation was associated with a decreased risk of postoperative respiratory complications in multivariable regression (adjusted odds ratio 0.90, 95% confidence interval 0.82 to 0.98, P=0.013). The results were similar in the propensity score matched cohort (odds ratio 0.89, 95% confidence interval 0.83 to 0.97, P=0.004). A PEEP of 5 cmH2O and median plateau pressures of 16 cmH2O or less were associated with the lowest risk of postoperative respiratory complications. CONCLUSIONS Intraoperative protective ventilation was associated with a decreased risk of postoperative respiratory complications. A PEEP of 5 cmH2O and a plateau pressure of 16 cmH2O or less were identified as protective mechanical ventilator settings. These findings suggest that protective thresholds differ for intraoperative ventilation in patients with normal lungs compared with those used for patients with acute lung injury.", "author" : [ { "dropping-particle" : "", "family" : "Ladha", "given" : "Karim", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Vidal Melo", "given" : "Marcos F", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "McLean", "given" : "Duncan J", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Wanderer", "given" : "Jonathan P", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Grabitz", "given" : "Stephanie D", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Kurth", "given" : "Tobias", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Eikermann", "given" : "Matthias", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" } ], "container-title" : "Bmj", "id" : "ITEM-1", "issued" : { "date-parts" : [ [ "2015" ] ] }, "page" : "h3646", "title" : "Intraoperative protective mechanical ventilation and risk of postoperative respiratory complications: hospital based registry study.", "type" : "article-journal", "volume" : "351" }, "uris" : [ "" ] } ], "mendeley" : { "formattedCitation" : "⁷", "plainTextFormattedCitation" : "7", "previouslyFormattedCitation" : "⁷" }, "properties" : { }, "schema" : "" }70-16491 (20.9%)467 (19.8%)493 (21.0%)0.00416179 (7.6%)252 (10.7%)238 (10.1%)>161 684 (71.5%)1 634 (69.4%)1 622 (68.9%) eTable 3. Characteristics of propensity score-matched study population by tertile of propensity score (continued).Intraoperative vasopressor useNo409 (17.4%)251 (10.7%)297 (12.6%)<0.001Yes1 945 (82.6%)2 102 (89.3%)2 056 (87.4%)Intraoperative neuromuscular blocking agents (multiples of ED95)8 1st quintile296 (12.6%)270 (11.5%)270 (11.5%)<0.0012nd quintile349 (14.8%)159 (6.8%)156 (6.6%)3rd quintile444 (18.9%)235 (10.0%)231 (9.8%)4th quintile587 (24.9%)465 (19.8%)464 (19.7%)5th quintile678 (28.8%)1 224 (52.0%)1 232 (52.4%)Median intraoperative SaO2/FiO2-ratio 0-150487 (20.7%)795 (33.8%)446 (19.0%)<0.001150-200862 (36.6%)704 (29.9%)537 (22.8%)200-300684 (29.1%)544 (23.1%)646 (27.5%)>300321 (13.6%)310 (13.2%)724 (30.8%) eTable 3. Characteristics of propensity score-matched study population by tertile of propensity score (continued)Principal surgical serviceVascular system220 (9.3%)314 (13.3%)475 (20.2%) Diagnostic radiology1 (<1%)1 (<1%)0 (0.0%) Digestive system691 (29.4%)649 (27.6%)284 (12.1%) Ear1 (<1%)4 (0.2%)3 (0.1%) Endocrine system119 (5.1%)24 (1.0%)16 (0.7%) Eye3 (0.1%)1 (<1%)4 (0.2%) Female genital organs99 (4.2%)31 (1.3%)5 (0.2%) Hematologic-lymphatic system41 (1.7%)19 (0.8%)9 (0.4%) Integumentary119 (5.1%)34 (1.4%)11 (0.5%) Male genital organs21 (0.9%)0 (0.0%)0 (0.0%) Miscellaneous2 (0.1%)1 (<1%)0 (0.0%) Musculoskeletal system519 (22.0%)146 (6.2%)36 (1.5%) Nervous system172 (7.3%)541 (23.0%)1280 (54.4%) Nose12 (0.5%)0 (0.0%)6 (0.3%) Obstetrics1 (<1%)1 (<1%)0 (0.0%) Respiratory system255 (10.8%)525 (22.3%)207 (8.8%) Urinary system78 (3.3%)62 (2.6%)17 (0.7%) High risk surgeryADDIN CSL_CITATION { "citationItems" : [ { "id" : "ITEM-1", "itemData" : { "DOI" : "10.1097/ALN.0b013e31829ce8fd", "ISBN" : "1528-1175", "ISSN" : "1528-1175", "PMID" : "23756454", "abstract" : "BACKGROUND The allocation of intensive care unit (ICU) beds for postoperative patients is a challenging daily task that could be assisted by the real-time detection of ICU needs. The goal of this study was to develop and validate an intraoperative predictive model for unplanned postoperative ICU use. METHODS With the use of anesthesia information management system, postanesthesia care unit, and scheduling data, a data set was derived from adult in-patient noncardiac surgeries. Unplanned ICU admissions were identified (4,847 of 71,996; 6.7%), and a logistic regression model was developed for predicting unplanned ICU admission. The model performance was tested using bootstrap validation and compared with the Surgical Apgar Score using area under the curve for the receiver operating characteristic. RESULTS The logistic regression model included 16 variables: age, American Society of Anesthesiologists physical status, emergency case, surgical service, and 12 intraoperative variables. The area under the curve was 0.905 (95% CI, 0.900-0.909). The bootstrap validation model area under the curves were 0.513 at booking, 0.688 at 3 h before case end, 0.738 at 2 h, 0.791 at 1 h, and 0.809 at case end. The Surgical Apgar Score area under the curve was 0.692. Unplanned ICU admissions had more ICU-free days than planned ICU admissions (5 vs. 4; P < 0.001) and similar mortality (5.6 vs. 6.0%; P = 0.248). CONCLUSIONS The authors have developed and internally validated an intraoperative predictive model for unplanned postoperative ICU use. Incorporation of this model into a real-time data sniffer may improve the process of allocating ICU beds for postoperative patients.", "author" : [ { "dropping-particle" : "", "family" : "Wanderer", "given" : "J P", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Anderson-Dam", "given" : "J", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Levine", "given" : "W", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Bittner", "given" : "E A", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" } ], "container-title" : "Anesthesiology", "id" : "ITEM-1", "issue" : "3", "issued" : { "date-parts" : [ [ "2013" ] ] }, "page" : "516-24", "title" : "Development and validation of an intraoperative predictive model for unplanned postoperative intensive care.", "type" : "article-journal", "volume" : "119" }, "uris" : [ "" ] } ], "mendeley" : { "formattedCitation" : "³", "plainTextFormattedCitation" : "3", "previouslyFormattedCitation" : "³" }, "properties" : { }, "schema" : "" }31447 (61.5%)2043 (86.8%)2236 (95.0%)<0.001Procedural complexityADDIN CSL_CITATION { "citationItems" : [ { "id" : "ITEM-1", "itemData" : { "DOI" : "10.1097/ALN.0b013e318219d5f9", "ISBN" : "1528-1175 (Electronic)\r0003-3022 (Linking)", "PMID" : "21519230", "abstract" : "BACKGROUND: Optimal risk adjustment is a requisite precondition for monitoring quality of care and interpreting public reports of hospital outcomes. Current risk-adjustment measures have been criticized for including baseline variables that are difficult to obtain and inadequately adjusting for high-risk patients. The authors sought to develop highly predictive risk-adjustment models for 30-day mortality and morbidity based only on a small number of preoperative baseline characteristics. They included the Current Procedural Terminology code corresponding to the patient's primary procedure (American Medical Association), American Society of Anesthesiologists Physical Status, and age (for mortality) or hospitalization (inpatient vs. outpatient, for morbidity). METHODS: Data from 635,265 noncardiac surgical patients participating in the American College of Surgeons National Surgical Quality Improvement Program between 2005 and 2008 were analyzed. The authors developed a novel algorithm to aggregate sparsely represented Current Procedural Terminology codes into logical groups and estimated univariable Procedural Severity Scores-one for mortality and morbidity, respectively-for each aggregated group. These scores were then used as predictors in developing respective risk quantification models. Models were validated with c-statistics, and calibration was assessed using observed-to-expected ratios of event frequencies for clinically relevant strata of risk. RESULTS: The risk quantification models demonstrated excellent predictive accuracy for 30-day postoperative mortality (c-statistic [95% CI] 0.915 [0.906-0.924]) and morbidity (0.867 [0.858-0.876]). Even in high-risk patients, observed rates calibrated well with estimated probabilities for mortality (observed-to-expected ratio: 0.93 [0.81-1.06]) and morbidity (0.99 [0.93-1.05]). CONCLUSION: The authors developed simple risk-adjustment models, each based on three easily obtained variables, that allow for objective quality-of-care monitoring among hospitals.", "author" : [ { "dropping-particle" : "", "family" : "Dalton", "given" : "J E", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Kurz", "given" : "A", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Turan", "given" : "A", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Mascha", "given" : "E J", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Sessler", "given" : "D I", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Saager", "given" : "L", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" } ], "container-title" : "Anesthesiology", "edition" : "2011/04/27", "id" : "ITEM-1", "issue" : "6", "issued" : { "date-parts" : [ [ "2011" ] ] }, "language" : "eng", "note" : "Dalton, Jarrod E\nKurz, Andrea\nTuran, Alparslan\nMascha, Edward J\nSessler, Daniel I\nSaager, Leif\nRandomized Controlled Trial\nValidation Studies\nUnited States\nAnesthesiology\nAnesthesiology. 2011 Jun;114(6):1336-44. doi: 10.1097/ALN.0b013e318219d5f9.", "page" : "1336-1344", "title" : "Development and validation of a risk quantification index for 30-day postoperative mortality and morbidity in noncardiac surgical patients", "type" : "article-journal", "volume" : "114" }, "uris" : [ "" ] } ], "mendeley" : { "formattedCitation" : "²", "plainTextFormattedCitation" : "2", "previouslyFormattedCitation" : "²" }, "properties" : { }, "schema" : "" }247.87 (15.16)59.64 (12.09)60.72 (11.61)<0.001Values are frequencies (percentages), mean (standard deviation) or median [interquartile range]. eTable 4. Subgroup analyses: Association between postoperative ICU vs. ward admission and postoperative hospital length of stay (primary outcome) in patient sub-cohorts.Sub-cohortsICU vs. ward admitted patientsPropensity score1st tertile2nd tertile3rd tertileIncident rate ratio [95% Confidence Interval], p valueUnmatched cohort4 950 vs. 58 5162.70 [2.40-3.03], p<0.0012.07 [1.93-2.22], p<0.0011.36 [1.33-1.39], p<0.001Unmatched cohort including intraoperative arterial blood gas1 560 vs.3 2301.32 [1.15-1.51], p<0.0011.31 [1.22-1.40], p<0.0010.96 [0.89-1.03], p=0.255Propensity matched cohort including intraoperative arterial blood gas751 vs. 7511.28 [1.17-1.41], p<0.0011.06 [0.95-1.17], p<0.0010.93 [0.84-1.03], p=0.173High risk surgical procedures32 664 vs.2 6641.67 [1.56-1.78], p<0.0011.15 [1.09-1.22], p<0.0010.95 [0.89-1.01], p=0.086Non-high risk surgical procedures647 vs. 6471.73 [1.52-1.97], p<0.0011.50 [1.33-1.68], p<0.0011.16 [1.04-1.30], p=0.007High comorbidity (CCI ≥3)1 738 vs.1 7381.79 [1.65-1.93], p<0.0011.17 [1.09-1.25], p<0.0010.91 [0.85-0.98], p=0.016Low comorbidity (CCI <3)1 843 vs.1 8431.64 [1.52-1.77], p<0.0011.13 [1.05-1.21], p=0.0010.96 [0.86-0.99], p=0.025ASA high (≥3)1 895 vs.1 8951.71 [1.58-1.84], p<0.0011.13 [1.06-1.21], p<0.0010.95 [0.88-1.02], p=0.883 eTable 4. Subgroup analyses: Association between postoperative ICU vs. ward admission and postoperative hospital length of stay (primary outcome) in patient sub-cohorts. (continued)ASA low (<3)1 612 vs.1 6121.57 [1.44-1.71], p<0.0011.28 [1.19-1.37], p<0.0010.97 [0.91-1.04], p=0.406Long duration of surgery(≥120 minutes)3 288 vs.3 2881.63 [1.54-1.73], p<0.0011.13 [1.08-1.19], p<0.0010.95 [0.90-1.0], p=0.071Non-emergent surgery3 142 vs.3 1421.71 [1.61-1.81], p<0.0011.15 [1.09-1.21], p<0.0010.92 [0.87-0.97], p=0.003Severe obesity (BMI ≥35)466 vs. 4661.85 [1.58-2.15], p<0.0011.08 [0.93-1.24], p=0.3250.83 [0.71-0.96], p=0.012Non-severe obesity (BMI <35)3 142 vs.3 1421.66 [1.56-1.76], p<0.0011.16 [1.09-1.22], p<0.0010.91 [0.86-0.96], p<0.001Thoracic surgery850 vs. 8501.53 [1.38-1.69], p<0.0011.24 [1.13-1.36], p<0.0010.95 [0.85-1.05], p=0.306Musculoskeletal surgery341 vs. 3411.69 [1.43-2.0], p<0.0011.23 [1.05-1.45], p=0.0131.10 [0.96-1.26], p=0.166Abdominal surgery785 vs. 7851.64 [1.46-1.84], p<0.0011.35 [1.22-1.49], p<0.0011.20 [1.10-1.31], p<0.001Male patients1 714 vs.1 7141.70 [1.57-1.85], p<0.0011.18 [1.10-1.26], p<0.0010.94 [0.87-1.02], p=0.121Female patients1 847 vs.1 8471.66 [1.54-1.79], p<0.0011.22 [1.14-1.31], p<0.0010.80 [0.75-0.86], p<0.001Male and female genital organ surgery72 vs. 722.35 [1.60-3.43], p<0.0011.83 [1.28-2.62], p=0.0011.29 [0.93-1.80], p=0.130Multivariable negative binomial and logistic regression models have been used to report results. eTable 5. Sensitivity analyses: Association between postoperative ICU vs. ward admission and postoperative hospital length of stay (primary outcome)Sensitivity analysisICU vs. ward admitted patientsPropensity score1st tertile2nd tertile3rd tertileIncident rate ratio [95% Confidence Interval], p valueAdjusting for SPOSA11.75 [1.66-1.85], p<0.0011.15 [1.10-1.21], p<0.0010.90 [0.85-0.94], p<0.001Adjusting for severe respiratory comorbidity (home oxygen requirement)3 530 vs. 3 5301.76 [1.65-1.88], p<0.0011.18 [1.12-1.24], p<0.0010.89 [0.84-0.94], p=0.001Adjusting for severe renal comorbidity (need for dialysis)3 530 vs. 3 5301.76, [1.65-1.88], p<0.0011.17, [1.12-1.24], p<0.0010.90, [0.85-0.95], p=0.001Adjusting for severe cardiac comorbidity (implanted cardiac defibrillator)3 530 vs. 3 5301.76, [1.65-1.88], p<0.0011.18, [1.12-1.24], p<0.0010.89, [0.84-0.95], p=0.001Adjusting for remote history of acute myocardial infarction (within one month prior to surgery)3 530 vs. 3 5301.76, [1.65-1.87], p<0.0011.18, [1.12-1.24], p<0.0010.89, [0.84-0.95], p<0.001Postoperative hospital length of stay defined as 28 days for patients who died in the hospital1.72 [1.63-1.83], p<0.0011.18 [1.12-1.24], p<0.0010.92 [0.87-0.97], p=0.002Cox regression analysis adjusting for competing risk of death1.87*, [1.72-2.04], p<0.0011.23*, [1.13-1.34], p<0.0010.87*,[0.81-0.95], p=0.001Adjusting for year of surgery3 530 vs. 3 5301.76 [1.65-1.88], p<0.0011.18 [1.12-1.24], p<0.0010.89 [0.84-0.94], p=0.001Adjusting for number of attending handovers3 525 vs. 3 5251.82 [1.71-1.93], p<0.0011.15 [1.09-1.21], p<0.0010.89 [0.84-0.94], p<0.001Mixed effect negative binomial regression analysis, adjusting for anesthesia provider preferences3 530 vs. 3 5301.68 [1.59-1.78], p<0.0011.16 [1.11-1.22], p<0.0010.89 [0.84-0.94], p<0.001Mixed effect negative binomial regression analysis, adjusting for surgical provider preferences3 530 vs. 3 5301.63 [1.54-1.73], p<0.0011.27 [1.20-1.33], p<0.0010.89 [0.84-0.94], p<0.001*Hazard ratio, derived from Cox regressioneTable 6. Summary description of surgical ICUs at the Massachusetts General HospitalDescription of surgical ICUs at MGHAbsolute numbers or ratiosSurgical ICUs6Surgical ICU beds93Adult Surgical ICU beds88ICU : ward bed ratio1.06 : 1Patient : nurse ratio1.15 : 1ICU: Intensive Care UniteTable 7. Predictors for calculating the propensity score of postoperative ICU or ward admissionPredictorDefinitionCategorization and/or referencePatient demographicsAgeContinuousSexMale vs. femaleBody mass index kg/m2ContinuousPatient comorbidityAmerican Society of Anesthesiologists (ASA) physical status classificationCategories: I - VCharlson Comorbidity IndexBased on ICD-9-CM diagnostic codes“Quan H, Sundararajan V, Halfon P, et al. Coding algorithms for defining comorbidities in ICD-9-CM and ICD-10 administrative data. Medical Care 2005; 43: 1130–9.”: continuousProcedural FactorPrincipal surgical proceduresICD-9 Volume-3 based anatomical region of surgery Vascular system, diagnostic radiological procedure, digestive system, endocrine system, female genital organs, hematolymphoid system, integumentary, male genital organs, musculoskeletal, nervous system, obstetrics, thoracic, urinary system, ear/nose/throat: categoricalDuration of surgeryTime between intubation and extubation, hoursContinuousProcedural risk for postoperative morbidity Procedural Severity Score (PSS) for morbidity, based on current procedural terminology (CPT) codes“Dalton JE, Kurz A, Turan A, Mascha EJ, Sessler DI, Saager L. Development and validation of a risk quantification index for 30-day postoperative mortality and morbidity in noncardiac surgical patients. Anesthesiology. 2011;114(6):1336-1344”: continuousWork relative value unitsMarker for hospital costs and surgical complexity, based on Current Procedural Terminology (CPT) codes“Centers for Medicare & Medicaid Services”: equally-sized quintilesEmergency statusSurgical procedures that took place within 30 minutes of booking the operating roomLetter "E" following American Society of Anesthesiologists (ASA) Classification booking: binaryHigh risk surgerySurgeries with reintubation rates above average“Wanderer JP, Anderson-Dam J, Levine W, Bittner EA. Development and validation of an intraoperative predictive model for unplanned postoperative intensive care. Anesthesiology 2013;119:516–24.”: binaryIntraoperatively administered drugs/ productsVolume of intraoperative colloid fluid administrationAlbumin (5%/20%/25%), hextend (5%);in milliliter (ml)ContinuousVolume of intraoperative crystalloid fluid administrationRinger's lactate, normal saline (0.9%/0.45%/3%), D5W (dextrose 5% in water), sodium bicarbonate-D5W;in milliliter (ml)ContinuousMarker of estimated blood loss during surgery Number of transfused packed red blood cell unitsContinuousIntraoperative dose of non-depolarizing neuromuscular blocking agents (NMBA)Expressed as the sum of multiples of NMBA-specific ED95 (the median effective dose required to achieve a 95% reduction in maximal twitch response from baseline)“Reference Table 34-4: Miller RD, Eriksson LI, Fleisher LA, et al: Miller’s Anesthesia. Eighth edition. Philadelphia, PA, Saunders, 2014”: equally-sized quintilesIntraoperative use of vasopressorsEpinephrine, norepinephrine, phenylephrine, dopamineBinaryIntraoperative physiologic parametersIntraoperative heart rateBeats per minuteCategories: <60, 60-99, ≥100Intraoperative mechanical ventilator settingsMedian positive end expiratory pressure (PEEP) in mmHg, median plateau pressure in mmHgPEEP categorized: <5, 5, >5Plateau pressure categorized: <16, 16, >16SaO2/FiO2 ratioMedian intraoperative ratio of arterial oxygen saturation to fraction of inspired oxygenCategories: 0-149, 150-299, ≥300Duration of intraoperative arterial hypotensionTime in minutes with mean arterial blood pressure below 55 mmHgCategories in multiple of 5 minutes of duration of hypotensioneFigure 3. Propensity score (A), formula for calculation of the propensity score (B), receiver operating characteristic (ROC) curve (C) and reliability plot (D) for postoperative ICU admissionReferencesADDIN Mendeley Bibliography CSL_BIBLIOGRAPHY 1. Shin CH, Grabitz SD, Timm FP, Mueller N, Chhangani K, Ladha K, Devine S, Kurth T, Eikermann M. Development and validation of a Score for Preoperative Prediction of Obstructive Sleep Apnea (SPOSA) and its perioperative outcomes. BMC Anesthesiol 2017;17:71.2. Dalton JE, Kurz A, Turan A, Mascha EJ, Sessler DI, Saager L. Development and validation of a risk quantification index for 30-day postoperative mortality and morbidity in noncardiac surgical patients. Anesthesiology 2011;114:1336–44.3. Wanderer JP, Anderson-Dam J, Levine W, Bittner EA. Development and validation of an intraoperative predictive model for unplanned postoperative intensive care. Anesthesiology 2013;119:516–24.4. Schaller SJ, Anstey M, Blobner M, Edrich T, Grabitz SD, Gradwohl-Matis I, Heim M, Houle T, Kurth T, Latronico N, Lee J, Meyer MJ, Peponis T, Talmor D, Velmahos GC, Waak K, Walz JM, Zafonte R, Eikermann M, International Early SOMS-guided Mobilization Research Initiative. Early, goal-directed mobilisation in the surgical intensive care unit: a randomised controlled trial. Lancet 2016;388:1377–88.5. Ladha KS, Zhao K, Quraishi SA, Kurth T, Eikermann M, Kaafarani HMA, Klein EN, Seethala R, Lee J. The Deyo-Charlson and Elixhauser-van Walraven Comorbidity Indices as predictors of mortality in critically ill patients. BMJ Open 2015;5:e008990.6. Shin CH, Long DR, McLean D, Grabitz SD, Ladha K, Timm FP, Thevathasan T, Pieretti A, Ferrone C, Hoeft A, Scheeren TWL, Thompson BT, Kurth T, Eikermann M. Effects of Intraoperative Fluid Management on Postoperative Outcomes. Ann Surg 2017:1.7. Ladha K, Vidal Melo MF, McLean DJ, Wanderer JP, Grabitz SD, Kurth T, Eikermann M. Intraoperative protective mechanical ventilation and risk of postoperative respiratory complications: hospital based registry study. Bmj 2015;351:h3646.8. Thevathasan T, Shih SL, Safavi KC, Berger DL, Burns SM, Grabitz SD, Glidden RS, Zafonte RD, Eikermann M, Schneider JC. Association between intraoperative non-depolarising neuromuscular blocking agent dose and 30-day readmission after abdominal surgery. BJA Br J Anaesth 2017;119:595–605. ................
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