Lippincott Williams & Wilkins



Supplementary Table 1: Summary of the general demographic and surgical/medical model input features.Feature DescriptionAgePatient's age at time of admissionAcute Kidney Injury (AKI) ClassCategorical variable; one of: 1, 2, 3 or NA. Computed using method of Mehta et alADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1186/cc5713","author":[{"dropping-particle":"","family":"Mehta","given":"Ravindra L","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Kellum","given":"John A","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"V","family":"Shah","given":"Sudhir","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Molitoris","given":"Bruce A","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Ronco","given":"Claudio","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Warnock","given":"David G","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Levin","given":"Adeera","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Critical Care","id":"ITEM-1","issue":"2","issued":{"date-parts":[["2007"]]},"page":"R31","title":"Acute Kidney Injury Network: report of an initiative to improve outcomes in acute kidney injury","type":"article-journal","volume":"11"},"uris":[""]}],"mendeley":{"formattedCitation":"¹","plainTextFormattedCitation":"1","previouslyFormattedCitation":"¹"},"properties":{"noteIndex":0},"schema":""}1 Alcohol ConsumptionMeasured in oz / weekASA ScoreAmerican Society of Anesthesiologists (ASA) score. Categorical variable; one of: 1, 2, 3, 4, 5 or NA Body Mass IndexMeasured in kg / m2 DispositionWhere patient was discharged to. Categorical variable; one of: Home or self care, Home health service, Skilled nursing facility, OtherDuration of SurgeryMeasured in minutesDuration of Mechanical VentilationTotal duration (in hours) of mechanical ventilation post-operatively. Computed using method of Gabel et alADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1213/ANE.0000000000001997","PMID":"28431419","abstract":"BACKGROUND In medical practice today, clinical data registries have become a powerful tool for measuring and driving quality improvement, especially among multicenter projects. Registries face the known problem of trying to create dependable and clear metrics from electronic medical records data, which are typically scattered and often based on unreliable data sources. The Society for Thoracic Surgery (STS) is one such example, and it supports manually collected data by trained clinical staff in an effort to obtain the highest-fidelity data possible. As a possible alternative, our team designed an algorithm to test the feasibility of producing computer-derived data for the case of postoperative mechanical ventilation hours. In this article, we study and compare the accuracy of algorithm-derived mechanical ventilation data with manual data extraction. METHODS We created a novel algorithm that is able to calculate mechanical ventilation duration for any postoperative patient using raw data from our EPIC electronic medical record. Utilizing nursing documentation of airway devices, documentation of lines, drains, and airways, and respiratory therapist ventilator settings, the algorithm produced results that were then validated against the STS registry. This enabled us to compare our algorithm results with data collected by human chart review. Any discrepancies were then resolved with manual calculation by a research team member. RESULTS The STS registry contained a total of 439 University of California Los Angeles cardiac cases from April 1, 2013, to March 31, 2014. After excluding 201 patients for not remaining intubated, tracheostomy use, or for having 2 surgeries on the same day, 238 cases met inclusion criteria. Comparing the postoperative ventilation durations between the 2 data sources resulted in 158 (66%) ventilation durations agreeing within 1 hour, indicating a probable correct value for both sources. Among the discrepant cases, the algorithm yielded results that were exclusively correct in 75 (93.8%) cases, whereas the STS results were exclusively correct once (1.3%). The remaining 4 cases had inconclusive results after manual review because of a prolonged documentation gap between mechanical and spontaneous ventilation. In these cases, STS and algorithm results were different from one another but were both within the transition timespan. This yields an overall accuracy of 99.6% (95% confidence interval, 98.7%-100%) for the algorithm when compare…","author":[{"dropping-particle":"","family":"Gabel","given":"Eilon","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Hofer","given":"Ira S.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Satou","given":"Nancy","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Grogan","given":"Tristan","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Shemin","given":"Richard","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Mahajan","given":"Aman","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Cannesson","given":"Maxime","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Anesthesia & Analgesia","id":"ITEM-1","issue":"5","issued":{"date-parts":[["2017","5"]]},"page":"1423-1430","title":"Creation and Validation of an Automated Algorithm to Determine Postoperative Ventilator Requirements After Cardiac Surgery","type":"article-journal","volume":"124"},"uris":[""]}],"mendeley":{"formattedCitation":"²","plainTextFormattedCitation":"2","previouslyFormattedCitation":"²"},"properties":{"noteIndex":0},"schema":""}2EthnicityPatient's ethnicity. Categorical variable; one of: Cuban, Hispanic/Latino, Hispanic/Spanish Origin Other, Mexican, Not Hispanico or Latino, Null, Patient Refused, Puerto Rican, UnknownFinancial ClassPatent's financial class. Categorical variable; one of: Commercial, Group Health Plan, International Payor, Medi-Cal, Medi-Cal Assigned, Medicare, Medicare Assigned, Null, Other, Self-Pay, Tricare, UCLA Managed Care, Worker’s CompIndicator for Admission with Primary Diagnosis-Related Group (DRG) code0/1 to indicate whether patient was admitted with a primary diagnosis-related group (DRG) codeIndicator for Alcohol Use0/1 to indicate whether patient consumes alcoholIndicator for Anesthesia Type: Bier Block0/1 indicators for various types of anesthesia (patients may have more than one type)Indicator for Anesthesia Type: EpiduralIndicator for Anesthesia Type: GeneralIndicator for Anesthesia Type: MACIndicator for Anesthesia Type: MAC with LocalIndicator for Anesthesia Type: Regional BlockIndicator for Anesthesia Type: SpinalIndicator for Hemodialysis0/1 for whether patient is on inpatient dialysisIndicator for Missing Alcohol Use Indicator0/1 for whether alcohol use indicator is missingIndicator for Missing Alcohol Consumption0/1 for whether alcohol consumption is missingIndicator for Missing Age0/1 for whether age is missingIndicator for Pain Management0/1 for whether patient has history of pre-admission pain managementIndicator for Smoking0/1 for whether patient is a smokerIndicator for Tracheostomy 0/1 for whether patient was administered tracheostomy during admission Number of Admissions in Previous YearNumber of prior admissions in one year period before current admission Patient ClassCategorical variable; one of: Emergency, Inpatient, Outpatient, Overnight recovery, Same day admit, Surgery outpatientPrimary LanguageCategorical variable; one of: English, Spanish, OtherRaceCategorical variable; one of: Asian, Black, White, OtherRelative Time to Surgery from AdmissionMeasured in hoursRisk, Injury, Failure, Sustained Loss and End-Stage (RIFLE) ClassCategorical variable; one of: 1, 2, 3 or NA; computed using method of Bellomo et alADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"author":[{"dropping-particle":"","family":"Bellomo","given":"Rinaldo","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Kellum","given":"John A","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Ronco","given":"Claudio","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Intensive care medicine","id":"ITEM-1","issue":"3","issued":{"date-parts":[["2007"]]},"page":"409-413","publisher":"Springer","title":"Defining and classifying acute renal failure: from advocacy to consensus and validation of the RIFLE criteria","type":"article-journal","volume":"33"},"uris":[""]}],"mendeley":{"formattedCitation":"³","plainTextFormattedCitation":"3","previouslyFormattedCitation":"³"},"properties":{"noteIndex":0},"schema":""}3SexCategorical variable; one of: Male, FemaleSurgical Service LineCategorical variable; one of: Cardiac, General, Liver Transplant, Neurosurgery, Obstetrics and Gynecology, Ophthalmology, Oral and Maxillofacial, Orthopaedics, Other, Otolaryngology, Pediatric, Plastic, Radiation Oncology, Radiology, Surgical Oncology, Thoracic Surgery, Urology, VascularTotal Blood TransfusedMeasured in mLTotal Colloid TransfusedMeasured in mLTotal Estimated Blood LossMeasured in mLTotal Fluid TransfusedMeasured in mLWeightPatient's weight (in kg) at admissionReferences: ADDIN Mendeley Bibliography CSL_BIBLIOGRAPHY 1. Mehta RL, Kellum JA, Shah S V, Molitoris BA, Ronco C, Warnock DG, Levin A. Acute Kidney Injury Network: report of an initiative to improve outcomes in acute kidney injury. Crit Care 2007;11:R31.2. Gabel E, Hofer IS, Satou N, Grogan T, Shemin R, Mahajan A, Cannesson M. Creation and Validation of an Automated Algorithm to Determine Postoperative Ventilator Requirements After Cardiac Surgery. Anesth Analg 2017;124:1423–30.3. Bellomo R, Kellum JA, Ronco C. Defining and classifying acute renal failure: from advocacy to consensus and validation of the RIFLE criteria. Intensive Care Med 2007;33:409–13. ................
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