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VISION-UK Study 2 statistical analysis plan 1.0 14 May 2018 ChronoVISION-UK Statistical Analysis Plan – study 2: timing of surgery and risk of postoperative morbidity: Version 1.0 14 May 2018Short title ChronoVISION-UK. UK Research ethics committee reference MREC:10/WNo03/25Investigators: Ackland, Abbott, PearseContents TOC \o "1-3" \h \z \u 2.Background PAGEREF _Toc517383914 \h 33.Aim PAGEREF _Toc517383915 \h 44.Objectives PAGEREF _Toc517383916 \h 55.Initial descriptive analysis PAGEREF _Toc517383917 \h 5i.Participants PAGEREF _Toc517383918 \h 5ii.Baseline characteristics PAGEREF _Toc517383919 \h 66.Primary analysis PAGEREF _Toc517383920 \h 67.Secondary analyses PAGEREF _Toc517383921 \h 7iii.Individual domains of the Post Operative Morbidity Survey within the first 7 days of surgery. PAGEREF _Toc517383922 \h 7iv.Clavien-Dindo defined cardiovascular complications. PAGEREF _Toc517383923 \h 7v.Myocardial injury, as defined by raised plasma troponin >14ng/l following in-patient surgery PAGEREF _Toc517383924 \h 8vi.Post-operative hospital stay & admission to critical care PAGEREF _Toc517383925 \h 8vii.Post-operative mortality PAGEREF _Toc517383926 \h 88.Handling of missing data PAGEREF _Toc517383927 \h 8viii.Data missing from database PAGEREF _Toc517383928 \h 8ix.Sensitivity Analysis PAGEREF _Toc517383929 \h 99.References PAGEREF _Toc517383930 \h 910.Appendix. Dummy tables and figures PAGEREF _Toc517383931 \h 12x.Figure 1: Flow diagram PAGEREF _Toc517383932 \h 12xi.Figure 2: Kaplan meier plot for time to become morbidity free within first 7 days of surgery, stratified by time of surgery (AM/PM). PAGEREF _Toc517383933 \h 13xii.Figure 3: Kaplan meier plot for length of hospital stay (adjusted for death), stratified by time of surgery (AM/PM). PAGEREF _Toc517383934 \h 13xiii.Table 1: Baseline characteristics. PAGEREF _Toc517383935 \h 14xiv.Table 2 PAGEREF _Toc517383936 \h 15xv.Table 3: AM/PM timing of surgery and postoperative troponin rise PAGEREF _Toc517383937 \h 16xvi.Table 4: Outcomes after surgery- POMS-defined morbidity. PAGEREF _Toc517383938 \h 16xvii.Table 5: Outcomes after surgery- Clavien-Dindo grading PAGEREF _Toc517383939 \h 17xviii.Table 6: Post-operative hospital measures PAGEREF _Toc517383940 \h 17Background Over 230 million patients undergo surgery worldwide each year with reported hospital mortality between 1 and 4%. ADDIN EN.CITE <EndNote><Cite><Author>Weiser</Author><Year>2008</Year><RecNum>20396</RecNum><DisplayText><style face="superscript">1</style></DisplayText><record><rec-number>20396</rec-number><foreign-keys><key app="EN" db-id="peff0sz06tfzdzezt0kx5rsapzaxfwp9prps" timestamp="1375448440">20396</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Weiser, T. G.</author><author>Regenbogen, S. E.</author><author>Thompson, K. D.</author><author>Haynes, A. B.</author><author>Lipsitz, S. R.</author><author>Berry, W. R.</author><author>Gawande, A. A.</author></authors></contributors><auth-address>Department of Health Policy and Management, Harvard School of Public Health, Boston, MA, USA. tweiser@hsph.harvard.edu</auth-address><titles><title>An estimation of the global volume of surgery: a modelling strategy based on available data</title><secondary-title>Lancet</secondary-title></titles><periodical><full-title>Lancet</full-title></periodical><pages>139-44</pages><volume>372</volume><number>9633</number><edition>2008/06/28</edition><keywords><keyword>Adolescent</keyword><keyword>Adult</keyword><keyword>Aged</keyword><keyword>Female</keyword><keyword>Humans</keyword><keyword>Life Expectancy</keyword><keyword>Linear Models</keyword><keyword>Male</keyword><keyword>Middle Aged</keyword><keyword>Population Surveillance/*methods</keyword><keyword>Surgical Procedures, Operative/classification/economics/*statistics &amp; numerical</keyword><keyword>data</keyword><keyword>World Health Organization</keyword></keywords><dates><year>2008</year><pub-dates><date>Jul 12</date></pub-dates></dates><isbn>1474-547X (Electronic)&#xD;0140-6736 (Linking)</isbn><accession-num>18582931</accession-num><urls><related-urls><url>(08)60878-8&#xD;S0140-6736(08)60878-8 [pii]</electronic-resource-num><language>eng</language></record></Cite></EndNote>1 Complications following major surgery are a leading cause of morbidity and mortality, of which myocardial injury is particularly common occurring in around 25% patients (as measured using high-sensitivity troponin assays).PEVuZE5vdGU+PENpdGU+PEF1dGhvcj5Xcml0aW5nIENvbW1pdHRlZSBmb3IgdGhlPC9BdXRob3I+

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ADDIN EN.CITE.DATA 2,3 Coronary artery disease, as quantified preoperatively by CT angiogram, does not appear to be account for the majority of perioperative myocardial injury. ADDIN EN.CITE <EndNote><Cite><Author>Sheth</Author><Year>2012</Year><RecNum>16205</RecNum><DisplayText><style face="superscript">4</style></DisplayText><record><rec-number>16205</rec-number><foreign-keys><key app="EN" db-id="peff0sz06tfzdzezt0kx5rsapzaxfwp9prps" timestamp="0">16205</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Sheth, T.</author><author>Butler, C.</author><author>Chow, B.</author><author>Chan, M. T.</author><author>Mitha, A.</author><author>Nagele, P.</author><author>Tandon, V.</author><author>Stewart, L.</author><author>Graham, M.</author><author>Choi, G. Y.</author><author>Kisten, T.</author><author>Woodard, P. K.</author><author>Crean, A.</author><author>Abdul Aziz, Y. F.</author><author>Karthikeyan, G.</author><author>Chow, C. K.</author><author>Szczeklik, W.</author><author>Markobrada, M.</author><author>Mastracci, T.</author><author>Devereaux, P. J.</author></authors></contributors><auth-address>Department of Medicine, McMaster University, Hamilton, Ontario, Canada.</auth-address><titles><title>The coronary CT angiography vision protocol: a prospective observational imaging cohort study in patients undergoing non-cardiac surgery</title><secondary-title>BMJ open</secondary-title><alt-title>BMJ Open</alt-title></titles><periodical><full-title>BMJ Open</full-title></periodical><alt-periodical><full-title>BMJ Open</full-title></alt-periodical><volume>2</volume><number>4</number><edition>2012/08/03</edition><dates><year>2012</year></dates><isbn>2044-6055 (Electronic)</isbn><accession-num>22855630</accession-num><urls><related-urls><url> This suggests that other mechanisms may contribute to cardiac morbidity. Endogenous circadian rhythms regulate brain, autonomic nervous system, immune and cardiovascular physiology. ADDIN EN.CITE <EndNote><Cite><Author>Chen</Author><Year>2015</Year><RecNum>35011</RecNum><DisplayText><style face="superscript">5</style></DisplayText><record><rec-number>35011</rec-number><foreign-keys><key app="EN" db-id="peff0sz06tfzdzezt0kx5rsapzaxfwp9prps" timestamp="1529875486">35011</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Chen, L.</author><author>Yang, G.</author></authors></contributors><auth-address>The Institute for Translational Medicine and Therapeutics, Perelman School of Medicine, University of Pennsylvania Philadelphia, PA, USA ; Department of Systems Pharmacology and Translational Therapeutics, Perelman School of Medicine, University of Pennsylvania Philadelphia, PA, USA.</auth-address><titles><title>Recent advances in circadian rhythms in cardiovascular system</title><secondary-title>Front Pharmacol</secondary-title></titles><periodical><full-title>Front Pharmacol</full-title></periodical><pages>71</pages><volume>6</volume><keywords><keyword>CVDs</keyword><keyword>blood pressure</keyword><keyword>circadian clock</keyword><keyword>circadian rhythm</keyword><keyword>myocardial infarction</keyword></keywords><dates><year>2015</year></dates><isbn>1663-9812 (Print)&#xD;1663-9812 (Linking)</isbn><accession-num>25883568</accession-num><urls><related-urls><url> Dysregulated circadian patterns are potentially deleterious in individuals susceptible to adverse cardiovascular events. The occurrence of stroke, myocardial infarction, and sudden cardiac death are strikingly more frequently in the morning. ADDIN EN.CITE <EndNote><Cite><Author>Thosar</Author><Year>2018</Year><RecNum>35014</RecNum><DisplayText><style face="superscript">6</style></DisplayText><record><rec-number>35014</rec-number><foreign-keys><key app="EN" db-id="peff0sz06tfzdzezt0kx5rsapzaxfwp9prps" timestamp="1529875842">35014</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Thosar, S. S.</author><author>Butler, M. P.</author><author>Shea, S. A.</author></authors></contributors><titles><title>Role of the circadian system in cardiovascular disease</title><secondary-title>J Clin Invest</secondary-title></titles><periodical><full-title>The Journal of clinical investigation</full-title><abbr-1>J Clin Invest</abbr-1></periodical><pages>2157-2167</pages><volume>128</volume><number>6</number><dates><year>2018</year><pub-dates><date>Jun 1</date></pub-dates></dates><isbn>1558-8238 (Electronic)&#xD;0021-9738 (Linking)</isbn><accession-num>29856365</accession-num><urls><related-urls><url> In heart failure, autonomic dysfunction is common and is linked to chronic disruptions of the circadian clock that exacerbate contribute to cardiovascular risk.PEVuZE5vdGU+PENpdGU+PEF1dGhvcj5XaXR0ZTwvQXV0aG9yPjxZZWFyPjIwMDA8L1llYXI+PFJl

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ADDIN EN.CITE.DATA 7 These observations extend to cardiac surgery, although as yet no studies have explored this hypothesis in noncardiac surgery.PEVuZE5vdGU+PENpdGU+PEF1dGhvcj5Nb250YWlnbmU8L0F1dGhvcj48WWVhcj4yMDE4PC9ZZWFy

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ZT5=

ADDIN EN.CITE PEVuZE5vdGU+PENpdGU+PEF1dGhvcj5Nb250YWlnbmU8L0F1dGhvcj48WWVhcj4yMDE4PC9ZZWFy

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ZT5=

ADDIN EN.CITE.DATA 8 Excess cardiovascular risk after noncardiac surgery also appears to be associated with systemic inflammation, which in turn is dysregulated by loss of circadian homeostasis.PEVuZE5vdGU+PENpdGU+PEF1dGhvcj5DdXJ0aXM8L0F1dGhvcj48WWVhcj4yMDE0PC9ZZWFyPjxS

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ADDIN EN.CITE.DATA 9,10VISION-UK is a national prospective cohort study designed to assess the relationship between timing of operation and post-operative myocardial injury, as measured by high-sensitivity plasma troponin, other complications and mortality in adult patients following elective surgery. The anticipated sample size is ~4200 patients from 4 different UK hospitals. The end of the study will be defined as the end of the 30-day period follow up period for the final participant in the study. This document is the proposed statistical analysis plan for the VISION-UK study of the relationship between timing of surgery and postoperative morbidity, including myocardial injury. The purpose of this statistical analysis plan is to set out the proposed analysis in advance of inspecting the data so that data derived decisions are avoided. AimTo demonstrate the relationship between timing of surgery and risk of postoperative morbidity.Objectives The primary outcome measure of this study will be morbidity free days within the first 7 days of surgery, as defined by the Post Operative Morbidity Survey. Initial descriptive analysis Participants All participating hospitals have been asked to keep a log of the data that is collected. Data included in the study, missing data and completeness of follow up will be illustrated using a CONSORT-style flow diagram. The inclusion criteria are all adult patients (age≥45 years) undergoing elective surgery in a participating hospital with a planned overnight stay. Patients undergoing planned day-case surgery or radiological procedure are excluded. Only hospitals returning valid data describing 20 or more patients will be included in the study. All eligible patients’ data should be uploaded to the online e-CRF. A thorough data cleaning procedure will be implemented as follows: ? A robust e-CRF is designed to ensure data entry errors are minimised. The e-CRF provides a warning message and asks the user to confirm the value of any data entered which lie outside the pre-determined validity range (hard and soft ranges), e.g. if haemoglobin is less than 30 g/L or age greater than 100 years. ? Checking for outliers. If there are extreme outliers, the data points will be excluded from the analysis. A secondary analysis will be conducted with all data included to gauge the difference in results. ? Duplicates will be checked for and removed using the software package NCSS 11. ? Handling of missing data is outlined in section 6.0. Baseline characteristics To give a broader understanding of the patients enrolled in the study, baseline characteristics of all the patients will be presented as outlined in Table 1. Numbers (%) or means (SD) and medians (IQR) will be given for each group as appropriate. ? Demographic: Age, gender, body mass index, ethnicity, smoking status and American Society of Anesthesiologists (ASA) Physical Status grade. ? Surgery related: Surgical procedure, laparoscopic surgery, cancer surgery, severity and duration of surgery. ? Co-morbidities: Coronary artery disease, Heart failure, Diabetes mellitus (insulin treated), Diabetes mellitus (non-insulin treated), metastases, cirrhosis, cerebrovascular disease, transient ischaemic disease, asthma, chronic obstructive pulmonary disease, chronic kidney disease.? Pre-operative blood test results: haemoglobin, leucocytes (including differential), plasma sodium and other electrolytes. Primary analysis The primary outcome measure of this study will be morbidity free days within the first 7 days of surgery, as defined by the Post Operative Morbidity Survey. The principal exposure will be comparing outcomes in patients with surgery commencing in the morning (defined as 0800-1300h) to patients undergoing surgery in the afternoon (defined as 1300-1900h). The primary effect estimate will be the odds ratio of POMS-defined morbidity, reported with 95% confidence intervals and p-value (Table 2). The significance level will be set at p<0.05. To adjust for potential confounding, a multivariable logistic regression model will be constructed, including all biologically plausible predictor variables. With the expected large sample size, a large number of predictors can be included in the model without compromising statistical power, thus predictors will be selected based on clinical suitability and assessment of correlated variables. The model will be adjusted for the following covariates: age, gender, smoking status, surgical procedure category and duration, ASA grade, presence of co-morbidities, anaesthetic technique, laparoscopic and cancer surgery). All predictors will be entered into the model using forced simultaneous entry. ADDIN EN.CITE <EndNote><Cite><Author>Thorpe</Author><Year>2017</Year><RecNum>35022</RecNum><DisplayText><style face="superscript">11</style></DisplayText><record><rec-number>35022</rec-number><foreign-keys><key app="EN" db-id="peff0sz06tfzdzezt0kx5rsapzaxfwp9prps" timestamp="1530007380">35022</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Thorpe, K. E.</author></authors></contributors><auth-address>Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada. kevin.thorpe@utoronto.ca.&#xD;Applied Health Research Centre (AHRC), Li Ka Shing Knowledge Institute of St. Michael&apos;s Hospital, Toronto, ON, Canada. kevin.thorpe@utoronto.ca.</auth-address><titles><title>How to construct regression models for observational studies (and how NOT to do it!)</title><secondary-title>Can J Anaesth</secondary-title></titles><periodical><full-title>Can J Anaesth</full-title></periodical><pages>461-470</pages><volume>64</volume><number>5</number><keywords><keyword>Anesthesiology</keyword><keyword>Humans</keyword><keyword>Logistic Models</keyword><keyword>Models, Statistical</keyword><keyword>Observational Studies as Topic/*methods/*statistics &amp; numerical data</keyword><keyword>*Regression Analysis</keyword><keyword>Research Design</keyword></keywords><dates><year>2017</year><pub-dates><date>May</date></pub-dates></dates><orig-pub>Comment batir des modeles de regression pour des etudes observationnelles (et comment ne PAS le faire!).</orig-pub><isbn>1496-8975 (Electronic)&#xD;0832-610X (Linking)</isbn><accession-num>28236060</accession-num><urls><related-urls><url> The results of the regression models will be reported as odds ratios with 95% confidence intervals and associated p-values (Table 2). Unadjusted odds ratios will also be presented for comparison in a supplementary file. Goodness-of-fit for the models will be performed using the Hosmer-Lemeshow test. For multivariable regression analysis, multi-collinearity (correlations among predictor variables) is expected and will be assessed using the Variance Inflation Factor (VIF) as required. A VIF>10 will be considered to be collinear and will be excluded from the analysis.Secondary analyses Individual domains of the Post Operative Morbidity Survey within the first 7 days of surgery.The primary analysis will be repeated defining the outcome according to individual domains of the Post Operative Morbidity Score.Clavien-Dindo defined complications.Data required to classify complications within 30 days of surgery according to the Clavien-Dindo grading system will also be presented, which provides information on the severity of postoperative morbidity. The number and percentage of patients in each Clavien-Dindo grade will be reported (Table 5). A sensitivity analysis will be conducted by repeating the primary analysis using Clavien-Dindo grading to classify complications. This will provide an understanding of how the findings are affected by the use of a different system of evaluating complications. Myocardial injury:Defined by raised plasma troponin >14ng/l following in-patient surgery. The primary analysis will be repeated replacing postoperative myocardial injury within 3 days after surgery as the outcome.Post-operative hospital stay & admission to critical care The median hospital length of stay (LOS) following the start of surgery, overall, by survival status and by complication status will be reported (Table 6). Post-operative LOS is the duration in days from the date of the end of surgery to the date of discharge from hospital. The number of critical care free days will also be presented, but will not be subjected to any statistical tests (Table 6). Post-operative mortality The number and percentage of deaths within 30 days of surgery will be reported for each NLR category. A logistic regression model with mortality as an outcome will be developed. The variable selection procedure will follow that of the primary analysis. The results will be reported as odds ratios with 95% confidence intervals and associated p-values. Handling of missing data Data missing from database A thorough approach will be undertaken by investigators to ensure completeness of data collection and data uploading. However, if data are still missing, then the following data handling technique will be used. If data are missing completely at random (MCAR), then case-wise deletion will be used to exclude the subjects from the analysis. If ≤10% of data is missing at random (MAR), then a complete case analysis will be conducted by excluding patients with missing data. If ≥10-25% of data is missing at random, then multiple imputation will be used. Multiple imputation substitutes a predicted value on the basis of other variables that are available for each subject. IF >25% of data are missing then this will handled by list-wise deletion. If data for any particular site are completely missing, then the site will be excluded from the analysis. Sensitivity Analysis A sensitivity approach will be taken if some data seem unrealistic. The primary analysis will be repeated excluding these patients. If relevant outcome data are missing, such as complications, the primary analysis will be repeated once, assuming that all patients with missing outcome data had no complications. The analysis will then be repeated again with the opposite outcome. This will provide an understanding of how the findings may be affected if the data were complete. Appendix. Dummy tables and figures Figure 1: Flow diagram 60579030480Total VISION-UK study cohort (n)Reason for exclusion (n)Patients with data available for inclusion into sub-study (n)Reason for exclusion (n)Dataset analysed (n): Time of surgery AM/PM0Total VISION-UK study cohort (n)Reason for exclusion (n)Patients with data available for inclusion into sub-study (n)Reason for exclusion (n)Dataset analysed (n): Time of surgery AM/PMFigure 2: Kaplan meier plot for time to become morbidity free within first 7 days of surgery, stratified by time of surgery (AM/PM). Figure 3: Kaplan meier plot for length of hospital stay (adjusted for death), stratified by time of surgery (AM/PM). Table 1: Baseline characteristics.All patients (n%) AM (n%) PM (n%) Age (mean, SD)Gender (%male)Smoker Ethnicity RCRIPOSSUMAtrial fibrillationCongestive heart failureCoronary artery diseaseCerebral vascular eventObstructive sleep apnoeaPeripheral vascular diseaseHTNCOPDDiabetesActive cancerHaemoglobinCKDASA gradeI II III IV Severity of surgery Minor Intermediate Major Duration of surgeryFluids during surgeryBlood loss during surgerySurgical Procedure category Orthopaedic Breast Thoracic Obstetrics & Gynaecology Upper gastro-intestinal Lower gastro-intestinal Hepato-biliary Vascular Urology & Kidney Head & Neck Leukocyte countsWhite cell countNeutrophil (n;%)Lymphocyte (n;%)Monocyte (n;%)Basophil (n;%)Eosinophil (n;%)HaemoglobinPlatelets (n)Table 2Multivariable logistic regression models for development of post-operative morbidity: data presented as n (%), odds ratios and 95% confidence intervals (CI).Postop morbidity n (%)Unadjusted OR (95% CI)Adjusted OR (95% CI)p valueAge in years (mean SD)MaleFrailtyCurrent smokerBMI (mean SD)Co-morbiditiesAtrial fibrillationCongestive heart failureCoronary artery diseaseCerebral vascular eventObstructive sleep apnoeaPeripheral vascular diseaseHTNCOPDDiabetesActive cancerHaemoglobinCKDUrgency of surgeryElectiveEmergencyType of surgeryVascularGeneralThoracicMajor urology or gynaecologyMajor orthopaedicMajor neurosurgeryOtherSurgical techniqueEndoscopicOpenPreoperative Leukocyte countNeutrophilLymphocyteMonocyteBasophilPlateletsTable 3: Cardiovascular complications:AM/PM timing of surgery and postoperative troponin riseAM PMOdds ratioP valueTroponin riseAbsolute troponin riseAny cardiovascular morbidityMyocardial infarctionMyocardial ischaemiaHypotensionArrhythmiasCardiogenic pulmonary oedemaTable 4: Outcomes after surgery- POMS-defined morbidity.Each domain will include individual sub-components.DAY 3DAY 7AMPMAMPMInfectionPulmonaryRenalGastrointestinalWoundNeurologicalHaematologyPainTable 5: Outcomes after surgery- Clavien-Dindo gradingPresented for AM/PM; (n;%)?Any c/v complicationC-D I?C-D II?C-D III?C-D IV?C-D VAMPMAMPMAMPMAMPMAMPMAMPMAny C/V eventMyocardial infarction ???????????Arrhythmia ???????????Pulmonary oedema ???????????Pulmonary embolism ???????????Stroke ???????????Cardiac arrest???????????Infectious complicationsOtherTable 6: Post-operative hospital measuresPresented for AM/PM timing of surgeryNumber of patientsHospital stay for all patients Hospital stay for patients with a complication Hospital stay for patients who died Critical care free days References ADDIN EN.REFLIST 1.Weiser TG, Regenbogen SE, Thompson KD, et al. An estimation of the global volume of surgery: a modelling strategy based on available data. Lancet. 2008;372(9633):139-144.2.Writing Committee for the VSI, Devereaux PJ, Biccard BM, et al. Association of Postoperative High-Sensitivity Troponin Levels With Myocardial Injury and 30-Day Mortality Among Patients Undergoing Noncardiac Surgery. JAMA. 2017;317(16):1642-1651.3.Devereaux PJ, Chan MT, Alonso-Coello P, et al. Association between postoperative troponin levels and 30-day mortality among patients undergoing noncardiac surgery. JAMA : the journal of the American Medical Association. 2012;307(21):2295-2304.4.Sheth T, Butler C, Chow B, et al. The coronary CT angiography vision protocol: a prospective observational imaging cohort study in patients undergoing non-cardiac surgery. BMJ open. 2012;2(4).5.Chen L, Yang G. Recent advances in circadian rhythms in cardiovascular system. Front Pharmacol. 2015;6:71.6.Thosar SS, Butler MP, Shea SA. Role of the circadian system in cardiovascular disease. J Clin Invest. 2018;128(6):2157-2167.7.Witte K, Hu K, Swiatek J, Mussig C, Ertl G, Lemmer B. Experimental heart failure in rats: effects on cardiovascular circadian rhythms and on myocardial beta-adrenergic signaling. Cardiovasc Res. 2000;47(2):350-358.8.Montaigne D, Marechal X, Modine T, et al. Daytime variation of perioperative myocardial injury in cardiac surgery and its prevention by Rev-Erbalpha antagonism: a single-centre propensity-matched cohort study and a randomised study. Lancet. 2018;391(10115):59-69.9.Curtis AM, Bellet MM, Sassone-Corsi P, O'Neill LA. Circadian clock proteins and immunity. Immunity. 2014;40(2):178-186.10.Brainard J, Gobel M, Bartels K, Scott B, Koeppen M, Eckle T. Circadian rhythms in anesthesia and critical care medicine: potential importance of circadian disruptions. Semin Cardiothorac Vasc Anesth. 2015;19(1):49-60.11.Thorpe KE. How to construct regression models for observational studies (and how NOT to do it!). Can J Anaesth. 2017;64(5):461-470. ................
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