Janny Xue Chen Ke1,2,3,4, Daniel I McIsaac5, Ronald B George6,7, Paula Branco8, E Francis Cook9, W Scott Beattie10, Robin Urquhart11, David B MacDonald6. 1. Department of Anesthesia, Pain Management & Perioperative Medicine, Dalhousie University, Halifax, NS, Canada. janny.ke@ubc.ca. 2. T. H. Chan School of Public Health, Harvard University, Boston, MB, USA. janny.ke@ubc.ca. 3. Department of Anesthesia, St. Paul's Hospital, Providence Health Care, 1081 Burrard Street, Vancouver, BC, V6Z 1Y6, Canada. janny.ke@ubc.ca. 4. Department of Anesthesiology, Pharmacology & Therapeutics, The University of British Columbia, Vancouver, BC, Canada. janny.ke@ubc.ca. 5. Department of Anesthesiology and Pain Medicine, University of Ottawa, Ottawa, ON, Canada. 6. Department of Anesthesia, Pain Management & Perioperative Medicine, Dalhousie University, Halifax, NS, Canada. 7. Department of Anesthesia and Perioperative Care, UCSF, San Francisco, CA, USA. 8. School of Electrical Engineering and Computer Science, University of Ottawa, Ottawa, ON, Canada. 9. T. H. Chan School of Public Health, Harvard University, Boston, MB, USA. 10. Department of Anesthesia, University of Toronto, Toronto, ON, Canada. 11. Department of Community Health and Epidemiology, Dalhousie University, Halifax, NS, Canada.
Abstract
PURPOSE: Accurate risk reassessment after surgery is crucial for postoperative planning for monitoring and disposition. Existing postoperative mortality risk prediction models using preoperative features do not incorporate intraoperative hemodynamic derangements that may alter risk stratification. Intraoperative vital signs may provide an objective and readily available prognostic resource. Our primary objective was to derive and internally validate a logistic regression (LR) model by adding intraoperative features to established preoperative predictors to predict 30-day postoperative mortality. METHODS: Following Research Ethics Board approval, we analyzed a historical cohort that included patients aged ≥ 45 undergoing noncardiac surgery with an overnight stay at two tertiary hospitals (2013 to 2017). Features included intraoperative vital signs (blood pressure, heart rate, end-tidal carbon dioxide partial pressure, oxygen saturation, and temperature) by threshold and duration of exposure, as well as patient, surgical, and anesthetic factors. The cohort was divided temporally 75:25 into derivation and validation sets. We constructed a multivariable LR model with 30-day all-cause mortality as the outcome and evaluated performance metrics. RESULTS: There were 30,619 patients in the cohort (mean [standard deviation] age, 66 [11] yr; 50.2% female; 2.0% mortality). In the validation set, the primary LR model showed a c-statistic of 0.893 (99% confidence interval [CI], 0.853 to 0.927), a Nagelkerke R-squared of 0.269, a scaled Brier score of 0.082, and an area under precision-recall curve of 0.158 (baseline 0.017 for an uninformative model). The addition of intraoperative vital signs to preoperative factors minimally improved discrimination and calibration. CONCLUSION: We derived and internally validated a model that incorporated vital signs to improve risk stratification after surgery. Preoperative factors were strongly predictive of mortality risk, and intraoperative predictors only minimally improved discrimination. External and prospective validations are needed. STUDY REGISTRATION: www. CLINICALTRIALS: gov (NCT04014010); registered on 10 July 2019.
PURPOSE: Accurate risk reassessment after surgery is crucial for postoperative planning for monitoring and disposition. Existing postoperative mortality risk prediction models using preoperative features do not incorporate intraoperative hemodynamic derangements that may alter risk stratification. Intraoperative vital signs may provide an objective and readily available prognostic resource. Our primary objective was to derive and internally validate a logistic regression (LR) model by adding intraoperative features to established preoperative predictors to predict 30-day postoperative mortality. METHODS: Following Research Ethics Board approval, we analyzed a historical cohort that included patients aged ≥ 45 undergoing noncardiac surgery with an overnight stay at two tertiary hospitals (2013 to 2017). Features included intraoperative vital signs (blood pressure, heart rate, end-tidal carbon dioxide partial pressure, oxygen saturation, and temperature) by threshold and duration of exposure, as well as patient, surgical, and anesthetic factors. The cohort was divided temporally 75:25 into derivation and validation sets. We constructed a multivariable LR model with 30-day all-cause mortality as the outcome and evaluated performance metrics. RESULTS: There were 30,619 patients in the cohort (mean [standard deviation] age, 66 [11] yr; 50.2% female; 2.0% mortality). In the validation set, the primary LR model showed a c-statistic of 0.893 (99% confidence interval [CI], 0.853 to 0.927), a Nagelkerke R-squared of 0.269, a scaled Brier score of 0.082, and an area under precision-recall curve of 0.158 (baseline 0.017 for an uninformative model). The addition of intraoperative vital signs to preoperative factors minimally improved discrimination and calibration. CONCLUSION: We derived and internally validated a model that incorporated vital signs to improve risk stratification after surgery. Preoperative factors were strongly predictive of mortality risk, and intraoperative predictors only minimally improved discrimination. External and prospective validations are needed. STUDY REGISTRATION: www. CLINICALTRIALS: gov (NCT04014010); registered on 10 July 2019.
Authors: Pavel S Roshanov; Tej Sheth; Emmanuelle Duceppe; Vikas Tandon; Amal Bessissow; Matthew T V Chan; Craig Butler; Benjamin J W Chow; James S Khan; P J Devereaux Journal: Anesthesiology Date: 2019-05 Impact factor: 7.892
Authors: Suneetha Ramani Moonesinghe; Michael G Mythen; Priya Das; Kathryn M Rowan; Michael P W Grocott Journal: Anesthesiology Date: 2013-10 Impact factor: 7.892
Authors: L McLean House; Khensani N Marolen; Paul J St Jacques; Matthew D McEvoy; Jesse M Ehrenfeld Journal: J Clin Anesth Date: 2016-06-08 Impact factor: 9.452
Authors: B B Abdelmalak; J P Cata; A Bonilla; J You; T Kopyeva; J D Vogel; S Campbell; D I Sessler Journal: Br J Anaesth Date: 2012-11-21 Impact factor: 9.166
Authors: Tom E F Abbott; Rupert M Pearse; R Andrew Archbold; Tahania Ahmad; Edyta Niebrzegowska; Andrew Wragg; Reitze N Rodseth; Philip J Devereaux; Gareth L Ackland Journal: Anesth Analg Date: 2018-06 Impact factor: 5.108
Authors: Danny J N Wong; Steve Harris; Arun Sahni; James R Bedford; Laura Cortes; Richard Shawyer; Andrew M Wilson; Helen A Lindsay; Doug Campbell; Scott Popham; Lisa M Barneto; Paul S Myles; S Ramani Moonesinghe Journal: PLoS Med Date: 2020-10-15 Impact factor: 11.069