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Derivation and validation of a clinical risk score to predict death among patients awaiting cardiac surgery in Ontario, Canada: a population-based study

BACKGROUND: Surgical delay may result in unintended harm to patients needing cardiac surgery, who are at risk for death if their condition is left untreated. Our objective was to derive and internally validate a clinical risk score to predict death among patients awaiting major cardiac surgery. METH...

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Autores principales: Sun, Louise Y., Wijeysundera, Harindra C., Lee, Douglas S., van Diepen, Sean, Ruel, Marc, Eddeen, Anan Bader, Mesana, Thierry G.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: CMA Impact Inc. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9259465/
https://www.ncbi.nlm.nih.gov/pubmed/35260467
http://dx.doi.org/10.9778/cmajo.20210031
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author Sun, Louise Y.
Wijeysundera, Harindra C.
Lee, Douglas S.
van Diepen, Sean
Ruel, Marc
Eddeen, Anan Bader
Mesana, Thierry G.
author_facet Sun, Louise Y.
Wijeysundera, Harindra C.
Lee, Douglas S.
van Diepen, Sean
Ruel, Marc
Eddeen, Anan Bader
Mesana, Thierry G.
author_sort Sun, Louise Y.
collection PubMed
description BACKGROUND: Surgical delay may result in unintended harm to patients needing cardiac surgery, who are at risk for death if their condition is left untreated. Our objective was to derive and internally validate a clinical risk score to predict death among patients awaiting major cardiac surgery. METHODS: We used the CorHealth Ontario Registry and linked ICES health administrative databases with information on all Ontario residents to identify patients aged 18 years or more who were referred for isolated coronary artery bypass grafting (CABG), valvular procedures, combined CABG–valvular procedures or thoracic aorta procedures between Oct. 1, 2008, and Sept. 30, 2019. We used a hybrid modelling approach with the random forest method for initial variable selection, followed by backward stepwise logistic regression modelling for clinical interpretability and parsimony. We internally validated the logistic regression model, termed the CardiOttawa Waitlist Mortality Score, using 200 bootstraps. RESULTS: Of the 112 266 patients referred for cardiac surgery, 269 (0.2%) died while awaiting surgery (118/72 366 [0.2%] isolated CABG, 81/24 461 [0.3%] valvular procedures, 63/12 046 [0.5%] combined CABG–valvular procedures and 7/3393 [0.2%] thoracic aorta procedures). Age, sex, surgery type, left main stenosis, Canadian Cardiovascular Society classification, left ventricular ejection fraction, heart failure, atrial fibrillation, dialysis, psychosis and operative priority were predictors of waitlist mortality. The model discriminated (C-statistic 0.76 [optimism-corrected 0.73]). It calibrated well in the overall cohort (Hosmer–Lemeshow p = 0.2) and across surgery types. INTERPRETATION: The CardiOttawa Waitlist Mortality Score is a simple clinical risk model that predicts the likelihood of death while awaiting cardiac surgery. It has the potential to provide data-driven decision support for managing access to cardiac care and preserve system capacity during the COVID-19 pandemic, the recovery period and beyond.
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spelling pubmed-92594652022-07-10 Derivation and validation of a clinical risk score to predict death among patients awaiting cardiac surgery in Ontario, Canada: a population-based study Sun, Louise Y. Wijeysundera, Harindra C. Lee, Douglas S. van Diepen, Sean Ruel, Marc Eddeen, Anan Bader Mesana, Thierry G. CMAJ Open Research BACKGROUND: Surgical delay may result in unintended harm to patients needing cardiac surgery, who are at risk for death if their condition is left untreated. Our objective was to derive and internally validate a clinical risk score to predict death among patients awaiting major cardiac surgery. METHODS: We used the CorHealth Ontario Registry and linked ICES health administrative databases with information on all Ontario residents to identify patients aged 18 years or more who were referred for isolated coronary artery bypass grafting (CABG), valvular procedures, combined CABG–valvular procedures or thoracic aorta procedures between Oct. 1, 2008, and Sept. 30, 2019. We used a hybrid modelling approach with the random forest method for initial variable selection, followed by backward stepwise logistic regression modelling for clinical interpretability and parsimony. We internally validated the logistic regression model, termed the CardiOttawa Waitlist Mortality Score, using 200 bootstraps. RESULTS: Of the 112 266 patients referred for cardiac surgery, 269 (0.2%) died while awaiting surgery (118/72 366 [0.2%] isolated CABG, 81/24 461 [0.3%] valvular procedures, 63/12 046 [0.5%] combined CABG–valvular procedures and 7/3393 [0.2%] thoracic aorta procedures). Age, sex, surgery type, left main stenosis, Canadian Cardiovascular Society classification, left ventricular ejection fraction, heart failure, atrial fibrillation, dialysis, psychosis and operative priority were predictors of waitlist mortality. The model discriminated (C-statistic 0.76 [optimism-corrected 0.73]). It calibrated well in the overall cohort (Hosmer–Lemeshow p = 0.2) and across surgery types. INTERPRETATION: The CardiOttawa Waitlist Mortality Score is a simple clinical risk model that predicts the likelihood of death while awaiting cardiac surgery. It has the potential to provide data-driven decision support for managing access to cardiac care and preserve system capacity during the COVID-19 pandemic, the recovery period and beyond. CMA Impact Inc. 2022-03-08 /pmc/articles/PMC9259465/ /pubmed/35260467 http://dx.doi.org/10.9778/cmajo.20210031 Text en © 2022 CMA Impact Inc. or its licensors https://creativecommons.org/licenses/by-nc-nd/4.0/This is an Open Access article distributed in accordance with the terms of the Creative Commons Attribution (CC BY-NC-ND 4.0) licence, which permits use, distribution and reproduction in any medium, provided that the original publication is properly cited, the use is noncommercial (i.e., research or educational use), and no modifications or adaptations are made. See: https://creativecommons.org/licenses/by-nc-nd/4.0/
spellingShingle Research
Sun, Louise Y.
Wijeysundera, Harindra C.
Lee, Douglas S.
van Diepen, Sean
Ruel, Marc
Eddeen, Anan Bader
Mesana, Thierry G.
Derivation and validation of a clinical risk score to predict death among patients awaiting cardiac surgery in Ontario, Canada: a population-based study
title Derivation and validation of a clinical risk score to predict death among patients awaiting cardiac surgery in Ontario, Canada: a population-based study
title_full Derivation and validation of a clinical risk score to predict death among patients awaiting cardiac surgery in Ontario, Canada: a population-based study
title_fullStr Derivation and validation of a clinical risk score to predict death among patients awaiting cardiac surgery in Ontario, Canada: a population-based study
title_full_unstemmed Derivation and validation of a clinical risk score to predict death among patients awaiting cardiac surgery in Ontario, Canada: a population-based study
title_short Derivation and validation of a clinical risk score to predict death among patients awaiting cardiac surgery in Ontario, Canada: a population-based study
title_sort derivation and validation of a clinical risk score to predict death among patients awaiting cardiac surgery in ontario, canada: a population-based study
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9259465/
https://www.ncbi.nlm.nih.gov/pubmed/35260467
http://dx.doi.org/10.9778/cmajo.20210031
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