Louise Y. Sun, H. Wijeysundera, Douglas S. Lee, S. van Diepen, M. Ruel, A. Eddeen, T. Mesana
{"title":"推导和验证在加拿大安大略省等待心脏手术的患者中预测死亡的临床风险评分:一项基于人群的研究","authors":"Louise Y. Sun, H. Wijeysundera, Douglas S. Lee, S. van Diepen, M. Ruel, A. Eddeen, T. Mesana","doi":"10.9778/cmajo.20210031","DOIUrl":null,"url":null,"abstract":"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.","PeriodicalId":93946,"journal":{"name":"CMAJ open","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Derivation and validation of a clinical risk score to predict death among patients awaiting cardiac surgery in Ontario, Canada: a population-based study\",\"authors\":\"Louise Y. Sun, H. Wijeysundera, Douglas S. Lee, S. van Diepen, M. Ruel, A. Eddeen, T. Mesana\",\"doi\":\"10.9778/cmajo.20210031\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"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.\",\"PeriodicalId\":93946,\"journal\":{\"name\":\"CMAJ open\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"CMAJ open\",\"FirstCategoryId\":\"0\",\"ListUrlMain\":\"https://doi.org/10.9778/cmajo.20210031\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"CMAJ open","FirstCategoryId":"0","ListUrlMain":"https://doi.org/10.9778/cmajo.20210031","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
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. 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.