Division of Cardiac Anesthesiology (Sun), University of Ottawa Heart Institute and the School of Epidemiology and Public Health, University of Ottawa; ICES uOttawa (Sun, Bader Eddeen), Ottawa, Ont.; ICES Central (Wijeysundera, Tam); Schulich Heart Program (Wijeysundera), Sunnybrook Health Sciences Centre; Division of Cardiology (Wijeysundera), Department of Medicine and Institute for Health Policy, Management and Evaluation, University of Toronto, Toronto, Ont.; Keele Cardiovascular Research Group (Mamas), Centre for Prognosis Research, Institutes of Applied Clinical Science and Primary Care and Health Sciences, Keele University, Staffordshire, UK; Department of Cardiology (Mamas), Royal Stoke University Hospital, Stoke-on-Trent, UK; Division of Cardiac Surgery (Tam), Sunnybrook Health Sciences Centre, Toronto, Ont.; Division of Cardiac Surgery (Mesana), University of Ottawa Heart Institute, Ottawa, Ont.
CMAJ. 2021 Aug 30;193(34):E1333-E1340. doi: 10.1503/cmaj.210170.
Waitlist management is a global challenge. For patients with severe cardiovascular diseases awaiting cardiac surgery, prolonged wait times are associated with unplanned hospitalizations. To facilitate evidence-based resource allocation, we derived and validated a clinical risk model to predict the composite outcome of death and cardiac hospitalization of patients on the waitlist for cardiac surgery.
We used the CorHealth Ontario Registry and linked ICES health care administrative databases, which have information on all Ontario residents. We included patients 18 years or older who waited at home for coronary artery bypass grafting, valvular or thoracic aorta surgeries between 2008 and 2019. The primary outcome was death or an unplanned cardiac hospitalizaton, defined as nonelective admission for heart failure, myocardial infarction, unstable angina or endocarditis. We randomly divided two-thirds of these patients into derivation and one-third into validation data sets. We derived the model using a multivariable Cox proportional hazard model with backward stepwise variable selection.
Among 62 375 patients, 41 729 patients were part of the derivation data set and 20 583 were part of the validation data set. Of the total, 3033 (4.9%) died or had an unplanned cardiac hospitalization while waiting for surgery. The area under the curve of our model at 15, 30, 60 and 89 days was 0.85, 0.82, 0.81 and 0.80, respectively, in the derivation cohort and 0.83, 0.80, 0.78 and 0.78, respctively, in the validation cohort. The model calibrated well at all time points.
We derived and validated a clinical risk model that provides accurate prediction of the risk of death and unplanned cardiac hospitalization for patients on the cardiac surgery waitlist. Our model could be used for quality benchmarking and data-driven decision support for managing access to cardiac surgery.
候补名单管理是一个全球性挑战。对于等待心脏手术的严重心血管疾病患者，等待时间延长与非计划性住院相关。为了促进基于证据的资源分配，我们开发并验证了一种临床风险模型，以预测心脏手术候补名单上患者死亡和心脏住院的复合结局。
我们使用了 CorHealth Ontario 注册中心和链接的 ICES 医疗保健管理数据库，这些数据库都包含所有安大略省居民的信息。我们纳入了 2008 年至 2019 年期间在家等待冠状动脉旁路移植术、瓣膜或胸主动脉手术的 18 岁及以上患者。主要结局是死亡或非计划性心脏住院，定义为因心力衰竭、心肌梗死、不稳定型心绞痛或心内膜炎而进行的非选择性住院。我们将这些患者的三分之二随机分为推导数据集和三分之一分为验证数据集。我们使用多变量 Cox 比例风险模型和向后逐步变量选择来推导模型。
在 62375 名患者中，41729 名患者是推导数据集的一部分，20583 名患者是验证数据集的一部分。在总人群中，3033 名（4.9%）患者在等待手术期间死亡或发生非计划性心脏住院。我们的模型在推导队列中，15、30、60 和 89 天时的曲线下面积分别为 0.85、0.82、0.81 和 0.80，在验证队列中，分别为 0.83、0.80、0.78 和 0.78。该模型在所有时间点都具有良好的校准。
我们开发并验证了一种临床风险模型，可准确预测心脏手术候补名单上患者死亡和非计划性心脏住院的风险。我们的模型可用于质量基准测试和数据驱动的决策支持，以管理心脏手术的准入。
