Derivation and validation of a clinical model to predict death or cardiac hospitalizations while on the cardiac surgery waitlist.
Derivation and validation of a clinical model to predict death or cardiac hospitalizations while on the cardiac surgery waitlist.
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DOI：
10.1503/cmaj.210170
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发表时间：
2021-08-30
期刊：
影响因子：
--
通讯作者：
Mesana TG
中科院分区：
文献类型：
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作者：
Sun LY;Eddeen AB;Wijeysundera HC;Mamas MA;Tam DY;Mesana TG
等待名单管理是一项全球性挑战。对于等待心脏手术的严重心血管疾病患者，延长等待时间与计划外住院有关。为了促进基于证据的资源分配，我们推导并验证了一个临床风险模型，以预测心脏手术等待名单上患者的死亡和心脏住院的复合结局。我们使用了CorHealth安大略登记处和ICES医疗保健管理数据库，其中有所有安大略居民的信息。我们纳入了2008年至2019年期间在家中等待冠状动脉旁路移植术，瓣膜或胸主动脉手术的18岁或以上患者。主要结局是死亡或计划外心脏病住院，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非选择性住院。我们将这些患者中的三分之二随机分为衍生数据集，三分之一分为验证数据集。我们使用多变量考克斯比例风险模型和向后逐步变量选择推导出该模型。在62 375例患者中，41 729例患者是推导数据集的一部分，20 583例患者是验证数据集的一部分。其中，3033例（4.9%）在等待手术期间死亡或发生计划外心脏住院。我们的模型在15、30、60和89天时的曲线下面积在推导队列中分别为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。模型在所有时间点均校准良好。我们推导并验证了一个临床风险模型，该模型可准确预测心脏手术等待名单上患者的死亡和计划外心脏住院风险。我们的模型可用于质量基准和数据驱动的决策支持，以管理心脏手术的访问。
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.