Modeling Postoperative Mortality in Older Patients by Boosting Discrete-Time Competing Risks Models

Elderly patients are at a high risk of suffering from postoperative death. Personalized strategies to improve their recovery after intervention are therefore urgently needed. A popular way to analyze postoperative mortality is to develop a prognostic model that incorporates risk factors measured at hospital admission, for example, comorbidities. When building such models, numerous issues must be addressed, including censoring and the presence of competing events (such as discharge from hospital alive). Here we present a novel survival modeling approach to investigate 30-day inpatient mortality following intervention. The proposed method accounts for both grouped event times, for example, measured in 24-hour intervals, and competing events. Conceptually, the method is embedded in the framework of generalized additive models for location, scale, and shape (GAMLSS). Model fitting is performed using a component-wise gradient boosting algorithm, which allows for additional regularization steps via stability selection. We used this new modeling approach to analyze data from the Peri-interventional Outcome Study in the Elderly (POSE), which is a recent cohort study that enrolled 9862 elderly inpatients undergoing intervention under anesthesia. Application of the proposed boosting algorithm yielded six important risk factors (including both clinical variables and interventional characteristics) that either contributed to the hazard of death or to discharge from hospital alive. Supplementary materials for this article are available online.

老年患者术后死亡风险极高，因此亟需制定个性化策略以改善其干预后的康复状况。当前分析术后死亡率的主流手段为构建预后模型，纳入入院时测定的风险因素（如合并症）。在构建此类模型时，需解决诸多问题，包括删失（censoring）以及竞争风险事件（competing events，如患者存活出院）的存在。本文提出一种全新的生存建模方法，用于探究干预后30天院内死亡率情况。该方法同时兼顾分组事件时间（例如以24小时间隔进行测量）与竞争风险事件。从概念上讲，该方法嵌入于位置、尺度和形状广义加性模型（generalized additive models for location, scale, and shape, GAMLSS）框架之中。模型拟合采用分量式梯度提升算法，该算法可通过稳定性选择实现额外的正则化步骤。我们运用这一新式建模方法，分析了老年患者围干预结局研究（Peri-interventional Outcome Study in the Elderly, POSE）的数据——该研究为一项近期队列研究，共纳入9862名接受麻醉干预的老年住院患者。应用所提出的梯度提升算法，最终筛选出6个重要风险因素（涵盖临床变量与干预特征），这些因素要么与死亡风险相关，要么与患者存活出院相关。本文的补充材料可在线获取。