Modeling Postoperative Mortality in Older Patients by Boosting Discrete-Time Competing Risks Models
提出一种新的生存建模方法，用于分析老年患者术后30天院内死亡率，该方法处理了分组事件时间和竞争事件（如活着出院），并基于GAMLSS框架和梯度提升算法，从9862名老年患者数据中识别出六个重要风险因素。
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.