Predicting the risk of death for chronic patients is highly valuable for informed medical decision-making. This paper proposes a general framework for dynamic prediction of the risk of death of a patient given her hospitalization history, which is generally available to physicians. Predictions are based on a joint model for the death and hospitalization processes, thereby avoiding the potential bias arising from selection of survivors. The framework accommodates various submodels for the hospitalization process. In particular, we study prediction of the risk of death in a renewal model for hospitalizations, a common approach to recurrent event modelling. In the renewal model, the distribution of hospitalizations throughout the follow-up period impacts the risk of death. This result differs from prediction in the Poisson model, previously studied, where only the number of hospitalizations matters. We apply our methodology to a prospective, observational cohort study of 401 patients treated for COPD in one of six outpatient respiratory clinics run by the Respiratory Service of Galdakao University Hospital, with a median follow-up of 4.16 years. We find that more concentrated hospitalizations increase the risk of death.

翻译：预测慢性病患者的死亡风险对于临床医疗决策具有重要价值。本文提出一个通用框架，可根据患者的住院史（临床医生通常可获取此类信息）动态预测其死亡风险。该预测基于死亡与住院过程的联合模型，从而避免因幸存者选择偏差带来的潜在影响。该框架可适配住院过程的各种子模型。具体而言，我们研究了基于再入院过程（一种常见的复发事件建模方法）的死亡风险预测。在再入院模型中，随访期间住院事件的分布特征会影响死亡风险——这一结论不同于先前研究的泊松模型（该模型仅考虑住院次数的影响）。我们将该方法应用于一项前瞻性观察队列研究，该研究纳入西班牙加尔达考大学医院呼吸科下属六家门诊呼吸诊所收治的401例慢性阻塞性肺疾病（COPD）患者，中位随访时间为4.16年。研究发现，住院事件越集中，死亡风险越高。