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 512 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 4.7 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知情医疗决策具有重要价值。本文提出一个通用框架，用于根据患者的住院史动态预测其死亡风险，这些信息通常可供医生获取。预测基于死亡过程与住院过程的联合建模，从而避免因选择幸存者而产生的潜在偏差。该框架可容纳住院过程的各种子模型。特别地，我们研究了在住院更新模型中的死亡风险预测，这是重复事件建模的常用方法。在更新模型中，随访期间住院时间的分布会影响死亡风险。这一结果与先前研究的泊松模型预测不同——在泊松模型中仅住院次数产生影响。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512名在加尔德考大学医院呼吸科下属六家门诊呼吸诊所接受治疗的COPD患者，中位随访时间为4.7年。研究发现住院时间越集中，死亡风险越高。