Epitelligence, Hong Kong SAR, China.
JC School of Public Health and Primary Care, The Chinese University of Hong Kong, Hong Kong SAR, China.
BMC Med Res Methodol. 2023 Jan 13;23(1):14. doi: 10.1186/s12874-022-01824-1.
Accurately estimating elderly patients' rehospitalisation risk benefits clinical decisions and service planning. However, research in rehospitalisation and repeated hospitalisation yielded only models with modest performance, and the model performance deteriorates rapidly as the prediction timeframe expands beyond 28 days and for older participants.
A temporal zero-inflated Poisson (tZIP) regression model was developed and validated retrospectively and prospectively. The data of the electronic health records (EHRs) contain cohorts (aged 60+) in a major public hospital in Hong Kong. Two temporal offset functions accounted for the associations between exposure time and parameters corresponding to the zero-inflated logistic component and the Poisson distribution's expected count. tZIP was externally validated with a retrospective cohort's rehospitalisation events up to 12 months after the discharge date. Subsequently, tZIP was validated prospectively after piloting its implementation at the study hospital. Patients discharged within the pilot period were tagged, and the proposed model's prediction of their rehospitalisation was verified monthly. Using a hybrid machine learning (ML) approach, the tZIP-based risk estimator's marginal effect on 28-day rehospitalisation was further validated, competing with other factors representing different post-acute and clinical statuses.
The tZIP prediction of rehospitalisation from 28 days to 365 days was achieved at above 80% discrimination accuracy retrospectively and prospectively in two out-of-sample cohorts. With a large margin, it outperformed the Cox proportional and linear models built with the same predictors. The hybrid ML revealed that the risk estimator's contribution to 28-day rehospitalisation outweighed other features relevant to service utilisation and clinical status.
A novel rehospitalisation risk model was introduced, and its risk estimators, whose importance outweighed all other factors of diverse post-acute care and clinical conditions, were derived. The proposed approach relies on four easily accessible variables easily extracted from EHR. Thus, clinicians could visualise patients' rehospitalisation risk from 28 days to 365 days after discharge and screen high-risk older patients for follow-up care at the proper time.
准确估计老年患者再入院风险有利于临床决策和服务规划。然而，再入院和多次住院的研究仅产生了性能适中的模型，并且随着预测时间范围超过 28 天和参与者年龄增加，模型性能迅速恶化。
开发并回顾性和前瞻性验证了一个时间零膨胀泊松（tZIP）回归模型。电子健康记录（EHR）的数据包含香港一家主要公立医院的队列（年龄在 60 岁以上）。两个时间偏移函数解释了暴露时间与零膨胀逻辑分量和泊松分布预期计数对应的参数之间的关系。tZIP 使用回顾性队列的再入院事件进行外部验证，随访时间截至出院日期后 12 个月。随后，在研究医院进行试点后，对 tZIP 进行了前瞻性验证。在试点期间出院的患者被标记，并且每月验证所提出模型对其再入院的预测。使用混合机器学习（ML）方法，进一步验证了基于 tZIP 的风险估计器对 28 天再入院的边际效应，与代表不同急性后和临床状态的其他因素竞争。
在两个样本外队列中，tZIP 对 28 天至 365 天的再入院预测在回顾性和前瞻性验证中均达到了 80%以上的区分准确性。与使用相同预测因子构建的 Cox 比例和线性模型相比，它具有很大的优势。混合 ML 表明，风险估计器对 28 天再入院的贡献大于与服务利用和临床状态相关的其他特征。
引入了一种新的再入院风险模型，并推导出其风险估计器，其重要性大于所有其他与急性后护理和临床状况不同的因素。该方法依赖于从 EHR 中提取的四个易于访问的变量。因此，临床医生可以从出院后 28 天到 365 天可视化患者的再入院风险，并在适当的时间为高危老年患者进行随访护理。
