Han Eunkyung, Kharrazi Hadi, Shi Leiyu
Ho-Young Institute of Community Health, Paju, Republic of Korea.
Asia Pacific Center For Hospital Management and Leadership Research, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States.
JMIR Aging. 2023 Nov 20;6:e42437. doi: 10.2196/42437.
Among older adults, nursing home admissions (NHAs) are considered a significant adverse outcome and have been extensively studied. Although the volume and significance of electronic data sources are expanding, it is unclear what predictors of NHA have been systematically identified in the literature via electronic health records (EHRs) and administrative data.
This study synthesizes findings of recent literature on identifying predictors of NHA that are collected from administrative data or EHRs.
The PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines were used for study selection. The PubMed and CINAHL databases were used to retrieve the studies. Articles published between January 1, 2012, and March 31, 2023, were included.
A total of 34 papers were selected for final inclusion in this review. In addition to NHA, all-cause mortality, hospitalization, and rehospitalization were frequently used as outcome measures. The most frequently used models for predicting NHAs were Cox proportional hazards models (studies: n=12, 35%), logistic regression models (studies: n=9, 26%), and a combination of both (studies: n=6, 18%). Several predictors were used in the NHA prediction models, which were further categorized into sociodemographic, caregiver support, health status, health use, and social service use factors. Only 5 (15%) studies used a validated frailty measure in their NHA prediction models.
NHA prediction tools based on EHRs or administrative data may assist clinicians, patients, and policy makers in making informed decisions and allocating public health resources. More research is needed to assess the value of various predictors and data sources in predicting NHAs and validating NHA prediction models externally.
在老年人中，入住养老院被视为一项重大不良后果，且已得到广泛研究。尽管电子数据源的数量和重要性在不断增加，但尚不清楚通过电子健康记录（EHR）和行政数据在文献中系统识别出的入住养老院的预测因素有哪些。
本研究综合了近期关于从行政数据或EHR中收集的入住养老院预测因素的文献研究结果。
采用PRISMA-ScR（系统评价和Meta分析扩展版的首选报告项目用于范围综述）指南进行研究选择。使用PubMed和CINAHL数据库检索研究。纳入2012年1月1日至2023年3月31日发表的文章。
共筛选出34篇论文最终纳入本综述。除入住养老院外，全因死亡率、住院和再住院也经常用作结局指标。预测入住养老院最常用的模型是Cox比例风险模型（研究数量：n = 12，35%）、逻辑回归模型（研究数量：n = 9，26%）以及两者结合的模型（研究数量：n = 6，18%）。入住养老院预测模型中使用了多个预测因素，这些因素进一步分为社会人口学、照护者支持、健康状况、医疗利用和社会服务利用因素。只有5项（15%）研究在其入住养老院预测模型中使用了经过验证的衰弱测量方法。
基于EHR或行政数据的入住养老院预测工具可能有助于临床医生、患者和政策制定者做出明智决策并分配公共卫生资源。需要更多研究来评估各种预测因素和数据源在预测入住养老院方面的价值，并对外验证入住养老院预测模型。
