Objective To provide focused evaluation of predictive modeling of electronic medical record (EMR) data to predict 30 day hospital readmission.
Design Systematic review.
Data source Ovid Medline, Ovid Embase, CINAHL, Web of Science, and Scopus from January 2015 to January 2019.
Eligibility criteria for selecting studies All studies of predictive models for 28 day or 30 day hospital readmission that used EMR data.
Outcome measures Characteristics of included studies, methods of prediction, predictive features, and performance of predictive models.
Results Of 4442 citations reviewed, 41 studies met the inclusion criteria. Seventeen models predicted risk of readmission for all patients and 24 developed predictions for patient specific populations, with 13 of those being developed for patients with heart conditions. Except for two studies from the UK and Israel, all were from the US. The total sample size for each model ranged between 349 and 1 195 640. Twenty five models used a split sample validation technique. Seventeen of 41 studies reported C statistics of 0.75 or greater. Fifteen models used calibration techniques to further refine the model. Using EMR data enabled final predictive models to use a wide variety of clinical measures such as laboratory results and vital signs; however, use of socioeconomic features or functional status was rare. Using natural language processing, three models were able to extract relevant psychosocial features, which substantially improved their predictions. Twenty six studies used logistic or Cox regression models, and the rest used machine learning methods. No statistically significant difference (difference 0.03, 95% confidence interval −0.0 to 0.07) was found between average C statistics of models developed using regression methods (0.71, 0.68 to 0.73) and machine learning (0.74, 0.71 to 0.77).
Conclusions On average, prediction models using EMR data have better predictive performance than those using administrative data. However, this improvement remains modest. Most of the studies examined lacked inclusion of socioeconomic features, failed to calibrate the models, neglected to conduct rigorous diagnostic testing, and did not discuss clinical impact.

中文翻译：

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在医院再入院风险预测模型的开发和验证中使用电子病历：系统评价。

目的对电子病历 (EMR) 数据的预测模型进行重点评估，以预测 30 天再入院。
设计系统审查。
数据来源2015 年 1 月至 2019 年 1 月的 Ovid Medline、Ovid Embase、CINAHL、Web of Science 和 Scopus
。选择研究的资格标准所有使用 EMR 数据的 28 天或 30 天住院再入院预测模型研究。
结果测量纳入研究的特征、预测方法、预测特征和预测模型的性能。
结果在审查的 4442 篇引文中，41 项研究符合纳入标准。17 个模型预测了所有患者再入院的风险，24 个模型针对患者特定人群进行了预测，其中 13 个模型是针对心脏病患者开发的。除来自英国和以色列的两项研究外，均来自美国。每个模型的总样本量在 349 到 1195640 之间。25 个模型使用了拆分样本验证技术。41 项研究中有 17 项报告 C 统计量为 0.75 或更高。十五个模型使用校准技术进一步完善模型。使用 EMR 数据使最终预测模型能够使用各种临床测量，例如实验室结果和生命体征；然而，很少使用社会经济特征或功能状态。使用自然语言处理，三个模型能够提取相关的社会心理特征，这大大改善了他们的预测。26 项研究使用了逻辑或 Cox 回归模型，其余的则使用了机器学习方法。使用回归方法（0.71，0.68 到 0.73）和机器学习（0.74，0.71 到 0.77）开发的模型的平均 C 统计量之间没有发现统计学上的显着差异（差异 0.03，95% 置信区间 -0.0 到 0.07）。
结论平均而言，使用 EMR 数据的预测模型比使用管理数据的预测模型具有更好的预测性能。然而，这种改善仍然不大。大多数检查的研究缺乏社会经济特征，未能校准模型，忽视进行严格的诊断测试，也没有讨论临床影响。