美国密歇根大学医学院Elham Mahmoudi研究组，对电子病历在再入院风险预测模型开发与验证中的应用进行了系统回顾。2020年4月8日，《英国医学杂志》发表了这一成果。
为了对电子病历（EMR）数据预测30天再入院率的模型进行集中评估，研究组对Ovid Medline、Ovid Embase等大型数据库中2015年1月至2019年1月的相关文献进行了系统审查，检索使用EMR数据预测模型评估28天或30天再入院率的研究。
共有41项研究符合纳入标准。有17种模型预测了所有患者的再入院风险，有24种针对特定人群患者进行预测，其中13种针对心脏病患者。除了来自英国和以色列的两项研究外，其他研究均来自美国。每个模型的总样本规模在349至1195640之间。
25个模型使用了拆分样本验证技术。41个研究中有17个报告的C统计值为0.75或更高。15个模型使用了校准技术来进一步完善模型。使用EMR数据让最终的预测模型能够使用各种临床指标，例如实验室结果和生命体征；但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，从而大大改善它们的预测。有26项研究使用了Logistic或Cox回归模型，其余研究则使用了机器学习方法。使用回归方法开发的模型平均C统计量为0.71，机器学习开发为0.74，两者之间无统计学差异。
总之，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，但改进并不大。大多数研究都缺乏社会经济特征，未能校准模型，忽略严格的诊断测试，且未讨论临床影响。
附：英文原文
Title: Use of electronic medical records in development and validation of risk prediction models of hospital readmission: systematic review
Author: Elham Mahmoudi, Neil Kamdar, Noa Kim, Gabriella Gonzales, Karandeep Singh, Akbar K Waljee
Issue&Volume: 2020/04/08
Abstract: 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 1195640. 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.
DOI: 10.1136/bmj.m958
Source: https://www.bmj.com/content/369/bmj.m958
BMJ-British Medical Journal:《英国医学杂志》，创刊于1840年。隶属于BMJ出版集团，最新IF：93.333
官方网址：http://www.bmj.com/
投稿链接：https://mc.manuscriptcentral.com/bmj
本期文章：《英国医学杂志》：Online/在线发表
