Abstract

Background

Approximately 20% of patients who are discharged from hospital for an acute exacerbation of COPD (AECOPD) are readmitted within 30 days. To reduce this, it is important both to identify all individuals admitted with AECOPD and to predict those who are at higher risk for readmission.

Objectives

To develop two clinical prediction models using data available in electronic medical records: 1) identifying patients admitted with AECOPD and 2) predicting 30-day readmission in patients discharged after AECOPD.

Methods

Two datasets were created using all admissions to General Internal Medicine from 2012 to 2018 at two hospitals: one cohort to identify AECOPD and a second cohort to predict 30-day readmissions. We fit and internally validated models with four algorithms.

Results

Of the 64,609 admissions, 3,620 (5.6%) were diagnosed with an AECOPD. Of those discharged, 518 (15.4%) had a readmission to hospital within 30 days. For identification of patients with a diagnosis of an AECOPD, the top-performing models were LASSO and a four-variable regression model that consisted of specific medications ordered within the first 72 hours of admission. For 30-day readmission prediction, a two-variable regression model was the top performing model consisting of number of COPD admissions in the previous year and the number of non-COPD admissions in the previous year.

Conclusion

We generated clinical prediction models to identify AECOPDs during hospitalization and to predict 30-day readmissions after an acute exacerbation from a dataset derived from available EMR data. Further work is needed to improve and externally validate these models.

中文翻译：

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开发多变量预测模型，用于识别因 COPD 恶化而入院的患者并预测再入院风险：使用电子病历数据的回顾性队列研究

摘要

背景

大约 20% 因 COPD 急性加重 (AECOPD) 出院的患者会在 30 天内再次入院。为了减少这种情况，重要的是要识别所有因 AECOPD 入院的患者，并预测那些再次入院风险较高的患者。

目标

利用电子病历中的可用数据开发两种临床预测模型：1) 识别因 AECOPD 入院的患者；2) 预测 AECOPD 出院患者 30 天再入院的情况。

方法

使用 2012 年至 2018 年两家医院普通内科的所有入院数据创建了两个数据集：一个队列用于识别 AECOPD，第二个队列用于预测 30 天的再入院率。我们使用四种算法拟合并内部验证模型。

结果

在 64,609 名入院患者中，3,620 名 (5.6%) 被诊断患有 AECOPD。在出院者中，518 人（15.4%）在 30 天内再次入院。为了识别诊断为 AECOPD 的患者，表现最好的模型是 LASSO 和四变量回归模型，该模型包含入院后 72 小时内订购的特定药物。对于 30 天再入院预测，双变量回归模型是表现最好的模型，该模型由前一年 COPD 入院人数和前一年非 COPD 入院人数组成。

结论

我们生成了临床预测模型，用于识别住院期间的 AECOPD，并根据可用 EMR 数据得出的数据集预测急性加重后 30 天的再入院情况。需要进一步的工作来改进和外部验证这些模型。