Development of Multivariable Prediction Models for the Identification of Patients Admitted to Hospital with an Exacerbation of COPD and the Prediction of Risk of Readmission: A Retrospective Cohort Study using Electronic Medical Record Data

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.

Keywords

• copd
• machine learning
• readmission
• acute exacerbations