Tracking COPD exacerbation patterns and forecasting readmission risks utilizing electronic medical records.

Introduction: Accurate evaluation of exacerbation frequency is an essential part of COPD assessment. But relying on just the prior-year exacerbation history may not capture the full picture of risk given the inherent year-to-year fluctuations in exacerbation rates. This study aimed to evaluate the predictive performance of models incorporating the 3-year exacerbation history based on electronic medical record.

Materials And Methods: This retrospective cohort study included 86,501 COPD hospitalized patients in Beijing from 2008 to 2014. The annual frequency of COPD exacerbation hospitalizations over a 3-year period after the index hospitalization was calculated, with patients segmented into seven distinct exacerbation trajectory groups. Logistic regression was used to evaluate the predictive capability of the 3-year exacerbation history for exacerbation readmission in the fourth year. Predictors included age, sex, comorbidities, and exacerbation hospitalization in previous 1-3 years. Model performance was evaluated using area under the receiver operating characteristic curve (AUC).

Results: Of the studied patients, 56.5% were men, and the mean age (SD) was 73.8 (10.3) years. The overall readmission rate for COPD exacerbation was 0.31 per person-year, with only 3.8% of patients persistently readmitted over three consecutive years. The 3-year trajectory of exacerbation frequency was associated with exacerbation risk in the fourth year. Compared to just the prior year, the inclusion of a 3-year exacerbation hospitalization history notably improved prediction accuracy, with AUC elevating from 0.731 (0.724-0.739) to 0.786 (0.779-0.792).

Conclusion: These results unveil the fluctuating nature of COPD exacerbation hospitalization frequency across years and demonstrate that integrating a more comprehensive 3-year exacerbation history significantly refines the prediction model for future risk, thus providing a more nuanced and actionable insight for clinical care.