Predicting 14-day readmission in middle-aged and elderly patients with pneumonia using emergency department data: a multicentre retrospective cohort study with a survival machine learning approach
Abstract
Objectives Unplanned pneumonia readmissions increase patient morbidity, mortality and healthcare costs. Among pneumonia patients, the middle-aged and elderly (≥45 years old) have a significantly higher risk of readmission compared with the young. Given that the 14-day readmission rate is considered a healthcare quality indicator, this study is the first to develop survival machine learning (ML) models using emergency department (ED) data to predict 14-day readmission risk following pneumonia-related admissions. Design A retrospective multicentre cohort study. Setting This study used the Taipei Medical University Clinical Research Database, including data from patients at three affiliated hospitals. Participants 11 989 hospital admissions for pneumonia among patients aged ≥45 years admitted from 2014 to 2021. Primary and secondary outcome measures The dataset was randomly split into training (80%), validation (10%) and independent test (10%) sets. Input features included demographics, comorbidities, clinical events, vital signs, laboratory results and medical interventions. Four survival ML models—CoxNet, Survival Tree, Gradient Boosting Survival Analysis and Random Survival Forest—were developed and compared on the validation set. The best performance model was tested on the independent test set. Results The RSF model outperformed the other models. Validation on an independent test set confirmed the model’s robustness (C-index=0.710; AUC=0.693). The most important predictive…
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Evidence Hub reads this as a cohort study, done in people. Its evidence level is 3 of 5 (Moderate · trial / cohort / secondary analysis). Trials without a large randomized design, cohorts and secondary analyses give moderate evidence.
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