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.

Objective: 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.

Methods: A retrospective multicentre cohort study. Methods: This study used the Taipei Medical University Clinical Research Database, including data from patients at three affiliated hospitals. Methods: 11 989 hospital admissions for pneumonia among patients aged ≥45 years admitted from 2014 to 2021. Methods: 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 features included creatinine levels, age, haematocrit levels, Charlson Comorbidity Index scores, and haemoglobin levels, with their predictive value changing over time.

Conclusions: The RSF model effectively predicts 14-day readmission risk among pneumonia patients. The ED data-based model allows clinicians to estimate readmission risk before ward admission or discharge from the ED, enabling timely interventions. Accurately predicting short-term readmission risk might also further support physicians in designing the optimal healthcare programme and controlling individual medical status to prevent readmissions.