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.

A retrospective multicentre cohort study.

This study used the Taipei Medical University Clinical Research Database, including data from patients at three affiliated hospitals.

11 989 hospital admissions for pneumonia among patients aged ≥45 years admitted from 2014 to 2021.

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.

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.

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.

To develop and validate a risk prediction model for adverse outcomes in patients with angina with non-obstructive coronary arteries (ANOCA) confirmed by invasive coronary angiography.

Retrospective cohort study.

A tertiary cardiovascular care centre in China.

From 17 816 consecutive patients undergoing coronary angiography for suspected coronary artery disease, 5934 met ANOCA criteria after rigorous exclusion: (1) significant stenosis (≥50% luminal narrowing), (2) established coronary artery disease history, (3) incomplete baseline/follow-up data, (4) non-cardiovascular life-limiting conditions.

The primary outcome was a composite of all-cause death, non-fatal myocardial infarction (MI), stroke and repeat percutaneous coronary intervention (PCI) or coronary artery bypass grafting (CABG). The secondary outcome was major adverse cardiovascular events, defined as cardiac-related death, non-fatal MI, non-fatal stroke, repeat PCI and CABG.

The derivation cohort (n=4452) and validation cohort (n=1482) demonstrated comparable baseline characteristics. The nomogram incorporated eight prognosticators: age, haemoglobin, serum urea, serum sodium, alanine aminotransferase/aspartate aminotransferase ratio, N-terminal pro-B-type natriuretic peptide (NT-proBNP), left atrial diameter and left ventricular ejection fraction. The prediction model showed robust discrimination for primary endpoint, achieving area under the curve (AUC) values of 0.82 (1 year), 0.90 (2 years) and 0.89 (3 years) in the derivation cohort, with corresponding validation cohort AUCs of 0.75, 0.77 and 0.78. Calibration plots revealed close alignment between predicted and actual event-free survival probabilities in both cohorts. Risk stratification identified two distinct prognostic groups with significant survival differences (log-rank p

This predictive model integrates routinely available clinical parameters to accurately stratify mortality and cardiovascular risk in ANOCA patients, providing a potential valuable decision-support tool for personalised therapeutic strategies.