Premature acute coronary syndrome (PACS) presents with a poor prognosis and significant risks. This study aimed to investigate the association between small-dense low-density lipoprotein cholesterol (sdLDL-C) levels and the severity of coronary lesions, as well as its potential role in risk stratification for PACS patients with multivessel disease (MVD).

Retrospective cross-sectional study.

First Affiliated Hospital of Xinjiang Medical University in China, between May 2022 and November 2023.

900 PACS patients with MVD confirmed by coronary angiography (CAG) and 600 age-matched and sex-matched controls with normal CAG results.

Patients with PACS and MVD were stratified by the Global Registry of Acute Coronary Events (GRACE) score, and sdLDL-C levels were compared among the different GRACE score groups. The association between sdLDL-C and the GRACE score was evaluated using Pearson’s correlation analysis. Multivariate logistic regression analysis was employed to identify factors associated with PACS and MVD. The discriminatory ability of sdLDL-C for PACS with MVD was assessed using receiver operating characteristic (ROC) curve analysis. Restriction cubic spline (RCS) analysis was used to examine the potential nonlinear association between sdLDL-C levels and the high-risk groups of PACS with MVD.

Patients with PACS and MVD exhibited significantly higher sdLDL-C levels compared with control group (p

Elevated sdLDL-C levels demonstrated a significant association with the risk of PACS and MVD. These findings indicate sdLDL-C may serve as a potential biomarker for risk stratification in this high-risk population. However, causal inferences require validation in prospective studies.

ChiCTR2300074166

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 evaluate the incidence and risk factors for psychiatric disorders, including depression and anxiety, and assess the risk of suicide in patients with polymyositis (PM) and dermatomyositis (DM).

Retrospective cohort study.

Data were obtained from Taiwan’s National Health Insurance Research Database (NHIRD) between 2000 and 2018.

A total of 3477 patients with PM/DM and 13 908 age- and sex-matched non-PM/DM controls were included in the study.

The primary outcome was the incidence and risk of psychiatric disorders in patients with PM/DM compared with controls. Secondary outcomes included the identification of risk factors for psychiatric disorders, mortality and suicide risk in the PM/DM cohort.

The incidence rate ratio (IRR) of psychiatric disorders was significantly higher in the PM/DM cohort than in controls (IRR 1.62, 95% CI 1.39 to 1.89), with depression being the most prevalent disorder (IRR 2.25, 95% CI 1.83 to 2.75). Key risk factors included female sex, intravenous steroid therapy, and high-dose oral steroid use. Additionally, the PM/DM cohort exhibited a higher mortality rate (IRR 3.4, 95% CI 3.15 to 3.67) and elevated suicide risk (IRR 1.99, 95% CI 0.96 to 3.86) compared with controls.

Patients with PM/DM face a significantly higher risk of psychiatric disorders, mortality and suicide. Integrating mental healthcare into the routine management of PM/DM is crucial to improving patient outcomes and reducing mortality. Future research should focus on the impact of early psychiatric interventions on survival outcomes in this population.