Postoperative pulmonary complications (PPCs) represent a significant cause of postoperative morbidity and even mortality. However, there is a lack of consensus regarding this composite endpoint, the definition of the individual components, classification and optimal outcome measures. This study aims to refine the PPCs composite framework by evaluating its construct validity, assessing the necessity and risks of a composite measure and exploring the feasibility of differentiating severity categories.

A Delphi consensus process will be conducted, engaging an international multidisciplinary group of 30–40 panellists, including clinicians, researchers, patients, public representatives and health economists. Through iterative rounds, the study will seek agreement on the individual components of the PPCs composite. Additionally, consensus will establish a framework for a composite outcome measure based on a standardised severity classification, appropriate timeframes and weighted grading of PPCs.

Consensus, defined by ≥75% concurrence in multiple choice questions or on Likert–scale statements, will be evaluated from round 2 onwards. Delphi rounds will be continued until all statements have reached stability of responses evaluated by ² tests or the Kruskal-Wallis test.

The study will be conducted in strict compliance with the principles of the Declaration of Helsinki and will adhere to ACCORD guidance for reporting. Ethics approval has been obtained for this study from the University of Wolverhampton, UK (SOABE/202425/staff/3). Informed consent will be obtained from all panellists before the commencement of the Delphi process. The results of the study will be published in a peer–reviewed journal with the authorship assigned in accordance with ICMJE requirements.

NCT06916598 (clinicaltrials.gov).

This study investigates the rates of military sexual trauma (MST) and its associations with adverse mental health among a sample of UK female ex-service personnel who served during the Iraq/Afghanistan eras.

Female ex-service personnel, who participated in the fourth phase (Phase 4) of the King’s Centre for Military Health Research (KCMHR) Health and Well-being Cohort Study (2022–2023) and consented to be recontacted for follow-up studies (n=295), are being invited to participate in an online questionnaire between July 2024 and February 2025. The questionnaire contains surveys and questions related to experiences of sexual harassment and sexual assault during and outside of military service, disordered eating and broader female health issues. While the questionnaire relates to several female health topics, this study focuses on the surveys related to experiences of sexual trauma and eating disorders. Sociodemographic variables and some health variables, including post-traumatic stress disorder (PTSD), complex PTSD, common mental disorders, alcohol misuse, physical somatisation and social support, will be extracted from participants’ pre-existing data collected in Phase 4 of the KCMHR Cohort Study. Analyses will assess rates of MST, and hierarchical multiple logistic regressions will investigate associated health impacts. Rates and ORs, employing 95% CIs, will be reported.

This study has been granted full ethical approval by the King’s College London Research Ethics Committee (Ref: HR/DP-23/24–39040). Participants provide informed consent before participating and have access to a signposting booklet containing contact details for a range of support services. A risk protocol is in place, which outlines the procedure to be undertaken if a participant contacts the research team in distress. Findings will form part of a PhD thesis and will be further disseminated through peer-reviewed publication and dissemination with veteran mental health services and charities, and relevant government departments.

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.