Mortality
Mortality refers to the occurrence of death in a defined population or clinical cohort over a specified period.
Mortality refers to the occurrence of death in a defined population or clinical cohort over a specified period. In medicine and epidemiology, it is a core clinical metric used to quantify fatal outcomes, compare disease burden, evaluate treatment effectiveness, and inform Risk Stratification. Mortality may be reported as All-cause mortality or cause-specific mortality, and it is frequently analyzed alongside morbidity, survival, disease progression, and prognosis to characterize clinical outcomes across patient groups.
In biomedical research, mortality is often modeled using statistical approaches such as the Cox proportional hazards model, hazard ratio estimation, odds ratio analysis, and propensity score matching, with performance or prediction studies frequently reporting sensitivity, specificity, Area Under the Curve, confidence interval, and P-value. Mortality is especially important in high-risk conditions such as infection, stroke, inflammation-related critical illness, and chronic cardiometabolic disease, where it serves as a key endpoint for evaluating clinical severity and the effectiveness of interventions or prognostic tools.
- Machine Learning Model for Predicting Risk Factor Analysis and a Mortality Prediction Model of Acute Cholangitis Complicated with Sepsis. PMID 42714037
Where the papers sit
12 papers study mortality directly. Those 12 are one subject: Clinical Risk and Outcomes. Risk prediction and outcome studies span sepsis, diabetes, HIV, transplantation and post-COVID cardiac events, using AI, real-world data and population surveillance. The common aim is to identify high-risk patients and quantify disease burden, rather than establish a single treatment strategy. No way of splitting those 12 scores better than chance.
Recent Findings on Mortality
Clinical Risk and Outcomes: Patients with sepsis, pediatric liver transplantation, cardiovascular disease, colorectal cancer, and COVID-19 can be stratified with clinical variables, scores, or imaging markers associated with mortality 42714037Sep42547621Aug42384558Jul42456539Jul42267655Jun. The acute cholangitis nomograms, Phoenix Sepsis Score, and XGBoost-SHAP model showed useful discrimination, with Area Under the Curve values reaching 0.904, 0.868, and 0.873, respectively 42714037Sep42547621Aug42384558Jul. Population surveillance linked rising type 2 diabetes burden and younger incidence in Caribbean countries mainly to high BMI, while diabetic foot ulcers marked advanced cardiometabolic disease in people with heart failure 42600892Aug42482123Jul. Associations varied across real-world treatment studies: GLP-1 receptor agonists were associated with lower all-cause mortality but not 3-point major adverse cardiovascular events, and diabetic foot ulcers predicted mortality only in the time-varying Cox model 42581431Aug42482123Jul. For unruptured giant intracranial aneurysms, endovascular and microsurgical management had higher three-year survival and more favorable neurological outcomes than conservative management, although endovascular treatment required more retreatment 42414088Jul. AI-based mortality prediction is moving toward external validation, prospective real-world evaluation, and implementation assessment, while the HIV prognostic literature is being prepared for systematic appraisal of predictive performance, risk of bias, reporting quality, and clinical readiness 42612087Aug42612074Aug.
Written from 12 PubMed abstracts, each one cited by PMID above. Published: 2026-08-19. Last written: 2026-09-11 by GPT. Drafted by language models from published abstracts; not medical advice.