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Incorporating pregnancy-related factors, especially hypertensive disorders of pregnancy, significantly improves the prediction of 10-year incident hypertension risk in women after their first childbirth, potentially guiding targeted cardiov
This study demonstrates that polygenic risk scores (PRS) independently predict type 2 diabetes risk in Japanese individuals, and healthy lifestyles can mitigate this risk even among genetically predisposed people, enhancing personalized pre
Surgeons’ preoperative subjective assessments quantified by visual analog scale significantly predict severe postoperative complications, enhancing traditional risk models for better perioperative prognosis.
An explainable machine learning model identifies bariatric surgery candidates at risk for obesity hypoventilation syndrome using seven accessible clinical variables, enhancing early diagnosis and perioperative risk management.
This study evaluates risk factors for intracranial haemorrhage after tenecteplase fibrinolysis in STEMI patients, developing a prediction model highlighting age, blood pressure, sex, weight, and dose interactions as key determinants.
The LIFE-Preserved model accurately predicts short-term and lifetime risks of heart failure hospitalization or cardiovascular death in HFpEF patients, enabling tailored clinical risk stratification and improved management.
A validated nomogram predicts hypothyroidism risk within 12 months after hemithyroidectomy using clinical and pathological variables, aiding tailored patient counseling and postoperative monitoring.
A novel AI-driven risk prediction model for ovarian cancer demonstrates high accuracy and potential to improve early detection in screening settings.
Weekly well-day capillary ketone testing maintains predictive accuracy for 1-month diabetic ketoacidosis risk, offering a practical and less burdensome monitoring strategy for patients with type 1 diabetes.
A novel machine learning-based model demonstrates improved risk prediction in transthyretin amyloid cardiomyopathy, surpassing traditional staging systems and supporting personalized patient management.
A recent study demonstrates that defining COPD exacerbation risk by any moderate or severe event over two years improves prediction accuracy and clinical decision-making compared to the standard one-year criteria.
Machine learning models can effectively predict major 30-day complications following glossectomy for tongue cancer, improving individualized risk assessment over traditional methods.
A large retrospective study from Pakistan shows that LACE and HOSPITAL scores have fair accuracy in predicting 30-day readmissions among internal medicine patients, supporting their use in LMIC hospitals to target interventions and improve
A novel risk score incorporating COPD and graded renal dysfunction improves prediction of one-year mortality post-TAVI, showing excellent internal validity but requiring external validation before clinical use.
This study identifies six plasma proteins associated with 3-year progression from impaired fasting glucose to diabetes, enhancing risk prediction and offering new mechanistic insights.
This review synthesizes recent advances in understanding the natural history of LMNA-related cardiomyopathies and introduces the first validated prediction model for severe heart failure events, enhancing early risk stratification and manag
Wearable accelerometer sleep-wake patterns were linked to later dementia risk in two UK cohorts and modestly improved prediction beyond age and standard risk factors.
In ARIC, ischemic stroke at age 80 years or older was more often embolic than thrombotic. Atrial fibrillation and left atrial disease explained much of the age effect, and adding embolic factors improved prediction.
A novel statistical model predicts chronic immune thrombocytopenia (ITP) in children at diagnosis, using age, sex, immunoglobulin levels, and lab values, offering early clinical guidance.
Researchers have developed and validated the TIP-CA model, a clinical tool using five key factors—anti-TIF1-γ, ILD absence, poikiloderma, DM subtype, and anemia—to accurately predict malignancy risk in patients with dermatomyositis, enablin
