Literature
A systematic review of machine learning algorithms for mortality risk, readmission and phenotype prediction in patients with heart failure: exploring key data sources, input variables and outcomes.
Evidence suggests that machine learning algorithms, particularly random forest and support vector machines, can effectively predict mortality and readmission in heart failure populations using clinical variables such as age, serum creatinine, and comorbidities. Worth noting that this systematic review of 109 studies identified key predictive variables that differ between mortality (age, serum creatinine, systolic blood pressure) and readmission (comorbidities, blood urea nitrogen) outcomes. Signal observed that while these findings address heart failure prediction broadly, the review focused exclusively on adult populations rather than pediatric cardiac cohorts.
Not applicable.
Relevance: Addresses heart failure prediction modeling, relevant to research interests and common endpoint in profile diagnoses, though focuses on adult rather than pediatric populations.
PMID: 42237288BMC medical informatics and decision making(Journal Article) Literature
Kidney and Survival Benefits of Semaglutide in Diabetes With Chronic Kidney Disease: FLOW Trial Cardiovascular Subgroup Analyses.
Evidence suggests that semaglutide reduced primary kidney outcomes and all-cause mortality in type 2 diabetes patients with chronic kidney disease, regardless of baseline cardiovascular status including heart failure. Worth noting that participants with heart failure showed particular benefit with a number needed to treat of 13 to prevent one primary kidney outcome at 3 years. Signal observed that these cardiovascular protective effects occurred in an adult diabetic population with established kidney disease, distinct from pediatric congenital heart disease populations.
(Evaluate Renal Function with Semaglutide Once Weekly [FLOW]; NCT03819153).
Relevance: Examines heart failure outcomes in clinical trial, but focuses on adult diabetic population with kidney disease rather than pediatric cardiac diagnoses, and drug not in profile formulary.
PMID: 42233552Journal of the American College of Cardiology(Journal Article) Literature
Association Between Predicting Risk of Cardiovascular Disease Events (PREVENT) Risk Scores and Subclinical Cardiovascular Disease: Insights From the Project Baseline Health Study.
Signal observed that PREVENT cardiovascular risk scores strongly correlate with subclinical heart failure, particularly left ventricular diastolic dysfunction, in asymptomatic adults. Evidence suggests that each unit increase in 10-year heart failure risk corresponded to 2.67-fold higher odds of diastolic dysfunction, with prevalence increasing from 5.8% in low-risk to 44.1% in intermediate-/high-risk groups. Worth noting that this risk stratification approach demonstrated strong predictive capability (area under the curve 0.81) for identifying subclinical cardiac dysfunction in community populations.
In an asymptomatic CVD-free community sample, subclinical CVD was increasingly associated with higher 10-year HF and ASCVD risks calculated by the Predicting Risk of CVD...
Relevance: Addresses heart failure phenotype identification and risk stratification, relevant to research interests, but focuses on adult prevention rather than pediatric congenital cardiac diagnoses.
PMID: 42216265Journal of the American Heart Association(Journal Article)