Trial reportCirculation. Cardiovascular quality and outcomes2011
Use of hundreds of electrocardiographic biomarkers for prediction of mortality in postmenopausal women: the Women's Health Initiative.
Trial report in Circulation. Cardiovascular quality and outcomes, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
29 citing papers in PubMed.
- A novel multimodal AI-based radiomics approach for precision assessment of pain intensity in chronic nonspecific low back pain.Journal of orthopaedic translation · 2026Article
- The cost of explainability in artificial intelligence-enhanced electrocardiogram models.NPJ digital medicine · 2025Article
- Analysis and development of risk prediction models for chronic opioid use after surgery: a cohort study using the nationwide database.Korean journal of anesthesiology · 2025Article
- A hybrid algorithm-based ECG risk prediction model for cardiovascular disease.European heart journal. Digital health · 2025Article
- Predicting in-hospital mortality among patients admitted with a diagnosis of heart failure: a machine learning approach.ESC heart failure · 2024Review
- Machine learning identifies prominent factors associated with cardiovascular disease: findings from two million adults in the Kashgar Prospective Cohort Study (KPCS).Global health research and policy · 2022Article
- The Impact of Time Horizon on Classification Accuracy: Application of Machine Learning to Prediction of Incident Coronary Heart Disease.JMIR cardio · 2022Article
- An algorithm to identify cases of pulmonary arterial hypertension from the electronic medical record.Respiratory research · 2022Article
- Induced Pluripotent Stem Cell-Based Drug Screening by Use of Artificial Intelligence.Pharmaceuticals (Basel, Switzerland) · 2022Review
- Selection of 51 predictors from 13,782 candidate multimodal features using machine learning improves coronary artery disease prediction.Patterns (New York, N.Y.) · 2021Article
- Clinician checklist for assessing suitability of machine learning applications in healthcare.BMJ health & care informatics · 2021Article
- Machine-learning algorithms for predicting hospital re-admissions in sickle cell disease.British journal of haematology · 2021Article
- Machine learning approach to predict postoperative opioid requirements in ambulatory surgery patients.PloS one · 2020Article
- Article
- Using big data analytics to improve HIV medical care utilisation in South Carolina: A study protocol.BMJ open · 2019Article
- Application of machine learning in rheumatic disease research.The Korean journal of internal medicine · 2019Review
- Evaluation of Machine-Learning Algorithms for Predicting Opioid Overdose Risk Among Medicare Beneficiaries With Opioid Prescriptions.JAMA network open · 2019Article
- The application of convolutional neural network to stem cell biology.Inflammation and regeneration · 2019Review
- Automated Deep Learning-Based System to Identify Endothelial Cells Derived from Induced Pluripotent Stem Cells.Stem cell reports · 2018Article
- Predicting Locations of High-Risk Plaques in Coronary Arteries in Patients Receiving Statin Therapy.IEEE transactions on medical imaging · 2018Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors.
Funding
Abstract
BACKGROUND- Simultaneous contribution of hundreds of electrocardiographic (ECG) biomarkers to prediction of long-term mortality in postmenopausal women with clinically normal resting ECGs is unknown. METHODS AND RESULTS- We analyzed ECGs and all-cause mortality in 33 144 women enrolled in the Women's Health Initiative trials who were without baseline cardiovascular disease or cancer and had normal ECGs by Minnesota and Novacode criteria. Four hundred and seventy-seven ECG biomarkers, encompassing global and individual ECG findings, were measured with computer algorithms. During a median follow-up of 8.1 years (range for survivors, 0.5 to 11.2 years), 1229 women died. For analyses, the cohort was randomly split into derivation (n=22 096; deaths, 819) and validation (n=11 048; deaths, 410) subsets. ECG biomarkers and demographic and clinical characteristics were simultaneously analyzed using both traditional Cox regression and random survival forest, a novel algorithmic machine-learning approach. Regression modeling failed to converge. Random survival forest variable selection yielded 20 variables that were independently predictive of long-term mortality, 14 of which were ECG biomarkers related to autonomic tone, atrial conduction, and ventricular depolarization and repolarization. CONCLUSIONS- We identified 14 ECG biomarkers from among hundreds that were associated with long-term prognosis using a novel random forest variable selection methodology. These biomarkers were related to autonomic tone, atrial conduction, ventricular depolarization, and ventricular repolarization. Quantitative ECG biomarkers have prognostic importance and may be markers of subclinical disease in apparently healthy postmenopausal women.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.