ArticleBMC medical informatics and decision making2024
Development a nomogram prognostic model for survival in heart failure patients based on the HF-ACTION data.
Article in BMC medical informatics and decision making, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Regularized win ratio regression for variable selection and risk prediction, with an application to a cardiovascular trial.BMC medical research methodology · 2025Article
- Correlation Between Coronary Arterial Dominance and the Degree of Coronary Artery Disease Using Computed Tomography Angiography.Journal of multidisciplinary healthcare · 2025Article
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7 authors.
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Abstract
backgroundThe risk assessment for survival in heart failure (HF) remains one of the key focuses of research. This study aims to develop a simple and feasible nomogram model for survival in HF based on the Heart Failure-A Controlled Trial Investigating Outcomes of Exercise TraiNing (HF-ACTION) to support clinical decision-making.
methodsThe HF patients were extracted from the HF-ACTION database and randomly divided into a training cohort and a validation cohort at a ratio of 7:3. Multivariate Cox regression was used to identify and integrate significant prognostic factors to form a nomogram, which was displayed in the form of a static nomogram. Bootstrap resampling (resampling = 1000) and cross-validation was used to internally validate the model. The prognostic performance of the model was measured by the concordance index (C-index), calibration curve, and the decision curve analysis.
resultsThere were 1394 patients with HF in the overall analysis. Seven prognostic factors, which included age, body mass index (BMI), sex, diastolic blood pressure (DBP), exercise duration, peak exercise oxygen consumption (peak VO
conclusionsThis study created a nomogram prognostic model for survival in HF based on a large American population, which can provide additional decision information for the risk prediction of HF.
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