ArticleAnnals of medicine2026
Predicting major adverse cardiovascular and cerebrovascular events in chronic heart failure: a machine learning study.
Article in Annals of medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundHeart failure (HF) is a clinical syndrome characterized by impaired cardiac diastolic and systolic function due to structural or functional damage to the myocardium. HF represents the end-stage manifestation of many cardiac diseases. Therefore, early identification of high-risk patients is crucial. This study aims to utilize machine learning (ML) methods to develop and validate a model to predict major adverse cardiovascular and cerebrovascular events (MACCE) in patients with HF and identify its key predictive features.
methodsThis study is a retrospective cohort study. We enrolled a total of 271 patients, who were divided into training and testing sets. Baseline data, including cardiopulmonary exercise testing (CPET) parameters and laboratory tests, were collected for all participants. Based on the presence or absence of MACCE during follow-up, they were categorized into a No-event group and MACCE group. We developed seven ML models to predict the incidence of MACCE in patients with chronic heart failure (CHF) using CPET parameters. The predictive performance of these models was systematically compared, and model interpretability was evaluated using Shapley Additive exPlanations (SHAP). Subsequently, retaining only those with HF with preserved ejection fraction (HFpEF) for a sensitivity analysis. Additionally, a subgroup analysis was conducted between No-event group and Worsening HF (WHF) group.
resultsWe used Boruta feature selection, four important predictive features were identified. Among the ML models constructed with these features, the Categorical Boosting (CatBoost) model demonstrated the best performance. SHAP analysis was applied to interpret the optimal model, revealing that lower values of heart rate recovery at 1 min (HRR1), as well as a higher carbon dioxide ventilation equivalent slope (VE/VCO
conclusionHRR1 and VE/VCO
Indexed as
Identifiers
What Socratic holds
Registered trials
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 Socratic graph.