ArticleCirculation reports2025
Machine-Learning-Based Prediction of Exercise Intolerance of Patients With Heart Failure Using Pragmatic Submaximal Exercise Parameters.
Article in Circulation reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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
2 citing papers in PubMed.
- Body Mass Index-Adjusted Rectus Femoris Muscle Thickness as a Simple Indicator for Exercise Tolerance in Patients With Heart Failure.Circulation reports · 2026Article
- Machine Learning in Adapted Physical Activity: Clinical Applications, Monitoring, and Implementation Pathways for Personalized Exercise in Chronic Conditions: A Narrative Review.Journal of functional morphology and kinesiology · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Low peak oxygen uptake (V̇O Methods and Results: We retrospectively analyzed the data for 343 patients with chronic HF and left ventricular ejection fraction <50% who underwent a symptom-limited cardiopulmonary exercise test and extracted 33 variables from their laboratory, echocardiographic, and exercise data up to the submaximal workload. The dataset was randomly divided into training and testing datasets in a 4 : 1 ratio. ML methods, including an exhaustive search for predictor selection, were used, and a support vector machine algorithm was applied for model optimization. We identified 5 important predictors: age, B-type natriuretic peptide, left ventricular end-diastolic diameter, V̇O Conclusions: Using readily available parameters, ML methods can enable accurate prediction of low peak V̇O
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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.