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February 21, 2026BMC Medical Informatics and Decision Making0 citationsOpen Access

Prediction Models for Cardiac Rehabilitation Adherence in Cardiovascular Disease Patients

Prediction models for adherence to cardiac rehabilitation programs in patients with cardiovascular disease: a scoping review

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Population

10 studies including patients with cardiovascular disease participating in or referred to cardiac…

Design

Systematic_review

Key result

Prediction models for adherence to cardiac rehabilitation programs in patients with cardiovascular disease demonstrated AUROC values ranging from 0.62 to 0.893, with non-adherence rates varying from 41% to 61.4%, but existing models lack external validation and methodological rigor.

Authors

CXChengyu XiaTongji UniversityGHGuo HtShantou UniversityLJLiuxia Ji

Discussion

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Overview

Existing models should not guide clinical decisions; leaves open need for externally validated, low-bias tools.

Key Points

  • The aim is to evaluate the quality of prediction models for adherence to cardiac rehabilitation in cardiovascular disease patients and suggest future research directions.
  • Conducted a scoping review based on the Arksey and O’Malley framework.
  • Systematically searched nine electronic databases for relevant studies published in English or Chinese.
  • Evaluated the methodological quality of prediction models using the PROBAST tool.
  • Included ten studies with varying adherence rates and methodological approaches.
  • Non-adherence rates to cardiac rehabilitation varied from 41% to 61.4%.
  • Significant methodological concerns were identified, including inadequate sample sizes and lack of external validation.
  • Logistic regression was the most common predictive method, with AUROC values ranging from 0.62 to 0.893.
  • Most models reviewed showed considerable heterogeneity and limited validation, hindering clinical applicability.

Study Design

Type

Scoping Review

Structured PICO

P
Population
10 studies including patients with cardiovascular disease (including AMI, post-PCI, post-cardiac events, stable angina, CABG, and heart failure) participating in or referred to cardiac rehabilitation programs. Study sample sizes ranged from 50 to 12,003 participants.
I
Intervention
Clinical prediction models (including logistic regression, decision tree, random forest, and artificial neural networks) for predicting adherence to cardiac rehabilitation programs
O
Outcome
Methodological quality (assessed via PROBAST framework) and predictive performance (AUROC, calibration, sensitivity/specificity) of prediction models for adherence to cardiac rehabilitation programs

Existing prediction models for cardiac rehabilitation adherence are at an early stage of development with high risk of bias and lack of external validation, meaning none can currently be recommended for clinical use.

Main Result

Effect estimate: AUROC 0.62 to 0.893

Absolute Event Rate: 41% vs 61.4%

Limitations

  • Included studies showed prevalent methodological concerns such as inadequate sample sizes and near-total lack of external validation.
  • Most studies relied on single-center, retrospective data with variable reporting quality.
  • Wide heterogeneity in sample sizes (50 to 12,003 participants) and predictor selection increases risk of overfitting in some models.
  • Lack of standardized adherence definitions and measurement methods across studies.
  • Limited use of machine learning models, with most studies using logistic regression and very few employing advanced algorithms.
  • inadequate sample sizes
  • near-total lack of external validation
  • reliance on single-center, retrospective data
  • variable reporting quality
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Cite This Study

Xia et al. (2026) conducted a scoping review in Adult patients (≥18 years) with cardiovascular disease participating in or referred to cardiac rehabilitation programs. Prediction models for adherence to cardiac rehabilitation programs was evaluated on Adherence to cardiac rehabilitation programs, measured by session completion rates, validated scales, or wearable device tracking (AUROC 0.62 to 0.893). Prediction models for adherence to cardiac rehabilitation programs in patients with cardiovascular disease demonstrated AUROC values ranging from 0.62 to 0.893, with non-adherence rates varying from 41% to 61.4%, but existing models lack external validation and methodological rigor.

synapsesocial.com/papers/69994bef873532290d020131https://doi.org/10.1186/s12911-026-03391-7
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Machine-Learning based Prediction Models for Healthcare Outcomes in Patients Participating in Cardiac Rehabilitation: A Systematic Review2024
  2. 2Development of Machine Learning Models for Predicting Effectiveness and Adherence in Cardiac Rehabilitation2025 · 2 citations
  3. 3Systematic Review and Critical Appraisal of Prediction Models for Readmission in Coronary Artery Disease Patients: Assessing Current Efficacy and Future Directions2024 · 3 citations
  4. 4Development of a Simple Clinical Tool for Predicting Early Dropout in Cardiac Rehabilitation2020 · 12 citations
  5. 5Risk prediction models for mortality and readmission in patients with acute heart failure: A protocol for systematic review, critical appraisal, and meta-analysis2023 · 3 citations