Diagnostic Accuracy of Cardiovascular Disease Prediction Models: A Systematic Review and Meta-Analysis of Validation
Mohammad Aziz Rasouli1,2, Farid Najafi3, Alireza Ansari-Moghaddam4
1Social Determinant of the Health Research Center, Research, Institute for Health Development Kurdistan University of Medical Sciences Sanandaj Iran.
Insights
This systematic review found that SCORE and COX models offer superior accuracy for predicting cardiovascular disease (CVD) risk compared to Framingham Risk Score (FRS) and ACC/AHA models. These findings aid in optimizing CVD prevention strategies globally.
Area of Science:
- Cardiology
- Public Health
- Epidemiology
Background:
- Cardiovascular diseases (CVD) represent a significant global health burden.
- Effective primary prevention strategies are crucial, especially in resource-constrained settings.
- Accurate CVD risk prediction tools are essential for targeted interventions.
Purpose of the Study:
- To conduct a systematic review and meta-analysis of diagnostic test accuracy for established CVD risk prediction tools.
- To provide high-certainty evidence on the real-world performance of these tools.
- To inform the optimization of primary CVD prevention strategies worldwide.
Main Methods:
- Systematic search of PubMed, Scopus, and Web of Science (2013-2024).
- Inclusion of 58 cohort studies with 7.5 million participants.
- Extraction of data to compute pooled sensitivity, specificity, likelihood ratios, DOR, and AUC; SROC curves and Fagan nomograms used for assessment.
Main Results:
- Pooled sensitivity and specificity varied across models: FRS (0.79/0.66), ACC/AHA (0.74/0.79), SCORE (0.77/0.69), COX (0.85/0.80).
- Highest pooled AUCs observed for SCORE (0.81) and COX (0.89), indicating superior discrimination.
- Significant CVD events noted: 47,276 in men and 33,931 in women across studies.
Conclusions:
- SCORE and COX models demonstrated superior performance based on AUC, a reliable threshold-independent metric.
- AUC provides a robust basis for comparing different CVD risk prediction models.
- Future research should focus on validating, locally adapting, and integrating novel predictors rather than developing redundant models.
Background And Aim:
Cardiovascular diseases (CVD) are widely accepted to be the most serious health care problem in the world. We performed a diagnostic test accuracy systematic review and meta-analysis to establish the real-world performance of established CVD risk prediction tools, providing high-certainty evidence to optimize primary prevention strategies in resource-constrained healthcare systems worldwide.
Methods:
Databases including PubMed, Scopus, and Web of Science were systematically searched for studies published from January 1, 2013, to December 30, 2024. A total of 58 cohort studies involving 7.5 million participants were ultimately included. Data on true positives, false positives, true negatives, and false negatives were extracted to compute pooled sensitivity, specificity, positive and negative likelihood ratios, diagnostic odds ratios (DOR), and area under the receiver operating characteristic curve (AUC). Summary receiver operating characteristic (SROC) curves were constructed, publication bias was evaluated using Deeks' funnel plot asymmetry test, and clinical utility was assessed through Fagan nomograms. Analyses were performed in STATA version 18.
Results:
Across 58 studies, 47,276 CVD events occurred in men and 33,931 in women. Pooled sensitivity and specificity were 0.79 (95% CI: 0.77-0.89) and 0.66 (95% CI: 0.50-0.79) for the Framingham Risk Score (FRS); 0.74 (95% CI: 0.68-0.80) and 0.79 (95% CI: 0.43-0.95) for the ACC/AHA model; 0.77 (95% CI: 0.57-0.89) and 0.69 (95% CI: 0.43-0.89) for SCORE; and 0.85 (95% CI: 0.81-0.88) and 0.80 (95% CI: 0.79-0.81) for COX models. The highest pooled AUCs were observed for SCORE (0.81, 95% CI: 0.77-0.84) and COX (0.89, 95% CI: 0.89-0.92), indicating superior discrimination.
Conclusions:
Based on pooled AUCs, the most reliable threshold-independent metric, SCORE and COX demonstrated superior performance, followed by FRS. While sensitivity/specificity and DOR provide classification insights, AUC offers the most robust basis for cross-model comparison. Future research should focus on validating and comparing established models, adapting them locally, and integrating novel predictors, rather than developing redundant ones.
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