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Comparing Non-Laboratory-Based and Laboratory-Based Cardiovascular Risk Predictions: Systematic Review and
Yihun Mulugeta Alemu1,2, Sisay M Alemu3, Nasser Bagheri1,4
1National Centre for Epidemiology and Population Health, College of Health and Medicine Australian National University Canberra Australia.
Insights
Non-laboratory-based cardiovascular disease (CVD) risk equations show strong agreement with lab-based methods. However, their interchangeability requires context-specific validation and recalibration for accurate risk prediction.
Area of Science:
- Cardiology
- Public Health
- Epidemiology
Background:
- Cardiovascular disease (CVD) is a leading global cause of death.
- Accurate CVD risk assessment is crucial for prevention and management.
- Evaluating non-laboratory-based risk equations against traditional laboratory-based ones is important for diverse clinical settings.
Purpose of the Study:
- To assess the agreement between non-laboratory-based and laboratory-based cardiovascular disease (CVD) risk equations.
- To analyze the concordance of these equations across various global settings.
- To determine the reliability and potential interchangeability of simplified CVD risk prediction tools.
Main Methods:
- Systematic literature search across major databases (PubMed, Scopus, Web of Science, etc.) up to March 2025.
- Meta-analysis using mixed-effects meta-regression on 33 identified studies (243,587 participants).
- Agreement measured using Spearman correlation coefficient and Kappa statistics.
Main Results:
- Pooled Spearman correlation was 0.954 and pooled kappa was 0.64, indicating strong agreement between equation types.
- Higher correlations observed in studies before 2000, high-income settings, and for equations predicting fatal outcomes only.
- Significant heterogeneity (I²=100% for correlation, I²=99% for kappa) noted across studies.
Conclusions:
- Non-laboratory-based CVD risk equations demonstrate strong correlation and substantial agreement with laboratory-based equations.
- Despite high concordance, predictive equivalence is not guaranteed; context-specific validation and recalibration are necessary for implementation.
- The findings support the potential utility of non-laboratory-based equations but highlight the need for careful consideration of their application.
Introduction:
Cardiovascular disease (CVD) remains the leading cause of global morbidity and mortality. This study assesses the agreement between non-laboratory-based and laboratory-based CVD risk equations across diverse settings.
Methods:
PubMed, Scopus, Web of Science, ProQuest Dissertations and Theses Global, and Google Scholar were systematically searched for studies published up to March 4, 2025. The protocol was registered with PROSPERO (CRD42021291936). Studies comparing laboratory-based and non-laboratory-based CVD risk equations were included, excluding those with participants who had CVD at baseline. A meta-analysis was conducted using mixed-effects meta-regression. The agreements for each study item (unit of analysis) were measured using the Spearman correlation coefficient and Kappa statistics.
Results:
A total of 33 studies, including 243,587 participants and nine CVD risk equations, were identified. The pooled Spearman correlation between the non-laboratory-based and laboratory-based equations was 0.954 (95% CI: 0.928-0.971, I2 = 100%, p < 0.0001), and the pooled kappa was 0.64 (95% CI: 0.61-0.67, I2 = 99%, p < 0.0001). Correlation was higher in studies conducted before 2000 (0.974; 95% CI: 0.970-0.978) compared to those conducted after 2000 (0.953; 95% CI: 0.948-0.958; p < 0.0001). Studies from high-income settings had higher correlations (0.967; 95% CI: 0.963-0.970) than those from low-income settings (0.945; 95% CI: 0.933-0.955). Equations predicting fatal outcomes had higher correlations (0.979; 95% CI: 0.977-0.982) than those predicting both fatal and non-fatal outcomes (0.942; 95% CI: 0.937-0.947).
Conclusion:
Non-laboratory-based CVD risk equations demonstrate strong correlation and substantial agreement with laboratory-based equations. Non-laboratory-based CVD risk equations show strong concordance with laboratory-based equations in many settings; however, strong correlation and substantial agreement do not necessarily indicate predictive equivalence, and their interchangeability and implementation should be considered context-specific and require external validation and recalibration.
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