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Comparative Study
. 2025 May 12;32(7):564-572.
doi: 10.1093/eurjpc/zwae242.

External validation and comparison of six cardiovascular risk prediction models in the Prospective Urban Rural Epidemiology (PURE)-Colombia study

Jose P Lopez-Lopez  1   2 , Angel A Garcia-Pena  3 , Daniel Martinez-Bello  1 , Ana M Gonzalez  3 , Maritza Perez-Mayorga  1   4 , Oscar Mauricio Muñoz Velandia  3 , Gabriela Ruiz-Uribe  1 , Alfonso Campo  5 , Sumathy Rangarajan  2   6 , Salim Yusuf  2   6 , Patricio Lopez-Jaramillo  1   7
Affiliations
Comparative Study

External validation and comparison of six cardiovascular risk prediction models in the Prospective Urban Rural Epidemiology (PURE)-Colombia study

Jose P Lopez-Lopez et al. Eur J Prev Cardiol. .

Abstract

Aims: To externally validate the SCORE2, AHA/ACC pooled cohort equation (PCE), Framingham Risk Score (FRS), Non-Laboratory INTERHEART Risk Score (NL-IHRS), Globorisk-LAC, and WHO prediction models and compare their discrimination and calibration capacity.

Methods and results: Validation in individuals aged 40-69 years with at least 10 years of follow-up and without baseline use of statins or cardiovascular diseases from the Prospective Urban Rural Epidemiology (PURE)-Colombia prospective cohort study. For discrimination, the C-statistic, and receiver operating characteristic curves with the integrated area under the curve (AUCi) were used and compared. For calibration, the smoothed time-to-event method was used, choosing a recalibration factor based on the integrated calibration index (ICI). In the NL-IHRS, linear regressions were used. In 3802 participants (59.1% women), baseline risk ranged from 4.8% (SCORE2 women) to 55.7% (NL-IHRS). After a mean follow-up of 13.2 years, 234 events were reported (4.8 cases per 1000 person-years). The C-statistic ranged between 0.637 (0.601-0.672) in NL-IHRS and 0.767 (0.657-0.877) in AHA/ACC PCE. Discrimination was similar between AUCi. In women, higher over-prediction was observed in the Globorisk-LAC (61%) and WHO (59%). In men, higher over-prediction was observed in FRS (72%) and AHA/ACC PCE (71%). Overestimations were corrected after multiplying by a factor derived from the ICI.

Conclusion: Six prediction models had a similar discrimination capacity, supporting their use after multiplying by a correction factor. If blood tests are unavailable, NL-IHRS is a reasonable option. Our results suggest that these models could be used in other countries of Latin America after correcting the overestimations with a multiplying factor.

Keywords: Cardiovascular disease; Latin-American; Prediction; Primary prevention; Scores; Validation.

Plain language summary

Detecting people at high risk of cardiovascular disease (CVD) and implementing preventive interventions in this population are key strategies in the primary prevention of CVD. Recently, new risk calculation tools have been developed, but before their application and routine use in populations different from those where it was developed, it is necessary to validate them. The recommendations for predicting cardiovascular risk in Colombia’s guidelines are based on studies with noteworthy limitations. This study involving 3802 healthy individuals in Colombia supports the recommendation of using these prediction models. The estimation result should be multiplied by a correction factor, because most of the prediction models overestimate cardiovascular risk. For example, the correction factors suggested in women for AHA/ACC PCE and SCORE2 are 0.54 and 0.75, respectively. In men, the correction factors suggested in AHA/ACC PCE and SCORE2 are 0.28 and 0.61, respectively. Therefore, the present study with a contemporary population provides additional evidence to update these recommendations in Colombia and perhaps in Latin America.

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Conflict of interest statement

Conflict of interest: none declared.

Figures

Figure 1
Figure 1
Time-dependent area under the receiver operating characteristic curve for cardiovascular risk prediction models. The diagonal line represents perfect chance (with a corresponding area under the receiver operating characteristic curve of 0.5), values may range to 1.0 (predict prediction).
Figure 2
Figure 2
Calibration of the six prediction models for women and men. Plots depict mean observed risk and predicted 10-year risk across deciles of predicted risk. Values below the 1 slope represent overestimation, while values above the 1 slope represent underestimation.
Figure 3
Figure 3
Calibration and recalibration plots of predicted and observed 10-year cardiovascular risk in the prediction models. The figure shows the relationship between observed/estimated events in the initial calibration (Calibrated) and after recalibration with the integrated calibration index for each model (Recalibrated).
See this image and copyright information in PMC

Comment in

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