Prediction of Cardiovascular Disease Mortality in a Middle Eastern Country: Performance of the Globorisk and Score Functions in Four Population-Based Cohort Studies of Iran.

Mohammad Talaei,Mohammad Ali Mansournia,Akbar Fotouhi,Mohammad Hassan Emamian,Sadaf G Sepanlou ,Fereidoun Azizi,Hossein Poustchi,Tahereh Samavat,Hamidreza Roohafza,Marjan Mansourian,Reza Malekzadeh,Ewout W. Steyerberg,David Van Klaveren,Akram Pourshams,Nizal Sarrafzadegan,Davood Khalili,Hassan Hashemi,Farzad Hadaegh,Maryam Sharafkhah,Noushin Fahimfar

doi:10.34172/ijhpm.2020.103

Abstract

Background: Considering the importance of cardiovascular disease (CVD) risk prediction for healthcare systems and the limited information available in the Middle East, we evaluated the SCORE and Globorisk models to predict CVD death in a country of this region. Methods: We included 24 427 participants (11 187 men) aged 40-80 years from four population-based cohorts in Iran. Updating approaches were used to recalibrate the baseline survival and the overall effect of the predictors of the models. We assessed the models’ discrimination using C-index and then compared the observed with the predicted risk of death using calibration plots. The sensitivity and specificity of the models were estimated at the risk thresholds of 3%, 5%, 7%, and 10%. An agreement between models was assessed using the intra-class correlation coefficient (ICC). We applied decision analysis to provide perception into the consequences of using the models in general practice; for this reason, the clinical usefulness of the models was assessed using the net benefit (NB) and decision curve analysis. The NB is a sensitivity penalized by a weighted false positive (FP) rate in population level. Results: After 154 522 person-years of follow-up, 437 cardiovascular deaths (280 men) occurred. The 10-year observed risks were 4.2% (95% CI: 3.7%-4.8%) in men and 2.1% (1.8-2%.5%) in women. The c-index for SCORE function was 0.784 (0.756-0.812) in men and 0.780 (0.744-0.815) in women. Corresponding values for Globorisk were 0.793 (0.766- 0.820) and 0.793 (0.757-0.829). The deviation of the calibration slopes from one reflected a need for recalibration; after which, the predicted-to-observed ratio for both models was 1.02 in men and 0.95 in women. Models showed good agreement (ICC 0.93 in men, and 0.89 in women). Decision curve showed that using both models results in the same clinical usefulness at the risk threshold of 5%, in both men and women; however, at the risk threshold of 10%, Globorisk had better clinical usefulness in women (Difference: 8%, 95% CI: 4%-13%). Conclusion: Original Globorisk and SCORE models overestimate the CVD risk in Iranian populations resulting in a high number of people who need intervention. Recalibration could adopt these models to precisely predict CVD mortality. Globorisk showed better performance clinically, only among high-risk women.

Highlights

Prediction models aim to estimate the probability of a specific disease at present or its occurrence in the future
Most of the cardiovascular disease (CVD) prediction models originated from the United States and Europe[2]; as such, before using a prediction model, its calibration should be among the main objectives of preventive programs in a country because a developed model might show noticeable under/overestimation that affects clinical decision-making.[3]
For the calibration of a risk prediction model in a new population, the average incidence of the outcome is needed; national data for cause-specific mortality rates are more trustworthy than disease incidence rates, especially in developing countries

Summary

Introduction

Prediction models aim to estimate the probability of a specific disease at present or its occurrence in the future. Cardiovascular disease (CVD) risk prediction has become essential in the prevention of these diseases and clinical judgments.[1] Most of the CVD prediction models originated from the United States and Europe[2]; as such, before using a prediction model, its calibration should be among the main objectives of preventive programs in a country because a developed model might show noticeable under/overestimation that affects clinical decision-making.[3]. For the calibration of a risk prediction model in a new population, the average incidence of the outcome is needed; national data for cause-specific mortality rates are more trustworthy than disease incidence rates, especially in developing countries. That is why the models based on CVD mortality may be more recalibrated than those on all CVD outcomes.[4] more than 80% of the premature deaths happen in low- and middle-income countries. In Iran, around 50% of premature deaths are caused by CVD.[5,6]

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Conclusion