Validation of the REduction of Atherothrombosis for Continued Health (REACH) prediction model for recurrent cardiovascular disease among United Arab Emirates Nationals

Abstract

Abstract Objectives Prediction models assist health-care providers in making patient care decisions. This study aimed to externally validate the REduction of Atherothrombosis for Continued Health (REACH) prediction model for recurrent cardiovascular disease (CVD) among the Emirati nationals. Results There are 204 patients with established CVD, attending Tawam Hospital from April 1, 2008. The data retrieved from electronic medical records for these patients were used to externally validate the REACH prediction model. Baseline results showed the following: 77.0% were men, 69.6% were diagnosed with coronary artery disease, and 87.3% have a single vascular bed involvement. The risk prediction model for cardiovascular mortality performed moderately well [C-statistic 0.74 (standard error 0.11)] in identifying those at high risk for cardiovascular death, whereas for recurrent CVD events, it performed poorly in predicting the next CVD event [C-statistic 0.63 (standard error 0.06)], over a 20-month follow-up. The calibration curve showed poor agreement indicating that the REACH model underestimated both recurrent CVD risk and cardiovascular death. With recalibration, the REACH cardiovascular death prediction model could potentially be used to identify patients who would benefit from aggressive risk reduction.

Keywords

• Calibration
• Discrimination
• External validation
• Prediction model
• Recurrent cardiovascular disease
• United arab emirates