Validation and comparison of four models to calculate pretest probability of obstructive coronary artery disease in a Chinese population: A coronary computed tomographic angiography study

Abstract Objective We sought to compare the performance of the updated Diamond–Forrester method (UDFM), Duke clinical score (DCS), Genders clinical model (GCM) and Genders extended model (GEM) in a Chinese population referred to coronary computed tomography angiography (coronary CTA). Background The reliability of existing models to calculate the pretest proability (PTP) of obstructive coronary artery disease (CAD) have not been fully investigated, especially in a Chinese population. Methods We identified 5743 consecutive patients with suspected stable CAD who underwent coronary calcium scoring (CCS) and coronary CCTA. Obstructive CAD was defined as with the presence of ≥50% diameter stenosis in coronary CTA or unassessable segments due to severe calcification. Area under the receiver operating characteristic curve (AUC), integrated discrimination improvement (IDI), net reclassification improvement (NRI) and Hosmer–Lemeshow goodness-of-fit statistic (H-L χ 2 ) were assessed to validate and compare these models. Results Overall, 1872 (32%) patients had obstructive CAD and 2467 (43%) had a CCS of 0. GEM demonstrated improved discrimination over the other models through the largest AUC (0.816 for GEM, 0.774 for GCM, 0.772 for DCS and 0.765 for UDFM). UDFM (−0.3255, p  2  = 137.82), DCS (H-L χ 2  = 156.70), GCM (H-L χ 2  = 51.17) and GEM (H-L χ 2  = 29.67), respectively. Conclusion Although GEM was superior for calculating PTP in a Chinese population referred for coronary CTA, developing new models allowing for more accurate and operational estimation are warranted.