Impact of sex-specific differences in calculating the pretest probability of obstructive coronary artery disease in symptomatic patients: a coronary computed tomographic angiography study.

Abstract

ObjectivesLittle is known about the impact of sex-specific differences in calculating the pretest probability (PTP) of obstructive coronary artery disease. We sought to determine whether the calculation of PTP differ by sex in symptomatic patients referred to coronary computed tomographic angiography (CCTA).Patients and methodsThe characteristics of 5777 men and women who underwent CCTA were compared. For each patient, PTP was calculated according to the updated Diamond–Forrester method (UDFM) and the Duke clinical score (DCS), respectively. Follow-up clinical data were also recorded. Area under the receiver operating characteristic curve, integrated discrimination improvement, net reclassification improvement, and the Hosmer–Lemeshow goodness-of-fit statistic were used to assess the models’ performance.ResultsThe area under the receiver operating characteristic curve of UDFM and DCS showed little difference in men (0.782 vs. 0.785, P=0.4708) and women (0.668 vs. 0.654, P=0.1255), and calibration of neither model was satisfactory. Compared with UDFM, DCS showed positive integrated discrimination improvement (10% in men, P<0.0001, and 8% in women, P<0.0001, respectively), net reclassification improvement (12.17% in men, P<0.0001, and 27.19% in women, P<0.0001, respectively), and obviously reduced unnecessary noninvasive testing for women with negative CCTA.ConclusionAlthough the performance of neither model was favorable, DCS offered a more accurate calculation of PTP than UDFM and application of DCS instead of UDFM would result in a significant decrease in inappropriate testing, especially in women.