A multi-classifier system to identify and subtype congenital adrenal hyperplasia based on circulating steroid hormones

Abstract Context Measurement of plasma steroids is necessary for diagnosis of congenital adrenal hyperplasia (CAH). We sought to establish an efficient strategy for detection and subtyping of CAH with a machine-learning algorithm. Methods Clinical phenotype and genetic testing were used to provide CAH diagnosis and subtype. We profiled 13 major steroid hormones by liquid chromatography–tandem mass spectrometry (LC-MS/MS). A multi-classifier system was established to distinguish 11β-hydroxylase deficiency (11βOHD), 17α-hydroxylase/17,20-lyase deficiency (17OHD) and 21α-hydroxylase deficiency (21OHD) in a discovery cohort (N=226). It was then validated in an independent cohort (N=111) and finally applied in a perspective cohort of 256 patients. The diagnostic performance on the basis of area under receiver operating characteristic curves was evaluated. Results A cascade logistic regression model, we named the “Steroidogenesis Score”, was able to discriminate the three most common CAH subtypes: 11βOHD, 17OHD, 21OHD. In the perspective application cohort, the Steroidogenesis Score had a high diagnostic accuracy for all three subtypes, 11βOHD (AUC, 0.994; 95% CI, 0.983-1.000), 17OHD (AUC, 0.993; 95% CI, 0.985-1.000) and 21OHD (AUC, 0.979; 95% CI, 0.964-0.994). For nonclassic 21OHD patients, the tool presented with significantly higher sensitivity compared with measurement of basal 17α-hydroxyprogesterone (17OHP) (0.973 vs 0.840, p=0.005) and was not inferior to measurement of basal versus stimulated 17OHP (0.973 vs 0.947, p=0.681). Conclusions The Steroidogenesis Score was biochemically interpretable and showed high accuracy in identifying CAH patients, especially for nonclassic 21OHD patients, thus offering a standardized approach to diagnose and subtype CAH.