A proof of concept of a machine learning algorithm to predict late-onset 21-hydroxylase deficiency in children with premature pubic hair

Abstract

In children with premature pubarche (PP), late onset 21-hydroxylase deficiency (21-OHD), also known as non-classical congenital adrenal hyperplasia (NCCAH), can be routinely ruled out by an adrenocorticotropic hormone (ACTH) test. Using liquid chromatography–tandem mass spectrometry (LC-MS/MS), a quantitative assay of the circulating steroidome can be obtained from a single blood sample. We hypothesized that, by applying multivariate machine learning (ML) models to basal steroid profiles and clinical parameters of 97 patients, we could distinguish children with PP from those with NCCAH, without the need for ACTH testing. Every child presenting with PP at the Trousseau Pediatric Endocrinology Unit between 2016 and 2018 had a basal and stimulated steroidome. Patients with central precocious puberty were excluded. The first set of patients (year 1, training set, n = 58), including 8 children with NCCAH verified by ACTH test and genetic analysis, was used to train the model. Subsequently, a validation set of an additional set of patients (year 2, n = 39 with 5 NCCAH) was obtained to validate our model. We designed a score based on an ML approach (orthogonal partial least squares discriminant analysis). A metabolic footprint was assigned for each patient using clinical data, bone age, and adrenal steroid levels recorded by LC-MS/MS. Supervised multivariate analysis of the training set (year 1) and validation set (year 2) was used to validate our score. Based on selected variables, the prediction score was accurate (100%) at differentiating premature pubarche from late onset 21-OHD patients. The most significant variables were 21-deoxycorticosterone, 17-hydroxyprogesterone, and 21-deoxycortisol steroids. We proposed a new test that has excellent sensitivity and specificity for the diagnosis of NCCAH, due to an ML approach.