Predicting hypogonadism in men based upon age, presence of erectile dysfunction, and depression

Abstract

Hypogonadism, a disorder associated with aging, can cause significant morbidity. As clinical manifestations of hypogonadism can be subtle, the challenge and the burden of diagnosis remain the responsibility of the clinician. Four different analytic methods were used to predict hypogonadism in men based upon age, the presence of erectile dysfunction (ED) and depression. 218 men were classified by age, serum testosterone level, the presence of ED and depression. Depression was determined by the Center for Epidemiologic Studies Depression Scale (CES-D). ED was assessed by the Sexual Health Inventory for Men (SHIM). Hypogonadism was defined as a serum testosterone level <300 ng/dl. An artificial neural network (ANN) was programmed and trained to predict hypogonadism based upon age, SHIM, and CES-D scores. Subject data was randomly partitioned into a training set of 148 (67.9%) and a test set of 70 (32.1%). The ANN processed the test set only after the training was complete. The discrete predicted binary output was set to (0) if testosterone level was <300 ng/dl or (1) if >300 ng/dl. The data was also analyzed by standard logistic regression (LR), linear and quadratic discriminant function analysis (LDFA and QDFA, respectively). Reverse regression (RR) analysis evaluated the statistical significance of each risk factor. The ANN can accurately predict hypogonadism in men based upon age, the presence of ED, and depression (receiver-operating characteristic=0.725). A four hidden node network was found to have the highest accuracy. RR revealed the depression index score to be most significant variable (P=0.0019), followed by SHIM score (P=0.00602), and then by age (P=0.015). Hypogonadism can be predicated by an ANN using the input factors of age, ED, and depression. This model can help clinicians assess the need for endocrinologic evaluation in men.