A Probabilistic Model for Cushing's Syndrome Screening in At-Risk Populations: A Prospective Multicenter Study

Context: Cushing's syndrome (CS) is challenging to diagnose. Increased prevalence of CS in specific patient populations has been reported, but routine screening for CS remains questionable. To decrease the diagnostic delay and improve disease outcomes, simple new screening methods for CS in at-risk populations are needed. Objective: To develop and validate a simple scoring system to predict CS based on clinical signs and an easy-to-use biochemical test. Design: Observational, prospective, multicenter. Setting: Referral hospital. Patients: A cohort of 353 patients attending endocrinology units for outpatient visits. Interventions: All patients were evaluated with late-night salivary cortisol (LNSC) and a low-dose dexamethasone suppression test for CS. Main Outcome Measures: Diagnosis or exclusion of CS. Results: Twenty-six cases of CS were diagnosed in the cohort. A risk scoring system was developed by logistic regression analysis, and cutoff values were derived from a receiver operating characteristic curve. This risk score included clinical signs and symptoms (muscular atrophy, osteoporosis, and dorsocervical fat pad) and LNSC levels. The estimated area under the receiver operating characteristic curve was 0.93, with a sensitivity of 96.2% and specificity of 82.9%. Conclusions: We developed a risk score to predict CS in an at-risk population. This score may help to identify at-risk patients in non-endocrinological settings such as primary care, but external validation is warranted. A prediction model for Cushing's syndrome diagnosis in at-risk populations was developed based on clinical variables and late night salivary cortisol levels.