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A Probabilistic Model for Cushing's Syndrome Screening in At-Risk Populations: A Prospective Multicenter Study
Antonio León-Justel1, Ainara Madrazo-Atutxa1, Ana I Alvarez-Rios1
1Medicine Department (A.L.-J.), Huelva University Hospital, Instituto de Biomedicina de Sevilla (IBiS), Hospital Universitario Virgen del Rocío/Consejo Superior de Investigaciones Científicas/Universidad de Sevilla, 41013 Sevilla, Spain; Unidad de Gestión Clínica de Endocrinología y Nutrición (A.M.-A., D.A.C., A.L.-C.), IBiS, Hospital Universitario Virgen del Rocío/Consejo Superior de Investigaciones Científicas/Universidad de Sevilla, 41013 Sevilla, Spain; Department of Clinical Biochemistry (A.I.A.-R.), Virgen del Rocío University Hospital (IBiS/CSIC/SAS/University of Seville), 41013 Sevilla, Spain; Servicio de Bioquímica (R.I.-F.), Sección Hormonas especiales, Hospital Universitario Virgen del Rocio, 41013 Sevilla, Spain; Department of Clinical Endocrinology and Nutrition (J.A.G.-A.), Carlos Haya Hospital, 29010 Málaga, Spain; Hospital Regional Universitario de Málaga (J.A.L.-M.), 29010 Málaga, Spain; Pituitary Disease Research Group/Department Endocrinology/Medicine (A.A.), Hospital Sant Pau, Universitat Autónoma de Barcelona and CIBERER U747, ISCIII, 08025 Bellaterra, Barcelona, Spain; Clinical Biochemistry Department (E.U.-R.), Hospital de Sant Pau, 08025 Barcelona, Spain; Sección de Endocrinología y Nutrición (M.B.), Hospital Universitario Insular, Instituto Universitario de Investigaciones Biomédicas y Sanitarias, Universidad de Las Palmas de Gran Canaria, 35001 Las Palmas de Gran Canaria, Spain; Servicio de Bioquímica Clínica (A.S.-d.-A.), Hospital Universitario Insular de Gran Canaria, 35016 Las Palmas de Gran Canaria, Spain; Hospital Universitario de La Ribera (C.F.-M., M.O.-A.), 46600 Alzira, Valencia, Spain; Servicio Endocrinología y Nutrición (I.S.-V.) and Servicio de Bioquímica (M.L.G.), Hospital Universitari Germans Trias i Pujol, 08916 Badalona, Barcelona, Spain.
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.
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