Development and Internal Validation of a Predictive Model for Adult GH Deficiency Prior to Stimulation Tests

• Published in: Frontiers in endocrinology (Lausanne) 2021-09, Vol.12, p.737947-737947
• Main Authors: Bioletto, Fabio, Parasiliti-Caprino, Mirko, Berton, Alessandro Maria, Prencipe, Nunzia, Cambria, Valeria, Ghigo, Ezio, Grottoli, Silvia, Gasco, Valentina
• Format: Article
• Language: English
• Subjects: Adult; Dwarfism, Pituitary - blood; Dwarfism, Pituitary - diagnosis; Endocrinology; Female; growth hormone deficiency; Human Growth Hormone - blood; Human Growth Hormone - deficiency; Humans; hypopituitarism; IGF-I; Insulin-Like Growth Factor I - metabolism; Male; Middle Aged; Models, Theoretical; pre-test probability; predictive model
• ISSN: 1664-2392; 1664-2392
• DOI: 10.3389/fendo.2021.737947

The diagnosis of adult GH deficiency (GHD) relies on a reduced GH response to provocative tests. Their diagnostic accuracy, however, is not perfect, and a reliable estimation of pre-test GHD probability could be helpful for a better interpretation of their results. Eighty patients showing concordant GH response to two provocative tests, i.e. the insulin tolerance test and the GHRH + arginine test, were enrolled. Data on IGF-I values and on the presence/absence of other pituitary deficits were collected and integrated for the estimation of GHD probability prior to stimulation tests. An independent statistically significant association with the diagnosis of GHD was found both for IGF-I SDS (OR 0.34, 95%-CI 0.18-0.65, p=0.001) and for the presence of other pituitary deficits (OR 6.55, 95%-CI 2.06-20.83, p=0.001). A low ( +0.91 in the presence of other pituitary deficits or IGF-I SDS > -0.52 in the absence of other pituitary deficits. A high (>75%) pre-test GHD probability could be predicted when IGF-I SDS < -0.82 in the presence of other pituitary deficits or IGF-I SDS < -2.26 in the absence of other pituitary deficits. This is the first study that proposes a quantitative estimation of GHD probability prior to stimulation tests. Our risk class stratification represents a simple tool that could be adopted for a Bayesian interpretation of stimulation test results, selecting patients who may benefit from a second stimulation test and possibly reducing the risk of wrong GHD diagnosis.