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

Abstract

Background: 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.

Methods: 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.

Results: 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 (<25%) pre-test GHD probability could be predicted when IGF-I SDS > +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.

Conclusion: 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.

Keywords: IGF-I; growth hormone deficiency; hypopituitarism; pre-test probability; predictive model.