HOMEPRODUCTSCOMPANYCONTACTFAQResearchDictionaryPharmaSign Up FREE or Login

Development and Internal Validation of Fatty Liver Prediction Models in Obese Children and Adolescents.

Abstract
To develop predictive models of fatty liver (FL), we performed a cross-sectional retrospective study of 1672 obese children with a median (interquartile range) age of 15 (13-16) years. The outcome variable was FL diagnosed by ultrasonography. The potential predictors were: (1) binary: sex; (2) continuous: age, body mass index (BMI), waist circumference (WC), alanine transaminase (ALT), aspartate transaminase, gamma-glutamyltransferase, glucose, insulin, homeostasis model assessment of insulin resistance (HOMA-IR), HDL-cholesterol, LDL-cholesterol, triglycerides, mean arterial pressure, uric acid, and c-reactive protein; (3) ordinal: Pubertal status. Bootstrapped multivariable logistic regression with fractional polynomials was used to develop the models. Two models were developed and internally validated, one using BMI and the other using WC as the anthropometric predictor. Both models included ALT, HOMA-IR, triglycerides, and uric acid as predictors, had similar discrimination (c-statistic = 0.81), and were similarly well calibrated as determined by calibration plots. These models should undergo external validation before being employed in clinical or research practice.
AuthorsGiorgio Bedogni, Sofia Tamini, Diana Caroli, Sabrina Cicolini, Marco Domenicali, Alessandro Sartorio
JournalJournal of clinical medicine (J Clin Med) Vol. 10 Issue 7 (Apr 02 2021) ISSN: 2077-0383 [Print] Switzerland
PMID33918271 (Publication Type: Journal Article)

Join CureHunter, for free Research Interface BASIC access!

Take advantage of free CureHunter research engine access to explore the best drug and treatment options for any disease. Find out why thousands of doctors, pharma researchers and patient activists around the world use CureHunter every day.
Realize the full power of the drug-disease research graph!


Choose Username:
Email:
Password:
Verify Password:
Enter Code Shown: