Machine Learning and BMI Improve the Prognostic Value of GAP Index in Treated IPF Patients

Machine Learning and BMI Improve the Prognostic Value of GAP Index in Treated IPF Patients

Patients affected by idiopathic pulmonary fibrosis (IPF) have a high mortality rate in the first 2–5 years from diagnosis. It is therefore necessary to identify a prognostic indicator that can guide the care process. The Gender-Age-Physiology (GAP) index and staging system is an easy-to-calculate pr...

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Bibliographic Details
Main Authors: Donato Lacedonia, Cosimo Carlo De Pace, Gaetano Rea, Ludovica Capitelli, Crescenzio Gallo, Giulia Scioscia, Pasquale Tondo, Marialuisa Bocchino
Format: Article
Language:English
Published: MDPI AG 2023-02-01
Series:Bioengineering
Subjects:
idiopathic pulmonary fibrosis
GAP index
machine learning
mortality
body mass index
nintedanib
Online Access:https://www.mdpi.com/2306-5354/10/2/251

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