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Journal Article Epublish 6 citations

PubMed: 36829744 DOI: 10.3390/bioengineering10020251

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

Bioengineering (Basel) 10(2)

Authors: Donato Lacedonia, Cosimo Carlo De Pace, Gaetano Rea, Ludovica Capitelli, Crescenzio Gallo, Giulia Scioscia, Pasquale Tondo, Marialuisa Bocchino

Affiliations

  • Department of Medical and Surgical Sciences, University of Foggia, 71121 Foggia, Italy.
  • Department of Radiology, Monaldi Hospital, AO dei Colli, 80131 Naples, Italy.
  • Respiratory Medicine Unit, Department of Clinical Medicine and Surgery, Federico II University of Naples, 80131 Naples, Italy.
  • Department of Clinical and Experimental Medicine, University of Foggia, 71121 Foggia, Italy.

Abstract

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 prediction tool, widely validated, and largely used in clinical practice to estimate the risk of mortality of IPF patients at 1-3 years. In our study, we analyzed the GAP index through machine learning to assess any improvement in its predictive power in a large cohort of IPF patients treated either with pirfenidone or nintedanib. In addition, we evaluated this event through the integration of additional parameters. As previously reported by Y. Suzuki et al., our data show that inclusion of body mass index (BMI) is the best strategy to reinforce the GAP performance in IPF patients under treatment with currently available anti-fibrotic drugs.

MeSH

Idiopathic Pulmonary Fibrosis, Mortality, Body Mass Index, Machine Learning

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