[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126547-en":3,"doc-seo-126547-105":31,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},126547,13056712833777,"Logic","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Machine Learning and BMI Improve the Prognostic Value of GAP Index in Treated IPF Patients","Patients with idiopathic pulmonary fibrosis (IPF) face high early mortality, making reliable prognostic stratification essential for guiding clinical care. The Gender-Age-Physiology (GAP) index is widely used for estimating 1–3 year mortality risk, but its predictive performance may be strengthened. This study applies machine learning to evaluate whether GAP can improve in treated cohorts receiving pirfenidone or nintedanib, and examines the added contribution of integrated parameters. Body mass index (BMI) proved the strongest strategy to enhance GAP performance under current anti-fibrotic therapy.","bioengineering  \nArticle  \nMachine Learning and BMI Improve the Prognostic Value of GAP Index in Treated IPF Patients  \nDonato Lacedonia 1, Cosimo Carlo De Pace 1, *, Gaetano Rea 2, Ludovica Capitelli 3, Crescenzio Gallo 4, Giulia Scioscia 1, Pasquale Tondo 1 and Marialuisa Bocchino 3  \nCitation: Lacedonia, D.; De Pace, C.C.; Rea, G.; Capitelli, L.; Gallo, C.; Scioscia, G.; Tondo, P.; Bocchino, M. Machine Learning and BMI Improve the Prognostic Value of GAP Index in Treated IPF Patients. Bioengineering 2023, 10, 251. [https://doi.org/](https://doi.org/)[ ](https://doi.org/)[10.3390/bioengineering10020251](10.3390/bioengineering10020251)  \nAcademic Editor: Yunfeng Wu  \nReceived: 30 December 2022  \nRevised: 10 February 2023  \nAccepted: 13 February 2023  \nPublished: 14 February 2023  \nCopyright: © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://](https://)[ ](https://)[creativecommons.org/licenses/by/](creativecommons.org/licenses/by/)[ ](creativecommons.org/licenses/by/)[4.0/](4.0/)) .  \n1 Department of Medical and Surgical Sciences, University of Foggia, 71121 Foggia, Italy  \n2 Department of Radiology, Monaldi Hospital, AO dei Colli, 80131 Naples, Italy  \n3 Respiratory Medicine Unit, Department of Clinical Medicine and Surgery, Federico II University of Naples, 80131 Naples, Italy  \n4 Department of Clinical and Experimental Medicine, University of Foggia, 71121 Foggia, Italy  \n* Correspondence: [cosimo.depace@unifg.it](cosimo.depace@unifg.it); Tel.: +39-088-173-3037  \nAbstract: 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.  \nKeywords: idiopathic pulmonary fibrosis; GAP index; machine learning; mortality; body mass index; nintedanib; pirfenidone  \n1. Introduction  \nIdiopathic pulmonary fibrosis (IPF) [1,2] is a rapidly progressive interstitial lung disease of unknown cause, which usually affects men over 65 years of age with a history of smoking. Diagnosis of IPF is often complex and based on many specialists' experience, not only pulmonologists but also radiologists and histopathologists. High-resolution computed tomography (HRCT) findings, especially after the update in the international guidelines, are crucial in the diagnostic approach, and in case of a radiological usual interstitial pneumonia (UIP) pattern, but also likely in a probable UIP pattern, the diagnosis of idiopathic pulmonary fibrosis is made. A multidisciplinary approach has become more and more relevant, especially in uncertain radiological and clinical conditions and in the absence of histopathologic samples, which requires a multi-specialist presence as evidenced by the newest published guidelines [3] .  \nSymptoms are commonly a dry cough, progressive dyspnea, fatigue, and a progressive decline in capability and independence during daily activities. The most characteristic sign identifiable during the physical examination of the patient is the “velcro-type” crackle during auscultation due to traction bronchiectasis but also, in the ","cbCaieAQ8fYpWece","https://ap.wps.com/l/cbCaieAQ8fYpWece","pdf",249812,3,1,9,"English","en",105,"# Introduction\n## Idiopathic pulmonary fibrosis and prognosis\n## Antifibrotic treatments: pirfenidone and nintedanib\n# Study aims and approach\n## Machine learning assessment of GAP performance\n## Integration of additional parameters (including BMI)","[{\"question\":\"What prognostic tool is commonly used for IPF mortality risk estimation?\",\"answer\":\"The Gender-Age-Physiology (GAP) index and staging system are widely used to estimate mortality risk of IPF patients at 1–3 years.\"},{\"question\":\"How does the study assess whether GAP predictions can improve?\",\"answer\":\"The study analyzes the GAP index using machine learning and evaluates predictive power in a large cohort of IPF patients treated with pirfenidone or nintedanib.\"},{\"question\":\"Which added parameter most strengthens GAP performance in treated IPF patients?\",\"answer\":\"Including body mass index (BMI) is reported as the best strategy to reinforce GAP performance in patients receiving current anti-fibrotic drugs.\"}]","Machine Learning and BMI Improve the Prognostic Value of GAP Index in Treated IPF Patients | 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prognostic tool is commonly used for IPF mortality risk estimation?","Question",{"text":76,"@type":77},"The Gender-Age-Physiology (GAP) index and staging system are widely used to estimate mortality risk of IPF patients at 1–3 years.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How does the study assess whether GAP predictions can improve?",{"text":81,"@type":77},"The study analyzes the GAP index using machine learning and evaluates predictive power in a large cohort of IPF patients treated with pirfenidone or nintedanib.",{"name":83,"@type":74,"acceptedAnswer":84},"Which added parameter most strengthens GAP performance in treated IPF patients?",{"text":85,"@type":77},"Including body mass index (BMI) is reported as the best strategy to reinforce GAP performance in patients receiving current anti-fibrotic 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