Machine Learning and BMI Improve the Prognostic Value of GAP Index in Treated IPF Patients
Abstract
:1. Introduction
2. Machine Learning Operation
3. Materials and Methods
- Logistic regression (a classification algorithm with LASSO regularization)
- Naive Bayes (a fast and simple probabilistic classifier based on Bayes’ theorem with the assumption of feature independence)
- Neural network (a multi-layer perceptron algorithm with backpropagation)
4. Results
5. Discussion
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
- Richeldi, L.; Collard, H.R.; Jones, M.G. Idiopathic pulmonary fibrosis. Lancet 2017, 389, 1941–1952. [Google Scholar] [CrossRef] [PubMed]
- King, T.E., Jr.; Pardo, A.; Selman, M. Idiopathic pulmonary fibrosis. Lancet 2011, 378, 1949–1961. [Google Scholar] [CrossRef] [PubMed]
- Raghu, G.; Remy-Jardin, M.; Richeldi, L.; Thomson, C.C.; Inoue, Y.; Johkoh, T.; Kreuter, M.; Lynch, D.A.; Maher, T.M.; Martinez, F.J.; et al. Idiopathic Pulmonary Fibrosis (an Update) and Progressive Pulmonary Fibrosis in Adults: An Official ATS/ERS/JRS/ALAT Clinical Practice Guideline. Am. J. Respir. Crit. Care Med. 2022, 205, e18–e47. [Google Scholar] [CrossRef] [PubMed]
- Noble, P.W.; Albera, C.; Bradford, W.Z.; Costabel, U.; Glassberg, M.K.; Kardatzke, D.; King, T.E., Jr.; Lancaster, L.; Sahn, S.A.; Szwarcberg, J.; et al. CAPACITY Study Group. Pirfenidone in patients with idiopathic pulmonary fibrosis (CAPACITY): Two randomised trials. Lancet 2011, 377, 1760–1769. [Google Scholar] [CrossRef]
- Lancaster, L.H.; de Andrade, J.A.; Zibrak, J.D.; Padilla, M.L.; Albera, C.; Nathan, S.D.; Wijsenbeek, M.S.; Stauffer, J.L.; Kirchgaessler, K.-U.; Costabel, U. Pirfenidone safety and adverse event management in idiopathic pulmonary fibrosis. Eur. Respir. Rev. Off. J. Eur. Respir. Soc. 2017, 26, 170057. [Google Scholar] [CrossRef] [PubMed]
- Richeldi, L.; du Bois, R.M.; Raghu, G.; Azuma, A.; Brown, K.K.; Costabel, U.; Cottin, V.; Flaherty, K.R.; Hansell, D.M.; Inoue, Y.; et al. INPULSIS Trial Investigators Efficacy and safety of nintedanib in idiopathic pulmonary fibrosis. N. Engl. J. Med. 2014, 370, 2071–2082. [Google Scholar] [CrossRef] [PubMed]
- Roth, G.J.; Binder, R.; Colbatzky, F.; Dallinger, C.; Schlenker-Herceg, R.; Hilberg, F.; Wollin, S.-L.; Kaiser, R. Nintedanib: From discovery to the clinic. J. Med. Chem. 2015, 58, 1053–1063. [Google Scholar] [CrossRef]
- Soccio, P.; Moriondo, G.; Lacedonia, D.; Tondo, P.; Quarato, C.M.I.; Foschino Barbaro, M.P.; Scioscia, G. EVs-miRNA: The New Molecular Markers for Chronic Respiratory Diseases. Life 2022, 12, 1544. [Google Scholar] [CrossRef]
- Lee, Y.J.; Choi, S.M.; Lee, Y.J.; Cho, Y.J.; Yoon, H.I.; Lee, J.H.; Lee, C.T.; Park, J.S. Clinical impact of depression and anxiety in patients with idiopathic pulmonary fibrosis. PLoS ONE 2017, 12, e0184300. [Google Scholar] [CrossRef]
- McDonnell, M.J.; Hunt, E.B.; Ward, C.; Pearson, J.P.; O’Toole, D.; Laffey, J.G.; Murphy, D.M.; Rutherford, R.M. Current therapies for gastro-oesophageal reflux in the setting of chronic lung disease: State of the art review. ERJ Open Res. 2020, 6, 00190–2019. [Google Scholar] [CrossRef]
- Laudisio, A.; Antonelli Incalzi, R.; Gemma, A.; Giovannini, S.; Lo Monaco, M.R.; Vetrano, D.L.; Padua, L.; Bernabei, R.; Zuccalà, G. Use of proton-pump inhibitors is associated with depression: A population-based study. Int. Psychogeriatr. 2018, 30, 153–159. [Google Scholar] [CrossRef]
- Shen, L.; Zhang, Y.; Su, Y.; Weng, D.; Zhang, F.; Wu, Q.; Chen, T.; Li, Q.; Zhou, Y.; Hu, Y.; et al. New pulmonary rehabilitation exercise for pulmonary fibrosis to improve the pulmonary function and quality of life of patients with idiopathic pulmonary fibrosis: A randomized control trial. Ann. Palliat. Med. 2021, 10, 7289–7297. [Google Scholar] [CrossRef] [PubMed]
- Scioscia, G.; De Pace, C.C.; Giganti, G.; Tondo, P.; Foschino Barbaro, M.P.; Lacedonia, D. Real life experience of molnupiravir as a treatment of SARS-CoV-2 infection in vaccinated and unvaccinated patients: A letter on its effectiveness at preventing hospitalization. Ir. J. Med. Sci. 2022, 1–3. [Google Scholar] [CrossRef] [PubMed]
- Steinmetz, A.; Bahlmann, S.; Bergelt, C.; Bröker, B.M.; Ewert, R.; Felix, S.B.; Flöel, A.; Fleischmann, R.; Hoffmann, W.; Holtfreter, S.; et al. The Greifswald Post COVID Rehabilitation Study and Research (PoCoRe)-Study Design, Characteristics and Evaluation Tools. J. Clin. Med. 2023, 12, 624. [Google Scholar] [CrossRef] [PubMed]
- Lacedonia, D.; Scioscia, G.; De Pace, C.C.; Laricchiuta, A.; Tondo, P.; Sabato, R.; Foschino Barbaro, M.P. How Are We Handling the Post-COVID Patients? The Dance of Uncertainties. Respir. Int. Rev. Thorac. Dis. 2022, 101, 210–213. [Google Scholar] [CrossRef]
- Glass, D.S.; Grossfeld, D.; Renna, H.A.; Agarwala, P.; Spiegler, P.; DeLeon, J.; Reiss, A.B. Idiopathic pulmonary fibrosis: Current and future treatment. Clin. Respir. J. 2022, 16, 84–96. [Google Scholar] [CrossRef]
- Li, D.; Liu, Y.; Wang, B. Single versus bilateral lung transplantation in idiopathic pulmonary fibrosis: A systematic review and meta-analysis. PLoS ONE 2020, 15, e0233732. [Google Scholar] [CrossRef]
- Ley, B.; Ryerson, C.J.; Vittinghoff, E.; Ryu, J.; Tomassetti, S.; Lee, J.S.; Poletti, V.; Buccioli, M.; Elicker, B.M.; Jones, K.D.; et al. A multidimensional index and staging system for idiopathic pulmonary fibrosis. Ann. Intern. Med. 2012, 156, 684–691. [Google Scholar] [CrossRef]
- Torrisi, S.E.; Ley, B.; Kreuter, M.; Wijsenbeek, M.; Vittinghoff, E.; Collard, H.R.; Vancheri, C. The added value of comorbidities in predicting survival in idiopathic pulmonary fibrosis: A multicentre observational study. Eur. Respir. J. 2019, 53, 1801587. [Google Scholar] [CrossRef]
- Zhang, X.; Ren, Y.; Xie, B.; Wang, S.; Geng, J.; He, X.; Jiang, D.; He, J.; Luo, S.; Wang, X.; et al. External validation of the GAP model in Chinese patients with idiopathic pulmonary fibrosis. Clin. Respir. J. 2022; early view. [Google Scholar] [CrossRef]
- Jouneau, S.; Rousseau, C.; Lederlin, M.; Lescoat, A.; Kerjouan, M.; Chauvin, P.; Luque-Paz, D.; Guillot, S.; Oger, E.; Vernhet, L.; et al. Malnutrition and decreased food intake at diagnosis are associated with hospitalization and mortality of idiopathic pulmonary fibrosis patients. Clin. Nutr. 2022, 41, 1335–1342. [Google Scholar] [CrossRef]
- Mann, J.; Goh, N.S.L.; Holland, A.E.; Khor, Y.H. Cough in Idiopathic Pulmonary Fibrosis. Front. Rehabil. Sci. 2021, 2, 751798. [Google Scholar] [CrossRef] [PubMed]
- Horio, Y.; Takihara, T.; Takahashi, F.; Enokida, K.; Nakamura, N.; Tanaka, J.; Tomomatsu, K.; Niimi, K.; Tajiri, S.; Hayama, N.; et al. Prognosis of acute exacerbation in idiopathic pulmonary fibrosis with pulmonary emphysema: A retrospective cohort study in Japan. BMJ Open 2022, 12, e062236. [Google Scholar] [CrossRef] [PubMed]
- Badenes-Bonet, D.; Rodó-Pin, A.; Castillo-Villegas, D.; Vicens-Zygmunt, V.; Bermudo, G.; Hernández-González, F.; Portillo, K.; Martínez-Llorens, J.; Chalela, R.; Caguana, O.; et al. Predictors and changes of physical activity in idiopathic pulmonary fibrosis. BMC Pulm. Med. 2022, 22, 340. [Google Scholar] [CrossRef] [PubMed]
- Caminati, A.; Lonati, C.; Cassandro, R.; Elia, D.; Pelosi, G.; Torre, O.; Zompatori, M.; Uslenghi, E.; Harari, S. Comorbidities in idiopathic pulmonary fibrosis: An underestimated issue. Eur. Respir. Rev. Off. J. Eur. Respir. Soc. 2019, 28, 190044. [Google Scholar] [CrossRef]
- Suzuki, Y.; Mori, K.; Aono, Y.; Kono, M.; Hasegawa, H.; Yokomura, K.; Naoi, H.; Hozumi, H.; Karayama, M.; Furuhashi, K.; et al. Combined assessment of the GAP index and body mass index at antifibrotic therapy initiation for prognosis of idiopathic pulmonary fibrosis. Sci. Rep. 2021, 11, 18579. [Google Scholar] [CrossRef]
- Scioscia, G.; Tondo, P.; Foschino Barbaro, M.P.; Sabato, R.; Gallo, C.; Maci, F.; Lacedonia, D. Machine learning-based prediction of adherence to continuous positive airway pressure (CPAP) in obstructive sleep apnea (OSA). Inform. Health Soc. Care 2022, 47, 274–282. [Google Scholar] [CrossRef]
- Mitchell, T.M. Machine Learning; McGraw-Hill: New York, NY, USA, 1997. [Google Scholar]
- Duch, W.; Wieczorek, T.; Biesiada, J.; Blachnik, M. Comparison of feature ranking methods based on information entropy. In Proceedings of the 2004 IEEE International Joint Conference on Neural Networks (IEEE Cat. No. 04CH37541), Budapest, Hungary, 25–29 July 2004; Volume 2, pp. 1415–1419. [Google Scholar]
- Gradojevic, N.; Caric, M. Predicting systemic risk with entropic indicators. J. Forecast. 2017, 36, 16–25. [Google Scholar] [CrossRef]
- Aremu, O.O.; Cody, R.A.; Hyland-Wood, D.; McAree, P.R. A relative entropy based feature selection framework for asset data in predictive maintenance. Comput. Ind. Eng. 2020, 145, 106536. [Google Scholar] [CrossRef]
- Pramokchon, P.; Piamsanga, P. An unsupervised, fast correlation-based filter for feature selection for data clustering. In Proceedings of the First International Conference on Advanced Data and Information Engineering (DaEng-2013); Springer: Singapore, 2014; pp. 87–94. [Google Scholar]
- Gopika, N.; Me, A.M.K. Correlation based feature selection algorithm for machine learning. In Proceedings of the 2018 3rd International Conference on Communication and Electronics Systems (ICCES), Coimbatore, India, 15–16 October 2018; pp. 692–695. [Google Scholar]
- Du, S.; Wang, S. An overview of correlation-filter-based object tracking. In IEEE Transactions on Computational Social Systems; Browse Journals & Magazines: Piscataway, NJ, USA, 2021. [Google Scholar]
- Mencar, C.; Gallo, C.; Mantero, M.; Tarsia, P.; Carpagnano, G.E.; Foschino Barbaro, M.P.; Lacedonia, D. Application of machine learning to predict obstructive sleep apnea syndrome severity. Health Inform. J. 2020, 26, 298–317. [Google Scholar] [CrossRef]
- Flietstra, B.; Markuzon, N.; Vyshedskiy, A.; Murphy, R. Automated analysis of crackles in patients with interstitial pulmonary fibrosis. Pulm. Med. 2011, 2011, 590506. [Google Scholar] [CrossRef] [PubMed]
- Furukawa, T.; Oyama, S.; Yokota, H.; Kondoh, Y.; Kataoka, K.; Johkoh, T.; Fukuoka, J.; Hashimoto, N.; Sakamoto, K.; Shiratori, Y.; et al. A comprehensible machine learning tool to differentially diagnose idiopathic pulmonary fibrosis from other chronic interstitial lung diseases. Respirology 2022, 27, 739–746. [Google Scholar] [CrossRef]
- Pan, J.; Hofmanninger, J.; Nenning, K.H.; Prayer, F.; Röhrich, S.; Sverzellati, N.; Poletti, V.; Tomassetti, S.; Weber, M.; Prosch, H.; et al. Unsupervised machine learning identifies predictive progression markers of IPF. Eur. Radiol. 2022, 33, 925–935. [Google Scholar] [CrossRef]
- Kim, E.S.; Choi, S.M.; Lee, J.; Park, Y.S.; Lee, C.-H.; Yim, J.-J.; Yoo, C.-G.; Kim, Y.W.; Han, S.K.; Lee, S.-M. Validation of the GAP score in Korean patients with idiopathic pulmonary fibrosis. Chest 2015, 147, 430–437. [Google Scholar] [CrossRef] [PubMed]
- Suissa, S.; Suissa, K. Antifibrotics and Reduced Mortality in Idiopathic Pulmonary Fibrosis: Immortal Time Bias. Am. J. Respir. Crit. Care Med. 2023, 207, 105–109. [Google Scholar] [CrossRef] [PubMed]
- Chandel, A.; Pastre, J.; Valery, S.; King, C.S.; Nathan, S.D. Derivation and validation of a simple multidimensional index incorporating exercise capacity parameters for survival prediction in idiopathic pulmonary fibrosis. Thorax 2022. [Google Scholar] [CrossRef] [PubMed]
Disease Stage | 3 Year Mortality Estimated by GAP | 3 Year Observed Mortality |
---|---|---|
GAP-I | 16% | 21% |
GAP-II | 40% | 46% |
GAP-III | 77% | 70% |
AUC | CA | F1 | |
---|---|---|---|
Logistic Regression | 0.70 | 0.71 | 0.71 |
Neural Network | 0.69 | 0.71 | 0.71 |
Naive Bayes | 0.67 | 0.71 | 0.71 |
Stochastic gradient descent | 0.68 | 0.71 | 0.71 |
Random Forest | 0.68 | 0.71 | 0.71 |
AUC | CA | F1 | |
---|---|---|---|
Naive Bayes | 0.78 | 0.70 | 0.70 |
Logistic Regression | 0.77 | 0.70 | 0.69 |
Support Vector Machine | 0.74 | 0.72 | 0.71 |
Random Forest | 0.73 | 0.70 | 0.70 |
K-Nearest Neighbors | 0.71 | 0.72 | 0.71 |
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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/10.3390/bioengineering10020251
Lacedonia D, De Pace CC, 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(2):251. https://doi.org/10.3390/bioengineering10020251
Chicago/Turabian StyleLacedonia, Donato, Cosimo Carlo De Pace, Gaetano Rea, Ludovica Capitelli, Crescenzio Gallo, Giulia Scioscia, Pasquale Tondo, and Marialuisa Bocchino. 2023. "Machine Learning and BMI Improve the Prognostic Value of GAP Index in Treated IPF Patients" Bioengineering 10, no. 2: 251. https://doi.org/10.3390/bioengineering10020251
APA StyleLacedonia, D., De Pace, C. C., Rea, G., Capitelli, L., Gallo, C., Scioscia, G., Tondo, P., & Bocchino, M. (2023). Machine Learning and BMI Improve the Prognostic Value of GAP Index in Treated IPF Patients. Bioengineering, 10(2), 251. https://doi.org/10.3390/bioengineering10020251