Identification of Idiopathic Pulmonary Fibrosis and Prediction of Disease Severity via Machine Learning Analysis of Comprehensive Metabolic Panel and Complete Blood Count Data

U.S. National Library of Medicine, N.I.H. Idiopathic pulmonary fibrosis. https://ghr.nlm.nih.gov/condition/idiopathic-pulmonary-fibrosis. Accessed 30 Aug 2019

Raghu G et al (2011) An official ATS/ERS/JRS/ALAT statement: idiopathic pulmonary fibrosis: evidence-based guidelines for diagnosis and management. Am J Respir Crit Care Med 183(6):788–824

Article  MathSciNet  PubMed  PubMed Central  Google Scholar 

Blackwell TS et al (2014) Future directions in idiopathic pulmonary fibrosis research. An NHLBI workshop report. Am J Respir Crit Care Med 189(2):214–222

Article  PubMed  PubMed Central  Google Scholar 

Lederer DJ, Martinez FJ (2018) Idiopathic pulmonary fibrosis. N Engl J Med 379(8):797–798

PubMed  Google Scholar 

Chung JH et al (2021) Differentiation of idiopathic pulmonary fibrosis from connective tissue disease-related interstitial lung disease using quantitative imaging. J Clin Med 10:12

Article  Google Scholar 

Suzuki A, Kondoh Y, Fischer A (2017) Recent advances in connective tissue disease related interstitial lung disease. Expert Rev Respir Med 11(7):591–603

Article  CAS  PubMed  Google Scholar 

Erre GL et al (2021) Antifibrotic drugs in connective tissue disease-related interstitial lung disease (CTD-ILD): from mechanistic insights to therapeutic applications. Drugs Context 10:1

Google Scholar 

Hayton C et al (2019) Breath biomarkers in idiopathic pulmonary fibrosis: a systematic review. Respir Res 20:1

Article  Google Scholar 

Hochhegger B et al (2019) Imaging in idiopathic pulmonary fibrosis: diagnosis and mimics. Clinics (Sao Paulo) 74:e225

Article  PubMed  Google Scholar 

Raghu G et al (2018) Diagnosis of idiopathic pulmonary fibrosis. An official ATS/ERS/JRS/ALAT Clinical Practice Guideline. Am J Respir Crit Care Med 198(5):e44–e68

Article  PubMed  Google Scholar 

Guiot J et al (2017) Blood biomarkers in idiopathic pulmonary fibrosis. Lung 195(3):273–280

Article  CAS  PubMed  PubMed Central  Google Scholar 

Hachisu Y et al (2019) Possible serological markers to predict mortality in acute exacerbation of idiopathic pulmonary fibrosis. Medicina (Kaunas) 55:5

Google Scholar 

Maher TM et al (2017) An epithelial biomarker signature for idiopathic pulmonary fibrosis: an analysis from the multicentre PROFILE cohort study. Lancet Respir Med 5(12):946–955

Article  CAS  PubMed  Google Scholar 

Zhang T et al (2021) KL-6 as an immunological biomarker predicts the severity, progression, acute exacerbation, and poor outcomes of interstitial lung disease: a systematic review and meta-analysis. Front Immunol 12:745233

Article  CAS  PubMed  PubMed Central  Google Scholar 

Zinellu A et al (2020) Blood cell count derived inflammation indexes in patients with idiopathic pulmonary fibrosis. Lung 198(5):821–827

Article  CAS  PubMed  PubMed Central  Google Scholar 

Wuyts WA et al (2020) Idiopathic pulmonary fibrosis: best practice in monitoring and managing a relentless fibrotic disease. Respiration 99(1):73–82

Article  PubMed  Google Scholar 

Fernández Fabrellas E et al (2018) Prognosis and follow-up of idiopathic pulmonary fibrosis. Med Sci 6(2):51

Google Scholar 

Stephan S et al (2007) Oxygen desaturation during a 4-minute step test: predicting survival in idiopathic pulmonary fibrosis. Sarcoidosis Vasc Diffuse Lung Dis 24(1):70–76

PubMed  Google Scholar 

Ley B, Collard HR, King TE Jr (2011) Clinical course and prediction of survival in idiopathic pulmonary fibrosis. Am J Respir Crit Care Med 183(4):431–440

Article  PubMed  Google Scholar 

Nishiyama O et al (2016) Prognostic value of forced expiratory volume in 1 second/forced vital capacity in idiopathic pulmonary fibrosis. Chron Respir Dis 13(1):40–47

Article  PubMed  Google Scholar 

Ley B et al (2016) Predictors of mortality poorly predict common measures of disease progression in idiopathic pulmonary fibrosis. Am J Respir Crit Care Med 194(6):711–718

Article  PubMed  PubMed Central  Google Scholar 

Travis WD et al (2013) An official American Thoracic Society/European Respiratory Society statement: update of the international multidisciplinary classification of the idiopathic interstitial pneumonias. Am J Respir Crit Care Med 188(6):733–748

Article  PubMed  PubMed Central  Google Scholar 

Julious SA (2005) Sample size of 12 per group rule of thumb for a pilot study. Pharm Stat 4:287–291

Article  Google Scholar 

Nguyen L-P, Harper RW, Louie S (2016) Using and interpreting carbon monoxide diffusing capacity (DLco) correctly. Consultant 56(5):440–445

Google Scholar 

Dempsey TM, Scanlon PD (2018) Pulmonary function tests for the generalist: a brief review. In: Mayo Clinic proceedings. Elsevier, Amsterdam

Johnson JD, Theurer WM (2014) A stepwise approach to the interpretation of pulmonary function tests. Am Fam Phys 89(5):359–366

Google Scholar 

Raghu G et al (2022) 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 205(9):e18–e47

Article  PubMed  PubMed Central  Google Scholar 

van Buuren S et al (2015) Package ‘mice’. Computer software

Waljee AK et al (2013) Comparison of imputation methods for missing laboratory data in medicine. BMJ Open 3(8):e002847

Article  PubMed  PubMed Central  Google Scholar 

Jadhav A, Pramod D, Ramanathan K (2019) Comparison of performance of data imputation methods for numeric dataset. Appl Artif Intell 33(10):913–933

Article  Google Scholar 

Stekhoven DJ, Bühlmann P (2012) MissForest—non-parametric missing value imputation for mixed-type data. Bioinformatics 28(1):112–118

Article  CAS  PubMed  Google Scholar 

Collins GS et al (2015) Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD Statement. BMC Med 13:1

Article  PubMed  PubMed Central  Google Scholar 

Miller HA, van Berkel VH, Frieboes HB (2022) Lung cancer survival prediction and biomarker identification with an ensemble machine learning analysis of tumor core biopsy metabolomic data. Metabolomics 18(8):1–12

Article  Google Scholar 

Grandini M, Bagli E, Visani G (2020) Metrics for multi-class classification: an overview. arXiv preprint arXiv:2008.05756

Fatourechi M et al (2008) Comparison of evaluation metrics in classification applications with imbalanced datasets. In: 2008 seventh international conference on machine learning and applications, 2008. IEEE

Katzenstein AL, Myers JL (2001) Idiopathic pulmonary fibrosis: to biopsy or not to biopsy. Am J Respir Crit Care Med 164(2):185–186

Article  CAS  PubMed  Google Scholar 

Hunninghake GW et al (2001) Utility of a lung biopsy for the diagnosis of idiopathic pulmonary fibrosis. Am J Respir Crit Care Med 164(2):193–196

Article  CAS  PubMed  Google Scholar 

Montesi SB et al (2020) Update in interstitial lung disease 2019. Am J Respir Crit Care Med 1:1

Google Scholar 

Vij R, Noth I (2012) Peripheral blood biomarkers in idiopathic pulmonary fibrosis. Transl Res 159(4):218–227

Article  CAS  PubMed  PubMed Central  Google Scholar 

Stainer A et al (2021) Molecular biomarkers in idiopathic pulmonary fibrosis: state of the art and future directions. Int J Mol Sci 22(12):6255

Article  CAS  PubMed  PubMed Central  Google Scholar 

Krauss E et al (2019) Exploring the ability of electronic nose technology to recognize Interstitial Lung Diseases (ILD) by Non-Invasive Breath Screening of Exhaled Volatile Compounds (VOC): a pilot study from the European IPF Registry (eurIPFreg) and Biobank. J Clin Med 8(10):1698

Article  CAS  PubMed  PubMed Central  Google Scholar 

Dragonieri S et al (2020) Exhaled volatile organic compounds analysis by e-nose can detect idiopathic pulmonary fibrosis. J Breath Res 14(4):047101

Article  CAS  PubMed  Google Scholar 

Hayton C et al (2019) Breath biomarkers in idiopathic pulmonary fibrosis: a systematic review. Respir Res 20(1):7

Article  PubMed  PubMed Central  Google Scholar 

Yamada YI et al (2017) Volatile organic compounds in exhaled breath of idiopathic pulmonary fibrosis for discrimination from healthy subjects. Lung 195(2):247–254

Article  CAS  PubMed  Google Scholar 

Plantier L et al (2022) The use of exhaled air analysis in discriminating interstitial lung diseases: a pilot study. Respir Res 23(1):12

Article  CAS  PubMed  PubMed Central  Google Scholar 

Taylor MJ et al (2024) Disease diagnosis and severity classification in pulmonary fibrosis using carbonyl volatile organic compounds in exhaled breath. Respir Med (in press)

Spagnolo P et al (2021) Idiopathic pulmonary fibrosis: disease mechanisms and drug development. Pharmacol Ther 222:107798

Article  CAS  PubMed 

留言 (0)

沒有登入
gif