Literature DB >> 31578022

Validation of the ILD-GAP Model and a Local Nomogram in a Singaporean Cohort.

Michelle L W Kam1, Hui Hua Li2, Yi Hern Tan3, Su Ying Low3.   

Abstract

BACKGROUND: The ILD-GAP model was developed and validated in a Western cohort to predict 1-, 2- and 3-year mortality in chronic interstitial lung disease (ILD).
OBJECTIVES: We aimed to validate the ILD-GAP model and identify predictors of mortality to derive a nomogram to predict mortality in our local Asian population.
METHODS: Characteristics of patients on follow-up in a tertiary ILD referral center were retrospectively reviewed.
RESULTS: There were 181 patients and 48 mortalities. 29.8% had idiopathic pulmonary fibrosis, 2.8% unclassifiable ILD, 33.1% connective tissue disease-associated interstitial lung disease (CTD-ILD), 28.7% idiopathic nonspecific interstitial pneumonia and 5.5% chronic hypersensitivity pneumonitis. Univariable analysis showed that a higher ILD-GAP index, unclassified ILD, males, older age, higher pulmonary artery systolic pressure, lower forced vital capacity percent predicted and carbon monoxide diffusion capacity (DLCO) correlated with increased mortality, and CTD had lower mortality. Multivariable analysis utilizing Akaike's information criterion stopping rule showed males and a lower DLCO predicted increased mortality, while CTD predicted lower mortality. These were used to generate a nomogram which predicted overall mortality better (C index 0.817, adequacy index 99.5%) than ILD-GAP (C index 0.777, adequacy index 60.7%) and provided superior estimates based on likelihood ratio testing. Calibration plots showed the nomogram predicted 1-year mortality better, whilst the ILD-GAP model predicted 2- and 3-year mortality closer to actual mortality rates but underpredicted 1-year mortality.
CONCLUSION: The nomogram performed better than ILD-GAP in predicting overall mortality and 1-year mortality. Both demonstrated good performance in predicting mortality risk.
© 2019 S. Karger AG, Basel.

Entities:  

Keywords:  Asian population; Clinical research; Interstitial lung disease; Interstitial pneumonias; Mortality

Year:  2019        PMID: 31578022     DOI: 10.1159/000502985

Source DB:  PubMed          Journal:  Respiration        ISSN: 0025-7931            Impact factor:   3.580


  2 in total

1.  Prediction of long-term mortality by using machine learning models in Chinese patients with connective tissue disease-associated interstitial lung disease.

Authors:  Di Sun; Yu Wang; Qing Liu; Tingting Wang; Pengfei Li; Tianci Jiang; Lingling Dai; Liuqun Jia; Wenjing Zhao; Zhe Cheng
Journal:  Respir Res       Date:  2022-01-07

2.  Use of machine learning models to predict prognosis of combined pulmonary fibrosis and emphysema in a Chinese population.

Authors:  Qing Liu; Di Sun; Yu Wang; Pengfei Li; Tianci Jiang; Lingling Dai; Mengjie Duo; Ruhao Wu; Zhe Cheng
Journal:  BMC Pulm Med       Date:  2022-08-29       Impact factor: 3.320

  2 in total

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