Comparison of CPI and GAP models in patients with idiopathic pulmonary fibrosis: a nationwide cohort study

Sang Hoon Lee,Jin Hwa Lee,Jong Sun Park,Hong Lyeol Lee,Jin Woo Song,Young Whan Kim,Sung Woo Park,Hyun Kyung Lee,Yong Bum Park,Yong Hyun Kim,Jong Wook Shin,Song Yee Kim,Sung Hwan Jeong,Eun Joo Lee,Dong Soon Kim,Man Pyo Chung,Choon-Sik Park ,Yangin Jegal,Soo Taek Uh,Moo Suk Park

doi:10.1038/s41598-018-23073-3

Abstract

The clinical course of idiopathic pulmonary fibrosis (IPF) is difficult to predict, partly owing to its heterogeneity. Composite physiologic index (CPI) and gender-age-physiology (GAP) models are easy-to-use predictors of IPF progression. This study aimed to compare the predictive values of these two models. From 2003 to 2007, the Korean Interstitial Lung Disease (ILD) Study Group surveyed ILD patients using the 2002 ATS/ERS criteria. A total of 832 patients with IPF were enrolled in this study. CPI was calculated as follows: 91.0 − (0.65 × %DLCO) − [0.53 × %FVC + [0.34 × %FEV1. GAP stage was calculated based on gender (0–1 points), age (0–2 points), and two physiologic lung function parameters (0–5 points). The two models had similar significant predictive values for patients with IPF (p < 0.001). The area under the curve (AUC) was higher for CPI than GAP for prediction of 1-, 2-, and 3-year mortality in this study. The AUC was higher for surgically diagnosed IPF patients than for clinically diagnosed patients. However, neither CPI nor GAP yielded good predictions of outcomes; the AUC was approximately 0.61~0.65. Although both CPI and GAP stage are significantly useful predictors for IPF, they have limited capability to accurately predict outcomes.

Highlights

Predictive models of Idiopathic pulmonary fibrosis (IPF) prognosis rely on numerous clinical factors, physiologic parameters, radiologic features, biomarkers, and pathologic findings[5,6,7,8,9,10,11,17,18]
We demonstrated that two simple-to-use models (GAP stage and composite physiologic index (CPI)) have important predictive values
CPI was developed in a British study and has the advantage of relying on pulmonary function tests (PFTs) data to predict IPF prognosis

Summary

Introduction

Previous studies used clinical factors (age, gender, smoking status, finger clubbing, dyspnea, 6-minute walking distance, and hospitalization), pulmonary function tests (PFTs), change in PFT, high-resolution computed tomography (HRCT) findings or scores, pulmonary hypertension, molecular biomarkers (metalloproteinase-7 and C-reactive protein [CRP]), and pathologic finding as variables in predictive models[5,6,7,8,9,10,11]. Most of these predictive models are too complex to use and have not been validated externally.

Objectives

Methods

Results

Conclusion