New prognostic scoring system for mortality in idiopathic pulmonary fibrosis by modifying the gender, age, and physiology model with desaturation during the six-minute walk test

BackgroundIdiopathic pulmonary fibrosis (IPF) is a progressive fibrosing interstitial lung disease (ILD) with variable and heterogeneous clinical course. The GAP (gender, age, and physiology) model had been used to predict mortality in patients with IPF, but does not contain exercise capacity. Therefore, our aim in this study was to develop new prognostic scoring system in the Korea IPF Cohort (KICO) registry.Materials and methodsThis is a retrospective study of Korean patients with IPF in KICO registry from June 2016 to August 2021. We developed new scoring system (the GAP6) based on the GAP model adding nadir saturation of percutaneous oxygen (SpO2) during six-minute walk test (6MWT) in the KICO registry and compared the efficacy of the GAP and the GAP6 model.ResultsAmong 2,412 patients in KICO registry, 966 patients were enrolled. The GAP6 model showed significant prognostic value for mortality between each stage [HR Stage II vs. Stage I = 2.89 (95% CI = 2.38–3.51), HR Stage III vs. Stage II = 2.68 (95% CI = 1.60–4.51)]. In comparison the model performance with area under curve (AUC) using receiver operating characteristic (ROC) curve analysis, the GAP6 model showed a significant improvement for predicting mortality than the GAP model (AUC the GAP vs. the GAP6, 0.646 vs. 0.671, p < 0.0019). Also, the C-index values slightly improved from 0.674 to 0.691 for mortality.ConclusionThe GAP6 model adding nadir SpO2 during 6WMT for an indicator of functional capacity improves prediction ability with C-index and AUC. Additional multinational study is needed to confirm these finding and validate the applicability and accuracy of this risk assessment system

Predicting survival of patients with idiopathic pulmonary fibrosis using GAP score: a nationwide cohort study

Background The clinical course of idiopathic pulmonary fibrosis (IPF) varies widely. Although the GAP model is useful for predicting mortality, survivals have not yet been validated for each GAP score. We aimed to elucidate how prognosis is related to GAP score and GAP stage in IPF patients. Methods The Korean Interstitial Lung Disease Study Group conducted a national survey to evaluate various characteristics in IPF patients from 2003 to 2007. Patients were diagnosed according to the 2002 criteria of the ATS/ERS. We enrolled 1,685 patients with IPF; 1,262 had undergone DLCO measurement. Patients were stratified based on GAP score (0–7): GAP score Group 0 (n = 26), Group 1 (n = 150), Group 2 (n = 208), Group 3 (n = 376), Group 4 (n = 317), Group 5 (n = 138), Group 6 (n = 39), and Group 7 (n = 8). Results Higher GAP score and GAP stage were associated with a poorer prognosis (p < 0.001, respectively). Survival time in Group 3 was lower than those in Groups 1 and 2 (p = 0.043 and p = 0.039, respectively), and higher than those in groups 4, 5, and 6 (p = 0.043, p = 0.032, and p = 0.003, respectively). Gender, age, and DLCO (%) differed significantly between Groups 2 and 3. All four variables in the GAP model differed significantly between Groups 3 and 4. Conclusion The GAP system showed significant predictive ability for mortality in IPF patients. However, prognosis in IPF patients with a GAP score of 3 were significantly different from those in the other stage I groups and stage II groups of Asian patients

Factors affecting treatment outcome in patients with idiopathic nonspecific interstitial pneumonia: a nationwide cohort study

Abstract Background The effects of corticosteroid-based therapy in patients with idiopathic nonspecific interstitial pneumonia (iNSIP), and factors affecting treatment outcome, are not fully understood. We aimed to investigate the long-term treatment response and factors affecting the treatment outcome in iNSIP patients from a multi-center study in Korea. Methods The Korean interstitial lung disease (ILD) Study Group surveyed ILD patients from 2003 to 2007. Patients were divided into two groups to compare the treatment response: response group (forced vital capacity (FVC) improves ≥10% after 1 year) and non-response group (FVC <10%). Factors affecting treatment response were evaluated by multivariate logistic regression analysis. Results A total of 261 patients with iNSIP were enrolled, and 95 patients were followed-up for more than 1 year. Corticosteroid treatment was performed in 86 patients. The treatment group showed a significant improvement in lung function after 1-year: FVC, 10.0%; forced expiratory volume (FEV1), 9.8%; diffusing capacity of the lung for carbon monoxide (DLco), 8.4% (p < 0.001). Sero-negative anti-nuclear antibody (ANA) was significantly related with lung function improvement. Sero-positivity ANA was significantly lower in the response group (p = 0.013), compared to that in the non-response group. A shorter duration of respiratory symptoms at diagnosis was significantly associated with a good response to treatment (p = 0.018). Conclusion Treatment with corticosteroids and/or immunosuppressants improved lung function in iNSIP patients, which was more pronounced in sero-negative ANA and shorter symptom duration patients. These findings suggest that early treatment should be considered in iNSIP patients, even in an early disease stage

Additional file 1: Table S1. of Predicting survival of patients with idiopathic pulmonary fibrosis using GAP score: a nationwide cohort study

Comorbidities of idiopathic pulmonary fibrosis patients according to GAP score. Table S2. Initial presenting symptoms of study population. Table S3. Survival analysis with Cox proportional hazard model including age, sex, FVC (%), DLCO (%), and smoking. (DOCX 23 kb

Additional file 1: Table S1. of Factors affecting treatment outcome in patients with idiopathic nonspecific interstitial pneumonia: a nationwide cohort study

Treatment modality in treatment group (n = 86). Table S2. Comorbidities of study population. Table S3. Comparison between initial and 1-year follow-up lung function according to treatment. Table S4. Analysis of risk factors that associated with treatment failure (by logistic regression). (DOCX 26 kb