Abstract
BACKGROUND: Predictors of survival for interstitial lung disease (ILD) in the Indian population have not been studied. The primary objective of the study was to assess the Modified-Gender Age and Physiology (M-GAP) score to predict survival in patients with ILD seen in clinical practice. We also analyzed the role of demographic and radiological characteristics in predicting the survival of patients with ILD. MATERIALS AND METHODS: In the ILD India registry, data were collected from 27 centers across 19 cities in India between March 2012 and June 2015. A single follow-up was conducted at 18 centers who agreed to participate in the follow-up in 2017. M-GAP score (range 0-5) was calculated with the following variables: age (≤60 years 0, 61-65 years 1, and >65 years 2), gender (female 0, male 1), and forced vital capacity% (>75% 0, 50%-75% 1, and >75% 2). A score of 0-3 and score of 4 and 5 were classified into Stage 1 and 2, respectively. Other predictors of survival, such as the history of tuberculosis, smoking, and the presence of honeycombing on computed tomography scan, were also evaluated. RESULTS: Nine hundred and seven patients were contacted in 2017. Among them, 309 patients were lost to follow-up; 399 were alive and 199 had died. M-GAP was significantly associated with survival. Similarly, other predictors of survival were ability to perform spirometry (hazard ratio [HR]: 0.49, 95% confidence interval [CI]: 0.34-0.72), past history of tuberculosis (HR: 1.57, 95% CI: 1.07-2.29), current or past history of smoking (HR: 1.51, 95% CI: 1.06-2.16), honeycombing (HR: 1.81, 95% CI: 1.29-2.55), a diagnosis of connective tissue disease -ILD (HR: 0.41, 95% CI: 0.22-0.76), and sarcoidosis (HR: 0.24, 95% CI: 0.08-0.77). CONCLUSION: In a subgroup of patients with newly diagnosed ILD enrolled in ILD India registry and who were available for follow-up, M-GAP score predicted survival. Honeycombing at the time of diagnosis, along with accurate history of smoking, and previous history of tuberculosis were useful indices for predicting survival.