Ho Cheol Kim1, Jeong Seok Lee2, Eun Young Lee2, You-Jung Ha3, Eun Jin Chae4, Minkyu Han5, Gary Cross6, Joseph Barnett7, Jacob Joseph8,9, Jin Woo Song1. 1. Department of Pulmonary and Critical Care Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea. 2. Division of Rheumatology, Department of Internal Medicine, Seoul National University College of Medicine, Seoul, Republic of Korea. 3. Division of Rheumatology, Department of Internal Medicine, Seoul National University Bundang Hospital, Seongnam, Republic of Korea. 4. Department of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea. 5. Clinical Epidemiology and Biostatistics, Asan Medical Center, Seoul, Republic of Korea. 6. Department of Radiology, Royal Free Hospital, Royal Free London NHS Foundation Trust, London, UK. 7. Department of Radiology, Royal Brompton Hospital, Royal Brompton and Harefield NHS Foundation Trust, London, UK. 8. Department of Respiratory Medicine, University College London, London, UK. 9. Centre for Medical Image Computing, University College London, London, UK.
Abstract
BACKGROUND AND OBJECTIVE: RA-ILD has a variable clinical course, and its prognosis is difficult to predict. Moreover, risk prediction models for prognosis remain undefined. METHODS: The prediction model was developed using retrospective data from 153 patients with RA-ILD and validated in an independent RA-ILD cohort (n = 149). Candidate variables for the prediction models were screened using a multivariate Cox proportional hazard model. C-statistics were calculated to assess and compare the predictive ability of each model. RESULTS: In the derivation cohort, the median follow-up period was 54 months, and 38.6% of the subjects exhibited a UIP pattern on HRCT imaging. In multivariate Cox analysis, old age (≥60 years, HR: 2.063), high fibrosis score (≥20% of the total lung extent, HR: 4.585), a UIP pattern (HR: 1.899) and emphysema (HR: 2.596) on HRCT were significantly poor prognostic factors and included in the final model. The prediction model demonstrated good performance in the prediction of 5-year mortality (C-index: 0.780, P < 0.001); furthermore, patients at risk were divided into three groups with 1-year mortality rates of 0%, 5.1% and 24.1%, respectively. Predicted and observed mortalities at 1, 2 and 3 years were similar in the derivation cohort, and the prediction model was also effective in predicting prognosis of the validation cohort (C-index: 0.638, P < 0.001). CONCLUSION: Our results suggest that a risk prediction model based on HRCT variables could be useful for patients with RA-ILD.
BACKGROUND AND OBJECTIVE:RA-ILD has a variable clinical course, and its prognosis is difficult to predict. Moreover, risk prediction models for prognosis remain undefined. METHODS: The prediction model was developed using retrospective data from 153 patients with RA-ILD and validated in an independent RA-ILD cohort (n = 149). Candidate variables for the prediction models were screened using a multivariate Cox proportional hazard model. C-statistics were calculated to assess and compare the predictive ability of each model. RESULTS: In the derivation cohort, the median follow-up period was 54 months, and 38.6% of the subjects exhibited a UIP pattern on HRCT imaging. In multivariate Cox analysis, old age (≥60 years, HR: 2.063), high fibrosis score (≥20% of the total lung extent, HR: 4.585), a UIP pattern (HR: 1.899) and emphysema (HR: 2.596) on HRCT were significantly poor prognostic factors and included in the final model. The prediction model demonstrated good performance in the prediction of 5-year mortality (C-index: 0.780, P < 0.001); furthermore, patients at risk were divided into three groups with 1-year mortality rates of 0%, 5.1% and 24.1%, respectively. Predicted and observed mortalities at 1, 2 and 3 years were similar in the derivation cohort, and the prediction model was also effective in predicting prognosis of the validation cohort (C-index: 0.638, P < 0.001). CONCLUSION: Our results suggest that a risk prediction model based on HRCT variables could be useful for patients with RA-ILD.