{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,16]],"date-time":"2026-07-16T15:14:09Z","timestamp":1784214849104,"version":"3.55.0"},"reference-count":45,"publisher":"BMJ","issue":"2","license":[{"start":{"date-parts":[[2024,4,24]],"date-time":"2024-04-24T00:00:00Z","timestamp":1713916800000},"content-version":"unspecified","delay-in-days":23,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"name":"Postdoctoral Fellowship Program of CPSF, and the Grant of State Key Laboratory of Respiratory Disease","award":["SKLRD-Z-202401"],"award-info":[{"award-number":["SKLRD-Z-202401"]}]},{"name":"Nanjing special fund for health science and technology development","award":["YKK23081"],"award-info":[{"award-number":["YKK23081"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["82303338"],"award-info":[{"award-number":["82303338"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100010031","name":"Postdoctoral Research Foundation of China","doi-asserted-by":"publisher","award":["2023M740835"],"award-info":[{"award-number":["2023M740835"]}],"id":[{"id":"10.13039\/501100010031","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100010031","name":"Postdoctoral Research Foundation of China","doi-asserted-by":"publisher","award":["2023T160146"],"award-info":[{"award-number":["2023T160146"]}],"id":[{"id":"10.13039\/501100010031","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Nanjing special fund for health science and technology development","award":["ZKX22021"],"award-info":[{"award-number":["ZKX22021"]}]},{"name":"the Special Fund for Clinical Research (Single Disease Database) from the Affiliated Drum Tower Hospital of Nanjing University Medical School","award":["2021-LCYJ-DBZ-06"],"award-info":[{"award-number":["2021-LCYJ-DBZ-06"]}]},{"name":"the Special Fund for Clinical Research from the Affiliated Drum Tower Hospital of Nanjing University Medical School","award":["2021-LCYJ-PY-05"],"award-info":[{"award-number":["2021-LCYJ-PY-05"]}]}],"content-domain":{"domain":["bmj.com"],"crossmark-restriction":true},"short-container-title":["RMD Open"],"accepted":{"date-parts":[[2024,2,24]]},"published-print":{"date-parts":[[2024,4]]},"abstract":"<jats:sec>\n                  <jats:title>Objectives<\/jats:title>\n                  <jats:p>Risk prediction for patients with polymyositis\/dermatomyositis-associated interstitial lung disease (PM\/DM-ILD) is challenging due to heterogeneity in the disease course. We aimed to develop a mortality risk prediction model for PM\/DM-ILD.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Methods<\/jats:title>\n                  <jats:p>This prognostic study analysed patients with PM\/DM-ILD admitted to Nanjing Drum Hospital from 2016 to 2021. The primary outcome was mortality within 1 year. We used a least absolute shrinkage and selection operator (LASSO) logistic regression model to identify predictive laboratory indicators. These indicators were used to create a laboratory risk score, and we developed a mortality risk prediction model by incorporating clinical factors. The evaluation of model performance encompassed discrimination, calibration, clinical utility and practical application for risk prediction and prognosis.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>Overall, 418 patients with PM\/DM-ILD were enrolled and randomly divided into development (n=282) and validation (n=136) cohorts. LASSO logistic regression identified four optimal features in the development cohort, forming a laboratory risk score: C reactive protein, lactate dehydrogenase, CD3+CD4+ T\u2009cell counts and PO2\/FiO2. The final prediction model integrated age, arthralgia, anti-melanoma differentiation-associated gene 5 antibody status, high-resolution CT pattern and the laboratory risk score. The prediction model exhibited robust discrimination (area under the receiver operating characteristic: 0.869, 95%\u2009CI 0.811 to 0.910), excellent calibration and valuable clinical utility. Patients were categorised into three risk groups with distinct mortality rates. The internal validation, sensitivity analyses and comparative assessments against previous models further confirmed the robustness of the prediction model.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Conclusions<\/jats:title>\n                  <jats:p>We developed and validated an evidence-based mortality risk prediction model with simple, readily accessible clinical variables in patients with PM\/DM-ILD, which may inform clinical decision-making.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1136\/rmdopen-2023-003850","type":"journal-article","created":{"date-parts":[[2024,4,25]],"date-time":"2024-04-25T02:40:59Z","timestamp":1714012859000},"page":"e003850","update-policy":"https:\/\/doi.org\/10.1136\/crossmarkpolicy","source":"Crossref","is-referenced-by-count":8,"title":["Pretreatment mortality risk prediction model in patients with polymyositis\/dermatomyositis-associated interstitial lung 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