{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,16]],"date-time":"2026-10-16T23:37:37Z","timestamp":1792193857255,"version":"4.4.0"},"reference-count":45,"publisher":"BMJ","issue":"1","license":[{"start":{"date-parts":[[2025,3,22]],"date-time":"2025-03-22T00:00:00Z","timestamp":1742601600000},"content-version":"unspecified","delay-in-days":21,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"name":"the Ministry of Science and Technology of the People\u2019s Republic of China","award":["2021YFC0863600"],"award-info":[{"award-number":["2021YFC0863600"]}]},{"name":"the Ministry of Science and Technology of the People\u2019s Republic of China","award":["2023YFC0872500"],"award-info":[{"award-number":["2023YFC0872500"]}]},{"name":"the Reform and Development Program of Beijing Institute of Respiratory Medicine to Xuan Huang","award":["Ggyfz202421"],"award-info":[{"award-number":["Ggyfz202421"]}]},{"name":"Beijing Hospitals Authority Youth Programme","award":["QML20230302"],"award-info":[{"award-number":["QML20230302"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["82200007"],"award-info":[{"award-number":["82200007"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"the Capital\u2019s Funds for Health Improvement and Research","award":["2022-1-1061"],"award-info":[{"award-number":["2022-1-1061"]}]}],"content-domain":{"domain":["bmj.com"],"crossmark-restriction":true},"short-container-title":["BMJ Open Resp Res"],"accepted":{"date-parts":[[2024,10,15]]},"published-print":{"date-parts":[[2025,3]]},"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Severe pneumonia has a poor prognosis and high mortality. Current severity scores such as Acute Physiology and Chronic Health Evaluation (APACHE-II) and Sequential Organ Failure Assessment (SOFA), have limited ability to help clinicians in classification and management decisions. The goal of this study was to analyse the clinical characteristics of severe pneumonia and develop a machine learning-based mortality-prediction model for patients with severe pneumonia.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Consecutive patients with severe pneumonia between 2013 and 2022 admitted to Beijing Chaoyang Hospital affiliated with Capital Medical University were included. In-hospital all-cause mortality was the outcome of this study. We performed a retrospective analysis of the cohort, stratifying patients into survival and non-survival groups, using mainstream machine learning algorithms (light gradient boosting machine, support vector classifier and random forest). We aimed to construct a mortality-prediction model for patients with severe pneumonia based on their accessible clinical and laboratory data. The discriminative ability was evaluated using the area under the receiver operating characteristic curve (AUC). The calibration curve was used to assess the fit goodness of the model, and decision curve analysis was performed to quantify clinical utility. By means of logistic regression, independent risk factors for death in severe pneumonia were figured out to provide an important basis for clinical decision-making.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>A total of 875 patients were included in the development and validation cohorts, with the in-hospital mortality rate of 14.6%. The AUC of the model in the internal validation set was 0.8779 (95% CI, 0.738 to 0.974), showing a competitive discrimination ability that outperformed those of traditional clinical scoring systems, that is, APACHE-II, SOFA, CURB-65 (confusion, urea, respiratory rate, blood pressure, age \u226565 years), Pneumonia Severity Index. The calibration curve showed that the in-hospital mortality in severe pneumonia predicted by the model fit reasonably with the actual hospital mortality. In addition, the decision curve showed that the net clinical benefit was positive in both training and validation sets of hospitalised patients with severe pneumonia. Based on ensemble machine learning algorithms and logistic regression technique, the level of ferritin, lactic acid, blood urea nitrogen, creatine kinase, eosinophil and the requirement of vasopressors were identified as top independent predictors of in-hospital mortality with severe pneumonia.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>A robust clinical model for predicting the risk of in-hospital mortality after severe pneumonia was successfully developed using machine learning techniques. The performance of this model demonstrates the effectiveness of these techniques in creating accurate predictive models, and the use of this model has the potential to greatly assist patients and clinical doctors in making well-informed decisions regarding patient care.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1136\/bmjresp-2023-001983","type":"journal-article","created":{"date-parts":[[2025,3,24]],"date-time":"2025-03-24T02:03:32Z","timestamp":1742781812000},"page":"e001983","update-policy":"https:\/\/doi.org\/10.1136\/crossmarkpolicy","source":"Crossref","is-referenced-by-count":16,"title":["Machine learning-based model for predicting all-cause mortality in severe pneumonia"],"prefix":"10.1136","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8785-1702","authenticated-orcid":false,"given":"Weichao","family":"Zhao","sequence":"first","affiliation":[{"name":"Department of Respiratory and Critical Care Medicine, Capital Medical University, Beijing, China"},{"name":"Department of Respiratory Medicine, the Ninth Medical Center, Chinese PLA General Hospital, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xuyan","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Respiratory and Critical Care Medicine, Capital Medical University, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lianjun","family":"Gao","sequence":"additional","affiliation":[{"name":"Beijing Boai hospital, Department of Respiratory and Critical Care Medicine, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhuang","family":"Ai","sequence":"additional","affiliation":[{"name":"Sinopharm Genomics Technology Co Ltd, Changzhou, Jiangsu, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yaping","family":"Lu","sequence":"additional","affiliation":[{"name":"Sinopharm Genomics Technology Co Ltd, Changzhou, Jiangsu, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9054-1975","authenticated-orcid":false,"given":"Jiachen","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Clinical Epidemiology, Capital Medical University, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dong","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Respiratory and Critical Care Medicine, Capital Medical University, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xinlou","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Medical Research, the Ninth Medical Center, Chinese PLA General Hospital, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nan","family":"Song","sequence":"additional","affiliation":[{"name":"Medical Research Center, Beijing Institute of Respiratory Medicine and Beijing Chao-Yang Hospital, Capital Medical University, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xuan","family":"Huang","sequence":"additional","affiliation":[{"name":"Medical Research Center, Beijing Institute of Respiratory Medicine and Beijing Chao-Yang Hospital, Capital Medical University, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhao-hui","family":"Tong","sequence":"additional","affiliation":[{"name":"Department of Respiratory and Critical Care Medicine, Capital Medical University, Beijing, China"},{"name":"Medical Research Center, Beijing Institute of Respiratory Medicine and Beijing Chao-Yang Hospital, Capital Medical University, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"239","published-online":{"date-parts":[[2025,3,22]]},"reference":[{"key":"2026010701502536000_12.1.e001983.1","doi-asserted-by":"crossref","DOI":"10.1136\/bmj-2021-065871","article-title":"Management of pneumonia in critically ill patients","volume":"375","author":"Cill\u00f3niz","year":"2021","journal-title":"BMJ"},{"key":"2026010701502536000_12.1.e001983.2","doi-asserted-by":"publisher","DOI":"10.1056\/NEJMoa1500245"},{"key":"2026010701502536000_12.1.e001983.3","doi-asserted-by":"crossref","first-page":"556","DOI":"10.1080\/22221751.2022.2035194","article-title":"Aetiology of severe community acquired pneumonia in adults identified by combined detection methods: a multi-centre prospective study in China","volume":"11","author":"Qu","year":"2022","journal-title":"Emerg Microbes Infect"},{"key":"2026010701502536000_12.1.e001983.4","doi-asserted-by":"crossref","DOI":"10.1186\/s13613-018-0430-6","article-title":"Ten-year trends in intensive care admissions for respiratory infections in the elderly","volume":"8","author":"Laporte","year":"2018","journal-title":"Ann Intensive Care"},{"key":"2026010701502536000_12.1.e001983.5","first-page":"531","article-title":"Community-acquired pneumonia as an emergency condition","volume":"24","author":"Cill\u00f3niz","year":"2018","journal-title":"Curr Opin Crit Care"},{"key":"2026010701502536000_12.1.e001983.6","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0145929","article-title":"Predictors of Severe Sepsis among Patients Hospitalized for Community-Acquired Pneumonia","volume":"11","author":"Montull","year":"2016","journal-title":"PLoS ONE"},{"key":"2026010701502536000_12.1.e001983.7","doi-asserted-by":"publisher","DOI":"10.1016\/j.chest.2020.03.051"},{"key":"2026010701502536000_12.1.e001983.8","doi-asserted-by":"publisher","DOI":"10.1164\/rccm.201908-1581ST"},{"key":"2026010701502536000_12.1.e001983.9","doi-asserted-by":"crossref","DOI":"10.1186\/cc4927","article-title":"Community-acquired pneumonia on the intensive care unit: secondary analysis of 17,869 cases in the ICNARC Case Mix Programme Database","volume":"10 Suppl 2","author":"Woodhead","year":"2006","journal-title":"Crit Care"},{"key":"2026010701502536000_12.1.e001983.10","doi-asserted-by":"crossref","first-page":"942","DOI":"10.1093\/cid\/ciaa183","article-title":"Derivation and Validation of a Novel Severity Scoring System for Pneumonia at Intensive Care Unit Admission","volume":"72","author":"Carmo","year":"2021","journal-title":"Clin Infect Dis"},{"key":"2026010701502536000_12.1.e001983.11","doi-asserted-by":"crossref","first-page":"56","DOI":"10.1016\/j.ejim.2017.02.013","article-title":"A new method to predict hospital mortality in severe community acquired pneumonia","volume":"40","author":"Wang","year":"2017","journal-title":"Eur J Intern Med"},{"key":"2026010701502536000_12.1.e001983.12","doi-asserted-by":"crossref","first-page":"144","DOI":"10.1590\/1806-9282.61.02.144","article-title":"In-hospital mortality risk factors in community acquired pneumonia: evaluation of immunocompetent adult patients without comorbidities","volume":"61","author":"Vicco","year":"2015","journal-title":"Rev Assoc Med Bras"},{"key":"2026010701502536000_12.1.e001983.13","doi-asserted-by":"publisher","DOI":"10.1093\/cid\/ciaa1140"},{"key":"2026010701502536000_12.1.e001983.14","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmi.2019.09.009"},{"key":"2026010701502536000_12.1.e001983.15","doi-asserted-by":"crossref","first-page":"4673","DOI":"10.1038\/s41598-021-83967-7","article-title":"Machine learning based predictors for COVID-19 disease severity","volume":"11","author":"Patel","year":"2021","journal-title":"Sci Rep"},{"key":"2026010701502536000_12.1.e001983.16","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1016\/j.chest.2022.07.005","article-title":"Machine-Learning Model for Mortality Prediction in Patients With Community-Acquired Pneumonia: Development and Validation Study","volume":"163","author":"Cilloniz","year":"2023","journal-title":"Chest"},{"key":"2026010701502536000_12.1.e001983.17","doi-asserted-by":"publisher","DOI":"10.1086\/511159"},{"key":"2026010701502536000_12.1.e001983.18","doi-asserted-by":"publisher","DOI":"10.7326\/M14-0697"},{"key":"2026010701502536000_12.1.e001983.19","doi-asserted-by":"crossref","first-page":"451","DOI":"10.2147\/IJGM.S344544","article-title":"Development and Validation of Nomogram for Hospital Mortality in Immunocompromised Patients with Severe Pneumonia in Intensive Care Units: A Single-Center, Retrospective Cohort Study","volume":"15","author":"Yang","year":"2022","journal-title":"Int J Gen Med"},{"key":"2026010701502536000_12.1.e001983.20","doi-asserted-by":"publisher","DOI":"10.1007\/s00134-011-2261-x"},{"key":"2026010701502536000_12.1.e001983.21","doi-asserted-by":"crossref","first-page":"197","DOI":"10.15441\/ceem.19.052","article-title":"Predicting 30-day mortality of patients with pneumonia in an emergency department setting using machine-learning models","volume":"7","author":"Kang","year":"2020","journal-title":"Clin Exp Emerg Med"},{"key":"2026010701502536000_12.1.e001983.22","doi-asserted-by":"publisher","DOI":"10.1016\/S0933-3657(96)00367-3"},{"key":"2026010701502536000_12.1.e001983.23","doi-asserted-by":"crossref","first-page":"903426","DOI":"10.3389\/fbioe.2022.903426","article-title":"Performance of Machine Learning Algorithms for Predicting Adverse Outcomes in Community-Acquired Pneumonia","volume":"10","author":"Xu","year":"2022","journal-title":"Front Bioeng Biotechnol"},{"key":"2026010701502536000_12.1.e001983.24","doi-asserted-by":"crossref","first-page":"3701","DOI":"10.2147\/RMHP.S317735","article-title":"Deep Learning-Based Available and Common Clinical-Related Feature Variables Robustly Predict Survival in Community-Acquired Pneumonia","volume":"14","author":"Feng","year":"2021","journal-title":"Risk Manag Healthc Policy"},{"key":"2026010701502536000_12.1.e001983.25","doi-asserted-by":"crossref","DOI":"10.21037\/atm.2020.01.09","article-title":"Machine learning for the prediction of severe pneumonia during posttransplant hospitalization in recipients of a deceased-donor kidney transplant","volume":"8","author":"Luo","year":"2020","journal-title":"Ann Transl Med"},{"key":"2026010701502536000_12.1.e001983.26","doi-asserted-by":"publisher","DOI":"10.1016\/j.arr.2022.101721"},{"key":"2026010701502536000_12.1.e001983.27","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pmed.1004287","article-title":"Assessing eligibility for lung cancer screening using parsimonious ensemble machine learning models: A development and validation study","volume":"20","author":"Callender","year":"2023","journal-title":"PLoS Med"},{"key":"2026010701502536000_12.1.e001983.28","doi-asserted-by":"crossref","DOI":"10.1038\/s41467-023-44595-z","article-title":"A Machine Learning-Driven Virtual Biopsy System For Kidney Transplant Patients","volume":"15","author":"Yoo","year":"2024","journal-title":"Nat Commun"},{"key":"2026010701502536000_12.1.e001983.29","doi-asserted-by":"publisher","DOI":"10.1016\/j.jacc.2016.08.062"},{"key":"2026010701502536000_12.1.e001983.30","doi-asserted-by":"crossref","DOI":"10.1186\/s12890-023-02567-5","article-title":"Development and validation of an in-hospital mortality risk prediction model for patients with severe community-acquired pneumonia in the intensive care unit","volume":"23","author":"Pan","year":"2023","journal-title":"BMC Pulm Med"},{"key":"2026010701502536000_12.1.e001983.31","doi-asserted-by":"crossref","DOI":"10.1186\/s12890-023-02314-w","article-title":"Development and validation of a survival prediction model in elder patients with community-acquired pneumonia: a MIMIC-population-based study","volume":"23","author":"Li","year":"2023","journal-title":"BMC Pulm Med"},{"key":"2026010701502536000_12.1.e001983.32","doi-asserted-by":"crossref","first-page":"107363","DOI":"10.1016\/j.rmed.2023.107363","article-title":"Novel pneumonia score based on a machine learning model for predicting mortality in pneumonia patients on admission to the intensive care unit","volume":"217","author":"Wang","year":"2023","journal-title":"Respir Med"},{"key":"2026010701502536000_12.1.e001983.33","doi-asserted-by":"crossref","first-page":"11527","DOI":"10.1038\/s41598-023-38765-8","article-title":"Machine learning-based prediction of in-ICU mortality in pneumonia patients","volume":"13","author":"Jeon","year":"2023","journal-title":"Sci Rep"},{"key":"2026010701502536000_12.1.e001983.34","doi-asserted-by":"crossref","DOI":"10.1136\/bmj.m1996","article-title":"Characterization and clinical course of 1000 patients with coronavirus disease 2019 in New York: retrospective case series","volume":"369","author":"Argenziano","year":"2020","journal-title":"BMJ"},{"key":"2026010701502536000_12.1.e001983.35","doi-asserted-by":"crossref","first-page":"e330","DOI":"10.1016\/S2666-5247(23)00031-9","article-title":"Infection and co-infection patterns of community-acquired pneumonia in patients of different ages in China from 2009 to 2020: a national surveillance study","volume":"4","author":"Liu","year":"2023","journal-title":"Lancet Microbe"},{"key":"2026010701502536000_12.1.e001983.36","doi-asserted-by":"crossref","DOI":"10.1186\/s13054-021-03841-w","article-title":"Clinical characteristics and risk factors associated with mortality in patients with severe community-acquired pneumonia and type 2 diabetes mellitus","volume":"25","author":"Huang","year":"2021","journal-title":"Crit Care"},{"key":"2026010701502536000_12.1.e001983.37","doi-asserted-by":"crossref","first-page":"570","DOI":"10.2337\/dc22-1760","article-title":"Severe COVID-19 Infection in Type 1 and Type 2 Diabetes During the First Three Waves in Sweden","volume":"46","author":"Edqvist","year":"2023","journal-title":"Diabetes Care"},{"key":"2026010701502536000_12.1.e001983.38","doi-asserted-by":"publisher","DOI":"10.1055\/s-0042-108071"},{"key":"2026010701502536000_12.1.e001983.39","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1016\/j.cca.2017.11.019","article-title":"Pretreatment albumin to globulin ratio can serve as a prognostic marker in human cancers: a meta-analysis","volume":"476","author":"Lv","year":"2018","journal-title":"Clin Chim Acta"},{"key":"2026010701502536000_12.1.e001983.40","doi-asserted-by":"publisher","DOI":"10.1007\/s00134-007-0563-9"},{"key":"2026010701502536000_12.1.e001983.41","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1097\/SHK.0000000000001353","article-title":"Serum Lactate and A Relative Change in Lactate as Predictors of Mortality in Patients With Cardiogenic Shock - Results from the Cardshock Study","volume":"53","author":"Lindholm","year":"2020","journal-title":"Shock"},{"key":"2026010701502536000_12.1.e001983.42","doi-asserted-by":"crossref","first-page":"e367","DOI":"10.1097\/PCC.0000000000001584","article-title":"Suspecting Hyperferritinemic Sepsis in Iron-Deficient Population: Do We Need a Lower Plasma Ferritin Threshold?*","volume":"19","author":"Ghosh","year":"2018","journal-title":"Pediatr Crit Care Med"},{"key":"2026010701502536000_12.1.e001983.43","doi-asserted-by":"publisher","DOI":"10.1016\/j.jaci.2020.04.021"},{"key":"2026010701502536000_12.1.e001983.44","doi-asserted-by":"crossref","DOI":"10.3390\/ijms22137075","article-title":"Emerging Evidence for Pleiotropism of Eosinophils","volume":"22","author":"Rodrigo-Mu\u00f1oz","year":"2021","journal-title":"Int J Mol Sci"},{"key":"2026010701502536000_12.1.e001983.45","doi-asserted-by":"crossref","DOI":"10.3389\/fimmu.2023.1170035","article-title":"Eosinophils as potential biomarkers in respiratory viral infections","volume":"14","author":"Macchia","year":"2023","journal-title":"Front Immunol"}],"container-title":["BMJ Open Respiratory Research"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/syndication.highwire.org\/content\/doi\/10.1136\/bmjresp-2023-001983","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,7]],"date-time":"2026-01-07T09:51:04Z","timestamp":1767779464000},"score":1,"resource":{"primary":{"URL":"https:\/\/bmjopenrespres.bmj.com\/lookup\/doi\/10.1136\/bmjresp-2023-001983"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,3]]},"references-count":45,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,3,22]]},"published-print":{"date-parts":[[2025,3]]}},"alternative-id":["10.1136\/bmjresp-2023-001983"],"URL":"https:\/\/doi.org\/10.1136\/bmjresp-2023-001983","relation":{},"ISSN":["2052-4439"],"issn-type":[{"value":"2052-4439","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,3]]}}}