{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,3]],"date-time":"2026-11-03T08:32:33Z","timestamp":1793694753902,"version":"4.5.1"},"reference-count":51,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,12,13]],"date-time":"2025-12-13T00:00:00Z","timestamp":1765584000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2026,1,8]],"date-time":"2026-01-08T00:00:00Z","timestamp":1767830400000},"content-version":"vor","delay-in-days":26,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"funder":[{"name":"The Tian Shan Innovation Team Program of the Science and Technology Department of Xinjiang Uygur Autonomous Region","award":["2024D14013"],"award-info":[{"award-number":["2024D14013"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["82460372"],"award-info":[{"award-number":["82460372"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Eur J Med Res"],"DOI":"10.1186\/s40001-025-03354-0","type":"journal-article","created":{"date-parts":[[2025,12,13]],"date-time":"2025-12-13T09:46:39Z","timestamp":1765619199000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Enhancing prognostic accuracy in sepsis-induced cardiomyopathy: a machine learning approach"],"prefix":"10.1186","volume":"31","author":[{"given":"Xiang","family":"Li","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Huixin","family":"Cheng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dina","family":"Ainiwaer","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xinxin","family":"Du","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chunbo","family":"Yang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ilzati","family":"Aizezi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yi","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiangyou","family":"Yu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhan","family":"Sun","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,12,13]]},"reference":[{"issue":"22","key":"3354_CR1","doi-asserted-by":"publisher","first-page":"2133","DOI":"10.1056\/NEJMra2403213","volume":"391","author":"NJ Meyer","year":"2024","unstructured":"Meyer NJ, Prescott HC. Sepsis and septic shock. N Engl J Med. 2024;391(22):2133\u201346. https:\/\/doi.org\/10.1056\/NEJMra2403213.","journal-title":"N Engl J Med"},{"issue":"4","key":"3354_CR2","doi-asserted-by":"publisher","first-page":"483","DOI":"10.7326\/0003-4819-100-4-483","volume":"100","author":"MM Parker","year":"1984","unstructured":"Parker MM, Shelhamer JH, Bacharach SL, et al. Profound but reversible myocardial depression in patients with septic shock. Ann Intern Med. 1984;100(4):483\u201390. https:\/\/doi.org\/10.7326\/0003-4819-100-4-483.","journal-title":"Ann Intern Med"},{"issue":"4","key":"3354_CR3","doi-asserted-by":"publisher","first-page":"625","DOI":"10.1097\/CCM.0000000000002777","volume":"46","author":"SJ Beesley","year":"2018","unstructured":"Beesley SJ, Weber G, Sarge T, et al. Septic cardiomyopathy. Crit Care Med. 2018;46(4):625\u201334. https:\/\/doi.org\/10.1097\/CCM.0000000000002777.","journal-title":"Crit Care Med"},{"issue":"2","key":"3354_CR4","doi-asserted-by":"publisher","DOI":"10.1161\/CIRCOUTCOMES.117.004040","volume":"11","author":"JF Frencken","year":"2018","unstructured":"Frencken JF, Donker DW, Spitoni C, et al. Myocardial injury in patients with sepsis and its association with long-term outcome. Circ Cardiovasc Qual Outcomes. 2018;11(2):e004040. https:\/\/doi.org\/10.1161\/CIRCOUTCOMES.117.004040.","journal-title":"Circ Cardiovasc Qual Outcomes"},{"issue":"3","key":"3354_CR5","doi-asserted-by":"publisher","DOI":"10.3390\/jcm14030986","volume":"14","author":"GE Zakynthinos","year":"2025","unstructured":"Zakynthinos GE, Giamouzis G, Xanthopoulos A, et al. Septic cardiomyopathy: difficult definition, challenging diagnosis, unclear treatment. J Clin Med. 2025;14(3):986. https:\/\/doi.org\/10.3390\/jcm14030986.","journal-title":"J Clin Med"},{"issue":"1","key":"3354_CR6","doi-asserted-by":"publisher","first-page":"112","DOI":"10.1186\/s13613-022-01089-3","volume":"12","author":"YM Lin","year":"2022","unstructured":"Lin YM, Lee MC, Toh HS, et al. Association of sepsis-induced cardiomyopathy and mortality: a systematic review and meta-analysis. Ann Intensive Care. 2022;12(1):112. https:\/\/doi.org\/10.1186\/s13613-022-01089-3.","journal-title":"Ann Intensive Care"},{"issue":"1","key":"3354_CR7","doi-asserted-by":"publisher","first-page":"15","DOI":"10.1186\/s13054-022-04290-9","volume":"27","author":"R Moreno","year":"2023","unstructured":"Moreno R, Rhodes A, Piquilloud L, et al. The sequential organ failure assessment (SOFA) score: has the time come for an update? Crit Care. 2023;27(1):15. https:\/\/doi.org\/10.1186\/s13054-022-04290-9.","journal-title":"Crit Care"},{"issue":"1","key":"3354_CR8","doi-asserted-by":"publisher","first-page":"374","DOI":"10.1186\/s13054-019-2663-7","volume":"23","author":"S Lambden","year":"2019","unstructured":"Lambden S, Laterre PF, Levy MM, Francois B. The SOFA score\u2014development, utility and challenges of accurate assessment in clinical trials. Crit Care. 2019;23(1):374. https:\/\/doi.org\/10.1186\/s13054-019-2663-7.","journal-title":"Crit Care"},{"issue":"2","key":"3354_CR9","doi-asserted-by":"publisher","first-page":"311","DOI":"10.1093\/ehjdh\/ztac025","volume":"3","author":"T Averbuch","year":"2022","unstructured":"Averbuch T, Sullivan K, Sauer A, et al. Applications of artificial intelligence and machine learning in heart failure. Eur Heart J. 2022;3(2):311\u201322. https:\/\/doi.org\/10.1093\/ehjdh\/ztac025.","journal-title":"Eur Heart J"},{"key":"3354_CR10","doi-asserted-by":"publisher","DOI":"10.1016\/j.clim.2024.110301","volume":"265","author":"X Li","year":"2024","unstructured":"Li X, Jiang S, Wang B, et al. Integrated multiomics analysis and machine learning developed diagnostic markers and prognostic model based on efferocytosis-associated signatures for septic cardiomyopathy. Clin Immunol. 2024;265:110301. https:\/\/doi.org\/10.1016\/j.clim.2024.110301.","journal-title":"Clin Immunol"},{"issue":"1","key":"3354_CR11","doi-asserted-by":"publisher","first-page":"349","DOI":"10.1186\/s13054-024-05138-0","volume":"28","author":"C Guan","year":"2024","unstructured":"Guan C, Gong A, Zhao Y, et al. Interpretable machine learning model for new-onset atrial fibrillation prediction in critically ill patients: a multicenter study. Crit Care. 2024;28(1):349. https:\/\/doi.org\/10.1186\/s13054-024-05138-0.","journal-title":"Crit Care"},{"issue":"1","key":"3354_CR12","doi-asserted-by":"publisher","first-page":"14","DOI":"10.1038\/s41746-023-00986-6","volume":"7","author":"A Boussina","year":"2024","unstructured":"Boussina A, Shashikumar SP, Malhotra A, et al. Impact of a deep learning sepsis prediction model on quality of care and survival. NPJ Digit Med. 2024;7(1):14. https:\/\/doi.org\/10.1038\/s41746-023-00986-6.","journal-title":"NPJ Digit Med"},{"issue":"1","key":"3354_CR13","doi-asserted-by":"publisher","first-page":"478","DOI":"10.1186\/s13054-020-03179-9","volume":"24","author":"PY Tseng","year":"2020","unstructured":"Tseng PY, Chen YT, Wang CH, et al. Prediction of the development of acute kidney injury following cardiac surgery by machine learning. Crit Care. 2020;24(1):478. https:\/\/doi.org\/10.1186\/s13054-020-03179-9.","journal-title":"Crit Care"},{"issue":"1","key":"3354_CR14","doi-asserted-by":"publisher","first-page":"26","DOI":"10.1007\/s00134-022-06928-2","volume":"49","author":"A Kalimouttou","year":"2023","unstructured":"Kalimouttou A, Lerner I, Cheurfa C, Jannot AS, Pirracchio R. Machine-learning-derived sepsis bundle of care. Intensive Care Med. 2023;49(1):26\u201336. https:\/\/doi.org\/10.1007\/s00134-022-06928-2.","journal-title":"Intensive Care Med"},{"issue":"1","key":"3354_CR15","doi-asserted-by":"publisher","first-page":"180","DOI":"10.1186\/s13054-024-04948-6","volume":"28","author":"S Bomrah","year":"2024","unstructured":"Bomrah S, Uddin M, Upadhyay U, et al. A scoping review of machine learning for sepsis prediction\u2014feature engineering strategies and model performance: a step toward explainability. Crit Care. 2024;28(1):180. https:\/\/doi.org\/10.1186\/s13054-024-04948-6.","journal-title":"Crit Care"},{"issue":"4","key":"3354_CR16","doi-asserted-by":"publisher","first-page":"608","DOI":"10.1007\/s00134-024-07383-x","volume":"50","author":"F Qian","year":"2024","unstructured":"Qian F, van den Boom W, See KC. The new global definition of acute respiratory distress syndrome: insights from the MIMIC-IV database. Intensive Care Med. 2024;50(4):608\u20139. https:\/\/doi.org\/10.1007\/s00134-024-07383-x.","journal-title":"Intensive Care Med"},{"issue":"1","key":"3354_CR17","doi-asserted-by":"publisher","first-page":"100","DOI":"10.1186\/s12933-024-02193-3","volume":"23","author":"Y Huang","year":"2024","unstructured":"Huang Y, Li Z, Yin X. Triglyceride-glucose index: a novel evaluation tool for all-cause mortality in critically ill hemorrhagic stroke patients\u2014a retrospective analysis of the MIMIC-IV database. Cardiovasc Diabetol. 2024;23(1):100. https:\/\/doi.org\/10.1186\/s12933-024-02193-3.","journal-title":"Cardiovasc Diabetol"},{"key":"3354_CR18","doi-asserted-by":"publisher","DOI":"10.1016\/j.jclinane.2024.111597","volume":"99","author":"Y Dong","year":"2024","unstructured":"Dong Y, Wei S, Liu Y, et al. Aspirin is associated with improved outcomes in patients with sepsis-induced myocardial injury: an analysis of the MIMIC-IV database. J Clin Anesth. 2024;99:111597. https:\/\/doi.org\/10.1016\/j.jclinane.2024.111597.","journal-title":"J Clin Anesth"},{"issue":"1","key":"3354_CR19","doi-asserted-by":"publisher","first-page":"W1","DOI":"10.7326\/M14-0698","volume":"162","author":"KG Moons","year":"2015","unstructured":"Moons KG, Altman DG, Reitsma JB, et al. Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): explanation and elaboration. Ann Intern Med. 2015;162(1):W1\u201373. https:\/\/doi.org\/10.7326\/M14-0698.","journal-title":"Ann Intern Med"},{"issue":"9","key":"3354_CR20","doi-asserted-by":"publisher","DOI":"10.1161\/JAHA.117.005930","volume":"6","author":"S Vallabhajosyula","year":"2017","unstructured":"Vallabhajosyula S, Sakhuja A, Geske JB, et al. Role of admission troponin-T and serial troponin-T testing in predicting outcomes in severe sepsis and septic shock. J Am Heart Assoc. 2017;6(9):e005930. https:\/\/doi.org\/10.1161\/JAHA.117.005930.","journal-title":"J Am Heart Assoc"},{"key":"3354_CR21","doi-asserted-by":"publisher","first-page":"2729","DOI":"10.2147\/IJGM.S318232","volume":"14","author":"R Xie","year":"2021","unstructured":"Xie R, Chen Q, He W, Zeng M. Association of cardiac troponin T concentration on admission with prognosis in critically ill patients without myocardial infarction: a cohort study. Int J Gen Med. 2021;14:2729\u201339. https:\/\/doi.org\/10.2147\/IJGM.S318232.","journal-title":"Int J Gen Med"},{"key":"3354_CR22","doi-asserted-by":"publisher","first-page":"1233147","DOI":"10.3389\/fcvm.2023.1233147","volume":"10","author":"S Chen","year":"2023","unstructured":"Chen S, Guan S, Yan Z, et al. The lactate to albumin ratio linked to all-cause mortality in critically ill patients with septic myocardial injury. Front Cardiovasc Med. 2023;10:1233147. https:\/\/doi.org\/10.3389\/fcvm.2023.1233147.","journal-title":"Front Cardiovasc Med"},{"issue":"9","key":"3354_CR23","doi-asserted-by":"publisher","DOI":"10.1016\/j.isci.2024.110787","volume":"27","author":"W Liu","year":"2024","unstructured":"Liu W, Dai J, Zhang P, et al. A novel vital sign pattern predicts sepsis-related myocardial injury mortality. iScience. 2024;27(9):110787. https:\/\/doi.org\/10.1016\/j.isci.2024.110787.","journal-title":"iScience"},{"key":"3354_CR24","doi-asserted-by":"publisher","first-page":"335","DOI":"10.1186\/s12967-023-04335-0","volume":"21","author":"Z Fan","year":"2023","unstructured":"Fan Z, Jiang J, Xiao C, et al. Construction and validation of prognostic models in critically ill patients with sepsis-associated acute kidney injury: interpretable machine learning approach. J Transl Med. 2023;21:335. https:\/\/doi.org\/10.1186\/s12967-023-04335-0.","journal-title":"J Transl Med"},{"key":"3354_CR25","doi-asserted-by":"publisher","first-page":"1403392","DOI":"10.3389\/fonc.2024.1403392","volume":"14","author":"T Sun","year":"2024","unstructured":"Sun T, Liu J, Yuan H, Li X, Yan H. Construction of a risk prediction model for lung infection after chemotherapy in lung cancer patients based on the machine learning algorithm. Front Oncol. 2024;14:1403392. https:\/\/doi.org\/10.3389\/fonc.2024.1403392.","journal-title":"Front Oncol"},{"issue":"1","key":"3354_CR26","doi-asserted-by":"publisher","first-page":"76","DOI":"10.1186\/s12933-025-02615-w","volume":"24","author":"H Kwiendacz","year":"2025","unstructured":"Kwiendacz H, Huang B, Chen Y, et al. Predicting major adverse cardiac events in diabetes and chronic kidney disease: a machine learning study from the Silesia Diabetes-Heart Project. Cardiovasc Diabetol. 2025;24(1):76. https:\/\/doi.org\/10.1186\/s12933-025-02615-w.","journal-title":"Cardiovasc Diabetol"},{"key":"3354_CR27","doi-asserted-by":"publisher","unstructured":"Yang H, Li J, Zhang C, et al. Predictive model for daily risk alerts in sepsis patients in the ICU: visualization and clinical analysis of risk indicators. Precis Clin Med. 2025;8(1):pnad001. https:\/\/doi.org\/10.1093\/pcmedi\/pnad001","DOI":"10.1093\/pcmedi\/pnad001"},{"key":"3354_CR28","doi-asserted-by":"publisher","DOI":"10.1007\/978-0-387-84858-7","volume-title":"The elements of statistical learning","author":"T Hastie","year":"2009","unstructured":"Hastie T, Tibshirani R, Friedman J. The elements of statistical learning. New York: Springer; 2009."},{"issue":"11","key":"3354_CR29","doi-asserted-by":"publisher","first-page":"1","DOI":"10.18637\/jss.v036.i11","volume":"36","author":"MB Kursa","year":"2010","unstructured":"Kursa MB, Rudnicki WR. Feature selection with the Boruta package. J Stat Softw. 2010;36(11):1\u201313. https:\/\/doi.org\/10.18637\/jss.v036.i11.","journal-title":"J Stat Softw"},{"issue":"1","key":"3354_CR30","doi-asserted-by":"publisher","first-page":"267","DOI":"10.1111\/j.2517-6161.1996.tb02080.x","volume":"58","author":"R Tibshirani","year":"1996","unstructured":"Tibshirani R. Regression shrinkage and selection via the lasso. J R Stat Soc Ser B Stat Methodol. 1996;58(1):267\u201388.","journal-title":"J R Stat Soc Ser B Stat Methodol"},{"issue":"4","key":"3354_CR31","doi-asserted-by":"publisher","first-page":"468","DOI":"10.1016\/j.jointm.2024.04.006","volume":"4","author":"J Gupta","year":"2024","unstructured":"Gupta J, Majumder AK, Sengupta D, et al. Investigating computational models for diagnosis and prognosis of sepsis based on clinical parameters: opportunities, challenges, and future research directions. J Intensive Med. 2024;4(4):468\u201377. https:\/\/doi.org\/10.1016\/j.jointm.2024.04.006.","journal-title":"J Intensive Med"},{"issue":"1","key":"3354_CR32","doi-asserted-by":"publisher","first-page":"228","DOI":"10.1186\/s12911-024-02630-z","volume":"24","author":"J Gao","year":"2024","unstructured":"Gao J, Lu Y, Ashrafi N, et al. Prediction of sepsis mortality in ICU patients using machine learning methods. BMC Med Inform Decis Mak. 2024;24(1):228. https:\/\/doi.org\/10.1186\/s12911-024-02630-z.","journal-title":"BMC Med Inform Decis Mak"},{"key":"3354_CR33","doi-asserted-by":"publisher","DOI":"10.1093\/burnst\/tkz006","volume":"8","author":"W Li","year":"2020","unstructured":"Li W, Wang M, Zhu B, Zhu Y, Xi X. Prediction of median survival time in sepsis patients by the SOFA score combined with different predictors. Burns Trauma. 2020;8:tkz006. https:\/\/doi.org\/10.1093\/burnst\/tkz006.","journal-title":"Burns Trauma"},{"issue":"4","key":"3354_CR34","doi-asserted-by":"publisher","first-page":"851","DOI":"10.1378\/chest.11-2164","volume":"142","author":"MT Keegan","year":"2012","unstructured":"Keegan MT, Gajic O, Afessa B. Comparison of APACHE III, APACHE IV, SAPS 3, and MPM0III and influence of resuscitation status on model performance. Chest. 2012;142(4):851\u20138. https:\/\/doi.org\/10.1378\/chest.11-2164.","journal-title":"Chest"},{"key":"3354_CR35","doi-asserted-by":"publisher","first-page":"5483","DOI":"10.2147\/JIR.S379905","volume":"15","author":"XD Shen","year":"2022","unstructured":"Shen XD, Zhang HS, Zhang R, et al. Progress in the clinical assessment and treatment of myocardial depression in critically ill patients with sepsis. J Inflamm Res. 2022;15:5483\u201390. https:\/\/doi.org\/10.2147\/JIR.S379905.","journal-title":"J Inflamm Res"},{"issue":"1","key":"3354_CR36","doi-asserted-by":"publisher","first-page":"8496","DOI":"10.1080\/21655979.2021.1985339","volume":"12","author":"Q Wang","year":"2021","unstructured":"Wang Q, Liu K, Jin C. Clinical value of microRNA-378a-3p in sepsis and its role in sepsis-induced inflammation and cardiac dysfunction. Bioengineered. 2021;12(1):8496\u2013504. https:\/\/doi.org\/10.1080\/21655979.2021.1985339.","journal-title":"Bioengineered"},{"issue":"6","key":"3354_CR37","doi-asserted-by":"publisher","first-page":"511","DOI":"10.4196\/kjpp.2022.26.6.511","volume":"26","author":"W Fu","year":"2022","unstructured":"Fu W, Fang X, Wu L, Hu W, Yang T. Neogambogic acid relieves myocardial injury induced by sepsis via p38 MAPK\/NF-\u03baB pathway. Korean J Physiol Pharmacol. 2022;26(6):511\u20138. https:\/\/doi.org\/10.4196\/kjpp.2022.26.6.511.","journal-title":"Korean J Physiol Pharmacol"},{"issue":"1","key":"3354_CR38","doi-asserted-by":"publisher","first-page":"18","DOI":"10.1038\/ajg.2016.517","volume":"112","author":"PY Kwo","year":"2017","unstructured":"Kwo PY, Cohen SM, Lim JK. ACG clinical guideline: evaluation of abnormal liver chemistries. Am J Gastroenterol. 2017;112(1):18\u201335. https:\/\/doi.org\/10.1038\/ajg.2016.517.","journal-title":"Am J Gastroenterol"},{"issue":"10","key":"3354_CR39","doi-asserted-by":"publisher","DOI":"10.1136\/bmjopen-2021-0586663","volume":"12","author":"A Katasako","year":"2022","unstructured":"Katasako A, Sasaki S, Raita Y, et al. Association between serum alkaline phosphatase and bacteraemia in hemodialysis outpatients: a multicenter retrospective cross-sectional study. BMJ Open. 2022;12(10):e058666. https:\/\/doi.org\/10.1136\/bmjopen-2021-0586663.","journal-title":"BMJ Open"},{"issue":"1","key":"3354_CR40","doi-asserted-by":"publisher","first-page":"68","DOI":"10.1007\/s00134-023-07271-w","volume":"50","author":"P Pickkers","year":"2024","unstructured":"Pickkers P, Angus DC, Bass K, et al. Phase-3 trial of recombinant human alkaline phosphatase for patients with sepsis-associated acute kidney injury (REVIVAL). Intensive Care Med. 2024;50(1):68\u201378. https:\/\/doi.org\/10.1007\/s00134-023-07271-w.","journal-title":"Intensive Care Med"},{"issue":"5","key":"3354_CR41","doi-asserted-by":"publisher","first-page":"731","DOI":"10.33549\/physiolres.932946","volume":"64","author":"B Bender","year":"2015","unstructured":"Bender B, Baranyi M, Kerekes A, et al. Recombinant human tissue nonspecific alkaline phosphatase successfully counteracts lipopolysaccharide-induced sepsis in mice. Physiol Res. 2015;64(5):731\u20138. https:\/\/doi.org\/10.33549\/physiolres.932946.","journal-title":"Physiol Res"},{"issue":"2","key":"3354_CR42","doi-asserted-by":"publisher","first-page":"144","DOI":"10.1016\/j.phrs.2012.04.006","volume":"66","author":"P Mart\u00ednez-Moya","year":"2012","unstructured":"Mart\u00ednez-Moya P, Ortega-Gonz\u00e1lez M, Gonz\u00e1lez R, et al. Exogenous alkaline phosphatase treatment complements endogenous enzyme protection in colonic inflammation and reduces bacterial translocation in rats. Pharmacol Res. 2012;66(2):144\u201353. https:\/\/doi.org\/10.1016\/j.phrs.2012.04.006.","journal-title":"Pharmacol Res"},{"issue":"2","key":"3354_CR43","doi-asserted-by":"publisher","DOI":"10.5546\/aap.2023-10149.eng","volume":"122","author":"JB Dartiguelongue","year":"2024","unstructured":"Dartiguelongue JB. Biological significance and clinical utility of lactate in sepsis. Arch Argent Pediatr. 2024;122(2):e202310149. https:\/\/doi.org\/10.5546\/aap.2023-10149.eng.","journal-title":"Arch Argent Pediatr"},{"issue":"13","key":"3354_CR44","doi-asserted-by":"publisher","DOI":"10.1097\/MD.0000000000010263","volume":"97","author":"HS Jeong","year":"2018","unstructured":"Jeong HS, Lee TH, Bang CH, Kim JH, Hong SJ. Risk factors and outcomes of sepsis-induced myocardial dysfunction and stress-induced cardiomyopathy in sepsis or septic shock: a comparative retrospective study. Medicine (Baltimore). 2018;97(13):e0263. https:\/\/doi.org\/10.1097\/MD.0000000000010263.","journal-title":"Medicine (Baltimore)"},{"issue":"6","key":"3354_CR45","doi-asserted-by":"publisher","first-page":"1161","DOI":"10.1038\/s42255-024-01036-5","volume":"6","author":"H Wang","year":"2024","unstructured":"Wang H, Wang J, Cui H, et al. Inhibition of fatty acid uptake by TGR5 prevents diabetic cardiomyopathy. Nat Metab. 2024;6(6):1161\u201377. https:\/\/doi.org\/10.1038\/s42255-024-01036-5.","journal-title":"Nat Metab"},{"key":"3354_CR46","doi-asserted-by":"publisher","DOI":"10.1016\/j.diabres.2025.112001","volume":"220","author":"W Lai","year":"2025","unstructured":"Lai W, Liu L, Wang S, Tang Q, Liu Y, Chai Y. The impact of diabetes on sepsis-induced cardiomyopathy. Diabetes Res Clin Pract. 2025;220:112001. https:\/\/doi.org\/10.1016\/j.diabres.2025.112001.","journal-title":"Diabetes Res Clin Pract"},{"key":"3354_CR47","doi-asserted-by":"publisher","DOI":"10.1016\/j.lfs.2024.122611","volume":"346","author":"E Casper","year":"2024","unstructured":"Casper E, El Wakeel L, Sabri N, Khorshid R, Fahmy SF. Melatonin: a potential protective multifaceted force for sepsis-induced cardiomyopathy. Life Sci. 2024;346:122611. https:\/\/doi.org\/10.1016\/j.lfs.2024.122611.","journal-title":"Life Sci"},{"key":"3354_CR48","doi-asserted-by":"publisher","DOI":"10.1016\/j.biopha.2023.115493","volume":"167","author":"Y Fan","year":"2023","unstructured":"Fan Y, Guan B, Xu J, Zhang H, Yi L, Yang Z. Role of toll-like receptor-mediated pyroptosis in sepsis-induced cardiomyopathy. Biomed Pharmacother. 2023;167:115493. https:\/\/doi.org\/10.1016\/j.biopha.2023.115493.","journal-title":"Biomed Pharmacother"},{"issue":"1","key":"3354_CR49","doi-asserted-by":"publisher","first-page":"8","DOI":"10.1159\/000521288","volume":"91","author":"ME Charlson","year":"2022","unstructured":"Charlson ME, Carrozzino D, Guidi J, Patierno C. Charlson comorbidity index: a critical review of clinimetric properties. Psychother Psychosom. 2022;91(1):8\u201335. https:\/\/doi.org\/10.1159\/000521288.","journal-title":"Psychother Psychosom"},{"issue":"17","key":"3354_CR50","doi-asserted-by":"publisher","DOI":"10.1161\/JAHA.124.034131","volume":"13","author":"MH Rohner","year":"2024","unstructured":"Rohner MH, Gebhard C, Luft A, H\u00e4nsel M, Wegener S. Cardiac findings following cerebrovascular disease. J Am Heart Assoc. 2024;13(17):e034131. https:\/\/doi.org\/10.1161\/JAHA.124.034131.","journal-title":"J Am Heart Assoc"},{"issue":"5","key":"3354_CR51","doi-asserted-by":"publisher","first-page":"466","DOI":"10.1016\/j.jemermed.2021.03.004","volume":"61","author":"E Boccio","year":"2021","unstructured":"Boccio E, Haimovich AD, Jacob V, Maciejewski KR, Wira CR, Belsky J. Sepsis fluid metric compliance and its impact on outcomes of patients with congestive heart failure, end-stage renal disease, or obesity. J Emerg Med. 2021;61(5):466\u201380. https:\/\/doi.org\/10.1016\/j.jemermed.2021.03.004.","journal-title":"J Emerg Med"}],"container-title":["European Journal of Medical Research"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s40001-025-03354-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s40001-025-03354-0","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s40001-025-03354-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,11]],"date-time":"2026-01-11T18:02:05Z","timestamp":1768154525000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1186\/s40001-025-03354-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,12,13]]},"references-count":51,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,12]]}},"alternative-id":["3354"],"URL":"https:\/\/doi.org\/10.1186\/s40001-025-03354-0","relation":{},"ISSN":["2047-783X"],"issn-type":[{"value":"2047-783X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,12,13]]},"assertion":[{"value":"31 May 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 October 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 December 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"This study was conducted in accordance with the principles of the Declaration of Helsinki. All data were obtained from the de-identified, HIPAA-compliant MIMIC-IV database; therefore, no personal health information was included. In compliance with institutional review board (IRB) requirements, the study protocol was submitted in writing and received official approval.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"All authors have reviewed and approved the final version of this manuscript and consent to its publication. The work has not been published previously and is not under consideration for publication elsewhere.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"46"}}