REFERENCES
1. Ammirati E, Moslehi JJ. Diagnosis and treatment of acute myocarditis: a review. JAMA. 2023;329:1098.
2. Sorella A, Galanti K, Iezzi L, et al. Diagnosis and management of dilated cardiomyopathy: a systematic review of clinical practice guidelines and recommendations. Eur Heart J Qual Care Clin Outcomes. 2025;11:206-22.
3. Martens P, Cooper LT, Tang WHW. Diagnostic approach for suspected acute myocarditis: considerations for standardization and broadening clinical spectrum. J Am Heart Assoc. 2023;12:e031454.
4. Montero S, Abrams D, Ammirati E, et al. Fulminant myocarditis in adults: a narrative review. J Geriatr Cardiol. 2022;19:137-51.
5. Wilson FP, Martin M, Yamamoto Y, et al. Electronic health record alerts for acute kidney injury: multicenter, randomized clinical trial. BMJ. 2021;372:m4786.
6. Hoste EAJ, Bagshaw SM, Bellomo R, et al. Epidemiology of acute kidney injury in critically ill patients: the multinational AKI-EPI study. Intensive Care Med. 2015;41:1411-23.
7. Chertow GM, Levy EM, Hammermeister KE, Grover F, Daley J. Independent association between acute renal failure and mortality following cardiac surgery. Am J Med. 1998;104:343-8.
8. Chertow GM, Burdick E, Honour M, Bonventre JV, Bates DW. Acute kidney injury, mortality, length of stay, and costs in hospitalized patients. J Am Soc Nephrol. 2005;16:3365-70.
9. Hobson C, Ozrazgat-Baslanti T, Kuxhausen A, et al. Cost and mortality associated with postoperative acute kidney injury. Ann Surg. 2015;261:1207-14.
10. Kellum JA, Sileanu FE, Bihorac A, Hoste EAJ, Chawla LS. Recovery after acute kidney injury. Am J Respir Crit Care Med. 2017;195:784-91.
11. Hickson LJ, Chaudhary S, Williams AW, et al. Predictors of outpatient kidney function recovery among patients who initiate hemodialysis in the hospital. Am J Kidney Dis. 2015;65:592-602.
12. Craven AMS, Hawley CM, McDonald SP, Rosman JB, Brown FG, Johnson DW. Predictors of renal recovery in Australian and New Zealand end-stage renal failure patients treated with peritoneal dialysis. Perit Dial Int. 2007;27:184-91.
13. Koyner JL, Carey KA, Edelson DP, Churpek MM. The development of a machine learning inpatient acute kidney injury prediction model. Crit Care Med. 2018;46:1070-7.
14. Luo X, Li B, Zhu R, et al. Development and validation of an interpretable machine learning model for predicting in-hospital mortality for ischemic stroke patients in ICU. Int J Med Inf. 2025;198:105874.
15. Jiang J, Shu H, Wang DW, et al. Chinese Society of Cardiology guidelines on the diagnosis and treatment of adult fulminant myocarditis. Sci China Life Sci. 2024;67:913-39.
16. Disease: Improving Global Outcomes (KDIGO). KDIGO clinical practice guideline for acute kidney injury. Kidney Int Suppl. 2012;2:1-138. Available from: https://kdigo.org/wp-content/uploads/2016/10/KDIGO-2012-AKI-Guideline-English.pdf [Last accessed on 20 Aug 2026].
17. Xu R, Chen Y, Yao Z, et al. Application of machine learning algorithms to identify people with low bone density. Front Public Health. 2024;12:1347219.
18. Mullick SS, Datta S, Das S. Adaptive learning-based k-nearest neighbor classifiers with resilience to class imbalance. IEEE Trans Neural Netw Learn Syst. 2018;29:5713-25.
19. Ke G, Meng Q, Finley T, et al. LightGBM: a highly efficient gradient boosting decision tree. Adv Neural Inf Process Syst. 2017;30:3146-54.
20. Yang Z, Ren J, Zhang Z, et al. A new three-way incremental naive bayes classifier. Electronics. 2023;12:1730.
21. Bisong E. The multilayer perceptron (MLP). In: Building machine learning and deep learning models on google cloud platform. Berkeley, CA: Apress; 2019. pp. 401-5.
23. Strobl C, Malley J, Tutz G. An introduction to recursive partitioning: rationale, application, and characteristics of classification and regression trees, bagging, and random forests. Psychol Methods. 2009;14:323-48.
24. Suthaharan S. Support vector machine. In: Machine learning models and algorithms for big data classification. Boston, MA: Springer; 2016. pp. 207-35.
25. Chen T, Guestrin C. XGBoost: a scalable tree boosting system. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining; San Francisco California USA. New York, NY, USA: ACM; 2016. pp. 785-94.
26. Zheng X, Zhang W, Yang F, Wang L, Yu B, Liang B. Evaluative performance of TyG-ABSI versus traditional indices in relation to cardiovascular disease and mortality: evidence from the U.S. NHANES. Cardiovasc Diabetol. 2025;24:344.
27. Yue S, Li S, Huang X, et al. Machine learning for the prediction of acute kidney injury in patients with sepsis. J Transl Med. 2022;20:215.
28. Tseng P, Chen Y, Wang C, et al. Prediction of the development of acute kidney injury following cardiac surgery by machine learning. Crit Care. 2020;24:478.
29. Kuno T, Mikami T, Sahashi Y, et al. Machine learning prediction model of acute kidney injury after percutaneous coronary intervention. Sci Rep. 2022;12:749.
30. Li G, Zhao Z, Yu Z, Liao J, Zhang M. Machine learning for risk prediction of acute kidney injury in patients with diabetes mellitus combined with heart failure during hospitalization. Sci Rep. 2025;15:10728.
31. Del Toro-Cisneros N, Antiga-López FJ, Felix-Bauer KC, et al. Development of a prediction index for persistent acute kidney injury following orthotopic liver transplant. Ann Hepatol. 2025;30:101923.
32. Song X, Liu X, Liu F, Wang C. Comparison of machine learning and logistic regression models in predicting acute kidney injury: a systematic review and meta-analysis. Int J Med Inf. 2021;151:104484.
33. Kociol RD, Cooper LT, Fang JC, et al. Recognition and initial management of fulminant myocarditis: a scientific statement from the American Heart Association. Circulation. 2020;141:e69-92.
35. Ho JS, Sia C, Chan MY, Lin W, Wong RC. Coronavirus-induced myocarditis: a meta-summary of cases. Heart Lung. 2020;49:681-5.
36. Barhoum P, Pineton De Chambrun M, Dorgham K, et al. Phenotypic heterogeneity of fulminant COVID-19-related myocarditis in adults. J Am Coll Cardiol. 2022;80:299-312.
37. Lobo MLS, Taguchi Â, Gaspar HA, Ferranti JF, Carvalho WBD, Delgado AF. Fulminant myocarditis associated with the H1N1 influenza virus: case report and literature review. Rev Bras Ter Intensiva. 2014;26:321-26.
38. Tian F, Xiao Y, Peng Z, et al. Fulminant myocarditis caused by influenza B virus in a male child: a case report and literature review. J Cardiothorac Surg. 2024;19:492.
39. Nirthanakumaran D, Pathan S, Parikh D, Pathan F. Fulminant myocarditis with normal inflammatory markers. JACC Case Rep. 2025;30:103878.
40. Zhao Y, Da M, Yang X, Xu Y, Qi J. A retrospective analysis of clinical characteristics and outcomes of pediatric fulminant myocarditis. BMC Pediatr. 2024;24:553.
41. Yuan J, Li L, Li F, et al. Analysis of risk factors affecting prognosis of fulminant myocarditis in children: a ten-year single-center study. BMC Pediatr. 2025;25:209.
42. Caforio ALP, Kaski JP, Gimeno JR, et al. Endomyocardial biopsy: safety and prognostic utility in paediatric and adult myocarditis in the European Society of Cardiology EURObservational Research Programme Cardiomyopathy and Myocarditis Long-Term Registry. Eur Heart J. 2024;45:2548-69.
43. Foltran D, Delmas C, Flumian C, et al. Myocarditis and pericarditis in adolescents after first and second doses of mRNA COVID-19 vaccines. Eur Heart J Qual Care Clin Outcomes. 2022;8:99-103.
44. Kanaoka K, Onoue K, Terasaki S, et al. Features and outcomes of histologically proven myocarditis with fulminant presentation. Circulation. 2022;146:1425-33.
45. Montero S, Aissaoui N, Tadié J, et al. Fulminant giant-cell myocarditis on mechanical circulatory support: management and outcomes of a French multicentre cohort. Int J Cardiol. 2018;253:105-12.
46. Gerfaud-Valentin M, Sève P, Iwaz J, et al. Myocarditis in adult-onset still disease. Medicine. 2014;93:280-9.
47. Zheng Y, Chen Z, Song W, et al. Cardiovascular adverse events associated with immune checkpoint inhibitors: a retrospective multicenter cohort study. Cancer Med. 2024;13:e7233.
48. Van Den Akker JP, Egal M, Groeneveld AJ. Invasive mechanical ventilation as a risk factor for acute kidney injury in the critically ill: a systematic review and meta-analysis. Crit Care. 2013;17:R98.
49. Imai Y, Parodo J, Kajikawa O, et al. Injurious mechanical ventilation and end-organ epithelial cell apoptosis and organ dysfunction in an experimental model of acute respiratory distress syndrome. JAMA. 2003;289:2104.
50. Yang H, Benos PV, Kitsios GD. Protecting the lungs but hurting the kidneys: causal inference study for the risk of ventilation-induced kidney injury in ARDS. Ann Transl Med. 2020;8:985.
51. Seubert ME, Goeijenbier M. Controlled mechanical ventilation in critically Ill patients and the potential role of venous bagging in acute kidney injury. J Clin Med. 2024;13:1504.
52. Ni W, Qin HD. Prognostic factors and evaluation methods of acute kidney injury among sepsis patients with pulmonary infection. Eur Rev Med Pharmacol Sci. 2023;27:10403-10.
53. Haines RW, Kirwan CJ, Prowle JR. Managing chloride and bicarbonate in the prevention and treatment of acute kidney injury. Semin Nephrol. 2019;39:473-83.
54. Zhou H. Total bilirubin level is associated with acute kidney injury in neonates admitted to the neonatal intensive care units: based on MIMIC-III database. Eur J Pediatr. 2024;183:4235-41.
55. Wang Z, Zhang H, Xie X, Cao F, Li F. Albumin-corrected anion gap predicts acute kidney injury in critically ill patients with acute pancreatitis: a retrospective cohort study. BMC Nephrol. 2025;26:348.





