In-hospital mortality prediction models for acute myocardial infarction: A systematic review and meta-analysis_Chinese Journal of Clinical Thoracic and Cardiovascular Surgery

Authors：

CHEN Youyou¹ ¹ , DAI Yu² ² , MA Hui³ ³ , WANG Huimin³ ³ ,  CHEN Duoxue³ ³

1. Department of Cardiology, Bozhou Hospital Affiliated to Anhui Medical University, Bozhou, 236800, Anhui, P. R. China;
2. Department of Nursing, Suzhou Municipal Hospital Affiliated to Anhui Medical University, Suzhou, 234000, Anhui, P. R. China;
3. Department of Cardiology, Bozhou Hospital Affiliated to Anhui Medical University, Bozhou, 236800, Anhui, P. R. China;

Corresponding author：

CHEN Duoxue³, Email: chenduoxue2005@163.com

Keywords：

Acute myocardial infarction; in-hospital mortality; prediction model; systematic review/meta-analysis

DOI：

10.7507/1007-4848.202506060

Video：

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Objective To systematically evaluate prediction models for in-hospital mortality risk in patients with acute myocardial infarction (AMI). Methods A comprehensive search was conducted in PubMed, Embase, Web of Science, Cochrane Library, and CNKI databases from inception to May 30, 2025, to identify studies related to AMI in-hospital mortality prediction models. Risk of bias and applicability were assessed using the Prediction Model Risk of Bias Assessment Tool (PROBAST). Relevant data were extracted for model quality assessment. Results A total of 29 studies involving 75 AMI in-hospital mortality prediction models were included. Key predictive factors identified included Killip classification, neutrophil count, renal insufficiency, age, systolic blood pressure, and left ventricular ejection fraction. The area under the receiver operating characteristic curve (AUC) ranged from 0.580 to 0.998. Internal validation was reported in 21 studies, external validation in 4, and both in 4 studies. Model calibration was evaluated in 23 studies. Most models were presented as nomograms. All studies demonstrated good applicability, though 25 were rated as high risk of bias overall. Conclusion Current AMI in-hospital mortality prediction models show generally good predictive performance, with some variables exhibiting stable predictive effects. However, the lack of external validation and high risk of bias remain prevalent issues. Future studies should focus on prospective, multicenter, high-quality designs to enhance the practical and clinical value of these models.

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2.	World Health Organization. Cardiovascular diseases (CVDs). [2023-06-11] (2025-05-23).
3.	国家心血管病中心, 中国心血管健康与疾病报告编写组. 中国心血管健康与疾病报告2024概要. 中国循环杂志, 2025, 40(6): 521-559.National Center for Cardiovascular Diseases, Writing Committee of China Cardiovascular Health and Disease Report. Report on cardiovascular health and diseases in China 2024: an updated summary. Chin Circ J, 2025, 40(6): 521-559.
4.	Ibanez B, James S, Agewall S, et al. 2017 ESC guidelines for the management of acute myocardial infarction in patients presenting with ST-segment elevation. Eur Heart J, 2018, 39(2): 119-177.
5.	Wang L, Wang Y, Jin F, et al. Risk prediction models for hospital mortality in general medical patients: a systematic review. J Gen Intern Med, 2023, 38(5): 1253-1265.
6.	Zhang Y, Liu H, Huang Q, et al. Predictive value of machine learning for in-hospital mortality risk in acute myocardial infarction: a systematic review and meta-analysis. Int J Med Inform, 2025, 198: 105875.
7.	Moons KGM, de Groot JAH, Bouwmeester W, et al. Critical appraisal and data extraction for systematic reviews of prediction modelling studies: the CHARMS checklist. PLoS Med, 2014, 11(10): e1001744.
8.	陈香萍, 张奕, 庄一渝, 等. PROBAST: 诊断或预后多因素预测模型研究偏倚风险的评估工具. 中国循证医学杂志, 2020, 20(6): 737-744.Chen XP, Zhang Y, Zhuang YY, et al. PROBAST: a tool for assessing risk of bias in the study of diagnostic or prognostic multi-factorial predictive models. Chin J Evid Based Med, 2020, 20(6): 737-744.
9.	Goriki Y, Tanaka A, Nishihira K, et al. A novel predictive model for in-hospital mortality based on a combination of multiple blood variables in patients with ST-segment-elevation myocardial infarction. J Clin Med, 2020, 9(3): 782.
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12.	Liao T, Xu Q, Shi R, et al. A nomogram for predicting hospital mortality in intensive care unit patients with acute myocardial infarction. Int J Gen Med, 2021, 14: 5863-5877.
13.	Ke J, Chen Y, Wang X, et al. Machine learning-based in-hospital mortality prediction models for patients with acute coronary syndrome. Am J Emerg Med, 2022, 53: 127-134.
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16.	Deng L, Zhao X, Su X, et al. Machine learning to predict no reflow and in-hospital mortality in patients with ST-segment elevation myocardial infarction that underwent primary percutaneous coronary intervention. BMC Med Inform Decis Mak, 2022, 22(1): 109.
17.	Wang Y, Li C, Yuan M, et al. Development of a complete blood count with differential-based prediction model for in-hospital mortality among patients with acute myocardial infarction in the coronary care unit. Front Cardiovasc Med, 2022, 9: 1001356.
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19.	Oliveira M, Seringa J, Pinto FJ, et al. Machine learning prediction of mortality in acute myocardial infarction. BMC Med Inform Decis Mak, 2023, 23(1): 70.
20.	Zhu X, Xie B, Chen Y, et al. Machine learning in the prediction of in-hospital mortality in patients with first acute myocardial infarction. Clin Chim Acta, 2024, 554: 117776.
21.	Shakhgeldyan KI, Kuksin NS, Domzhalov IG, et al. Interpretable machine learning for in-hospital mortality risk prediction in patients with ST-elevation myocardial infarction after percutaneous coronary interventions. Comput Biol Med, 2024, 170: 107953.
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24.	Zhang Q, Xu L, Xie Z, et al. Machine learning-based prediction of mortality in acute myocardial infarction with cardiogenic shock. Front Cardiovasc Med, 2024, 11: 1402503.
25.	Xu BZ, Wang B, Chen JP, et al. Construction and validation of a personalized risk prediction model for in-hospital mortality in patients with acute myocardial infarction undergoing percutaneous coronary intervention. Clinics (Sao Paulo), 2025, 80: 100580.
26.	Gawinski L, Milewska A, Marczak M, et al. Nomogram predicting in-hospital mortality in patients with myocardial infarction treated with primary coronary interventions based on logistic and angiographic predictors. Biomedicines, 2025, 13(3): 612.
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31.	贾吉, 陶四明. 基于血浆渗透压建立急性ST段抬高型心肌梗死重症患者发生院内死亡风险预测模型. 昆明医科大学学报, 2022, 43(12): 58-65.Jia J, Tao SM. Development of a plasma osmolality prediction model for the risk of in-hospital death in critically ill patients with acute ST-segment elevation myocardial infarction. J Kunming Med Univ, 2022, 43(12): 58-65.
32.	李其华, 易秋艳, 徐广纳, 等. 急性ST段抬高型心肌梗死患者直接PCI术后院内死亡风险预测模型的建立和验证. 中国循证心血管医学杂志, 2022, 14(12): 1470-1475.Li QH, Yi QY, Xu GN, et al. Establishment and validation of prediction model of in-hospital mortality risk in patients with acute ST-segment elevation myocardial infarction after primary percutaneous coronary intervention. Chin J Evid Based Cardiovasc Med, 2022, 14(12): 1470-1475.
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34.	张晔, 孙婷婷, 张海峰, 等. 急性心肌梗死合并心源性休克院内死亡的预测模型建立与验证. 中西医结合心脑血管病杂志, 2023, 21(24): 4570-4575.Zhang Y, Sun TT, Zhang HF, et al. Development and validation of a prediction model for in-hospital mortality in acute myocardial infarction with cardiogenic shock. Chin J Integr Med Cardio Cerebrovasc Dis, 2023, 21(24): 4570-4575.
35.	李金滨, 全美玲, 李晓雪. 急性心肌梗死合并心源性休克发生院内死亡预测模型的建立. 中国循证心血管医学杂志, 2024, 16(3): 283-287.Li JB, Quan ML, Li XX. Development of a predictive model for in-hospital death in acute myocardial infarction combined with cardiogenic shock. Chin J Evid Based Cardiovasc Med, 2024, 16(3): 283-287.
36.	周明英, 周丽珍, 罗文慧, 等. 急性心肌梗死患者死亡危险因素分析及预测模型建立. 现代实用医学, 2022, 34(10): 1349-1351,1402.Zhou MY, Zhou LZ, Luo WH, et al. Analysis of risk factors for mortality and construction of a prediction model in acute myocardial infarction patients. Mod Pract Med, 2022, 34(10): 1349-1351,1402.
37.	张杰, 马礼坤, 张理想, 等. 急性心肌梗死患者院内死亡风险列线图预测模型的构建. 临床心血管病杂志, 2020, 36(4): 311-317.Zhang J, Ma LK, Zhang LX, et al. Construction of predictive model of in-hospital death risk in patients with acute myocardial infarction. J Clin Cardiol, 2020, 36(4): 311-317.
38.	Carreras-Mora J, Vidal-Burdeus M, Rodriguez-Gonzalez C, et al. Killip scale reclassification according to lung ultrasound: Killip pLUS. Eur Heart J Acute Cardiovasc Care, 2024, 13(7): 566-569.
39.	Abduljabbar AS, Fawzi HM. The association of neutrophil to lymphocyte ratio and other complete blood count parameters with global registry of acute coronary events risk score in patients with non-ST segment elevation - acute coronary syndrome: a single-center study. Med J Islam Repub Iran, 2024, 38: 109.
40.	Li X, Qiao Y, Ruan L, et al. Stress hyperglycemia ratio as an independent predictor of acute kidney injury in critically ill patients with acute myocardial infarction: a retrospective U. S. cohort study. Ren Fail, 2025, 47(1): 2471018.
41.	Jalaja PP, Kommineni D, Mishra A, et al. Predictors of mortality in acute myocardial infarction: insights from the healthcare cost and utilization project (HCUP) nationwide readmission database. Cureus, 2025, 17(5): e83675.
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1. Thygesen K, Alpert JS, Jaffe AS, et al. Fourth universal definition of myocardial infarction (2018). Glob Heart, 2018, 13(4): 305-338.
2. World Health Organization. Cardiovascular diseases (CVDs). [2023-06-11] (2025-05-23).
3. 国家心血管病中心, 中国心血管健康与疾病报告编写组. 中国心血管健康与疾病报告2024概要. 中国循环杂志, 2025, 40(6): 521-559.National Center for Cardiovascular Diseases, Writing Committee of China Cardiovascular Health and Disease Report. Report on cardiovascular health and diseases in China 2024: an updated summary. Chin Circ J, 2025, 40(6): 521-559.
4. Ibanez B, James S, Agewall S, et al. 2017 ESC guidelines for the management of acute myocardial infarction in patients presenting with ST-segment elevation. Eur Heart J, 2018, 39(2): 119-177.
5. Wang L, Wang Y, Jin F, et al. Risk prediction models for hospital mortality in general medical patients: a systematic review. J Gen Intern Med, 2023, 38(5): 1253-1265.
6. Zhang Y, Liu H, Huang Q, et al. Predictive value of machine learning for in-hospital mortality risk in acute myocardial infarction: a systematic review and meta-analysis. Int J Med Inform, 2025, 198: 105875.
7. Moons KGM, de Groot JAH, Bouwmeester W, et al. Critical appraisal and data extraction for systematic reviews of prediction modelling studies: the CHARMS checklist. PLoS Med, 2014, 11(10): e1001744.
8. 陈香萍, 张奕, 庄一渝, 等. PROBAST: 诊断或预后多因素预测模型研究偏倚风险的评估工具. 中国循证医学杂志, 2020, 20(6): 737-744.Chen XP, Zhang Y, Zhuang YY, et al. PROBAST: a tool for assessing risk of bias in the study of diagnostic or prognostic multi-factorial predictive models. Chin J Evid Based Med, 2020, 20(6): 737-744.
9. Goriki Y, Tanaka A, Nishihira K, et al. A novel predictive model for in-hospital mortality based on a combination of multiple blood variables in patients with ST-segment-elevation myocardial infarction. J Clin Med, 2020, 9(3): 782.
10. Aziz F, Malek S, Ibrahim KS, et al. Short- and long-term mortality prediction after an acute ST-elevation myocardial infarction (STEMI) in Asians: a machine learning approach. PLoS One, 2021, 16(8): e0254894.
11. Zhao J, Zhao P, Li C, et al. Optimized machine learning models to predict in-hospital mortality for patients with ST-segment elevation myocardial infarction. Ther Clin Risk Manag, 2021, 17: 951-961.
12. Liao T, Xu Q, Shi R, et al. A nomogram for predicting hospital mortality in intensive care unit patients with acute myocardial infarction. Int J Gen Med, 2021, 14: 5863-5877.
13. Ke J, Chen Y, Wang X, et al. Machine learning-based in-hospital mortality prediction models for patients with acute coronary syndrome. Am J Emerg Med, 2022, 53: 127-134.
14. Wang Y, Liu L, Li X, et al. Nomogram for predicting in-hospital mortality in patients with acute ST-elevation myocardial infarction complicated by cardiogenic shock after primary percutaneous coronary intervention. J Interv Cardiol, 2022, 2022: 8994106.
15. Wang Y, Wang W, Jia S, et al. Development of a nomogram for the prediction of in-hospital mortality in patients with acute ST-elevation myocardial infarction after primary percutaneous coronary intervention: a multicentre, retrospective, observational study in Hebei province, China. BMJ Open, 2022, 12(2): e056101.
16. Deng L, Zhao X, Su X, et al. Machine learning to predict no reflow and in-hospital mortality in patients with ST-segment elevation myocardial infarction that underwent primary percutaneous coronary intervention. BMC Med Inform Decis Mak, 2022, 22(1): 109.
17. Wang Y, Li C, Yuan M, et al. Development of a complete blood count with differential-based prediction model for in-hospital mortality among patients with acute myocardial infarction in the coronary care unit. Front Cardiovasc Med, 2022, 9: 1001356.
18. Li R, Shen L, Ma W, et al. Use of machine learning models to predict in-hospital mortality in patients with acute coronary syndrome. Clin Cardiol, 2023, 46(2): 184-194.
19. Oliveira M, Seringa J, Pinto FJ, et al. Machine learning prediction of mortality in acute myocardial infarction. BMC Med Inform Decis Mak, 2023, 23(1): 70.
20. Zhu X, Xie B, Chen Y, et al. Machine learning in the prediction of in-hospital mortality in patients with first acute myocardial infarction. Clin Chim Acta, 2024, 554: 117776.
21. Shakhgeldyan KI, Kuksin NS, Domzhalov IG, et al. Interpretable machine learning for in-hospital mortality risk prediction in patients with ST-elevation myocardial infarction after percutaneous coronary interventions. Comput Biol Med, 2024, 170: 107953.
22. Yang J, Li Y, Li X, et al. A machine learning model for predicting in-hospital mortality in Chinese patients with ST-segment elevation myocardial infarction: findings from the China myocardial infarction registry. J Med Internet Res, 2024, 26: e50067.
23. Yu F, Xu Y, Peng J, et al. Evaluation of a nomogram model for predicting in-hospital mortality risk in patients with acute ST-elevation myocardial infarction and acute heart failure post-PCI. Scand Cardiovasc J, 2024, 58(1): 2387001.
24. Zhang Q, Xu L, Xie Z, et al. Machine learning-based prediction of mortality in acute myocardial infarction with cardiogenic shock. Front Cardiovasc Med, 2024, 11: 1402503.
25. Xu BZ, Wang B, Chen JP, et al. Construction and validation of a personalized risk prediction model for in-hospital mortality in patients with acute myocardial infarction undergoing percutaneous coronary intervention. Clinics (Sao Paulo), 2025, 80: 100580.
26. Gawinski L, Milewska A, Marczak M, et al. Nomogram predicting in-hospital mortality in patients with myocardial infarction treated with primary coronary interventions based on logistic and angiographic predictors. Biomedicines, 2025, 13(3): 612.
27. 王禹丹. STEMI患者PCI后院内死亡危险因素分析及风险预测模型构建. 河北医科大学, 2022.Wang YD. Analysis of risk factors and construction of risk prediction model for STEMI patients who died in hospital after PCI. Hebei Medical University, 2022.
28. 张志宇. 多因素联合评估模型对心梗后心源性休克患者院内死亡的预测价值. 吉林大学, 2023.Zhang ZY. The value of multi-factor combined evaluation model in predicting hospital death in patients with cardiogenic shock after myocardial infarction. Jilin University, 2023.
29. 赵鹏宇. 机器学习在急性心肌梗死诊疗中的应用研究. 天津大学, 2022.Zhao PY. Research on the application of machine learning in the diagnosis and treatment of acute myocardial infarction. Tianjin University, 2022.
30. 孙雨可. 基于MIMIC-Ⅳ数据库对ICU病房急性心肌梗死患者院内死亡风险预测模型的构建与验证. 兰州大学, 2023.Sun YK. Construction and validation of in-hospital death risk prediction model for patients with acute myocardial infarction in ICU ward based on MIMIC-IV database. Lanzhou University, 2023.
31. 贾吉, 陶四明. 基于血浆渗透压建立急性ST段抬高型心肌梗死重症患者发生院内死亡风险预测模型. 昆明医科大学学报, 2022, 43(12): 58-65.Jia J, Tao SM. Development of a plasma osmolality prediction model for the risk of in-hospital death in critically ill patients with acute ST-segment elevation myocardial infarction. J Kunming Med Univ, 2022, 43(12): 58-65.
32. 李其华, 易秋艳, 徐广纳, 等. 急性ST段抬高型心肌梗死患者直接PCI术后院内死亡风险预测模型的建立和验证. 中国循证心血管医学杂志, 2022, 14(12): 1470-1475.Li QH, Yi QY, Xu GN, et al. Establishment and validation of prediction model of in-hospital mortality risk in patients with acute ST-segment elevation myocardial infarction after primary percutaneous coronary intervention. Chin J Evid Based Cardiovasc Med, 2022, 14(12): 1470-1475.
33. 谈子恒. 急性ST段抬高型心肌梗死患者院内死亡预测模型的构建: 一项中国的多中心回顾性研究. 南昌大学, 2023.Tan ZH. Construction of a predictive model for in-hospital mortality in patients with acute ST-segment elevation myocardial infarction: a multicenter retrospective study in. Nanchang University, 2023.
34. 张晔, 孙婷婷, 张海峰, 等. 急性心肌梗死合并心源性休克院内死亡的预测模型建立与验证. 中西医结合心脑血管病杂志, 2023, 21(24): 4570-4575.Zhang Y, Sun TT, Zhang HF, et al. Development and validation of a prediction model for in-hospital mortality in acute myocardial infarction with cardiogenic shock. Chin J Integr Med Cardio Cerebrovasc Dis, 2023, 21(24): 4570-4575.
35. 李金滨, 全美玲, 李晓雪. 急性心肌梗死合并心源性休克发生院内死亡预测模型的建立. 中国循证心血管医学杂志, 2024, 16(3): 283-287.Li JB, Quan ML, Li XX. Development of a predictive model for in-hospital death in acute myocardial infarction combined with cardiogenic shock. Chin J Evid Based Cardiovasc Med, 2024, 16(3): 283-287.
36. 周明英, 周丽珍, 罗文慧, 等. 急性心肌梗死患者死亡危险因素分析及预测模型建立. 现代实用医学, 2022, 34(10): 1349-1351,1402.Zhou MY, Zhou LZ, Luo WH, et al. Analysis of risk factors for mortality and construction of a prediction model in acute myocardial infarction patients. Mod Pract Med, 2022, 34(10): 1349-1351,1402.
37. 张杰, 马礼坤, 张理想, 等. 急性心肌梗死患者院内死亡风险列线图预测模型的构建. 临床心血管病杂志, 2020, 36(4): 311-317.Zhang J, Ma LK, Zhang LX, et al. Construction of predictive model of in-hospital death risk in patients with acute myocardial infarction. J Clin Cardiol, 2020, 36(4): 311-317.
38. Carreras-Mora J, Vidal-Burdeus M, Rodriguez-Gonzalez C, et al. Killip scale reclassification according to lung ultrasound: Killip pLUS. Eur Heart J Acute Cardiovasc Care, 2024, 13(7): 566-569.
39. Abduljabbar AS, Fawzi HM. The association of neutrophil to lymphocyte ratio and other complete blood count parameters with global registry of acute coronary events risk score in patients with non-ST segment elevation - acute coronary syndrome: a single-center study. Med J Islam Repub Iran, 2024, 38: 109.
40. Li X, Qiao Y, Ruan L, et al. Stress hyperglycemia ratio as an independent predictor of acute kidney injury in critically ill patients with acute myocardial infarction: a retrospective U. S. cohort study. Ren Fail, 2025, 47(1): 2471018.
41. Jalaja PP, Kommineni D, Mishra A, et al. Predictors of mortality in acute myocardial infarction: insights from the healthcare cost and utilization project (HCUP) nationwide readmission database. Cureus, 2025, 17(5): e83675.
42. Zordok M, Buda KG, Etiwy M, et al. Comparative analysis of the DanGer shock trial to randomized cardiogenic shock trials and real-world registries. Cardiovasc Revasc Med, 2025, 48: 102-110.
43. Kunkel JB, Soholm H, Holle SLD, et al. Neurohormonal response is associated with mortality in women with ST-elevation myocardial infarction. Eur Heart J Acute Cardiovasc Care, 2025, 14(1): 31-39.
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Latest ArticlesIn-hospital mortality prediction models for acute myocardial infarction: A systematic review and meta-analysis

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