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).
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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.	陳香萍, 張奕, 莊一渝, 等. PROBAST: 診斷或預后多因素預測模型研究偏倚風險的評估工具. 中國循證醫學雜志, 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.
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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.
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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.
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32.	李其華, 易秋艷, 徐廣納, 等. 急性ST段抬高型心肌梗死患者直接PCI術后院內死亡風險預測模型的建立和驗證. 中國循證心血管醫學雜志, 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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35.	李金濱, 全美玲, 李曉雪. 急性心肌梗死合并心源性休克發生院內死亡預測模型的建立. 中國循證心血管醫學雜志, 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, 周麗珍, 羅文慧, 等. 急性心肌梗死患者死亡危險因素分析及預測模型建立. 現代實用醫學, 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.	張杰, 馬禮坤, 張理想, 等. 急性心肌梗死患者院內死亡風險列線圖預測模型的構建. 臨床心血管病雜志, 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.
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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.
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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. 國家心血管病中心, 中國心血管健康與疾病報告編寫組. 中國心血管健康與疾病報告2024概要. 中國循環雜志, 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. 陳香萍, 張奕, 莊一渝, 等. PROBAST: 診斷或預后多因素預測模型研究偏倚風險的評估工具. 中國循證醫學雜志, 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后院內死亡危險因素分析及風險預測模型構建. 河北醫科大學, 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. 張志宇. 多因素聯合評估模型對心梗后心源性休克患者院內死亡的預測價值. 吉林大學, 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. 趙鵬宇. 機器學習在急性心肌梗死診療中的應用研究. 天津大學, 2022.Zhao PY. Research on the application of machine learning in the diagnosis and treatment of acute myocardial infarction. Tianjin University, 2022.
30. 孫雨可. 基于MIMIC-Ⅳ數據庫對ICU病房急性心肌梗死患者院內死亡風險預測模型的構建與驗證. 蘭州大學, 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. 賈吉, 陶四明. 基于血漿滲透壓建立急性ST段抬高型心肌梗死重癥患者發生院內死亡風險預測模型. 昆明醫科大學學報, 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. 李其華, 易秋艷, 徐廣納, 等. 急性ST段抬高型心肌梗死患者直接PCI術后院內死亡風險預測模型的建立和驗證. 中國循證心血管醫學雜志, 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. 談子恒. 急性ST段抬高型心肌梗死患者院內死亡預測模型的構建: 一項中國的多中心回顧性研究. 南昌大學, 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. 張曄, 孫婷婷, 張海峰, 等. 急性心肌梗死合并心源性休克院內死亡的預測模型建立與驗證. 中西醫結合心腦血管病雜志, 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. 李金濱, 全美玲, 李曉雪. 急性心肌梗死合并心源性休克發生院內死亡預測模型的建立. 中國循證心血管醫學雜志, 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, 周麗珍, 羅文慧, 等. 急性心肌梗死患者死亡危險因素分析及預測模型建立. 現代實用醫學, 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. 張杰, 馬禮坤, 張理想, 等. 急性心肌梗死患者院內死亡風險列線圖預測模型的構建. 臨床心血管病雜志, 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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Latest ArticlesIn-hospital mortality prediction models for acute myocardial infarction: A systematic review and meta-analysis

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