Risk prediction models for 30-day unplanned readmission in patients undergoing coronary artery bypass grafting: A systematic review_Chinese Journal of Clinical Thoracic and Cardiovascular Surgery

Authors：

YE Junjie ¹ , HUANG Sirui ¹ , BIAN Yufeng ² , HE Jiaojiao ¹ ,  WANG Ying ²

1. Graduate School, Tianjin University of Traditional Chinese Medicine, Tianjin, 301617, P. R. China;
2. Tianjin First Central Hospital, Tianjin, 300190, P. R. China;

Corresponding author：

WANG Ying, Email: Wangy2009@sina.com

Keywords：

Coronary artery bypass grafting; readmission; prediction model; systematic review

DOI：

10.7507/1007-4848.202508012

Video：

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Objective To systematically evaluate risk prediction models for 30-day unplanned readmission in patients undergoing coronary artery bypass grafting (CABG). Methods We searched PubMed, EMbase, Cochrane Library, Web of Science, CINAHL, CNKI, CBM, WanFang, and VIP databases from inception to June 25, 2025. Two investigators independently screened literature, extracted data, and assessed bias risk/applicability using PROBAST criteria. Results Thirteen studies comprising 17 prediction models were included. Ten models reported the area under the receiver operating characteristic curve (AUC) for modeling (0.597-0.906), ten models reported the AUC for internal validation (0.57-0.92), and twelve models reported the AUC for external validation (0.537-0.865). Core predictors included age, female sex, diabetes, and heart failure. All studies had a high risk of bias. Conclusion The research on risk prediction models for 30-day unplanned readmission in patients undergoing CABG is still in its exploratory stages. Some models exhibit insufficient performance, and there is a need to enhance the processes of model validation and performance evaluation. It is expected that future efforts will focus on developing prediction models with excellent performance and high applicability, to assist healthcare providers in the early identification of high-risk patients for readmission.

1.	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, The Writing Committee of the Report on Cardiovascular Health and Diseases in China. Report on cardiovascular health and diseases in China 2024: an updated summary. Chin Circ J, 2025, 40(6): 521-559.
2.	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2023概要. 中国循环杂志, 2024, 39(7): 625-660.National Center for Cardiovascular Diseases, The Writing Committee of the Report on Cardiovascular Health and Diseases in China. Report on cardiovascular health and diseases in China 2023: an updated summary. Chin Circ J, 2024, 39(7): 625-660.
3.	Simsek B, Rynders BD, Okeson BK, et al. Coronary angiography within 30 days from coronary artery bypass graft surgery: indications, findings, and outcomes. J Invasive Cardiol, 2023, 35(5): E248-E253.
4.	Alghafees MA, Alsubaie NA, Alsadoon LK, et al. Thirty-day readmission rates and associated risk factors after coronary artery bypass grafting. J Taibah Univ Med Sci, 2020, 15(4): 292-297.
5.	Case R, George J, Li Q, et al. Unplanned 30-day readmission after coronary artery bypass in patients with acute myocardial infarction. Cardiovasc Revasc Med, 2020, 21(4): 518-521.
6.	Amritphale A, Fonarow GC, Amritphale N, et al. All-cause unplanned readmissions in the United States: insights from the Nationwide Readmission Database. Intern Med J, 2023, 53(2): 262-270.
7.	Shah RM, Zhang Q, Chatterjee S, et al. Incidence, cost, and risk factors for readmission after coronary artery bypass grafting. Ann Thorac Surg, 2019, 107(6): 1782-1789.
8.	Rosenblum JM, Lovasik BP, Hunting JC, et al. Predicted risk of mortality score predicts 30-day readmission after coronary artery bypass grafting. Gen Thorac Cardiovasc Surg, 2019, 67(8): 661-668.
9.	Brown JR, Parker DM, Stabler ME, et al. Improving the prediction of long-term readmission and mortality using a novel biomarker panel. J Card Surg, 2021, 36(11): 4213-4223.
10.	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.
11.	鲁小丹, 卫建华, 沈建通, 等. 预测模型系统评价的制作方法与步骤. 中国循证医学杂志, 2023, 23(5): 602-609.Lu XD, Wei JH, Shen JT, et al. Methods and processes for producing a systematic review of predictive model studies. Chin J Evid Based Med, 2023, 23(5): 602-609.
12.	Engoren M, Habib RH, Dooner JJ, et al. Use of genetic programming, logistic regression, and artificial neural nets to predict readmission after coronary artery bypass surgery. J Clin Monit Comput, 2013, 27(4): 455-464.
13.	Shahian DM, He X, O'Brien SM, et al. Development of a clinical registry-based 30-day readmission measure for coronary artery bypass grafting surgery. Circulation, 2014, 130(5): 399-409.
14.	Lancey R, Kurlansky P, Argenziano M, et al. Uniform standards do not apply to readmission following coronary artery bypass surgery: a multi-institutional study. J Thorac Cardiovasc Surg, 2015, 149(3): 850-857.
15.	Fanari Z, Elliott D, Russo CA, et al. Predicting readmission risk following coronary artery bypass surgery at the time of admission. Cardiovasc Revasc Med, 2017, 18(2): 95-99.
16.	Benuzillo J, Caine W, Evans RS, et al. Predicting readmission risk shortly after admission for CABG surgery. J Card Surg, 2018, 33(4): 163-170.
17.	Zywot A, Lau CSM, Glass N, et al. Preoperative scale to determine all-cause readmission after coronary artery bypass operations. Ann Thorac Surg, 2018, 105(4): 1086-1093.
18.	Deo SV, Raza S, Altarabsheh SE, et al. Risk calculator to predict 30-day readmission after coronary artery bypass: a strategic decision support tool. Heart Lung Circ, 2019, 28(12): 1896-1903.
19.	Liu G, Zhang Y, Zhang W, et al. A risk prediction model of readmission for Chinese patients after coronary artery bypass grafting. Heart Surg Forum, 2021, 24(3): E479-E483.
20.	Manyam R, Zhang YQ, Carter S, et al. Unraveling the impact of time-dependent perioperative variables on 30-day readmission after coronary artery bypass surgery. J Thorac Cardiovasc Surg, 2022, 164(3): 943-952.
21.	Zea-Vera R, Ryan CT, Havelka J, et al. Machine learning to predict outcomes and cost by phase of care after coronary artery bypass grafting. Ann Thorac Surg, 2022, 114(3): 711-719.
22.	丁琰俊, 杭琤, 张明, 等. 冠状动脉旁路移植术患者术后早期再入院风险预测模型的构建. 护士进修杂志, 2022, 37(12): 1125-1129.Ding YJ, Hang C, Zhang M, et al. Construction of a risk prediction model for early readmission in patients after coronary artery bypass grafting. J Nurses Train, 2022, 37(12): 1125-1129.
23.	朱艳美. 冠状动脉旁路移植术后非计划再入院风险预测模型的构建与验证. 北京: 北京协和医学院, 2024.Zhu YM. Risk prediction model and validation of unplanned readmission after coronary artery bypass transplantation. Beijing: Peking Union Medical College, 2024.
24.	曹勖, 王梧圩, 姜宏伟, 等. 单纯冠状动脉旁路移植术出院后30 d内非计划再入院风险预测模型构建与验证. 中国胸心血管外科临床杂志, 2025, 32(5): 646-654.Cao X, Wang WX, Jiang HW, et al. Construction and validation of a risk prediction model of unplanned 30-day readmission in patients after isolated coronary artery bypass grafting. Chin J Clin Thorac Cardiovasc Surg, 2025, 32(5): 646-654.
25.	Shawon MSR, Odutola M, Falster MO, et al. Patient and hospital factors associated with 30-day readmissions after coronary artery bypass graft (CABG) surgery: a systematic review and meta-analysis. J Cardiothorac Surg, 2021, 16(1): 172.
26.	Sadeghi R. Coronary artery bypass grafting in advance aged patients. ARYA Atheroscler, 2023, 19(4): 37-45.
27.	Mitsutake S, Ishizaki T, Tsuchiya-Ito R, et al. Association of cognitive impairment severity with potentially avoidable readmissions: a retrospective cohort study of 8897 older patients. Alzheimers Dement (Amst), 2021, 13(1): e12147.
28.	Osherov A, Gallego-Colon E, Abu-Alkean I, et al. Gender differences in the incidence of saphenous vein graft intervention. J Cardiothorac Surg, 2024, 19(1): 643.
29.	Sabe SA, Sabe MA, Kennedy KF, et al. Risk factors for heart failure readmission after cardiac surgery. JACC Adv, 2023, 2(8): 100599.
30.	Yang DR, Wang MY, Zhang CL, et al. Endothelial dysfunction in vascular complications of diabetes: a comprehensive review of mechanisms and implications. Front Endocrinol (Lausanne), 2024, 15: 135925.
31.	崔严奇, 杨鲸蓉, 倪琳, 等. 基于机器学习算法构建浸润性肺腺癌预后预测模型. 中国胸心血管外科临床杂志, 2025, 32(1): 80-86.Cui YQ, Yang JR, Ni L, et al. Construction of a prognostic prediction model for invasive lung adenocarcinoma based on machine learning. Chin J Clin Thorac Cardiovasc Surg, 2025, 32(1): 80-86.
32.	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 the risk of bias in the study of diagnostic or prognostic multi-factorial predictive models. Chin J Evid Based Med, 2020, 20(6): 737-744.
33.	Asiimwe IG, Ndzamba BS, Mouksassi S, et al. Machine-learning assisted screening of correlated covariates: application to clinical data of desipramine. AAPS J, 2024, 26(4): 63.
34.	高湘金, 肇晖, 王瑞平. 限制性立方样条在临床研究数据分析中的应用. 上海医药, 2024, 45(13): 29-33.Gao XJ, Zhao H, Wang RP, et al. Application of restricted cubic splines in clinical research data analysis. Shanghai Med Pharm J, 2024, 45(13): 29-33.
35.	Wolff RF, Moons KGM, Riley RD, et al. PROBAST: a tool to assess the risk of bias and applicability of prediction model studies. Ann Intern Med, 2019, 170(1): 51-58.
36.	高瑞磊, 王丹, 戴国华, 等. 心力衰竭患者出院后再入院风险预测模型的系统评价. 中国胸心血管外科临床杂志, 2025, 32(5): 677-684.Gao RL, Wang D, Dai GH, et al. Re-admission risk prediction models for patients with heart failure after discharge: a systematic review. Chin J Clin Thorac Cardiovasc Surg, 2025, 32(5): 677-684.

1. 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, The Writing Committee of the Report on Cardiovascular Health and Diseases in China. Report on cardiovascular health and diseases in China 2024: an updated summary. Chin Circ J, 2025, 40(6): 521-559.
2. 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2023概要. 中国循环杂志, 2024, 39(7): 625-660.National Center for Cardiovascular Diseases, The Writing Committee of the Report on Cardiovascular Health and Diseases in China. Report on cardiovascular health and diseases in China 2023: an updated summary. Chin Circ J, 2024, 39(7): 625-660.
3. Simsek B, Rynders BD, Okeson BK, et al. Coronary angiography within 30 days from coronary artery bypass graft surgery: indications, findings, and outcomes. J Invasive Cardiol, 2023, 35(5): E248-E253.
4. Alghafees MA, Alsubaie NA, Alsadoon LK, et al. Thirty-day readmission rates and associated risk factors after coronary artery bypass grafting. J Taibah Univ Med Sci, 2020, 15(4): 292-297.
5. Case R, George J, Li Q, et al. Unplanned 30-day readmission after coronary artery bypass in patients with acute myocardial infarction. Cardiovasc Revasc Med, 2020, 21(4): 518-521.
6. Amritphale A, Fonarow GC, Amritphale N, et al. All-cause unplanned readmissions in the United States: insights from the Nationwide Readmission Database. Intern Med J, 2023, 53(2): 262-270.
7. Shah RM, Zhang Q, Chatterjee S, et al. Incidence, cost, and risk factors for readmission after coronary artery bypass grafting. Ann Thorac Surg, 2019, 107(6): 1782-1789.
8. Rosenblum JM, Lovasik BP, Hunting JC, et al. Predicted risk of mortality score predicts 30-day readmission after coronary artery bypass grafting. Gen Thorac Cardiovasc Surg, 2019, 67(8): 661-668.
9. Brown JR, Parker DM, Stabler ME, et al. Improving the prediction of long-term readmission and mortality using a novel biomarker panel. J Card Surg, 2021, 36(11): 4213-4223.
10. 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.
11. 鲁小丹, 卫建华, 沈建通, 等. 预测模型系统评价的制作方法与步骤. 中国循证医学杂志, 2023, 23(5): 602-609.Lu XD, Wei JH, Shen JT, et al. Methods and processes for producing a systematic review of predictive model studies. Chin J Evid Based Med, 2023, 23(5): 602-609.
12. Engoren M, Habib RH, Dooner JJ, et al. Use of genetic programming, logistic regression, and artificial neural nets to predict readmission after coronary artery bypass surgery. J Clin Monit Comput, 2013, 27(4): 455-464.
13. Shahian DM, He X, O'Brien SM, et al. Development of a clinical registry-based 30-day readmission measure for coronary artery bypass grafting surgery. Circulation, 2014, 130(5): 399-409.
14. Lancey R, Kurlansky P, Argenziano M, et al. Uniform standards do not apply to readmission following coronary artery bypass surgery: a multi-institutional study. J Thorac Cardiovasc Surg, 2015, 149(3): 850-857.
15. Fanari Z, Elliott D, Russo CA, et al. Predicting readmission risk following coronary artery bypass surgery at the time of admission. Cardiovasc Revasc Med, 2017, 18(2): 95-99.
16. Benuzillo J, Caine W, Evans RS, et al. Predicting readmission risk shortly after admission for CABG surgery. J Card Surg, 2018, 33(4): 163-170.
17. Zywot A, Lau CSM, Glass N, et al. Preoperative scale to determine all-cause readmission after coronary artery bypass operations. Ann Thorac Surg, 2018, 105(4): 1086-1093.
18. Deo SV, Raza S, Altarabsheh SE, et al. Risk calculator to predict 30-day readmission after coronary artery bypass: a strategic decision support tool. Heart Lung Circ, 2019, 28(12): 1896-1903.
19. Liu G, Zhang Y, Zhang W, et al. A risk prediction model of readmission for Chinese patients after coronary artery bypass grafting. Heart Surg Forum, 2021, 24(3): E479-E483.
20. Manyam R, Zhang YQ, Carter S, et al. Unraveling the impact of time-dependent perioperative variables on 30-day readmission after coronary artery bypass surgery. J Thorac Cardiovasc Surg, 2022, 164(3): 943-952.
21. Zea-Vera R, Ryan CT, Havelka J, et al. Machine learning to predict outcomes and cost by phase of care after coronary artery bypass grafting. Ann Thorac Surg, 2022, 114(3): 711-719.
22. 丁琰俊, 杭琤, 张明, 等. 冠状动脉旁路移植术患者术后早期再入院风险预测模型的构建. 护士进修杂志, 2022, 37(12): 1125-1129.Ding YJ, Hang C, Zhang M, et al. Construction of a risk prediction model for early readmission in patients after coronary artery bypass grafting. J Nurses Train, 2022, 37(12): 1125-1129.
23. 朱艳美. 冠状动脉旁路移植术后非计划再入院风险预测模型的构建与验证. 北京: 北京协和医学院, 2024.Zhu YM. Risk prediction model and validation of unplanned readmission after coronary artery bypass transplantation. Beijing: Peking Union Medical College, 2024.
24. 曹勖, 王梧圩, 姜宏伟, 等. 单纯冠状动脉旁路移植术出院后30 d内非计划再入院风险预测模型构建与验证. 中国胸心血管外科临床杂志, 2025, 32(5): 646-654.Cao X, Wang WX, Jiang HW, et al. Construction and validation of a risk prediction model of unplanned 30-day readmission in patients after isolated coronary artery bypass grafting. Chin J Clin Thorac Cardiovasc Surg, 2025, 32(5): 646-654.
25. Shawon MSR, Odutola M, Falster MO, et al. Patient and hospital factors associated with 30-day readmissions after coronary artery bypass graft (CABG) surgery: a systematic review and meta-analysis. J Cardiothorac Surg, 2021, 16(1): 172.
26. Sadeghi R. Coronary artery bypass grafting in advance aged patients. ARYA Atheroscler, 2023, 19(4): 37-45.
27. Mitsutake S, Ishizaki T, Tsuchiya-Ito R, et al. Association of cognitive impairment severity with potentially avoidable readmissions: a retrospective cohort study of 8897 older patients. Alzheimers Dement (Amst), 2021, 13(1): e12147.
28. Osherov A, Gallego-Colon E, Abu-Alkean I, et al. Gender differences in the incidence of saphenous vein graft intervention. J Cardiothorac Surg, 2024, 19(1): 643.
29. Sabe SA, Sabe MA, Kennedy KF, et al. Risk factors for heart failure readmission after cardiac surgery. JACC Adv, 2023, 2(8): 100599.
30. Yang DR, Wang MY, Zhang CL, et al. Endothelial dysfunction in vascular complications of diabetes: a comprehensive review of mechanisms and implications. Front Endocrinol (Lausanne), 2024, 15: 135925.
31. 崔严奇, 杨鲸蓉, 倪琳, 等. 基于机器学习算法构建浸润性肺腺癌预后预测模型. 中国胸心血管外科临床杂志, 2025, 32(1): 80-86.Cui YQ, Yang JR, Ni L, et al. Construction of a prognostic prediction model for invasive lung adenocarcinoma based on machine learning. Chin J Clin Thorac Cardiovasc Surg, 2025, 32(1): 80-86.
32. 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 the risk of bias in the study of diagnostic or prognostic multi-factorial predictive models. Chin J Evid Based Med, 2020, 20(6): 737-744.
33. Asiimwe IG, Ndzamba BS, Mouksassi S, et al. Machine-learning assisted screening of correlated covariates: application to clinical data of desipramine. AAPS J, 2024, 26(4): 63.
34. 高湘金, 肇晖, 王瑞平. 限制性立方样条在临床研究数据分析中的应用. 上海医药, 2024, 45(13): 29-33.Gao XJ, Zhao H, Wang RP, et al. Application of restricted cubic splines in clinical research data analysis. Shanghai Med Pharm J, 2024, 45(13): 29-33.
35. Wolff RF, Moons KGM, Riley RD, et al. PROBAST: a tool to assess the risk of bias and applicability of prediction model studies. Ann Intern Med, 2019, 170(1): 51-58.
36. 高瑞磊, 王丹, 戴国华, 等. 心力衰竭患者出院后再入院风险预测模型的系统评价. 中国胸心血管外科临床杂志, 2025, 32(5): 677-684.Gao RL, Wang D, Dai GH, et al. Re-admission risk prediction models for patients with heart failure after discharge: a systematic review. Chin J Clin Thorac Cardiovasc Surg, 2025, 32(5): 677-684.

Chinese Journal of Clinical Thoracic and Cardiovascular Surgery

Latest ArticlesRisk prediction models for 30-day unplanned readmission in patients undergoing coronary artery bypass grafting: A systematic review

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