Skip to main content
Key Specialties
Anesthesiology Cardiology Dermatology Diabetology Endocrinology Gastroenterology General Medicine Geriatrics Hematology Infectious Diseases Internal Medicine Nephrology Neurology Obstetrics and Gynecology Oncology Pediatrics Psychiatry Radiology Respiratory Medicine Rheumatology Surgery Urology
Congress Coverage
2024 ESMO Congress 2024 ERS Congress 2024 EASD Congress 2024 ESC Congress 2024 EAN Congress 2024 ASCO Annual Meeting 2024 ACC Congress
Clinical Collections
Advances in Alzheimer’s

Keynote series | Spotlight on menopause

Menopause can have a significant impact on the body, with effects ranging beyond the endocrine and reproductive systems. Learn about the systemic effects of menopause, so you can help patients in your clinics through the transition.

Published: Thursday 12th December 2024
ADVANCED SEARCH
Log in
Top
BMC Medical Informatics and Decision Making
Published in:

Open Access 01-12-2024 | CABG | Research

Risk factors and prediction model for acute ischemic stroke after off-pump coronary artery bypass grafting based on Bayesian network

Authors: Wenlong Zou, Haipeng Zhao, Ming Ren, Chaoxiong Cui, Guobin Yuan, Boyi Yuan, Zeyu Ji, Chao Wu, Bin Cai, Tingting Yang, Jinjun Zou, Guangzhi Liu

Published in: BMC Medical Informatics and Decision Making | Issue 1/2024

Login to get access

Abstract

Background

This study aimed to identify the risk factors of acute ischemic stroke (AIS) occurring during hospitalization in patients following off-pump coronary artery bypass grafting (OPCABG) and utilize Bayesian network (BN) methods to establish predictive models for this disease.

Methods

Data were collected from the electronic health records of adult patients who underwent OPCABG at Beijing Anzhen Hospital from January 2018 to December 2022. Patients were allocated to the training and test sets in an 8:2 ratio according to the principle of randomness. Subsequently, a BN model was established using the training dataset and validated against the testing dataset. The BN model was developed using a tabu search algorithm. Finally, receiver operating characteristic (ROC) and calibration curves were plotted to assess the extent of disparity in predictive performance between the BN and logistic models.

Results

A total of 10,184 patients (mean (SD) age, 62.45 (8.7) years; 2524 (24.7%) females) were enrolled, including 151 (1.5%) with AIS and 10,033 (98.5%) without AIS. Female sex, history of ischemic stroke, severe carotid artery stenosis, high glycated albumin (GA) levels, high D-dimer levels, high erythrocyte distribution width (RDW), and high blood urea nitrogen (BUN) levels were strongly associated with AIS. Type 2 diabetes mellitus (T2DM) was indirectly linked to AIS through GA and BUN. The BN models exhibited superior performance to logistic regression in both the training and testing sets, achieving accuracies of 72.64% and 71.48%, area under the curve (AUC) of 0.899 (95% confidence interval (CI), 0.876–0.921) and 0.852 (95% CI, 0.769–0.935), sensitivities of 91.87% and 89.29%, and specificities of 72.35% and 71.24% (using the optimal cut-off), respectively.

Conclusion

Female gender, IS history, carotid stenosis (> 70%), RDW-CV, GA, D-dimer, BUN, and T2DM are potential predictors of IS in our Chinese cohort. The BN model demonstrated greater efficiency than the logistic regression model. Hence, employing BN models could be conducive to the early diagnosis and prevention of AIS after OPCABG.
Appendix
Available only for authorised users
Literature
1.
Sun LY, Gaudino M, Chen RJ, Bader Eddeen A, Ruel M. Long-term outcomes in patients with severely reduced left ventricular ejection fraction undergoing percutaneous coronary intervention vs coronary artery bypass grafting. JAMA Cardiol. 2020;5:631–41.CrossRefPubMedPubMedCentral
2.
Bakaeen FG, Gaudino M, Whitman G, Doenst T, Ruel M, Taggart DP, Stulak JM, Benedetto U, Anyanwu A, Chikwe J, Bozkurt B, Puskas JD, Silvestry SC, Velazquez E, Slaughter MS, McCarthy PM, Soltesz EG, Moon MR. 2021: The american association for thoracic surgery expert consensus document: Coronary artery bypass grafting in patients with ischemic cardiomyopathy and heart failure. J Thorac Cardiovasc Surg. 2021; 162: 829 – 50.e1.
3.
Head SJ, Milojevic M, Domanski MJ, Farkouh ME, Kappetein AP: Reply. Stroke risk following anaortic off-pump cabg versus pci. J Am Coll Cardiol. 2018;72:2681.CrossRefPubMed
4.
Lorusso R, Moscarelli M, Di Franco A, Grazioli V, Nicolini F, Gherli T, De Bonis M, Taramasso M, Villa E, Troise G, Scrofani R, Antona C, Mariscalco G, Beghi C, Miceli A, Glauber M, Ranucci M, De Vincentiis C, Gaudino M. Association between coronary artery bypass surgical techniques and postoperative stroke. J Am Heart Assoc. 2019;8:e013650.CrossRefPubMedPubMedCentral
5.
Mohamed MO, Hirji S, Mohamed W, Percy E, Braidley P, Chung J, Aranki S, Mamas MA. Incidence and predictors of postoperative ischemic stroke after coronary artery bypass grafting. Int J Clin Pract. 2021;75:e14067.CrossRefPubMed
6.
Jonsson K, Barbu M, Nielsen SJ, Hafsteinsdottir B, Gudbjartsson T, Jensen EM, Silverborn M, Jeppsson A. Perioperative stroke and survival in coronary artery bypass grafting patients: a swedeheart study. Eur J Cardiothorac Surg. 2022;62:ezac025.
7.
Sultan I, Bianco V, Kilic A, Jovin T, Jadhav A, Jankowitz B, Aranda-Michel E, D’Angelo MP, Navid F, Wang Y, Thoma F, Gleason TG. Predictors and outcomes of ischemic stroke after cardiac surgery. Ann Thorac Surg. 2020;110:448–56.CrossRefPubMed
8.
Shahian DM, Jacobs JP, Badhwar V, Kurlansky PA, Furnary AP, Cleveland JC Jr., Lobdell KW, Vassileva C, Wyler von Ballmoos MC, Thourani VH, Rankin JS, Edgerton JR, D’Agostino RS, Desai ND, Feng L, He X, O’Brien SM. The society of thoracic surgeons 2018 adult cardiac surgery risk models: part 1-background, design considerations, and model development. Ann Thorac Surg. 2018;105:1411–8.CrossRefPubMed
9.
Nashef SA, Roques F, Sharples LD, Nilsson J, Smith C, Goldstone AR, Lockowandt U. Euroscore ii. Eur J Cardiothorac Surg. 2012; 41: 734 – 44; discussion 44 – 5.
10.
Choi JW, Hwang HY, Kim KB. The performance of the current risk prediction scoring systems in patients undergoing anaortic off-pump coronary artery bypass grafting. J Korean Med Sci. 2021;36:e163.CrossRefPubMedPubMedCentral
11.
Song Y, Zhai W, Ma S, Wu Y, Ren M, Van den Eynde J, Nardi P, Pang PYK, Ali JM, Han J, Guo Z. Machine learning-based prediction of off-pump coronary artery bypass grafting-associated acute kidney injury. J Thorac Dis. 2024;16:4535–42.CrossRefPubMedPubMedCentral
12.
Parise O, Parise G, Vaidyanathan A, Occhipinti M, Gharaviri A, Tetta C, Bidar E, Maesen B, Maessen JG, La Meir M, Gelsomino S. Machine learning to identify patients at risk of developing new-onset atrial fibrillation after coronary artery bypass. J Cardiovasc Dev Dis. 2023; 10:82.
13.
Song W, Qiu L, Qing J, Zhi W, Zha Z, Hu X, Qin Z, Gong H, Li Y. Using bayesian network model with mmhc algorithm to detect risk factors for stroke. Math Biosci Eng. 2022;19:13660–74.CrossRefPubMed
14.
Lee S, Song IU, Na SH, Jeong DS, Chung SW. Association between long-term functional outcome and change in Hs-Crp level in patients with acute ischemic stroke. Neurologist. 2020;25:122–5.CrossRefPubMed
15.
Latchaw RE, Alberts MJ, Lev MH, Connors JJ, Harbaugh RE, Higashida RT, Hobson R, Kidwell CS, Koroshetz WJ, Mathews V, Villablanca P, Warach S, Walters B. Recommendations for imaging of acute ischemic stroke: a scientific statement from the American heart association. Stroke. 2009;40:3646–78.CrossRefPubMed
16.
Wintermark M, Sanelli PC, Albers GW, Bello JA, Derdeyn CP, Hetts SW, Johnson MH, Kidwell CS, Lev MH, Liebeskind DS, Rowley HA, Schaefer PW, Sunshine JL, Zaharchuk G, Meltzer CC. Imaging recommendations for acute stroke and transient ischemic attack patients: a joint statement by the American society of neuroradiology, the American college of radiology and the society of neurointerventional surgery. J Am Coll Radiol. 2013;10:828–32.CrossRefPubMedPubMedCentral
17.
Mao Z, Zhong X, Yin J, Zhao Z, Hu X, Hackett ML. Predictors associated with stroke after coronary artery bypass grafting: a systematic review. J Neurol Sci. 2015;357:1–7.CrossRefPubMed
18.
Tatlisuluoglu D, Tezcan B, Mungan İ, Çakirli YA, Tümer NB, Taşoğlu İ. Predicting postoperative ischemic stroke problems in patients following coronary bypass surgery using neutrophil-lymphocyte ratio, platelet-lymphocyte ratio, and red blood cell distribution width values. Kardiochir Torakochirurgia Pol. 2022;19:90–5.PubMedPubMedCentral
19.
Tibshirani R. Regression shrinkage and selection via the lasso. J Royal Stat Soc Ser B-Statistical Methodol. 1996;58:267–88.CrossRef
20.
Arora P, Boyne D, Slater JJ, Gupta A, Brenner DR, Druzdzel MJ. Bayesian networks for risk prediction using real-world data: a tool for precision medicine. Value Health. 2019;22:439–45.CrossRefPubMed
21.
Song W, Qin Z, Hu X, Han H, Li A, Zhou X, Li Y, Li R. Using bayesian networks with tabu-search algorithm to explore risk factors for hyperhomocysteinemia. Sci Rep. 2023;13:1610.CrossRefPubMedPubMedCentral
22.
Hajian-Tilaki K. The choice of methods in determining the optimal cut-off value for quantitative diagnostic test evaluation. Stat Methods Med Res. 2018;27:2374–83.CrossRefPubMed
23.
Rubens FD, Fremes SE, Grubic N, Fergusson D, Taljaard M, van Walraven C. Outcomes following coronary artery bypass grafting with multiple arterial grafting by pump status in men and women. J Thorac Cardiovasc Surg. 2024; 167: 1796 – 807.e15.
24.
Qu WW, Wei JW, Binongo JN, Keeling WB. Sex differences in failure-to-rescue after coronary artery bypass grafting. Ann Thorac Surg. 2022;114:1596–602.CrossRefPubMed
25.
Bryce Robinson N, Naik A, Rahouma M, Morsi M, Wright D, Hameed I, Di Franco A, Girardi LN, Gaudino M. Sex differences in outcomes following coronary artery bypass grafting: a meta-analysis. Interact Cardiovasc Thorac Surg. 2021;33:841–7.CrossRefPubMedPubMedCentral
26.
Mehta A, Choxi R, Gleason T, Wechsler L, Jovin T, Thirumala PD. Carotid artery disease as a predictor of in-hospital postoperative stroke after coronary artery bypass grafting from 1999 to 2011. J Cardiothorac Vasc Anesth. 2018;32:1587–96.CrossRefPubMed
27.
Wang S, Ran Y, Cheng S, Lyu Y, Liu J. Determinants and clinical outcomes of stroke following revascularization among patients with reduced ejection fraction. Brain Behav. 2023;13:e2927.CrossRefPubMedPubMedCentral
28.
Chen S, Mi C, Zhang S, Li Y, Yun Y, Zhang X, Chen J, Li Y, Zhang H, Gao T, Zou C, Ma X. The role of carotid artery stenosis in predicting stroke after coronary artery bypass grafting in a Chinese cohort study. Sci Rep. 2023;13:21536.CrossRefPubMedPubMedCentral
29.
Masabni K, Raza S, Blackstone EH, Gornik HL, Sabik JF 3. Does preoperative carotid stenosis screening reduce perioperative stroke in patients undergoing coronary artery bypass grafting? J Thorac Cardiovasc Surg. 2015;149:1253–60.
30.
Klarin D, Patel VI, Zhang S, Xian Y, Kosinski A, Yerokun B, Badhwar V, Thourani VH, Sundt TM, Shahian D, Melnitchouk S. Concomitant carotid endarterectomy and cardiac surgery does not decrease postoperative stroke rates. J Vasc Surg. 2020;72:589–e963.CrossRefPubMed
31.
Yazdanpanah S, Rabiee M, Tahriri M, Abdolrahim M, Rajab A, Jazayeri HE, Tayebi L. Evaluation of glycated albumin (Ga) and ga/hba1c ratio for diagnosis of diabetes and glycemic control: a comprehensive review. Crit Rev Clin Lab Sci. 2017;54:219–32.CrossRefPubMed
32.
Kim Y, Lee SH, Kang MK, Kim TJ, Jeong HY, Lee EJ, Bae J, Jeon K, Nam KW, Yoon BW. Glycated albumin, a novel biomarker for short-term functional outcomes in acute ischemic stroke. Brain Sci. 2021;11:337.
33.
Selvin E, Rawlings AM, Lutsey PL, Maruthur N, Pankow JS, Steffes M, Coresh J. Fructosamine and glycated albumin and the risk of cardiovascular outcomes and death. Circulation. 2015;132:269–77.CrossRefPubMedPubMedCentral
34.
Yang ZK, Shen Y, Shen WF, Pu LJ, Meng H, Zhang RY, Zhang Q, Chen QJ, De Caterina R, Lu L. Elevated glycated albumin and reduced endogenous secretory receptor for advanced glycation endproducts levels in serum predict major adverse cardio-cerebral events in patients with type 2 diabetes and stable coronary artery disease. Int J Cardiol. 2015;197:241–7.CrossRefPubMed
35.
Li J, Wang Y, Wang D, Lin J, Wang A, Zhao X, Liu L, Wang C, Wang Y. Glycated albumin predicts the effect of dual and single antiplatelet therapy on recurrent stroke. Neurology. 2015;84:1330–6.CrossRefPubMed
36.
Zhao X, Li Q, Tu C, Zeng Y, Ye Y. High glycated albumin is an independent predictor of low response to clopidogrel in acs patients: a cross-sectional study. Cardiovasc Diabetol. 2020;19:171.CrossRefPubMedPubMedCentral
37.
Peng R, Liu K, Li W, Yuan Y, Niu R, Zhou L, Xiao Y, Gao H, Yang H, Zhang C, Zhang X, He M, Wu T. Blood urea nitrogen, blood urea nitrogen to creatinine ratio and incident stroke: the dongfeng-tongji cohort. Atherosclerosis. 2021;333:1–8.CrossRefPubMed
38.
Xie Y, Bowe B, Li T, Xian H, Yan Y, Al-Aly Z. Higher blood urea nitrogen is associated with increased risk of incident diabetes mellitus. Kidney Int. 2018;93:741–52.CrossRefPubMed
39.
Koppe L, Nyam E, Vivot K, Manning Fox JE, Dai XQ, Nguyen BN, Trudel D, Attané C, Moullé VS, MacDonald PE, Ghislain J, Poitout V. Urea impairs β cell glycolysis and insulin secretion in chronic kidney disease. J Clin Invest. 2016;126:3598–612.CrossRefPubMedPubMedCentral
40.
41.
Xie KH, Liu LL, Liang YR, Su CY, Li H, Liu RN, Chen QQ, He JS, Ruan YK, He WK. Red cell distribution width: a novel predictive biomarker for stroke risk after transient ischaemic attack. Ann Med. 2022;54:1167–77.CrossRefPubMedPubMedCentral
42.
Xue J, Zhang D, Zhang XG, Zhu XQ, Xu XS, Yue YH. Red cell distribution width is associated with stroke severity and unfavorable functional outcomes in ischemic stroke. Front Neurol. 2022;13:938515.CrossRefPubMedPubMedCentral
43.
Hamatani Y, Nagai T, Nakai M, Nishimura K, Honda Y, Nakano H, Honda S, Iwakami N, Sugano Y, Asaumi Y, Aiba T, Noguchi T, Kusano K, Toyoda K, Yasuda S, Yokoyama H, Ogawa H, Anzai T. Elevated plasma d-dimer level is associated with short-term risk of ischemic stroke in patients with acute heart failure. Stroke. 2018;49:1737–40.CrossRefPubMed
44.
Nahab F, Sharashidze V, Liu M, Rathakrishnan P, El Jamal S, Duncan A, Hoskins M, Marmarchi F, Belagaje S, Bianchi N, Belair T, Henriquez L, Monah K, Rangaraju S. Markers of coagulation and hemostatic activation aid in identifying causes of cryptogenic stroke. Neurology. 2020;94:e1892–9.CrossRefPubMedPubMedCentral
45.
Ohara T, Farhoudi M, Bang OY, Koga M, Demchuk AM. The emerging value of serum d-dimer measurement in the work-up and management of ischemic stroke. Int J Stroke. 2020;15:122–31.CrossRefPubMed
Metadata
Title
Risk factors and prediction model for acute ischemic stroke after off-pump coronary artery bypass grafting based on Bayesian network
Authors
Wenlong Zou
Haipeng Zhao
Ming Ren
Chaoxiong Cui
Guobin Yuan
Boyi Yuan
Zeyu Ji
Chao Wu
Bin Cai
Tingting Yang
Jinjun Zou
Guangzhi Liu
Publication date
01-12-2024
Publisher
BioMed Central
Published in
BMC Medical Informatics and Decision Making / Issue 1/2024
Electronic ISSN: 1472-6947
DOI
https://doi.org/10.1186/s12911-024-02762-2

Other articles of this Issue 1/2024

A national platform for advancing self-care processes for the most common illnesses and conditions: designing, evaluating, and implementing

  • Open Access
  • Research

TECRR: a benchmark dataset of radiological reports for BI-RADS classification with machine learning, deep learning, and large language model baselines

  • Open Access
  • Database

Predictive model for congenital heart disease in children of Pakistan by using structural equation modeling

  • Open Access
  • Research

End-to-end pseudonymization of fine-tuned clinical BERT models

  • Open Access
  • Research

Optimized polycystic ovarian disease prognosis and classification using AI based computational approaches on multi-modality data

Development of a quantitative index system for evaluating the quality of electronic medical records in disease risk intelligent prediction

  • Open Access
  • Research