Machine Learning Methods for Prediction of Hospital Mortality in Patients with Coronary Heart Disease after Coronary Artery Bypass Grafting

Aim      To compare the accuracy of predicting an in-hospital fatal outcome for models based on current machine-learning technologies in patients with ischemic heart disease (IHD) after coronary bypass (CB) surgery.

Material and methods  A retrospective analysis of 866 electronic medical records was performed for patients (685 men and 181 women) who have had a CB surgery for IHD in 2008–2018. Results of clinical, laboratory, and instrumental evaluations obtained prior to the CB surgery were analyzed. Patients were divided into two groups: group 1 included 35 (4 %) patients who died within the first 20 days of CB, and group 2 consisted of 831 (96 %) patients with a beneficial outcome of the surgery. Predictors of the in-hospital fatal outcome were identified by a multistep selection procedure with analysis of statistical hypotheses and calculation of weight coefficients. For construction of models and verification of predictors, machine-learning methods were used, including the multifactorial logistic regression (LR), random forest (RF), and artificial neural networks (ANN). Model accuracy was evaluated by three metrics: area under the ROC curve (AUC), sensitivity, and specificity. Cross validation of the models was performed on test samples, and the control validation was performed on a cohort of patients with IHD after CB, whose data were not used in development of the models.

Results The following 7 risk factors for in-hospital fatal outcome with the greatest predictive potential were isolated from the EuroSCORE II scale: ejection fraction (EF) <30 %, EF 30-50 %, age of patients with recent MI, damage of peripheral arterial circulation, urgency of CB, functional class III-IV chronic heart failure, and 5 additional predictors, including heart rate, systolic blood pressure, presence of aortic stenosis, posterior left ventricular (LV) wall relative thickness index (RTI), and LV relative mass index (LVRMI). The models developed by the authors using LR, RF and ANN methods had higher AUC values and sensitivity compared to the classical EuroSCORE II scale. The ANN models including the RTI and LVRMI predictors demonstrated a maximum level of prognostic accuracy, which was illustrated by values of the quality metrics, AUC 93 %, sensitivity 90 %, and specificity 96 %. The predictive robustness of the models was confirmed by results of the control validation.

Conclusion      The use of current machine-learning technologies allowed developing a novel algorithm for selection of predictors and highly accurate models for predicting an in-hospital fatal outcome after CB.

1. Bokeria L.A., Sokolskaya N.O., Kopylova N.S., Alshibaya M.M. Echocardiographic predictors of the severity of the early postoperative period in patients after surgical myocardial revascularization. Russian Journal of Anаеsthesiology and Reanimatology. 2015;60(5):8–11.

2. Nashef SAM, Roques F, Sharples LD, Nilsson J, Smith C, Goldstone AR et al. EuroSCORE II. European Journal of Cardio-Thoracic Surgery. 2012;41(4):734–45. DOI: 10.1093/ejcts/ezs043

3. Ad N, Holmes SD, Patel J, Pritchard G, Shuman DJ, Halpin L. Comparison of EuroSCORE II, Original EuroSCORE, and The Society of Thoracic Surgeons Risk Score in Cardiac Surgery Patients. The Annals of Thoracic Surgery. 2016;102(2):573–9. DOI: 10.1016/j.athoracsur.2016.01.105

4. Yamaoka H, Kuwaki K, Inaba H, Yamamoto T, Kato TS, Dohi S et al. Comparison of modern risk scores in predicting operative mortality for patients undergoing aortic valve replacement for aortic stenosis. Journal of Cardiology. 2016;68(2):135–40. DOI: 10.1016/j.jjcc.2015.08.017

5. Allyn J, Allou N, Augustin P, Philip I, Martinet O, Belghiti M et al. A Comparison of a Machine Learning Model with EuroSCORE II in Predicting Mortality after Elective Cardiac Surgery: A Decision Curve Analysis. PLOS ONE. 2017;12(1):e0169772. DOI: 10.1371/journal.pone.0169772

6. Kilic A, Goyal A, Miller JK, Gjekmarkaj E, Tam WL, Gleason TG et al. Predictive Utility of a Machine Learning Algorithm in Estimating Mortality Risk in Cardiac Surgery. The Annals of Thoracic Surgery. 2020;109(6):1811–9. DOI: 10.1016/j.athoracsur.2019.09.049

7. Boughorbel S, Jarray F, El-Anbari M. Optimal classifier for imbalanced data using Matthews Correlation Coefficient metric. PLOS ONE. 2017;12(6):e0177678. DOI: 10.1371/journal.pone.0177678

8. Steele AJ, Denaxas SC, Shah AD, Hemingway H, Luscombe NM. Machine learning models in electronic health records can outperform conventional survival models for predicting patient mortality in coronary artery disease. PLOS ONE. 2018;13(8):e0202344. DOI: 10.1371/journal.pone.0202344

9. Cuocolo R, Perillo T, De Rosa E, Ugga L, Petretta M. Current applications of big data and machine learning in cardiology. Journal of Geriatric Cardiology. 2019;16(8):601–7. DOI: 10.11909/j. issn.1671-5411.2019.08.002

10. Ranganathan P, Pramesh CS, Aggarwal R. Common pitfalls in statistical analysis: Logistic regression. Perspectives in Clinical Research. 2017;8(3):148–51. DOI: 10.4103/picr.PICR_87_17

11. Strobl C, Boulesteix A-L, Kneib T, Augustin T, Zeileis A. Conditional variable importance for random forests. BMC Bioinformatics. 2008;9(1):307. DOI: 10.1186/1471-2105-9-307

12. Luan L, Hu H, Li S-C. A Review of Studies of Quality of Life for Chinese-Speaking Patients with Ischemic Heart Disease. Value in Health Regional Issues. 2018;15:82–90. DOI: 10.1016/j.vhri.2017.08.013

13. Alizadehsani R, Abdar M, Roshanzamir M, Khosravi A, Kebria PM, Khozeimeh F et al. Machine learning-based coronary artery disease diagnosis: A comprehensive review. Computers in Biology and Medicine. 2019;111:103346. DOI: 10.1016/j.compbiomed.2019.103346