PERSONALIZED MODEL FOR SELECTING OPTIMAL REVASCULARIZATION STRATEGY IN PATIENTS WITH SIMULTANEOUS CAROTID AND CORONARY ARTERY DISEASE: PROGNOSIS OF IN-HOSPITAL OUTCOMES

Aim: To develop a prediction model for selecting op-timal revascularization strategy for patients with polyvascular disease based on the evaluation of hospital out-comes of various surgical treatment strategies for patients with simultaneous coronary and carotid artery disease.

Methods: 391 patients with combined hemodynamically significant atherosclerotic lesions of the coronary and internal carotid arteries underwent reconstruction heart surgeries. Group 1 patients (n = 151, 38.6%) underwent coronary artery bypass grafting (CABG) and subsequent carotid endarterectomy (CEA). Group 2 patients (n = 141, 36%) underwent combined CABG and CEA. Group 3 patients (n = 28, 7.2%) underwent hybrid percutaneous coronary intervention (PCI) and CEA. Group 4 patients (n = 71, 18.2%) underwent CEA and subsequent CABG. 3 groups of risk factors (clinical and demographic risk factors, risk factors of coronary and cerebrovascular adverse events), were selected to develop the prediction model for evaluating the probability of in-hospital complications, such as death, myocardial infarction, acute cerebrovascular accident / transient ischemic attack, significant hemorrhagic complications > BARC type 3 bleeding. The prognostic coefficients of all risk factors for each surgical treatment strategy were evaluated to select optimal revascularization strategy. Then, the integrated indicators, representing a comprehensive assessment of the risk factors related to each surgical treatment strategy, were calculated. The ROC-analysis was performed to set a threshold allowing improving the quality of the prognosis (sensitivity and specificity).

Results: Clinical and demographic factors and cere-brovascular risk factors were significantly associated with adverse prognosis in Group 1 and 2 patients according to the regression analysis. Coronary and cerebrovascular factors were reported to affect prognosis in patients undergoing hybrid revascularization, whereas clinical and demographic risk factors and cerebrovascular risk factors worsen the prognosis of patients undergoing CEA and subsequent CABG. The prediction model allowed creating an interactive calculator able to determine the probability of adverse cardiovascular events in patients with polyvascular disease undergoing four types of surgical treatment strategies and to select the optimal one that will be associated with a minimal risk of in-hospital complications.

Conclusion: The prediction model and personalized calculator for selecting an optimal surgical treatment strategy based on the comprehensive risk assessment for adverse outcomes (clinical and demographic factors, cor-onary and cerebrovascular factors) allows predicting the probability of adverse cardiovascular events in patients with simultaneous carotid and coronary artery disease.

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