Results of Coronary Artery Bypass Grafting in Obese Patients

This study is planned to assess the effect of obesity (BMI over 30 kg / m2) on hospital outcomes of isolated coronary artery bypass grafting in patients with chronic ischemic heart disease.

It is planned to retrospectively enroll around 500 patients who underwent coronary artery bypass grafting for chronic ischemic heart disease at the Tomsk Research Institute of Cardiology. It is planned to develop a register of isolated coronary bypass grafting procedures, to conduct a comparative analysis of hospital results in obese patients (BMI more than 30 kg / m2) and without (BMI less than 30 kg / m2), to identify risk factors for adverse outcomes in patients of this group of patients.

Planning methods:

1. Clinical status of the patient: collection of complaints, medical history, physical examination, assessment of anthropometric indicators (measurement of body weight, height, BMI), and demographic indicators before and in the control period after surgical treatment.
2. General clinical examination: standard 12-lead ECG, general urine analysis, general blood analysis, blood chemistry, coagulation, chest x-ray, ultrasound of the carotid and femoral arteries.
3. Coronarography to assess the anatomy and condition of the coronary arteries.
4. Echocardiography to assess the following parameters: mass of viable left ventricular myocardium; volumes of cardiac cavities; diameter of the mitral and tricuspid valve rings with the determination of regurgitation degree; the long axis of the ventricle; left ventricular ejection fraction; visualization of akinetic and hypokinetic myocardium; the presence of blood clots in the cavities of the heart; All surgical procedures will be performed via median sternotomy Intraoperative information will be collected from the anesthetic record, surgical notes and perfusion records. Intraoperative data collection will include total operative time, CPB time, cross-clamp time, intraoperative red blood cell transfusion (units), highest dose/agent used for intraoperative inotrope or vasopressor support. Intraoperative flowmetry of coronary grafts during coronary artery bypass grafting using the Medistim VeriQ System will be performed.

Postoperative data will include valuation of following indicators: mortality (hospital mortality and death from any cause); neurological injury (TIA, stroke, delirium), acute kidney injury (creatinine level prior and 1 postoperative day, urine output-up to 24-48 h, renal replacement therapy (dialysis); time of mechanical ventilation; re-exploration for bleeding, tamponade or other reasons; postoperative transfusion (packed red blood cells, platelets, fresh frozen platelets, cryoprecipitate); postoperative myocardial infarction (electrocardiogram and troponins); inotropic support during 24-48 h (agent and dose (VIS)); length of stay (intensive care unit and total hospital days).

Structured collection of patient data will be performed in a database formed on the platform of the Microsoft Excel 2010 software (Microsoft Corp., USA). Statistical processing of the results will be carried out using the SPSS 23.0 for Windows software package (IBM Corp., Armonk, NY, USA). The normality of the law of distribution of quantitative indicators will be checked using the Shapiro-Wilks criterion. Normally distributed parameters will be presented as mean value (M) and standard deviation (StD) in the form M ± StD; not normally distributed parameters will be presented as median (Me) and the 1st and 3rd interquantile intervals (Q25 - Q75) in the form of Me [Q25; Q75]. Qualitative data will be described by the frequency of occurrence or its percentage. To find statistical dependences, to determine their strength and direction, the Pearson correlation coefficient (r) (for normally distributed parameters) and Spearman correlation coefficient (for for not normally distributed parameters and for qualitative indicators in the ordinal scale) will be calculated. Using logistic regression, significant predictors will be identified for the values of reverse remodeling in the long term after surgical treatment. When conducting a multivariate analysis of interconnections, first, by means of a univariate analysis, the main parameters that influence the studied value will be identified, then, based on the search for intergroup correlations, the signs that have a moderate or strong relationship will be eliminated, and multivariate modeling of the relationships will be performed.

During the work, the methods of statistical analysis can be revised and (or) supplemented.