MULTIFACTOR PREDICTION OF LONG-TERM OUTCOMES OF ACUTE CORONARY SYNDROME WITH SUSTAINED ST SEGMENT ELEVATION - N. G. Lozhkina; V. N. Maksimov; Yu. I. Ragino; A. D. Kuimov; M. I. Voevoda (Российский кардиологический журнал, 2015)
Aim. Prediction of long-term (annual) adverse and benefit outcomes of acute coronary syndrome with sustained ST elevation (STEMI) for the improvement of rehabilitation, secondary prevention and personified approach to treatment.Material and methods.   Totally 145 patients included with STEMI (106 men, 39 women), hospitalized to the CCH №1 of Novosibirsk during the year 2010. Mean age of the patients was 59,1±6,1 y. Diagnosis of ACS was set by a collection of criteria developed by European Society of Cardiology and American Heart Association (2000). All patients underwent clinical and instrumental investigation as following: clinical examination, electrocardiography, echocardiography, Holter monitoring, measurement of the inflammatory cytokines and molecular-genetic test. By the methods of factor and correlation analysis, the study made it to define the influence of the each of the factors studied on probability of adverse annual prognosis.Results.   As the result of the study, we invented a brand new method of multifactor prediction of long-term (annual) adverse and positive outcomes of the acute coronary syndrome with sustained ST elevation. The model includes the check for the absence or existence in the patient of diabetes mellitus (DM), ejection fraction of the left ventricle (EF LV), ultrasensitive C-reactive protein concentration (hsCRP), and genotype for polymorphism rs1376251 of the gene TAS2R50. Sensitivity of the proposed method of prediction for adverse outcomes was 82%, positive outcomes — 80%.Conclusion. The model proposed was created on the basis of the parameters measured in Russian patients and includes the activity of atherosclerotic inflammation and the patient’s genotype. An equation for the probability calculation of an adverse outcome shows, what is the weight multiplier of one or another factor to influence the probability of adverse outcome. The model is simple in use and makes it possible to individualize secondary prevention for this type of patients.