Development of one-year major adverse cardiac events risk index in patients with acute coronary syndrome and diabetes mellitus who underwent percutaneous coronary intervention

Abstract

Background/Aim. Patients with acute coronary syndrome (ACS) and diabetes mellitus (DM) have an increased risk of major adverse cardiovascular events (MACE) after percutaneous coronary intervention (PCI), which is not estimated sufficiently-multidimensionally in terms of type and severity of the ACS and/or DM and angiographic findings. The study was intended to validate and develop an index of metabolic, angiographic, anatomic and clinical risk factors for one-year MACE after conducted PCI in patients with ACS and DM. Methods. A prospective cross-section study was performed in patients with DM and ACS. In the PCI period the following risk factors where were recorded: 1) age and metabolic variablesglycosylated hemoglobin (HbA1c), total cholesterol, triglyceridemia; 2) endocrinological variables-DM therapy, tip of DM; 3) ACS modality; 4) radiological / anatomical variable-SYNTAX score and 5) clinical variables in modified ACEF score. One-year MACE were recorded. Results. From a total of 136 consecutive patients, 55 of them developed at least one MACE in one-year followup. A high predictive risk index was evaluated that assessed particularly or associated risks for one-year MACE (c statistic = 0.879) in the study population, defined by: SYNTAX score > 21, modified ACEF score > 1.38, HbA1c ≥ 8 (%), triglyceridemia ≥ 2.3 (mmol/L) in patients with insulin therapy, and ACS modalityunstable angina pectoris. The constructed Risk Index for one-year MACE (MACERI) has better predictive characteristics than SYNTAX score (c statistic = 0.798), as well as ACF score (c statistic = 0.744). Conclusions. MACERI can potentially have great application in future risk factors studies for one-year MACE in patients with DM and ACS who underwent PCI, because with it the effects of these factors measure multidimensionally at valid and accurate manner.

Statistical methods applied in pharmacotherapy

“…In regression models, one or more dependent variables are brought into operation with one or more independent variables (the predictor). When the dependent variable is continuous, the methods applied are ANOVA, MANOVA [19,20], ANCOVA, MANCOVA, nonlinear functions, multiple linear regression, survival methods, Cox proportional analysis, while in cases where the dependent variable is categorical, then logistic regression [21][22][23][24][25] or discriminatory analysis are applied, which in the narrower sense also belong to classifi cation techniques.…”