Keyword: All-Cause Mortality

ABSTRACT: Coronary artery disease remains a major cause of death and disability worldwide. Chronic Coronary disease one of its most common clinical forms, reflects the combined effects of age, obesity, hypertension, diabetes, and other chronic conditions. This study analyzed national administrative data from Kazakhstan between 2014 and 2021 to explore trends in Chronic Coronary disease incidence, comorbidities, and mortality among patients coded under ICD-10 codes I20–I20.9 within the Unified National Electronic Healthcare System (UNEHS). A total of 624,852 patients with Chronic Coronary disease were identified through the national electronic health system. Demographic, clinical, and outcome indicators were examined to assess trends and disparities across sex, ethnicity, and place of residence. During the study period, the recorded incidence of Chronic Coronary disease decreased from 584 to 211 cases per 100,000 population, whereas the mortality rate rose from 19 to 100 per 100,000. The 2014 incidence figure should be interpreted with caution as it likely reflects a prevalent pool effect at the inception of systematic UNEHS data capture. Mortality was highest among men, older adults, ethnic Russians, and rural residents. Patients undergoing coronary artery bypass grafting showed better survival than those treated with percutaneous coronary intervention, though this comparison should be interpreted cautiously given potential confounding by indication. Hypertension, diabetes, and multiple comorbidities substantially increased the risk of death and adverse cardiovascular events. These results underline widening health inequalities and the urgent need for improved prevention, equitable access to care, and integrated management of chronic cardiovascular disease in Kazakhstan.

ABSTRACT: Aim: To examine all-cause mortality predictors among lab indicators and drugs administered to stroke patients in Kazakhstan.
Methods: This retrospective study analyzed data from 272 patient records derived from the UNEHS database (2014-2019). The GPT-4o model was used to dissect the records and assist in the extraction of lab and medication data; other clinical and demographic data were derived from the previous study [6]. Statistical analyses included univariate, multivariate, and imputed multivariate logistic regressions (STATA version 16.1).    
Results: In our cohort, deceased patients were older (66.1 vs. 58.6, p < 0.0001). Adjusted logistic regression revealed age (OR 1.04), hemoglobin (OR 0.97), and piracetam (OR 3.17) as independent predictors. After multiple imputation, Russian ethnicity (OR 2.79), PTI (OR 0.97), and dopamine (OR 5.23) became independent predictors, while piracetam lost its significance.
Conclusions: Findings suggest the importance of future research on stroke predictors in bigger cohorts to facilitate the introduction of innovations to stroke patients’ care to potentially decrease the burden on the Kazakhstani population.