To estimate impact of factors such as patients’ comorbidities, age and level of the hospital to 30-day and one year mortality in Latvian hospitals after hospitalization with acute myocardial infarction (AMI). Administrative data for 15321 patients admitted to the hospitals with AMI in 2014-2017, data on 30-day mortality and mortality within one year after admission were used to estimate odds ratio associated with age, index of comorbidities (Charlson comorbidity index was used) of patient and level of the hospital. Binary logistic regression models were constructed to estimate impact of every factor and its’ statistical significance. Binary logistic regression model identified that 30-day and one year mortality odds ratio when compared between age groups, comorbidity (Charlson) index groups and hospitals’ level groups are statistically significant. 30-day mortality odds ratio when compared age group >80 years with age group <60 years is 8,77 (95% CI: 7,33-10,50). 30 day mortality odds ratio when compared patients with more than two comorbidity (Charlson index >2) with no comorbidities is 1,90 (95% CI: 1,65-2,19). 30-day mortality odds ratio when compared regional hospitals with university hospitals is 1,37 ((95% CI:1,27-1,52). One year mortality odds ratio when compared between group of patients in age group >80 years with age group <60 years is 9,98 (95% CI: 8,58-11,61). Although main predictors of 30-day and one year mortality after admission to the hospitals with AMI in Latvia remain age of the patient and status of comorbidities, the level of the hospital care provided associated with the level of the hospital is by itself statistically significant.
- 3.4. Other publications in conference proceedings (including local)