Survival analysis of coronary care unit patients from MIMIC IV database

Autor: Pedro G. Lanzieri, Dayanna Q. Palmer, Ronaldo A. Gismondi, Valéria T. Baltar, Flavio L. Seixas
Rok vydání: 2022
Popis: The profile of hospitalizations in coronary care units (CCU) includes patients with different age groups, multiple comorbidities and causes of hospitalization that may or may not be primarily cardiac. This study aimed to estimate survival time and evaluate the association and impact of different factors on this time in a cohort of patients admitted to CCU. A cohort of 7120 adult patients admitted to CCU was analyzed from a subset of data from the MIMIC-IV database (Medical Information Mart for Intensive Care, version 4). A descriptive analysis was performed using Kaplan-Meier survival analysis, with a Log-rank test to establish comparisons between groups. Survival regression was modeled using Cox’s proportional risk models for the multiple analysis. The p-value was defined as ¡ 0.05 as statistically significant. In patients who died during hospitalization, there was a higher average age, longer hospital stay, and a higher rate of heart and respiratory rate, all with p ¡ 0.001. Median overall survival was 28 days (95% CI 26-30 days). The survival probability curve presented a higher inclination in the first weeks, reaching a stable value close to 20% at 10 weeks after hospitalization. When Cox’s regression adjusted for age, gender and comorbidities was performed, hyperpotassemia was shown to be an independent risk factor for in-hospital mortality (RR = 1.22, 95% CI: 1.14-1.30) in this group of patients. These results reinforced that the electronic health record may contain, already in the first hours of hospitalization, relevant information to understand the progression of diseases and identify future directions for research. This study is expected to clarify important topics related to the MIMIC-IV database and enable further research using this patient database. Knowledge of the characteristics of the CCU population can allow better management of physical and human hospital resources.
Databáze: OpenAIRE