Impact Of Country On Adverse Outcome In Acutely Intoxicated Patients In ICU: A Generalized Linear Mixed Models Analysis
Summary
Our study aimed to investigate poor outcomes among ICU patients with acute poisoning, with a specific focus on discerning international variations. Additionally, we assessed the efficacy of Generalized Linear Mixed Models (GLMM) when compared to Generalized Linear Models (GLMs). We found that patient outcomes significantly diverge across countries, even after accounting for various factors. Notably, countries introduced as random effects within the GLMM exerted a substantial influence on patient outcomes, reflecting the influence of distinct healthcare systems, sociocultural factors, and resource availability. In terms of modelling, GLMM outperformed GLMs. The inclusion of laboratory results alongside patient characteristics notably improved the ability to discriminate poor
outcomes. The most effective model incorporated a comprehensive set of parameters, encompassing physiological indicators, laboratory findings, and exposure types. Our analysis unveiled the complexity of the data, indicating that relying solely on physiological and laboratory parameters does not suffice to explain patient outcomes fully. Exposure types, as revealed in this study, significantly contribute to our understanding, aligning with prior research on the mortality implications of intoxication. However, while exposures remained relevant, their impact was comparatively modest. The study also highlighted challenges related to categorizing exposures due to methodological disparities. To tackle this challenge, we must explore and evaluate new exposure categories.
Furthermore, concerns emerged regarding patients exposed to multiple substances, warranting the need for a refined approach to exposure categorization and prudent consideration of exposure dosages. To enhance our models, we should consider transforming physiological and laboratory parameters to establish linear relationships with outcomes, thus addressing challenges posed by right-skewed distributions and outliers. In conclusion, our study sheds light on the varying patient outcomes following intoxication, emphasizing the effectiveness of GLMM as an analytical tool, emphasizing the importance of considering exposure types alongside physiological and laboratory parameters. This comprehensive approach emphasizes the need for future research to refine exposure categorization, consider exposure dosages, and carefully handle other parameters to establish linear relationships with outcomes.