Title : A bayesian genomic-enhanced precision critical care clinical decision/probability mathematical model for septic shock outcomes
Abstract:
Background: Septic shock, a severe manifestation of sepsis, is characterized by life-threatening circulatory and metabolic abnormalities, leading to organ dysfunction. Despite global efforts to standardize sepsis management, septic shock remains a significant contributor to mortality, with a high degree of patient variability in treatment response. Precision medicine, which tailors interventions to individual patient characteristics, has emerged as a promising approach to address the complexity of septic shock. Integrating clinical and genomic data into decision-making could revolutionize treatment outcomes in septic shock patients.
Objective: This study aims to develop a Bayesian genomic-enhanced precision critical care clinical decision/probability mathematical model to predict outcomes in septic shock patients.
Methods: By utilizing scoring systems that use physiologic and laboratory data alongside a genomic-based sepsis score in a Bayesian mathematical modeling approach. Prior clinical studies data, including the Miami Sepsis Score and Point of Care Lactate combined with a previously developed genomic septic shock score to account for genetic variability and underlying biological mechanisms. Sensitivity and specificity were attained from prior research including pooled meta-analysis data to build a sequential Bayesian model. Absolute and relative Bayesian diagnostic gains were calculated. Statistical significance was assessed via t-test, chi-square and odds ratio. P-value was set to 0.05.
Results: A paired t-test compared the effectiveness of genomic data versus the Bayesian approach in predicting septic shock outcomes. The two-tailed p-value was < 0.05, indicating that the difference between the two models is not statistically significant. The mean difference between the Genomic and Bayesian groups was -19.8900, with a 95% confidence interval ranging from -39.7944 to 0.0144. The results suggest a trend toward improved outcomes with the Bayesian approach, with statistical significance (t = 2.4451, df = 6, standard error of difference = 8.135).
Further examination of the data revealed that the Genomic group had a mean score of 72.86 (SD = 15.4196, SEM = 7.7098, N = 4), while the Bayesian group had a higher mean score of 92.75 (SD = 5.1881, SEM = 2.5941, N = 4). It suggests that the Genomic approach holds potential, but the Bayesian model offers more consistent performance in predicting outcomes.
Conclusion: The results suggest that this Bayesian approach may offer more precise predictions for septic shock outcomes compared to a genomic-based model alone. The Bayesian framework's ability to continuously integrate new clinical and genomic data allows for real-time adjustments, offering a promising strategy for precision critical care. Future studies with larger sample sizes and further model refinement are needed to confirm these findings and explore the full potential of genomic data integration in improving patient outcomes.
