A Bayesian genomic-enhanced precision critical care early warning model for septic shock outcomes

Background: Septic shock remains a significant contributor to mortality, with high patient variability in treatment response. Precision medicine offers promise in addressing this complexity. This study aimed to develop a Bayesian genomic-enhanced precision critical care clinical decision/probability model to predict outcomes in septic shock patients. Methods: Utilizing Bayesian modeling, we integrated clinical scoring systems (e.g., the Miami sepsis score and point of care lactate) with genomic data. Sensitivity and specificity from prior studies were incorporated into the model. The effectiveness of a genomic-based approach versus a Bayesian model was evaluated using paired t -tests with a significance threshold of P < 0.05. Results: 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 combined Bayesian model, integrating clinical, lactate, and genomic data outperformed standalone genomic models. Mean score for the Bayesian model was 92.75 (standard deviation [SD] = 5.1881), significantly higher than the genomic model’s 72.86 (SD = 15.4196) ( P < 0.05). This suggests that the Bayesian model offers more consistent performance in predicting septic shock outcomes and a statistically significant improvement in accuracy. These findings underscore the Bayesian framework’s potential for real-time decision-making in precision critical care. Conclusion: A Bayesian genomic-enhanced model offers a promising pathway for improving septic shock outcomes. Future studies with larger datasets and further model refinement are warranted to validate its potential and explore its broader application in critical care.