International “Resc-IA-Sepsis” protocol: a reinforcement learning system for multidisciplinary surgical intervention guided by biomarkers in septic shock and multiorgan failure

Abstract

Introduction: Septic shock and multiorgan failure represent the most serious complications of sepsis, with mortality ranging from 28% to 50% according to reported series (He et al., 2026). Clinical decision support systems based on artificial intelligence have emerged as promising tools to optimize the management of these critically ill patients (MORE-CLEAR, 2026). Reinforcement learning (RL), in particular, offers a framework for sequential decision-making in dynamic environments such as intensive care units (rECMOmender, 2026). Objective: To describe the international "Resc-IA-Sepsis" protocol, a reinforcement learning system for multidisciplinary surgical intervention guided by biomarkers in septic shock and multiorgan failure. Methodology: Systematic review following PRISMA 2020 guidelines (Page et al., 2021). A search was conducted in PubMed, LILACS, SciELO and Cochrane for studies published between 2020 and 2026 on reinforcement learning systems in sepsis, organ failure predictive models, and biomarkers in septic shock. Results: Seven relevant studies documenting the application of RL and machine learning models in sepsis were identified. RL models have demonstrated the ability to optimize therapeutic decisions, with systems such as MORE-CLEAR integrating structured data and clinical notes to improve patient state representation (MORE-CLEAR, 2026). Machine learning-based predictive models have shown AUCs of up to 0.95 for heart failure prediction and 0.93 for liver failure in septic patients (He et al., 2026). The integration of biomarkers such as presepsin and procalcitonin in predictive models has demonstrated additional prognostic value, with an odds ratio of 5.80 for the development of postoperative complications when combining two presepsin-based risk factors (Presepsin Trial, 2026). Conclusion: The "Resc-IA-Sepsis" protocol represents an innovative approach that integrates reinforcement learning, biomarkers, and predictive models to guide multidisciplinary surgical intervention in septic shock and multiorgan failure. Implementation of this system requires prospective validation in multicenter cohorts to establish its safety and effectiveness (Hybrid Sepsis Model, 2026).