Septic Shock in Hematological Malignancies: Role of Artificial Intelligence in Predicting Outcomes

Abstract

Septic shock is a life-threatening complication of sepsis, particularly in patients with hematologic diseases who are highly susceptible to it due to profound immune dysregulation. Recent advances in artificial intelligence offer promising tools for improving septic shock diagnosis, prognosis, and treatment in this vulnerable population. In detail, these innovative models analyzing electronic health records, immune function, and real-time physiological data have demonstrated superior performance compared to traditional scoring systems such as Sequential Organ Failure Assessment. In patients with hematologic malignancies, machine learning approaches have shown strong accuracy in predicting the sepsis risk using biomarkers like lactate and red cell distribution width, the latter emerging as a powerful, cost-effective predictor of mortality. Deep reinforcement learning has enabled the dynamic modelling of immune responses, facilitating the design of personalized treatment regimens helpful in reducing simulated mortality. Additionally, algorithms driven by artificial intelligence can optimize fluid and vasopressor management, corticosteroid use, and infection risk. However, challenges related to data quality, transparency, and ethical concerns must be addressed to ensure their safe integration into clinical practice. Clinically, AI could enable earlier detection of septic shock, better patient triage, and tailored therapies, potentially lowering mortality and the number of ICU admissions. However, risks like misclassification and bias demand rigorous validation and oversight. A multidisciplinary approach is crucial to ensure that AI tools are implemented responsibly, with patient-centered outcomes and safety as primary goals. Overall, artificial intelligence holds transformative potential in managing septic shock among hematologic patients by enabling timely, individualized interventions, reducing overtreatment, and improving survival in this high-risk group of patients.

Keywords: antibiotics; artificial intelligence; bundles; hematological malignancies; infections; septic shock; septic shock outcomes.

Figure 1

Pathogenesis of septic shock. PRR: pattern recognition receptor.