Early prediction of sepsis and septic shock via machine learning

A comparative narrative review and implementation analysis in the "golden hour"

Abstract

Sepsis and septic shock represent critical challenges for intensive care medicine due to high mortality and diagnostic complexity. The present study aims to analyze the comparative efficacy between traditional screening protocols, specifically the Quick Sequential Organ Failure Assessment (qSOFA), and predictive models based on Machine Learning (ML) for the early identification of the syndrome. The methodology comprises a narrative review of scientific literature published between 2023 and 2026, selecting studies that apply Artificial Intelligence algorithms to optimize therapeutic interventions within the "Golden Hour." The analysis examines updated epidemiology, pathophysiology, and the clinical application of advanced techniques such as Deep Learning and Natural Language Processing. The results demonstrate that ML models consistently outperform conventional clinical scores in sensitivity and specificity by processing dynamic and multimodal vital variables in real-time. It is observed that this technological superiority mitigates the risk of underdiagnosis inherent in manual methods. It is concluded that the adoption of automated predictive tools constitutes a necessary paradigm shift, although effective clinical implementation demands overcoming barriers related to algorithmic interpretability and data privacy to ensure patient safety.