[Precision fluid management and artificial intelligence-assisted decision-making in severe pneumonia induced septic shock].

Abstract

Severe pneumonia-induced septic shock is one of the most representative complex syndromes in pulmonary critical care medicine. Its pathophysiology results from the combined effects of alveolar-capillary barrier injury, infection-associated vasodilation, microcirculatory perfusion abnormalities, cardiopulmonary interactions related to mechanical ventilation, and multiple organ dysfunction. Therefore, fluid management should emphasize a dynamic balance between lung injury, circulatory benefit, and etiological control. In recent years, international guidelines and clinical studies have indicated that the focus of treatment for severe pneumonia-induced septic shock has shifted from conventional fluid resuscitation to precision resuscitation based on dynamic fluid responsiveness, fluid tolerance, and phase-specific goals. In the setting of acute respiratory distress syndrome (ARDS) or at high risk of ARDS, the importance of early vasopressor administration and negative fluid balance management has further increased. Meanwhile, advances in artificial intelligence-including the identification of ARDS subphenotypes, early prediction of sepsis-associated ARDS, sepsis early warning systems have provided a foundation for identifying "fluid-intolerant" subpopulations, optimizing the timing of negative fluid balance, and constructing multimodal closed-loop decision-support systems. This article reviews the hemodynamic characteristics of severe pneumonia-induced septic shock, phenotype identification, etiological control, and an integrated closed-loop pathway combining ventilator waveforms, hemodynamics, ultrasonography, and artificial intelligence. It aims to provide a reference for clinical practice and future research.