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Research Snapshot Theater: Shock, Adult III

Associations Between PPI Trajectories and Clinical Outcomes in Shock Patients in ICU

Sunday, March 22, 2026
12:15 PM - 1:15 PM Central Time
Location: Connections Central - RST 16

  • ZHOU RAN, n/a (she/her/hers)

    West China Hospital, Sichuan, China (People's Republic)

    Disclosure(s): No relevant financial relationship(s) to disclose.

  • WY

    Wanhong Yin, n/a

    Dr.
    West China Hospital

    Disclosure information not submitted.

  • YK

    Yan Kang, N/A

    Prof.
    Department of Critical Care Medicine, West China School of Medicine, West China Hospital, Sichuan University

    Disclosure information not submitted.

  • YL

    Yi Li, N/A

    Associate chief physician
    West China Hospital Sichuan University

    Disclosure information not submitted.

  • LY

    LV YANG, n/a

    Dr.
    West China Hospital, Sichuan University

    Disclosure information not submitted.

Introduction: One of the core pathological features of shock is microcirculatory dysfunction, where impaired tissue perfusion drives progressive organ failure. The Perfusion Pressure Index (PPI) enables non-invasive, real-time hemodynamic assessment, offering critical insights into microvascular perfusion. Although requiring further validation, PPI demonstrates strong prognostic utility for guiding resuscitation strategies and predicting patient outcomes. This study leverages Group-Based Trajectory Modeling (GBTM) to decode PPI dynamics and their clinical implications.


Methods: We conducted a prospective multicenter observational study across 20 hospitals in Southwest China, enrolling ICU patients with shock. All participants underwent comprehensive monitoring including shock-related hemodynamic parameters, laboratory tests, and ultrasonographic indicators. Regular PPI monitoring was performed for all patients. Group-Based Trajectory Modeling (GBTM) was employed to analyze the dynamic patterns of PPI and their association with clinical outcomes in shock patients.


Results: A total of 421 patients were included. Using the GBTM model, PI dynamics were classified into 5 groups with optimal fit (average posterior probability >0.7, relative entropy=0.819):Class1 (21.14%): Low baseline, slow increase, Class2 (50.36%): Low baseline, moderate increase, Class3 (21.85%): Moderate baseline, slow increase,Class4 (1.19%): Low baseline, rapid increase, Class5 (5.46%): High baseline, slow increase
. Trajectory groups were significantly associated with discharge outcomes (χ²=27.10, P< 0.001) and ICU discharge outcomes (χ²=26.84, P< 0.001). Class1 (21.14%) had the highest mortality rate (48.31%), while no deaths occurred in Class4 (1.19%). For discharge outcomes, KM curves showed significant intergroup differences at 7, 10, and 15 days (P < 0.05). Cox regression indicated lower discharge risk in Classes2–5 versus Class1; at 28 days, unadjusted HR=0.632 (P=0.017) and adjusted HR=0.658 (P=0.032) for Class2, and unadjusted HR=0.415 (P=0.002) and adjusted HR=0.452 (P=0.005) for Class3.


Conclusions: Our study identified five distinct PPI trajectory patterns during shock resuscitation, with their early-phase dynamic changes demonstrating predictive value for clinical outcomes.