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. 2025 Aug 13;25(1):388.
doi: 10.1186/s12890-025-03856-x.

Trajectory-based analysis of early mechanical ventilation PEEP levels and physiological parameter trajectories in ARDS patients and their association with the occurrence of gastrointestinal injury events

Shi-Tao Huang  1 , Kai-Hua Yu  1 , Xiong-Bin Ma  1 , Yi-Bo Sun  1 , Zhong-Ya Huang  1 , Di Chen  1 , Hong-Lei Li  1 , Li-Ping Liu  2
Affiliations

Trajectory-based analysis of early mechanical ventilation PEEP levels and physiological parameter trajectories in ARDS patients and their association with the occurrence of gastrointestinal injury events

Shi-Tao Huang et al. BMC Pulm Med. .

Abstract

Background: Mechanical ventilation is crucial in the treatment of acute respiratory distress syndrome (ARDS), but is associated with complications, including gastrointestinal injury (GI). This study investigates the association between early mechanical ventilation parameter trajectories and the occurrence of GI events in ARDS patients.

Methods: A retrospective cohort study using the MIMIC-IV database employed group-based trajectory modeling (GBTM) to identify trajectory patterns during early mechanical ventilation in ARDS patients. Kaplan-Meier analysis estimated GI events incidence across trajectory groups, and Cox regression models assessed independent risk associations, adjusting for clinical confounders. We evaluated and compared the model in order to enhance its robustness.

Results: A total of 1,344 ARDS patients were included, with four trajectory groups identified. Patients in Group 1 (low PEEP, moderate VT, lower frequency spontaneous breathing) exhibited the highest GI risk, while Group 2 (high PEEP, low tidal volume, controlled spontaneous breathing) had the lowest risk (HR: 0.606, 95% CI: 0.418-0.879, P = 0.008). Kaplan-Meier curves revealed a consistent pattern with Group 1 showing the highest cumulative incidence of GI events. Aspirin use, heparin therapy, renal replacement therapy, and APACHE II score were identified as independent factors for GI events. Subgroup analysis did not show intergroup differences. Sensitivity analyses confirmed model robustness.

Conclusions: Ventilation parameter trajectories are associated with GI injury risk in ARDS patients. Strategies optimizing PEEP levels and spontaneous breathing may mitigate this risk, supporting the need for individualized ventilation approaches.

Keywords: ARDS; Gastrointestinal injury; Group-based trajectory modeling; MIMICIV Database; Mechanical ventilation; PEEP.

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Conflict of interest statement

Declarations. Ethics approval and consent to participate: The data used in this study were derived from the MIMICIV database, and database access was approved for sharing with informed consent amnesty by the Institutional Review Boards of the Massachusetts Institute of Technology and Beth Israel Deaconess Medical Centre. One of the authors (STH) passed the appropriate tests and was granted permission to extract the data (Permission ID: 58312537). Consent for publication: Not applicable. Competing interests: The authors declare no competing interests.

Figures

Fig. 1
Fig. 1
Flow chart and study design. ARDS: acute respiratory distress syndrome; MIMIC-IV database: Medical Information Mart for Intensive Care IV database; ECMO: Extra Corporeal Membrane Oxygenation; ICD code, International Classification of Diseases code; ICU, Intensive Care Unit
Fig. 2
Fig. 2
Frequency of gastrointestinal injury events by their type
Fig. 3
Fig. 3
Multi-trajectory groups of PEEP settings, observed tidal volume, and spontaneous respiratory rate using group-based multi-trajectory modeling. Dots show group-specific mean observed levels, solid lines represent fitted trajectories, and the gray lines indicate 95% CIs. PEEP: positive end expiratory pressure; VT: observed tidal volume; RR: spontaneous respiratory rate
Fig. 4
Fig. 4
Kaplan Meier curve analysis of gastrointestinal injury events based on multi-trajectory recognition of groups
Fig. 5
Fig. 5
Time-dependent ROC curve analysis of the multi-variate model predicting gastrointestinal injury events based on multi-trajectory groups. (A) AUC curves at 7, 30, and 60 days for predicting gastrointestinal injury events. (B) Time-dependent ROC curves showing predictive accuracy over time
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