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Objective <p>The purpose of this study was to determine whether dynamic Glasgow Coma Scale (GCS) trajectories are associated with mortality risk and provide incremental prognostic information beyond selected baseline clinical variables in intensive care unit (ICU)-admitted patients with non-traumatic stroke (NTS).</p> Methods <p>In this retrospective cohort study of 7876 patients from the MIMIC-IV, eICU, and NSICU databases, latent class growth modeling (LCGM) was used to identify GCS trajectories. The associations of trajectory groups and threshold-based GCS metrics with in-hospital mortality were evaluated using multivariable Cox regression. The incremental value of trajectories was assessed using variable sets selected by Boruta, least absolute shrinkage and selection operator (LASSO), and best subset selection (BSS).</p> Results <p>Four distinct GCS trajectories were identified: stable high (35.7%), rapid improvement (38.0%), persistent moderate (18.8%), and persistent low (7.5%). Compared with the stable high group, mortality risk increased stepwise in fully adjusted models, with HRs of 2.94 (95% CI 2.24–3.84), 8.11 (95% CI 6.18–10.66), and 17.72 (95% CI 13.30–23.59) for rapid improvement, persistent moderate, and persistent low, respectively. Threshold-based mean GCS AUC metrics were also associated with in-hospital mortality. A distinct gradual deterioration trajectory was observed in elderly patients (≥ 65&#xa0;years). Adding GCS trajectories to the Boruta-selected baseline model improved predictive performance, increasing the AUC from 0.800 to 0.860, with an IDI of 0.085 and a continuous NRI of 0.283 (all <i>P</i> &lt; 0.001).</p> Conclusions <p>Trajectory-based and threshold-based longitudinal GCS assessments were strongly associated with in-hospital mortality in critically ill patients with NTS, supporting the value of dynamic GCS monitoring as a complementary tool for risk stratification in neurocritical care.</p> Graphical Abstract <p>Dynamic GCS monitoring in 7876 ICU-admitted patients with non-traumatic stroke identified four distinct trajectory patterns in the overall cohort, with a gradual deterioration pattern observed only in elderly patients (≥ 65 years). Threshold-based AUC metrics quantified cumulative exposure above clinically relevant GCS thresholds. Both trajectory- and threshold-based approaches provided complementary prognostic information for mortality risk stratification.</p>

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Dynamic GCS Trajectories and Mortality Risk in ICU Patients with Stroke: A Multicenter, Age-Stratified Study

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月20日

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  • Juan Wang,
  • Wei Dai,
  • Man-man Xu,
  • Chun-Hua Hang,
  • Wen-Juan Li,
  • Peng-lai Zhao

摘要

Objective

The purpose of this study was to determine whether dynamic Glasgow Coma Scale (GCS) trajectories are associated with mortality risk and provide incremental prognostic information beyond selected baseline clinical variables in intensive care unit (ICU)-admitted patients with non-traumatic stroke (NTS).

Methods

In this retrospective cohort study of 7876 patients from the MIMIC-IV, eICU, and NSICU databases, latent class growth modeling (LCGM) was used to identify GCS trajectories. The associations of trajectory groups and threshold-based GCS metrics with in-hospital mortality were evaluated using multivariable Cox regression. The incremental value of trajectories was assessed using variable sets selected by Boruta, least absolute shrinkage and selection operator (LASSO), and best subset selection (BSS).

Results

Four distinct GCS trajectories were identified: stable high (35.7%), rapid improvement (38.0%), persistent moderate (18.8%), and persistent low (7.5%). Compared with the stable high group, mortality risk increased stepwise in fully adjusted models, with HRs of 2.94 (95% CI 2.24–3.84), 8.11 (95% CI 6.18–10.66), and 17.72 (95% CI 13.30–23.59) for rapid improvement, persistent moderate, and persistent low, respectively. Threshold-based mean GCS AUC metrics were also associated with in-hospital mortality. A distinct gradual deterioration trajectory was observed in elderly patients (≥ 65 years). Adding GCS trajectories to the Boruta-selected baseline model improved predictive performance, increasing the AUC from 0.800 to 0.860, with an IDI of 0.085 and a continuous NRI of 0.283 (all P < 0.001).

Conclusions

Trajectory-based and threshold-based longitudinal GCS assessments were strongly associated with in-hospital mortality in critically ill patients with NTS, supporting the value of dynamic GCS monitoring as a complementary tool for risk stratification in neurocritical care.

Graphical Abstract

Dynamic GCS monitoring in 7876 ICU-admitted patients with non-traumatic stroke identified four distinct trajectory patterns in the overall cohort, with a gradual deterioration pattern observed only in elderly patients (≥ 65 years). Threshold-based AUC metrics quantified cumulative exposure above clinically relevant GCS thresholds. Both trajectory- and threshold-based approaches provided complementary prognostic information for mortality risk stratification.