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Background &amp; Objective <p>Early and accessible prognostication after acute ischemic stroke (AIS) is critical for risk stratification, yet translating systemic immuno-metabolic responses into bedside tools remains challenging. This study aimed to develop and externally validate an interpretable prognostic framework based on routine immuno-metabolic biomarkers for predicting 90-day functional outcomes after AIS.</p> Methods <p>In this dual-center retrospective cohort study, consecutive AIS patients from two tertiary hospitals in the Huaibei Economic Zone were enrolled. The primary endpoint was unfavorable functional outcome at 90 days, defined as a modified Rankin Scale score ≥ 3. After feature selection via classically solved Quadratic Unconstrained Binary Optimization (QUBO) with simulated annealing, a CRITIC-Optimized Poly-Ensemble (COPE) model integrating six base learners was constructed. A Full Model (including cellular population data) and a Core Model (excluding cellular population data) were internally locked and then evaluated in a held-out external validation cohort. Model interpretation was performed using CRITIC-weighted SHapley Additive exPlanations. Supporting biological contextualization utilized Mendelian randomization, single-cell RNA sequencing, and unsupervised clustering.</p> Results <p>A total of 3,812 patients were included (3,093 in the development cohort, 719 in external validation, including 107 with an unfavorable outcome). The QUBO algorithm identified a 10-feature panel comprising neurological severity, inflammatory, and metabolic indices. In external validation, the Full COPE Model achieved an area under the receiver operating characteristic curve of 0.860 (95% CI = 0.815–0.898), a Brier score of 0.118 (95% CI = 0.106–0.130), a specificity of 0.931, and a positive predictive value of 0.592. The Core Model retained comparable discrimination (AUC = 0.870). SHAP analysis revealed that admission neurological severity and inflammatory burden dominated predictions, and unsupervised clustering identified two reproducible sub-phenotypes with divergent outcomes. Mendelian randomization and transcriptomic data provided supportive biological context linking neuroinflammatory signals to prognosis.</p> Conclusion <p>The framework offers an interpretable, laboratory-based prognostic tool for 90-day AIS outcomes by integrating routine immuno-metabolic biomarkers. Its balanced performance and clinical accessibility support potential utility in risk stratification, though prospective multicenter validation across diverse populations is required before clinical implementation.</p>

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Immuno-metabolic biomarkers for 90-day prognostication after acute ischemic stroke: a classically solved QUBO-COPE modeling and biological contextualization study

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月3日

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  • Haozhou Tan,
  • Li Zhao,
  • Jingyuan Zhang,
  • Mengyao Huang,
  • Abulikemu Tulapu,
  • Chenggang Zhu,
  • Qian Feng,
  • Ying Li

摘要

Background & Objective

Early and accessible prognostication after acute ischemic stroke (AIS) is critical for risk stratification, yet translating systemic immuno-metabolic responses into bedside tools remains challenging. This study aimed to develop and externally validate an interpretable prognostic framework based on routine immuno-metabolic biomarkers for predicting 90-day functional outcomes after AIS.

Methods

In this dual-center retrospective cohort study, consecutive AIS patients from two tertiary hospitals in the Huaibei Economic Zone were enrolled. The primary endpoint was unfavorable functional outcome at 90 days, defined as a modified Rankin Scale score ≥ 3. After feature selection via classically solved Quadratic Unconstrained Binary Optimization (QUBO) with simulated annealing, a CRITIC-Optimized Poly-Ensemble (COPE) model integrating six base learners was constructed. A Full Model (including cellular population data) and a Core Model (excluding cellular population data) were internally locked and then evaluated in a held-out external validation cohort. Model interpretation was performed using CRITIC-weighted SHapley Additive exPlanations. Supporting biological contextualization utilized Mendelian randomization, single-cell RNA sequencing, and unsupervised clustering.

Results

A total of 3,812 patients were included (3,093 in the development cohort, 719 in external validation, including 107 with an unfavorable outcome). The QUBO algorithm identified a 10-feature panel comprising neurological severity, inflammatory, and metabolic indices. In external validation, the Full COPE Model achieved an area under the receiver operating characteristic curve of 0.860 (95% CI = 0.815–0.898), a Brier score of 0.118 (95% CI = 0.106–0.130), a specificity of 0.931, and a positive predictive value of 0.592. The Core Model retained comparable discrimination (AUC = 0.870). SHAP analysis revealed that admission neurological severity and inflammatory burden dominated predictions, and unsupervised clustering identified two reproducible sub-phenotypes with divergent outcomes. Mendelian randomization and transcriptomic data provided supportive biological context linking neuroinflammatory signals to prognosis.

Conclusion

The framework offers an interpretable, laboratory-based prognostic tool for 90-day AIS outcomes by integrating routine immuno-metabolic biomarkers. Its balanced performance and clinical accessibility support potential utility in risk stratification, though prospective multicenter validation across diverse populations is required before clinical implementation.