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<p>Accurately predicting surgical and transfusion needs in traumatic brain injury (TBI) patients remains challenging in emergency settings. We developed multiomics data fusion (MDF) models integrating clinical biomarkers, neural radiological imaging, and clinical text mining for predicting surgical intervention and blood transfusion requirements across four multicenter cohorts (<i>N</i> = 2219). The MDF models provided predictions a median of 3 hours before interventions, with surgical model F1 scores of 0.63–0.85 across external testing, outperforming single-domain approaches. The transfusion model demonstrated strong cross-center performance (validation/external F1 scores: 0.78, 0.74) and correlated well with actual transfusion volumes (validation: <i>R</i> = 0.687, external: <i>R</i> = 0.580). SHapley additive exPlanations revealed radiological features drove surgical predictions, while clinical parameters (lactate, GCS scores, pupillary reflex, and hemoglobin) were crucial for transfusion predictions. We also developed a simplified emergency model maintaining robust performance (validation AUC: 0.81, external AUC: 0.75). These models demonstrate cross-center generalizability and practical utility for emergency settings, supporting clinical implementation for improved TBI patient management.</p>

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Interpretable Multiomics Models for Predicting Surgical Interventions and Blood Transfusion Requirements in Traumatic Brain Injury

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年11月19日

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  • Jiang Deng,
  • Tao Deng,
  • Yan-Chun Zhang,
  • Ning Zhao,
  • Kai Liu,
  • Chao-Jie Wang,
  • Zi-Jian Zhang,
  • Jing-Han Ma,
  • Hao Wang,
  • Li-Ping Lv,
  • Ping Ma,
  • Xiang-Yan Huang,
  • Tao Wu,
  • Yan-Yu Zhang

摘要

Accurately predicting surgical and transfusion needs in traumatic brain injury (TBI) patients remains challenging in emergency settings. We developed multiomics data fusion (MDF) models integrating clinical biomarkers, neural radiological imaging, and clinical text mining for predicting surgical intervention and blood transfusion requirements across four multicenter cohorts (N = 2219). The MDF models provided predictions a median of 3 hours before interventions, with surgical model F1 scores of 0.63–0.85 across external testing, outperforming single-domain approaches. The transfusion model demonstrated strong cross-center performance (validation/external F1 scores: 0.78, 0.74) and correlated well with actual transfusion volumes (validation: R = 0.687, external: R = 0.580). SHapley additive exPlanations revealed radiological features drove surgical predictions, while clinical parameters (lactate, GCS scores, pupillary reflex, and hemoglobin) were crucial for transfusion predictions. We also developed a simplified emergency model maintaining robust performance (validation AUC: 0.81, external AUC: 0.75). These models demonstrate cross-center generalizability and practical utility for emergency settings, supporting clinical implementation for improved TBI patient management.