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<p>Sudden cardiac arrest (SCA) remains a critical public health challenge with mortality rates close to 90%. Current prognostication methods commonly analyze data of individual modalities separately and delay assessment until 72 hours post-arrest, creating a critical gap in early decision-making. Here, we introduce contrastive language and image reasoning with masked autoencoders (CLAIR), a novel multimodal framework integrating head computed tomography (CT) imaging with non-imaging clinical patient information through a cross-attention mechanism and contrastive learning approach to predict cerebral performance category (CPC) score in patients after cardiac arrest. In a retrospective study of 208 patients, we evaluated CLAIR against CT-based imaging-only assessment, as well as clinical evaluation by two experienced ICU neurologists. Our method achieved an AUC-ROC of 0.94 (CI: 0.90-0.97) when trained on a combination of multiplanar CT reconstructions and non-imaging clinical data, significantly outperforming CT scan-based imaging-only methods (AUC-ROC: 0.80, CI: 0.74-0.86) with statistical significance (p = 0.03). In a structured evaluation, the clinicians suggested that CLAIR assisted assessments resulted in fewer prognostic errors than non-assisted evaluations. Further, we demonstrate the applicability of our approach for early neurologic outcome prediction using CT scans obtained within the first 24 hours post-arrest (median acquisition time: 3.1 hours). Our results suggest that CLAIR can contribute value as a clinical assistive tool aiming at reliable early prognostication for post-cardiac arrest patients, potentially enabling more timely clinical decision-making, family counseling, and resource allocation.</p>

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Multimodal contrastive prognostication framework for early neurological outcome prediction in post-cardiac arrest patients

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年3月31日

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  • Akhil Kasturi,
  • Ashley R. Proctor,
  • Ali Vosoughi,
  • Chloe T. Zhang,
  • Nathan Hadjiyski,
  • Sydney V. Palka,
  • Jenna Gonillo Davis,
  • Lisa M. Cardamone,
  • Samantha Helmy,
  • Jeronimo Cardona,
  • Thomas W. Johnson,
  • Yang Gu,
  • Mark A. Marinescu,
  • Olga Selioutski,
  • Regine Choe,
  • Imad R. Khan,
  • Axel Wismüller

摘要

Sudden cardiac arrest (SCA) remains a critical public health challenge with mortality rates close to 90%. Current prognostication methods commonly analyze data of individual modalities separately and delay assessment until 72 hours post-arrest, creating a critical gap in early decision-making. Here, we introduce contrastive language and image reasoning with masked autoencoders (CLAIR), a novel multimodal framework integrating head computed tomography (CT) imaging with non-imaging clinical patient information through a cross-attention mechanism and contrastive learning approach to predict cerebral performance category (CPC) score in patients after cardiac arrest. In a retrospective study of 208 patients, we evaluated CLAIR against CT-based imaging-only assessment, as well as clinical evaluation by two experienced ICU neurologists. Our method achieved an AUC-ROC of 0.94 (CI: 0.90-0.97) when trained on a combination of multiplanar CT reconstructions and non-imaging clinical data, significantly outperforming CT scan-based imaging-only methods (AUC-ROC: 0.80, CI: 0.74-0.86) with statistical significance (p = 0.03). In a structured evaluation, the clinicians suggested that CLAIR assisted assessments resulted in fewer prognostic errors than non-assisted evaluations. Further, we demonstrate the applicability of our approach for early neurologic outcome prediction using CT scans obtained within the first 24 hours post-arrest (median acquisition time: 3.1 hours). Our results suggest that CLAIR can contribute value as a clinical assistive tool aiming at reliable early prognostication for post-cardiac arrest patients, potentially enabling more timely clinical decision-making, family counseling, and resource allocation.