Scholarlink

<p>Routinely collected blood tests can reflect underlying pathophysiological processes. We demonstrate that the dynamics of routinely collected blood tests hold prediction validity in acute Spinal Cord Injury (SCI). Using MIMIC data (<i>n</i> = 2615) for modeling and TRACK-SCI study data (<i>n</i> = 137) for validation, we identified multiple trajectories for common blood markers. We developed machine learning models for the dynamic prediction of in-hospital mortality, SCI occurrence in spine trauma patients, and SCI severity (motor complete vs. incomplete). The in-hospital mortality model achieved an out-of-train ROC-AUC of 0.79 [0.77–0.81] day one post-injury, improving to 0.89 [0.88–0.89] by day 21. For detecting the presence of SCI after spine trauma, the highest ROC-AUC was 0.71 [0.69–0.72] achieved by day 21. By day seven, the ROC-AUC for SCI severity was 0.81 [0.77–0.85]. Our full models outperformed the severity score SAPS II following seven days of hospitalization.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Modeling trajectories of routine blood tests as dynamic biomarkers for outcome in spinal cord injury

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年7月22日

查看全文
  • Marzieh Mussavi Rizi,
  • Daniel Fernández,
  • John L. K. Kramer,
  • Rajiv Saigal,
  • Anthony M. DiGiorgio,
  • Michael S. Beattie,
  • Adam R. Ferguson,
  • Nikos Kyritsis,
  • Abel Torres-Espín,
  • Prakruthi Amar Kumar,
  • Michael S. Beattie,
  • Jacqueline C. Bresnahan,
  • Anthony M. DiGiorgio,
  • Xuan Duong-Fernandez,
  • Adam R. Ferguson,
  • Debra D. Hemmerle,
  • J. Russel Huie,
  • Anastasia Keller,
  • Nikos Kyritsis,
  • Nicole Lai,
  • Geoffrey T. Manley,
  • Jonathan Z. Pan,
  • Lisa U. Pascual,
  • Rajiv Saigal,
  • Vineeta Singh,
  • Jason F. Talbott,
  • Abel Torres-Espin,
  • Phil Weinstein,
  • William D. Whetstone

摘要

Routinely collected blood tests can reflect underlying pathophysiological processes. We demonstrate that the dynamics of routinely collected blood tests hold prediction validity in acute Spinal Cord Injury (SCI). Using MIMIC data (n = 2615) for modeling and TRACK-SCI study data (n = 137) for validation, we identified multiple trajectories for common blood markers. We developed machine learning models for the dynamic prediction of in-hospital mortality, SCI occurrence in spine trauma patients, and SCI severity (motor complete vs. incomplete). The in-hospital mortality model achieved an out-of-train ROC-AUC of 0.79 [0.77–0.81] day one post-injury, improving to 0.89 [0.88–0.89] by day 21. For detecting the presence of SCI after spine trauma, the highest ROC-AUC was 0.71 [0.69–0.72] achieved by day 21. By day seven, the ROC-AUC for SCI severity was 0.81 [0.77–0.85]. Our full models outperformed the severity score SAPS II following seven days of hospitalization.