常规血液检查作为脊髓损伤预后动态生物标志物的建模轨迹

IF 15.1 1区 医学 Q1 HEALTH CARE SCIENCES & SERVICES
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
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引用次数: 0

摘要

常规采集的血液检查可以反映潜在的病理生理过程。我们证明了常规采集血液测试的动态在急性脊髓损伤(SCI)中具有预测有效性。使用MIMIC数据(n = 2615)进行建模,使用TRACK-SCI研究数据(n = 137)进行验证,我们确定了常见血液标志物的多个轨迹。我们开发了机器学习模型,用于动态预测住院死亡率、脊柱创伤患者的脊髓损伤发生率和脊髓损伤严重程度(运动完全性与不完全性)。院内死亡率模型在伤后第1天的ROC-AUC为0.79[0.77-0.81],到第21天提高到0.89[0.88-0.89]。对于脊柱创伤后脊髓损伤的检测,在第21天达到最高的ROC-AUC为0.71[0.69-0.72]。第7天,SCI严重程度的ROC-AUC为0.81[0.77-0.85]。我们的完整模型在住院7天后的严重程度评分优于SAPS II。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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

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

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.

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来源期刊
CiteScore
25.10
自引率
3.30%
发文量
170
审稿时长
15 weeks
期刊介绍: npj Digital Medicine is an online open-access journal that focuses on publishing peer-reviewed research in the field of digital medicine. The journal covers various aspects of digital medicine, including the application and implementation of digital and mobile technologies in clinical settings, virtual healthcare, and the use of artificial intelligence and informatics. The primary goal of the journal is to support innovation and the advancement of healthcare through the integration of new digital and mobile technologies. When determining if a manuscript is suitable for publication, the journal considers four important criteria: novelty, clinical relevance, scientific rigor, and digital innovation.
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