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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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.
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.