Predictors of Intensive Care Unit Stay in Patients with Acute Traumatic Spinal Cord Injury Above T6

Esmorís-Arijón, Inés; Galeiras, Rita; Velasco, M.E. Ferreiro; Pértega-Díaz, Sonia

doi:10.1016/j.wneu.2022.07.072

Cited by 1 publication

(1 citation statement)

References 53 publications

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“… 102 The Spinal Cord Injury Risk Score, specifically designed to integrate overall trauma burden in acute SCI, demonstrated superior mortality prediction compared to ISS in a machine learning analysis. 103 A multi-variate Fine-Gray cumulative risk model predicted the duration of intensive care unit (ICU) stay and mortality by incorporating neurological level, total motor score, and organ failure defined from laboratory values and ventilatory status at Day 4 post-SCI, 104 demonstrating that ISNCSCI could be modeled together with critical care variables.…”

Section: Current Sci Classificationmentioning

confidence: 99%

Development of a Systems Medicine Approach to Spinal Cord Injury

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Kelly-Hedrick

Williamson

et al. 2023

Journal of Neurotrauma

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Traumatic spinal cord injury (SCI) causes a sudden onset multi-system disease, permanently altering homeostasis with multiple complications. Consequences include aberrant neuronal circuits, multiple organ system dysfunctions, and chronic phenotypes such as neuropathic pain and metabolic syndrome. Reductionist approaches are used to classify SCI patients based on residual neurological function. Still, recovery varies due to interacting variables, including individual biology, comorbidities, complications, therapeutic side effects, and socioeconomic influences for which data integration methods are lacking. Infections, pressure sores, and heterotopic ossification are known recovery modifiers. However, the molecular pathobiology of the disease-modifying factors altering the neurological recovery-chronic syndrome trajectory is mainly unknown, with significant data gaps between intensive early treatment and chronic phases. Changes in organ function such as gut dysbiosis, adrenal dysregulation, fatty liver, muscle loss, and autonomic dysregulation disrupt homeostasis, generating progression-driving allostatic load. Interactions between interdependent systems produce emergent effects, such as resilience, that preclude single mechanism interpretations. Due to many interacting variables in individuals, substantiating the effects of treatments to improve neurological outcomes is difficult. Acute injury outcome predictors, including blood and cerebrospinal fluid biomarkers, neuroimaging signal changes, and autonomic system abnormalities, often do not predict chronic SCI syndrome phenotypes. In systems medicine, network analysis of bioinformatics data is used to derive molecular control modules. To better understand the evolution from acute SCI to chronic SCI multi-system states, we propose a topological phenotype framework integrating bioinformatics, physiological data, and allostatic load tested against accepted established recovery metrics. This form of correlational phenotyping may reveal critical nodal points for intervention to improve recovery trajectories. This study examines the limitations of current classifications of SCI and how these can evolve through systems medicine.

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Section: Current Sci Classificationmentioning

confidence: 99%