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Observational Study
. 2022 Mar;36(2):663-671.
doi: 10.1111/jvim.16352. Epub 2022 Jan 10.

Developing a predictive model for spinal shock in dogs with spinal cord injury

Rebecca McBride  1 , Elizabeth Parker  2 , Rebecca B Garabed  1 , Natasha J Olby  3 , Andrea Tipold  4 , Veronika Maria Stein  5 , Nicolas Granger  6 , Ashley C Hechler  1 , Page E Yaxley  1 , Sarah A Moore  1 ; CANSORT-SCI study investigators
Collaborators, Affiliations
Observational Study

Developing a predictive model for spinal shock in dogs with spinal cord injury

Rebecca McBride et al. J Vet Intern Med. 2022 Mar.

Abstract

Background: Reduced pelvic limb reflexes in dogs with spinal cord injury typically suggests a lesion of the L4-S3 spinal cord segments. However, pelvic limb reflexes might also be reduced in dogs with a T3-L3 myelopathy and concurrent spinal shock.

Hypothesis/objectives: We hypothesized that statistical models could be used to identify clinical variables associated with spinal shock in dogs with spinal cord injuries.

Animals: Cohort of 59 dogs with T3-L3 myelopathies and spinal shock and 13 dogs with L4-S3 myelopathies.

Methods: Data used for this study were prospectively entered by partner institutions into the International Canine Spinal Cord Injury observational registry between October 2016 and July 2019. Univariable logistic regression analyses were performed to assess the association between independent variables and the presence of spinal shock. Independent variables were selected for inclusion in a multivariable logistic regression model if they had a significant effect (P ≤ .1) on the odds of spinal shock in univariable logistic regression.

Results: The final multivariable model included the natural log of weight (kg), the natural log of duration of clinical signs (hours), severity (paresis vs paraplegia), and pelvic limb tone (normal vs decreased/absent). The odds of spinal shock decreased with increasing weight (odds ratio [OR] = 0.28, P = .09; confidence interval [CI] 0.07-1.2), increasing duration (OR = 0.44, P = .02; CI 0.21-0.9), decreased pelvic limb tone (OR = 0.04, P = .003; CI 0.01-0.36), and increased in the presence of paraplegia (OR = 7.87, P = .04; CI 1.1-56.62).

Conclusions and clinical importance: A formula, as developed by the present study and after external validation, could be useful for assisting clinicians in determining the likelihood of spinal shock in various clinical scenarios and aid in diagnostic planning.

Keywords: fibrocartilaginous infarct; herniation; intervertebral disc disease; neurology; spinal cord disease; spinal shock.

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Conflict of interest statement

Andrea Tipold serves as Associate Editor for the Journal of Veterinary Internal Medicine. She was not involved in review of this manuscript. No other authors have a conflict of interest.

Figures

FIGURE 1
FIGURE 1
Calibration belt curve used for internal validation of the model used to predict the presence of spinal shock in dogs
FIGURE 2
FIGURE 2
Flowchart documenting the numbers of dogs available from the CANSORT database and exclusion criteria applied to identify the T3‐L3 myelopathy with spinal shock and L4‐S3 myelopathy groups included in the present study
FIGURE 3
FIGURE 3
Graph demonstrating the relationship between duration of clinical signs in hours, paraparesis, and the probability of spinal shock in a 10 kg dog. With increasing duration of clinical signs and the presence of paraparesis, the probability of spinal shock decreases
FIGURE 4
FIGURE 4
Graph demonstrating the relationship between weight in kilograms, paraparesis, and the probability of spinal shock for dogs that are presented at 10 hours after the onset of clinical signs. With increasing weight and the presence of paraparesis, the probability of spinal shock decreases
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