Highlighting discrepancies in walking prediction accuracy for patients with traumatic spinal cord injury: an evaluation of validated prediction models using a Canadian Multicenter Spinal Cord Injury Registry.
Functional outcomes
Logistic regression
Predictive accuracy
Prognosis
Traumatic spinal cord injury
Walking
Journal
The spine journal : official journal of the North American Spine Society
ISSN: 1878-1632
Titre abrégé: Spine J
Pays: United States
ID NLM: 101130732
Informations de publication
Date de publication:
04 2019
04 2019
Historique:
received:
06
06
2018
revised:
27
08
2018
accepted:
27
08
2018
pubmed:
5
9
2018
medline:
20
2
2020
entrez:
5
9
2018
Statut:
ppublish
Résumé
Models for predicting recovery in traumatic spinal cord injury (tSCI) patients have been developed to optimize care. Several models predicting tSCI recovery have been previously validated, yet recent findings question their accuracy, particularly in patients whose prognoses are the least predictable. To compare independent ambulatory outcomes in AIS (ASIA [American Spinal Injury Association] Impairment Scale) A, B, C, and D patients, as well as in AIS B+C and AIS A+D patients by applying two existing logistic regression prediction models. A prospective cohort study. Individuals with tSCI enrolled in the pan-Canadian Rick Hansen SCI Registry (RHSCIR) between 2004 and 2016 with complete neurologic examination and Functional Independence Measure (FIM) outcome data. The FIM locomotor score was used to assess independent walking ability at 1-year follow-up. Two validated prediction models were evaluated for their ability to predict walking 1-year postinjury. Relative prognostic performance was compared with the area under the receiver operating curve (AUC). In total, 675 tSCI patients were identified for analysis. In model 1, predictive accuracies for 675 AIS A, B, C, and D patients as measured by AUC were 0.730 (95% confidence interval [CI] 0.622-0.838), 0.691 (0.533-0.849), 0.850 (0.771-0.928), and 0.516 (0.320-0.711), respectively. In 160 AIS B+C patients, model 1 generated an AUC of 0.833 (95% CI 0.771-0.895), whereas model 2 generated an AUC of 0.821 (95% CI 0.754-0.887). The AUC for 515 AIS A+D patients was 0.954 (95% CI 0.933-0.975) with model 1 and 0.950 (0.928-0.971) with model 2. The difference in prediction accuracy between the AIS B+C cohort and the AIS A+D cohort was statistically significant using both models (p=.00034; p=.00038). The models were not statistically different in individual or subgroup analyses. Previously tested prediction models demonstrated a lower predictive accuracy for AIS B+C than AIS A+D patients. These models were unable to effectively prognosticate AIS A+D patients separately; a failure that was masked when amalgamating the two patient populations. This suggests that former prediction models achieved strong prognostic accuracy by combining AIS classifications coupled with a disproportionately high proportion of AIS A+D patients.
Sections du résumé
BACKGROUND CONTEXT
Models for predicting recovery in traumatic spinal cord injury (tSCI) patients have been developed to optimize care. Several models predicting tSCI recovery have been previously validated, yet recent findings question their accuracy, particularly in patients whose prognoses are the least predictable.
PURPOSE
To compare independent ambulatory outcomes in AIS (ASIA [American Spinal Injury Association] Impairment Scale) A, B, C, and D patients, as well as in AIS B+C and AIS A+D patients by applying two existing logistic regression prediction models.
STUDY DESIGN
A prospective cohort study.
PARTICIPANT SAMPLE
Individuals with tSCI enrolled in the pan-Canadian Rick Hansen SCI Registry (RHSCIR) between 2004 and 2016 with complete neurologic examination and Functional Independence Measure (FIM) outcome data.
OUTCOME MEASURES
The FIM locomotor score was used to assess independent walking ability at 1-year follow-up.
METHODS
Two validated prediction models were evaluated for their ability to predict walking 1-year postinjury. Relative prognostic performance was compared with the area under the receiver operating curve (AUC).
RESULTS
In total, 675 tSCI patients were identified for analysis. In model 1, predictive accuracies for 675 AIS A, B, C, and D patients as measured by AUC were 0.730 (95% confidence interval [CI] 0.622-0.838), 0.691 (0.533-0.849), 0.850 (0.771-0.928), and 0.516 (0.320-0.711), respectively. In 160 AIS B+C patients, model 1 generated an AUC of 0.833 (95% CI 0.771-0.895), whereas model 2 generated an AUC of 0.821 (95% CI 0.754-0.887). The AUC for 515 AIS A+D patients was 0.954 (95% CI 0.933-0.975) with model 1 and 0.950 (0.928-0.971) with model 2. The difference in prediction accuracy between the AIS B+C cohort and the AIS A+D cohort was statistically significant using both models (p=.00034; p=.00038). The models were not statistically different in individual or subgroup analyses.
CONCLUSIONS
Previously tested prediction models demonstrated a lower predictive accuracy for AIS B+C than AIS A+D patients. These models were unable to effectively prognosticate AIS A+D patients separately; a failure that was masked when amalgamating the two patient populations. This suggests that former prediction models achieved strong prognostic accuracy by combining AIS classifications coupled with a disproportionately high proportion of AIS A+D patients.
Identifiants
pubmed: 30179672
pii: S1529-9430(18)31104-5
doi: 10.1016/j.spinee.2018.08.016
pii:
doi:
Types de publication
Evaluation Study
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
703-710Informations de copyright
Copyright © 2019. Published by Elsevier Inc.