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

医学 置信区间 接收机工作特性 逻辑回归 脊髓损伤 回廊的 队列 前瞻性队列研究 功能独立性测度 物理疗法 曲线下面积 内科学 脊髓 康复 精神科
作者
Philippe Phan,Jason Weatherald,Qiong Zhang,Carly S. Rivers,Vanessa K. Noonan,Tova Plashkes,Eugene K. Wai,Jérôme Paquet,Darren M. Roffey,Eve C. Tsai,Nader Fallah
出处
期刊:The Spine Journal [Elsevier BV]
卷期号:19 (4): 703-710 被引量:27
标识
DOI:10.1016/j.spinee.2018.08.016
摘要

Abstract

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.
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