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same b-vector), this artifact is not visible (mountSinai03, right panel). Such an artifact could be problematic for image registration with regularization along the S-I axis, or for performing diffusion tractography. (e) b = 0 image from a DWI scan (perform02) acquired with poor shimming and resulting signal dropout. (f) Another example of poor shimming resulting in sub-efficient fat saturation, with the fat being aliased on top of the SC. Here we show the mean DWI scan of a participant from the single subject database (perform). (g) Effect of pulsatile movement on a non-cardiac gated acquisition (single subject, juntendoAchieva). Diffusion-weighted scans (sagittal view) acquired at three b-vecs fairly orthogonal to the SC (i.e., diffusion-specific signal attenuation should be minimum in the SC), showing abrupt signal drop at a few slices (red arrows), likely due to cardiac-related pulsatile effects.

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Background and Purpose: Predicting future outdoor walking ability after spinal cord injury (SCI) is important, as this is associated with community engagement and social participation. A clinical prediction rule (CPR) was derived for predicting outdoor walking 1 year after SCI. While promising, this CPR has not been validated, which is necessary to establish its clinical value. The objective of this study was to externally validate the CPR using a multisite dataset. Methods: This was a retrospective analysis of US SCI Model Systems data from 12 centers. L3 motor score, L5 motor score, and S1 sensory score were used as predictor variables. The dataset was split into testing and training datasets. The testing dataset was used as a holdout dataset to provide an unbiased estimate of prediction performance. The training dataset was used to determine the optimal CPR threshold through a “leave-one-site-out” cross-validation framework. The primary outcome was self-reported outdoor walking ability 1 year after SCI. Results: A total of 3721 participants' data were included. Using the optimal CPR threshold (CPR ≥ 33 threshold value), we were able to predict outdoor walking 1 year with high cross-validated accuracy and prediction performance. For the entire dataset, area under receiver operator characteristic curve was 0.900 (95% confidence interval: 0.890-0.910; P < 0.0001). Discussion and Conclusions: The outdoor walking CPR has been externally validated. Future research should conduct a clinical outcomes and cost-benefit impact analysis for implementing this CPR. Our results support that clinicians may use this 3-variable CPR for prediction of future outdoor walking ability. Video Abstract available for more insights from the authors (see the Video, Supplemental Digital Content 1, available at: http://links.lww.com/JNPT/A411).