Multicenter Spinal Cord Injury Research Articles

469 Recovery Rate of Motor Function During Rehabilitation After Traumatic Cervical Spinal Cord Injury Predicts Functional Status at Years 1 and 5 After Surgery: A Multicenter, Longitudinal Cohort Study

INTRODUCTION: Traumatic spinal cord injury (SCI) is a devastating condition. Early surgical decompression and rehabilitative efforts in cervical SCI patients has been shown to improve neurological outcomes. METHODS: A longitudinal, retrospective cohort study was conducted from the multicenter Spinal Cord Injury Model Systems (SCIMS) database on patients with motor Functional Independence Measure (FIM) scores at admission to and discharge from rehabilitation, year 1, and year 5. Patients who underwent surgical decompression with neurological levels of injury limited to the cervical region and those with American Spinal Injury Association (ASIA) Impairment Scale (AIS) grades of A and B were included. The FIM score was utilized to calculate changes in motor FIM scores over each respective time period. Multivariate logistical regression was performed to identify longitudinal predictors associated with functional independence. RESULTS: A total of 351 patients met the inclusion criteria. The change in motor FIM score during the inpatient rehab period was a significant predictor of achieving FI at 1 and 5 years (year 1: aOR = 15.6, 95% CI 3.94-77.6, p < 0.0001; year 5: aOR = 28.0, 95% CI 6.13-173.6, p < 0.0001). For patients not achieving FI at year 1, the change in motor FIM score during inpatient rehab was a greater predictor of FI at year 5 (aOR = 64.7, 95% CI 5.15-1717.3, p = 0.0004). CONCLUSIONS: Inpatient rehabilitation efforts may lead to greater long-term functional status and carry emphasized importance for patients who do not attain functional independence by year 1.

Prediction of independence in bowel function after spinal cord injury: validation of a logistic regression model

Study designRetrospective analysis of prospectively collected data.ObjectivesRecently, logistic regression models were developed to predict independence in bowel function 1 year after spinal cord injury (SCI) on a multicenter European SCI (EMSCI) dataset. Here, we evaluated the external validity of these models against a prospectively accrued North American SCI dataset.SettingTwenty-five SCI centers in the United States and Canada.MethodsTwo logistic regression models developed by the EMSCI group were applied to data for 277 patients derived from three prospective multicenter SCI studies based in North America. External validation was evaluated for both models by assessing their discrimination, calibration, and clinical utility. Discrimination and calibration were assessed using ROC curves and calibration curves, respectively, while clinical utility was assessed using decision curve analysis.ResultsThe simplified logistic regression model, which used baseline total motor score as the predictor, demonstrated the best performance, with an area under the ROC curve of 0.869 (95% confidence interval: 0.826–0.911), a sensitivity of 75.5%, and a specificity of 88.5%. Moreover, the model was well calibrated across the full range of observed probabilities and displayed superior clinical benefit on the decision curve.ConclusionsA logistic regression model using baseline total motor score as a predictor of independent bowel function 1 year after SCI was successfully validated against an external dataset. These findings provide evidence supporting the use of this model to enhance the care for individuals with SCI.

Development of a Clinical Prediction Model for Central Cord Syndrome: An Evaluation of Motor Recovery and the Effectiveness of Early Surgery

Abstract INTRODUCTION There remains a paucity of data on the outcomes of central cord syndrome (CCS) and their predictors in the modern era. Further, the efficacy of early surgical decompression, within 24 hr, for CCS remains unclear. In patients with CCS, we therefore sought to: 1) develop a clinical prediction model for neurological outcome; and 2) evaluate the effect of early surgery (<24 hr) on neurological recovery. METHODS Patients with CCS (AIS grade C or D; LEMS UEMS ≥5) were identified from two prospective, multi-center spinal cord injury (SCI) datasets (NACTN; STASCIS). A clinical prediction model was developed by multiple linear regression; the outcome was change in American Spinal Injury Association (ASIA) motor score (AMS) at 1-yr. Covariates were chosen a priori: age, baseline AMS, baseline AIS grade, time to surgery (early [<24 hr] vs late [≥24 hr]), and time to surgery AIS grade. Effect sizes were summarized by ß coefficients. Internal validation was performed by bootstrapping. The model was externally validated in a cohort of patients from the NASCIS III trial. RESULTS A total of 211 patients were eligible. ß coefficients were significant for all variables in the model: age (−0.12, P = .04); baseline AMS (−0.71, P < .01); AIS grade (9.69, P = .01); time to surgery (12.67, P < .01); AIS grade × time to surgery (−13.18, P < .01). The mean R2 value across bootstraps was 0.66. In patients with AIS C injury, early surgery resulted in significantly improved motor recovery (marginal mean: +12.7, 95% CI 5.8-19.6); there was no difference in recovery with early surgery in patients with AIS D injury (marginal mean: −0.5, 95% CI −4.4-3.3). The model showed good external validity (R2 = 0.65 in validation cohort, N = 38). CONCLUSION Motor recovery after CCS may be predicted by age, AMS, AIS grade, and time to surgery. Early surgery improves recovery, particularly in patients with more severe injury.

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

BACKGROUND CONTEXTModels 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. PURPOSETo 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 DESIGNA prospective cohort study. PARTICIPANT SAMPLEIndividuals 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 MEASURESThe FIM locomotor score was used to assess independent walking ability at 1-year follow-up. METHODSTwo 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). RESULTSIn 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. CONCLUSIONSPreviously 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.

Wednesday, September 26, 2018 10:35 AM – 12:00 PM Cervical Spine Trauma: 22. A simplified clinical prediction rule for prognosticating independent walking after spinal cord injury: a prospective study from a Canadian multicenter spinal cord injury registry

BACKGROUND CONTEXT Models for predicting recovery in patients affected with traumatic spinal cord injury (tSCI) have been developed in recent years to optimize patient-centered care. Several databases and analytical models have been validated in their ability to predict tSCI recovery, yet recent findings have queried the accuracy of these models in patients whose prognoses are least predictable, ie, AIS B and C patients. PURPOSE To examine the predictive accuracy of van Middendorp's five-variable and Hicks’ three-variable LR models in the subset of AIS B and C patients. Secondly, to assess the predictive accuracy of a novel, seven-variable logistic regression model in this cohort. Thirdly, to investigate the utility of advanced computing models based on artificial intelligence in the context of AIS B and C prognostication. STUDY DESIGN/SETTING Prospective cohort study using the Rick Hansen Spinal Cord Injury Registry, a pan-Canadian prospective observational registry. PATIENT SAMPLE Patients who sustained tSCI between 2004 and 2016. OUTCOME MEASURES Neurological severity (AIS) at time of admission and level of injury was assessed within 15 days of injury according to the International Standards for Neurological Classification of Spinal Cord Injury (ISNCSCI). Independent walking ability was assessed by the FIM at ≥12 months from time of injury, by which time neurologic and functional outcomes are known to plateau (16-20). METHODS Three logistic regression models and two artificial intelligence models were evaluated for their ability to predict walking ability one-year postinjury. Comparison of prognostic performance between models was calculated by area under the receiver operating characteristic curve (AUC). RESULTS A total of 675 tSCI patients within the RHSCIR were identified for our study. From 160 AIS B and C patients, our modified LR model demonstrated an AUC of 0.854 (95% CI 0.808–0.900). For the same cohort, van Middendorp et al.’s LR model generated an AUC of 0.833 (95% CI 0.771–0.895), while the artificial intelligence model presenting the highest degree of prognostic ability (neural networking) generated an AUC of 0.872. There were no significant differences in predictive accuracy between models (p>.05). The AUC for 515 AIS A and D patients utilizing van Middendorp et al.’s LR model was 0.954. CONCLUSIONS Application of our novel, seven-variable LR model, two previously tested LR models, and two artificial intelligence models demonstrated limited predictive accuracy for AIS B and C patients. These findings suggest that former models achieved strong prognostic accuracy through a high proportion of AIS A and D patients in their analysis cohort; furthermore, present models cannot effectively predict ambulatory outcomes in AIS B and C patients, regardless of methodology. FDA DEVICE/DRUG STATUS This abstract does not discuss or include any applicable devices or drugs.

PD36-02 VOLITIONAL VOIDING OF THE BLADDER AFTER SPINAL CORD INJURY: VALIDATION OF BILATERAL LOWER EXTREMITY MOTOR FUNCTION AS A KEY PREDICTOR

You have accessJournal of UrologyUrodynamics/Lower Urinary Tract Dysfunction/Female Pelvic Medicine: Neurogenic Voiding Dysfunction II1 Apr 2018PD36-02 VOLITIONAL VOIDING OF THE BLADDER AFTER SPINAL CORD INJURY: VALIDATION OF BILATERAL LOWER EXTREMITY MOTOR FUNCTION AS A KEY PREDICTOR Christopher Elliott, Kai Dallas, Dimitar Zlatev, Craig Comiter, James Crew, and Kazuko Shem Christopher ElliottChristopher Elliott More articles by this author , Kai DallasKai Dallas More articles by this author , Dimitar ZlatevDimitar Zlatev More articles by this author , Craig ComiterCraig Comiter More articles by this author , James CrewJames Crew More articles by this author , and Kazuko ShemKazuko Shem More articles by this author View All Author Informationhttps://doi.org/10.1016/j.juro.2018.02.1725AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookTwitterLinked InEmail INTRODUCTION AND OBJECTIVES For many persons with spinal cord injury (SCI), a return of volitional bladder voiding is considered more important than regaining motor function. Recently a predictive model using only composite bilateral lower extremity motor (LEM) scores for levels L2-S1 (range 0-50) was put forth by the European Multicenter Spinal Cord Injury (EMSCI) study group that showed excellent predictive power with an area under the curve (AUC) of 0.912. However appropriate validation of this model is lacking. We sought to further validate the EMSCI model using a national SCI cohort. METHODS We created models of volitional voiding using the United States National SCI Database for the years 2007-2016. In addition to testing LEM scores, we evaluated other patient variables that we hypothesized might affect volitional voiding including age, gender, rectal motor/sensory preservation, AIS classification and sensory level. Logistic regression modeling and receiver operator characteristic (ROC) curves were created. RESULTS In the cohort of 4327 individuals, volitional voiding was present in 1333 (30.8%) at 1-year follow-up. While younger age, female gender, improved AIS classification and more caudal sensory level were all predictors of volitional voiding, LEM scores were the most predictive (AUC=0.919). Addition of the other patient characteristics did little to improve the predictive power of LEM scores (AUC=0.932 for the full model). Further analysis of the predictive power of LEM scores suggests that the negative predictive value is better than the positive predictive value as LEM AUC decreases in groups more likely to volitionally void (AIS C=0.724 and AIS D=0.644). CONCLUSIONS Our study verifies the validity of the EMSCI predictive model of volitional voiding after SCI. The differing ability of LEM scores to predict voiding based on neurologic class of injury should be noted. © 2018FiguresReferencesRelatedDetails Volume 199Issue 4SApril 2018Page: e726-e727 Advertisement Copyright & Permissions© 2018MetricsAuthor Information Christopher Elliott More articles by this author Kai Dallas More articles by this author Dimitar Zlatev More articles by this author Craig Comiter More articles by this author James Crew More articles by this author Kazuko Shem More articles by this author Expand All Advertisement Advertisement PDF downloadLoading ...

Clinical prediction rules: the importance of the validation phase

Commentary On: Hicks K.E., Zhao Y., Fallah N, Rivers C, Noonan V, Plashkes T, Wai EK, Roffey DM, Tsai E, Paquet J, Attabib N, Marion T, Ahn H, Phan P. A simplified clinical prediction rule for prognosticating independent walking after spinal cord injury: a prospective study from a Canadian multicenter spinal cord injury registry. The Spine Journal. Article in press.

Volitional Voiding After Spinal Cord Injury—Who Will and Who Will Not

A long-term absence of volitional voiding after spinal cord injury is common. This review serves to highlight predictive models of volitional voiding after spinal cord injury and evaluate current therapies aimed at improving voiding efficiency after injury. In a new predictive model, using data from the European Multicenter Spinal Cord Injury study, lower extremity motor preservation is the most important physical exam finding to predict volitional voiding. Using a simple composite motor score ranging from 0 to 50 for the bilateral levels of L2-S1, an impressive area under the curve of 0.91 is achieved. While our ability to predict volitional voiding after spinal cord injury has improved, the treatment of an inability to void remains lacking. Neuromodulation techniques may provide the best chance of recovery in those that do not spontaneously improve.

A simplified clinical prediction rule for prognosticating independent walking after spinal cord injury: a prospective study from a Canadian multicenter spinal cord injury registry

Background ContextTraumatic spinal cord injury (SCI) is a debilitating condition with limited treatment options for neurologic or functional recovery. The ability to predict the prognosis of walking post injury with emerging prediction models could aid in rehabilitation strategies and reintegration into the community. PurposeTo revalidate an existing clinical prediction model for independent ambulation (van Middendorp et al., 2011) using acute and long-term post-injury follow-up data, and to investigatethe accuracy of a simplified model using prospectively collected data from a Canadian multicenter SCI database, the Rick Hansen Spinal Cord Injury Registry (RHSCIR). Study DesignProspective cohort study. Participant SampleThe analysis cohort consisted of 278 adult individuals with traumatic SCI enrolled in the RHSCIR for whom complete neurologic examination data and Functional Independence Measure (FIM) outcome data were available. Outcome MeasuresThe FIM locomotor score was used to assess independent walking ability (defined as modified or complete independence in walk or combined walk and wheelchair modality) at 1-year follow-up for each participant. MethodsA logistic regression (LR) model based on age and four neurologic variables was applied to our cohort of 278 RHSCIR participants. Additionally, a simplified LR model was created. The Hosmer-Lemeshow goodness of fit test was used to check if the predictive model is applicable to our data set. The performance of the model was verified by calculating the area under the receiver operating characteristic curve (AUC). The accuracy of the model was tested using a cross-validation technique. This study was supported by a grant from The Ottawa Hospital Academic Medical Organization ($50,000 over 2 years). The RHSCIR is sponsored by the Rick Hansen Institute and is supported by funding from Health Canada, Western Economic Diversification Canada, and the provincial governments of Alberta, British Columbia, Manitoba, and Ontario. ET and JP report receiving grants from the Rick Hansen Institute (approximately $60,000 and $30,000 per year, respectively). DMR reports receiving remuneration for consulting services provided to Palladian Health, LLC and Pacira Pharmaceuticals, Inc ($20,000-$30,000 annually), although neither relationship presents a potential conflict of interest with the submitted work. KEH received a grant for involvement in the present study from the Government of Canada as part of the Canada Summer Jobs Program ($3,000). JP reports receiving an educational grant from Medtronic Canada outside of the submitted work ($75,000 annually). TM reports receiving educational fellowship support from AO Spine, AO Trauma, and Medtronic; however, none of these relationships are financial in nature. All remaining authors have no conflicts of interest to disclose. ResultsThe fitted prediction model generated 85% overall classification accuracy, 79% sensitivity, and 90% specificity. The prediction model was able to accurately classify independent walking ability (AUC 0.889, 95% confidence interval [CI] 0.846–0.933, p<.001) compared with the existing prediction model, despite the use of a different outcome measure (FIM vs. Spinal Cord Independence Measure) to qualify walking ability. A simplified, three-variable LR model based on age and two neurologic variables had an overall classification accuracy of 84%, with 76% sensitivity and 90% specificity, demonstrating comparable accuracy with its five-variable prediction model counterpart. The AUC was 0.866 (95% CI 0.816–0.916, p<.01), only marginally less than that of the existing prediction model. ConclusionsA simplified predictive model with similar accuracy to a more complex model for predicting independent walking was created, which improves utility in a clinical setting. Such models will allow clinicians to better predict the prognosis of ambulation in individuals who have sustained a traumatic SCI.

Prediction of Bladder Outcomes after Traumatic Spinal Cord Injury: A Longitudinal Cohort Study.

BackgroundNeurogenic bladder dysfunction represents one of the most common and devastating sequelae of traumatic spinal cord injury (SCI). As early prediction of bladder outcomes is essential to counsel patients and to plan neurourological management, we aimed to develop and validate a model to predict urinary continence and complete bladder emptying 1 y after traumatic SCI.Methods and FindingsUsing multivariate logistic regression analysis from the data of 1,250 patients with traumatic SCI included in the European Multicenter Spinal Cord Injury study, we developed two prediction models of urinary continence and complete bladder emptying 1 y after traumatic SCI and performed an external validation in 111 patients. As predictors, we evaluated age, gender, and all variables of the International Standards for Neurological Classification of Spinal Cord Injury (ISNCSCI) and of the Spinal Cord Independence Measure (SCIM). Urinary continence and complete bladder emptying 1 y after SCI were assessed through item 6 of SCIM. The full model relies on lower extremity motor score (LEMS), light-touch sensation in the S3 dermatome of ISNCSI, and SCIM subscale respiration and sphincter management: the area under the receiver operating characteristics curve (aROC) was 0.936 (95% confidence interval [CI]: 0.922–0.951). The simplified model is based on LEMS only: the aROC was 0.912 (95% CI: 0.895–0.930). External validation of the full and simplified models confirmed the excellent predictive power: the aROCs were 0.965 (95% CI: 0.934–0.996) and 0.972 (95% CI 0.943–0.999), respectively. This study is limited by the substantial number of patients with a missing 1-y outcome and by differences between derivation and validation cohort.ConclusionsOur study provides two simple and reliable models to predict urinary continence and complete bladder emptying 1 y after traumatic SCI. Early prediction of bladder function might optimize counselling and patient-tailored rehabilitative interventions and improve patient stratification in future clinical trials.

Developing Artificial Neural Network Models to Predict Functioning One Year After Traumatic Spinal Cord Injury

ObjectiveTo develop mathematical models for predicting level of independence with specific functional outcomes 1 year after discharge from inpatient rehabilitation for spinal cord injury. DesignStatistical analyses using artificial neural networks and logistic regression. SettingRetrospective analysis of data from the national, multicenter Spinal Cord Injury Model Systems (SCIMS) Database. ParticipantsSubjects (N=3142; mean age, 41.5y) with traumatic spinal cord injury who contributed data for the National SCIMS Database longitudinal outcomes studies. InterventionsNot applicable. Main Outcome MeasuresSelf-reported ambulation ability and FIM-derived indices of level of assistance required for self-care activities (ie, bed-chair transfers, bladder and bowel management, eating, toileting). ResultsModels for predicting ambulation status were highly accurate (>85% case classification accuracy; areas under the receiver operating characteristic curve between .86 and .90). Models for predicting nonambulation outcomes were moderately accurate (76%–86% case classification accuracy; areas under the receiver operating characteristic curve between .70 and .82). The performance of models generated by artificial neural networks closely paralleled the performance of models analyzed using logistic regression constrained by the same independent variables. ConclusionsAfter further prospective validation, such predictive models may allow clinicians to use data available at the time of admission to inpatient spinal cord injury rehabilitation to accurately predict longer-term ambulation status, and whether individual patients are likely to perform various self-care activities with or without assistance from another person.

Methylprednisolone for the Treatment of Patients with Acute Spinal Cord Injuries: A Propensity Score-Matched Cohort Study from a Canadian Multi-Center Spinal Cord Injury Registry

Methylprednisolone for the Treatment of Patients with Acute Spinal Cord Injuries: A Propensity Score-Matched Cohort Study from a Canadian Multi-Center Spinal Cord Injury Registry

Methylprednisolone for the Treatment of Patients with Acute Spinal Cord Injuries: A Propensity Score-Matched Cohort Study from a Canadian Multi-Center Spinal Cord Injury Registry.

In prior analyses of the effectiveness of methylprednisolone for the treatment of patients with acute traumatic spinal cord injuries (TSCIs), the prognostic importance of patients' neurological levels of injury and their baseline severity of impairment has not been considered. Our objective was to determine whether methylprednisolone improved motor recovery among participants in the Rick Hansen Spinal Cord Injury Registry (RHSCIR).We identified RHSCIR participants who received methylprednisolone according to the Second National Spinal Cord Injury Study (NASCIS-II) protocol and used propensity score matching to account for age, sex, time of neurological exam, varying neurological level of injury, and baseline severity of neurological impairment. We compared changes in total, upper extremity, and lower extremity motor scores using the Wilcoxon signed-rank test and performed sensitivity analyses using negative binomial regression.Forty-six patients received methylprednisolone and 1555 received no steroid treatment. There were no significant differences between matched participants for each of total (13.7 vs. 14.1, respectively; p=0.43), upper extremity (7.3 vs. 6.4; p=0.38), and lower extremity (6.5 vs. 7.7; p=0.40) motor recovery. This result was confirmed using a multivariate model and, as predicted, only cervical (C1–T1) rather than thoracolumbar (T2–L3) injury levels (p<0.01) and reduced baseline injury severity (American Spinal Injury Association [ASIA] Impairment Scale grades; p<0.01) were associated with greater motor score recovery. There was no in-hospital mortality in either group; however, the NASCIS-II methylprednisolone group had a significantly higher rate of total complications (61% vs. 36%; p=0.02)NASCIS-II methylprednisolone did not improve motor score recovery in RHSCIR patients with acute TSCIs in either the cervical or thoracic spine when the influence of anatomical level and severity of injury were included in the analysis. There was a significantly higher rate of total complications in the NASCIS-II methylprednisolone group. These findings support guideline recommendations against routine administration of methylprednisolone in acute TSCI.