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

Predictive modeling of ambulatory outcomes after spinal cord injury using machine learning

STUDY DESIGN

Retrospective multi-site cohort study.

OBJECTIVES

To develop an accurate machine learning predictive model using predictor variables from the acute rehabilitation period to determine ambulatory status in spinal cord injury (SCI) one year post injury.

SETTING

Model SCI System (SCIMS) database between January 2000 and May 2019.

METHODS

Retrospective cohort study using data that were previously collected as part of the SCI Model System (SCIMS) database. A total of 4523 patients were analyzed comparing traditional models (van Middendorp and Hicks) compared to machine learning algorithms including Elastic Net Penalized Logistic Regression (ENPLR), Gradient Boosted Machine (GBM), and Artificial Neural Networks (ANN).

RESULTS

Compared with GBM and ANN, ENPLR was determined to be the preferred model based on predictive accuracy metrics, calibration, and variable selection. The primary metric to judge discrimination was the area under the receiver operating characteristic curve (AUC). When compared to the van Middendorp all patients (0.916), ASIA A and D (0.951) and ASIA B and C (0.775) and Hicks all patients (0.89), ASIA A and D (0.934) and ASIA B and C (0.775), ENPLR demonstrated improved AUC for all patients (0.931), ASIA A and D (0.965) ASIA B and C (0.803).

CONCLUSIONS

Utilizing artificial intelligence and machine learning methods are feasible for accurately classifying outcomes in SCI and may provide improved sensitivity in identifying which individuals are less likely to ambulate and may benefit from augmentative strategies, such as neuromodulation. Future directions should include the use of additional variables to further refine these models.

Machine Learning Web Application for Predicting Functional Outcomes in Patients With Traumatic Spinal Cord Injury Following Inpatient Rehabilitation

Accurately predicting functional outcomes in patients with spinal cord injury (SCI) helps clinicians set realistic functional recovery goals and improve the home environment after discharge. The present study aimed to develop and validate machine learning (ML) models to predict functional outcomes in patients with SCI and deploy the models within a web application. The study included data from the Japan Association of Rehabilitation Database from January 1, 1991, to December 31, 2015. Patients with SCI who were admitted to an SCI center or transferred to a participating post-acute rehabilitation hospital after receiving acute treatment were enrolled in this database. The primary outcome was functional ambulation at discharge from the rehabilitation hospital. The secondary outcome was the total motor Functional Independence Measure (FIM) score at discharge. We used binary classification models to predict whether functional ambulation was achieved, as well as regression models to predict total motor FIM scores at discharge. In the training dataset (70% random sample) using demographic characteristics and neurological and functional status as predictors, we built prediction performance matrices of multiple ML models and selected the best one for each outcome. We validated each model's predictive performance in the test dataset (the remaining 30%). Among the 4181 patients, 3827 were included in the prediction model for the total motor FIM score. The mean (standard deviation [SD]) age was 50.4 (18.7) years, and 3211 (83.9%) patients were male. There were 3122 patients included in the prediction model for functional ambulation. The CatBoost Classifier and regressor models showed the best performances in the training dataset. On the test dataset, the CatBoost Classifier had an area under the receiver operating characteristic curve of 0.8572 and an accuracy of 0.7769 for predicting functional ambulation. Likewise, the CatBoost Regressor performed well, with an R2 of 0.7859, a mean absolute error of 9.2957, and a root mean square error of 13.4846 for predicting the total motor FIM score. The final models were deployed in a web application to provide functional predictions. The application can be found at http://3.138.174.54:8501. In conclusion, our prediction models developed using ML successfully predicted functional outcomes in patients with SCI and were deployed in an open-access web application.

Clinical Subsets of Central Cord Syndrome: Is it a Distinct Entity from Other Forms of Incomplete Tetraplegia for Research?

Central cord syndrome (CCS) is the most prevalent and debated incomplete spinal cord injury (SCI) syndrome, with its hallmark feature being more pronounced weakness of the upper extremities compared to the lower extremities. Varying definitions encapsulate multiple clinical features under the single umbrella term of CCS, complicating evaluation of its frequency, prognosis discussions, and outcomes research. Oftentimes, people with CCS are excluded from research protocols, as it is thought to have a favorable prognosis, but the vague nature of CCS raises doubts about the validity of this practice. The objective of this study was to categorize CCS into specific subsets with clear quantifiable differences, to assess whether this would enhance the ability to determine if individuals with CCS or its subsets exhibit distinct neurological and functional outcomes relative to others with incomplete tetraplegia. This study retrospectively reviewed individuals with new motor incomplete tetraplegia from traumatic SCI who enrolled in the Spinal Cord Injury Model Systems (SCIMS) database from 2010 to 2020. Through an assessment of the prevailing criteria for CCS, coupled with data analysis, we used two key criteria, including the severity of distal upper extremity weakness (i.e., hands and fingers) and extent of symmetry, to delineate three CCS subsets: Full CCS, Unilateral CCS, and Borderline CCS. Of the 1,490 participants in our sample, 17.5% had Full, 25.6% Unilateral, and 9% Borderline CCS, together encompassing more than 50% of motor incomplete tetraplegia cases. Despite the increased sensitivity and specificity of these subsets compared to existing quantifiable criteria, substantial variability in clinical presentation was still observed. Overall, individuals meeting CCS subset criteria showed a higher likelihood of AIS D grade compared to those with motor incomplete tetraplegia without CCS, from admission to the 1-year follow-up. The upper extremity motor score (UEMS) for those with CCS was lower on admission, a difference that diminished by discharge, while their lower extremity motor score (LEMS) consistently remained higher compared to those without CCS. However, these neurological distinctions did not result in significant functional differences, as lower and upper extremity functional outcomes at discharge were mostly similar to those with motor incomplete tetraplegia, with some significant differences observed within those with AIS D grade. The AIS grade seems to remain the foremost determinant influencing neurological and functional outcomes, rather than the diagnosis of CCS. We recommend that future studies consider incorporating motor incomplete tetraplegia into their inclusion/exclusion criteria, instead of relying on criteria specific to CCS. While there remains clinical value in characterizing an injury pattern as CCS and perhaps using the different subsets to better characterize the impairments, it does not appear to be a useful research criterion.

Guidelines for neuroprognostication in adults with traumatic spinal cord injury

BACKGROUND

Traumatic spinal cord injury (tSCI) impacts patients and their families acutely and often for the long term. The ability of clinicians to share prognostic information about mortality and functional outcomes allows patients and their surrogates to engage in decision-making and plan for the future. These guidelines provide recommendations on the reliability of acute-phase clinical predictors to inform neuroprognostication and guide clinicians in counseling adult patients with tSCI or their surrogates.

METHODS

A narrative systematic review was completed using Grading of Recommendations Assessment, Development, and Evaluation methodology. Candidate predictors, including clinical variables and prediction models, were selected based on clinical relevance and presence of an appropriate body of evidence. The Population/Intervention/Comparator/Outcome/Timing/Setting question was framed as "When counseling patients or surrogates of critically ill patients with traumatic spinal cord injury, should < predictor, with time of assessment if appropriate > be considered a reliable predictor of < outcome, with time frame of assessment >?" Additional full-text screening criteria were used to exclude small and lower quality studies. Following construction of an evidence profile and summary of findings, recommendations were based on four Grading of Recommendations Assessment, Development, and Evaluation criteria: quality of evidence, balance of desirable and undesirable consequences, values and preferences, and resource use. Good practice recommendations addressed essential principles of neuroprognostication that could not be framed in the Population/Intervention/Comparator/Outcome/Timing/Setting format. Throughout the guideline development process, an individual living with tSCI provided perspective on patient-centered priorities.

RESULTS

Six candidate clinical variables and one prediction model were selected. Out of 11,132 articles screened, 369 met inclusion criteria for full-text review and 35 articles met eligibility criteria to guide recommendations. We recommend pathologic findings on magnetic resonance imaging, neurological level of injury, and severity of injury as moderately reliable predictors of American Spinal Cord Injury Impairment Scale improvement and the Dutch Clinical Prediction Rule as a moderately reliable prediction model of independent ambulation at 1 year after injury. No other reliable or moderately reliable predictors of mortality or functional outcome were identified. Good practice recommendations include considering the complete clinical condition as opposed to a single variable and communicating the challenges of likely functional deficits as well as potential for improvement and for long-term quality of life with SCI-related deficits to patients and surrogates.

CONCLUSIONS

These guidelines provide recommendations about the reliability of acute-phase predictors of mortality, functional outcome, American Spinal Injury Association Impairment Scale grade conversion, and recovery of independent ambulation for consideration when counseling patients with tSCI or their surrogates and suggest broad principles of neuroprognostication in this context.

Are Clinical Prediction Rules Used in Spinal Cord Injury Care? A Survey of Practice

Background

Accurate outcome prediction is desirable post spinal cord injury (SCI), reducing uncertainty for patients and supporting personalized treatments. Numerous attempts have been made to create clinical prediction rules that identify patients who are likely to recover function. It is unknown to what extent these rules are routinely used in clinical practice.

Objectives

To better understand knowledge of, and attitudes toward, clinical prediction rules amongst SCI clinicians in the United Kingdom.

Methods

An online survey was distributed via mailing lists of clinical special interest groups and relevant National Health Service Trusts. Respondents answered questions about their knowledge of existing clinical prediction rules and their general attitudes to using them. They also provided information about their level of experience with SCI patients.

Results

One hundred SCI clinicians completed the survey. The majority (71%) were unaware of clinical prediction rules for SCI; only 8% reported using them in clinical practice. Less experienced clinicians were less likely to be aware. Lack of familiarity with prediction rules was reported as being a barrier to their use. The importance of clinical expertise when making prognostic decisions was emphasized. All respondents reported interest in using clinical prediction rules in the future.

Conclusion

The results show widespread lack of awareness of clinical prediction rules amongst SCI clinicians in the United Kingdom. However, clinicians were positive about the potential for clinical prediction rules to support decision-making. More focus should be directed toward refining current rules and improving dissemination within the SCI community.

A Single Dermatome Clinical Prediction Rule for Independent Walking 1 Year After Spinal Cord Injury

OBJECTIVE

To derive and validate a simple, accurate CPR to predict future independent walking ability after SCI at the bedside that does not rely on motor scores and is predictive for those initially classified in the middle of the SCI severity spectrum.

DESIGN

Retrospective cohort study. Binary variables were derived, indicating degrees of sensation to evaluate predictive value of pinprick and light touch variables across dermatomes. The optimal single sensory modality and dermatome was used to derive our CPR, which was validated on an independent dataset.

SETTING

Analysis of SCI Model Systems dataset.

PARTICIPANTS

Individuals with traumatic SCI. The data of 3679 participants (N=3679) were included with 623 participants comprising the derivation dataset and 3056 comprising the validation dataset.

INTERVENTIONS

Not applicable.

MAIN OUTCOME MEASURES

Self-reported ability to walk both indoors and outdoors.

RESULTS

Pinprick testing at S1 over lateral heels, within 31 days of SCI, accurately identified future independent walkers 1 year after SCI. Normal pinprick in both lateral heels provided good prognosis, any pinprick sensation in either lateral heel provided fair prognosis, and no sensation provided poor prognosis. This CPR performed satisfactorily in the middle SCI severity subgroup.

CONCLUSIONS

In this large multi-site study, we derived and validated a simple, accurate CPR using only pinprick sensory testing at lateral heels that predicts future independent walking after SCI.

Using the Standing and Walking Assessment Tool at Discharge Predicts Community Outdoor Walking Capacity in Persons With Traumatic Spinal Cord Injury

OBJECTIVE

The Standing and Walking Assessment Tool (SWAT) standardizes the timing and content of walking assessments during inpatient rehabilitation by combining 12 stages ranging from lowest to highest function (0, 0.5, 1A, 1B, 1C, 2A, 2B, 2C, 3A, 3B, 3C, and 4) with 5 standard measures: the Berg Balance Scale, the modified Timed "Up & Go" test, the Activities-specific Balance Confidence Scale, the modified 6-Minute Walk Test, and the 10-Meter Walk Test (10MWT). This study aimed to determine if the SWAT at rehabilitation discharge could predict outdoor walking capacity 1-year after discharge in people with traumatic spinal cord injury.

METHODS

This retrospective study used data obtained from the Rick Hansen Spinal Cord Injury Registry from 2014 to 2020. Community outdoor walking capacity was measured using the Spinal Cord Independence Measure III (SCIM III) outdoor mobility score obtained 12 (±4) months after discharge. Of 206 study participants, 90 were community nonwalkers (ie, SCIM III score 0-3), 41 were community walkers with aids (ie, SCIM III score 4-6), and 75 were independent community walkers (ie, SCIM III score 7-8). Bivariate, multivariable regression, and an area under the receiver operating characteristic curve analyses were performed.

RESULTS

At rehabilitation discharge, 3 significant SWAT associations were confirmed: 0-3A with community nonwalkers, 3B/higher with community walkers with and without an aid, and 4 with independent community walkers. Moreover, at discharge, a higher (Berg Balance Scale, Activities-specific Balance Confidence Scale), faster (modified Timed "Up & Go," 10MWT), or further (10MWT) SWAT measure was significantly associated with independent community walking. Multivariable analysis indicated that all SWAT measures, except the 10MWT were significant predictors of independent community walking. Furthermore, the Activities-Specific Balance Confidence Scale had the highest area under the receiver operating characteristic score (0.91), demonstrating an excellent ability to distinguish community walkers with aids from independent community walkers.

CONCLUSION

The SWAT stage and measures at discharge can predict community outdoor walking capacity in persons with traumatic spinal cord injury. Notably, a patient's confidence in performing activities plays an important part in achieving walking ability in the community.

IMPACT

The discharge SWAT is useful to optimize discharge planning.

Performance of the Dutch clinical prediction rule for the ambulation outcome after spinal cord injury in a middle-income country clinical setting: an external validation study in the Thai retrospective cohort

OBJECTIVE

To perform external geographic and domain validation of the clinical prediction rule (CPR) of the ambulation outcome of patients with traumatic spinal cord injury (SCI) originally developed by van Middendorp, et al. (2011) in Thais with traumatic and non-traumatic SCI.

STUDY DESIGN

Retrospective cohort study.

SETTING

A tertiary rehabilitation facility in Chiang Mai, Thailand.

METHODS

A validation data set, including predictive (age and four neurological variables) and outcome (ambulation status) parameters was retrospectively collected from medical records of patients with traumatic and non-traumatic SCI admitted between December 2007 and December 2019. The performance of the original model was evaluated in both discrimination and calibration aspects, using an area under the receiver-operating characteristic curve (auROC) and calibration curves, respectively.

RESULTS

Three hundred and thirty-three patients with SCI were included in the validation set. The prevalence of ambulators was 59% (197 of 333 participants). An auROC of 0.93 (95% CI 0.90-0.96) indicated excellent discrimination whereas the calibration curve demonstrated underestimation, especially in patients with AIS grade D. Performance of the CPR was decreased but acceptable in patients with non-traumatic SCI.

CONCLUSIONS

Our external validation study demonstrated excellent discrimination but slightly underestimated calibration of the CPR of ambulation outcome after SCI. Regardless of the geographic and etiologic background of the population, the Dutch CPR could be applied to predict the ambulation outcome in patients with SCI.

Spasticity Predicts Motor Recovery for Patients with Subacute Motor Complete Spinal Cord Injury

OBJECTIVE

A motor complete spinal cord injury (SCI) results in the loss of voluntary motor control below the point of injury. Some of these patients can regain partial motor function through inpatient rehabilitation; however, there is currently no biomarker to easily identify which patients have this potential. Evidence indicates that spasticity could be that marker. Patients with motor complete SCI who exhibit spasticity show preservation of descending motor pathways, the pathways necessary for motor signals to be carried from the brain to the target muscle. We hypothesized that the presence of spasticity predicts motor recovery after subacute motor complete SCI.

METHODS

Spasticity (Modified Ashworth Scale and pendulum test) and descending connectivity (motor evoked potentials) were tested in the rectus femoris muscle in patients with subacute motor complete (n = 36) and motor incomplete (n = 30) SCI. Motor recovery was assessed by using the International Standards for Neurological Classification of Spinal Cord Injury and the American Spinal Injury Association Impairment Scale (AIS). All measurements were taken at admission and discharge from inpatient rehabilitation.

RESULTS

We found that motor complete SCI patients with spasticity improved in motor scores and showed AIS conversion to either motor or sensory incomplete. Conversely, patients without spasticity showed no changes in motor scores and AIS conversion. In incomplete SCI patients, motor scores improved and AIS conversion occurred regardless of spasticity.

INTERPRETATION

These findings suggest that spasticity represents an easy-to-use clinical outcome that might help to predict motor recovery after severe SCI. This knowledge can improve inpatient rehabilitation effectiveness for motor complete SCI patients. ANN NEUROL 2023.

Limb accelerations during sleep are related to measures of strength, sensation, and spasticity among individuals with spinal cord injury

BACKGROUND

To evaluate the relationship between measures of neuromuscular impairment and limb accelerations (LA) collected during sleep among individuals with chronic spinal cord injury (SCI) to provide evidence of construct and concurrent validity for LA as a clinically meaningful measure.

METHODS

The strength (lower extremity motor score), sensation (summed lower limb light touch scores), and spasticity (categorized lower limb Modified Ashworth Scale) were measured from 40 adults with chronic (≥ 1 year) SCI. Demographics, pain, sleep quality, and other covariate or confounding factors were measured using self-report questionnaires. Each participant then wore ActiGraph GT9X Link accelerometers on their ankles and wrist continuously for 1-5 days to measure LA from movements during sleep. Regression models with built-in feature selection were used to determine the most relevant LA features and the association to each measure of impairment.

RESULTS

LA features were related to measures of impairment with models explaining 69% and 73% of the variance (R²) in strength and sensation, respectively, and correctly classifying 81.6% (F1-score = 0.814) of the participants into spasticity categories. The most commonly selected LA features included measures of power and frequency (frequency domain), movement direction (correlation between axes), consistency between movements (relation to recent movements), and wavelet energy (signal characteristics). Rolling speed (change in angle of inclination) and movement smoothness (median crossings) were uniquely associated with strength. When LA features were included, an increase of 72% and 222% of the variance was explained for strength and sensation scores, respectively, and there was a 34% increase in spasticity classification accuracy compared to models containing only covariate features such as demographics, sleep quality, and pain.

CONCLUSION

LA features have shown evidence of having construct and concurrent validity, thus demonstrating that LA are a clinically-relevant measure related to lower limb strength, sensation, and spasticity after SCI. LA may be useful as a more detailed measure of impairment for applications such as clinical prediction models for ambulation.

The potential of prediction models of functioning remains to be fully exploited: A scoping review in the field of spinal cord injury rehabilitation

OBJECTIVE

The study aimed to explore existing prediction models of functioning in spinal cord injury (SCI).

STUDY DESIGN AND SETTING

The databases PubMed, EBSCOhost CINAHL Complete, and IEEE Xplore were searched for relevant literature. The search strategy included published search filters for prediction model and impact studies, index terms and keywords for SCI, and relevant outcome measures able to assess functioning as reflected in the International Classification of Functioning, Disability and Health (ICF). The search was completed in October 2020.

RESULTS

We identified seven prediction model studies reporting twelve prediction models of functioning. The identified prediction models were mainly envisioned to be used for rehabilitation planning, however, also other possible applications were stated. The method predominantly used was regression analysis and the investigated predictors covered mainly the ICF-components of body functions and activities and participation, next to characteristics of the health condition and health interventions.

CONCLUSION

Findings suggest that the development of prediction models of functioning for use in clinical practice remains to be fully exploited. By providing a comprehensive overview of what has been done, this review informs future research on prediction models of functioning in SCI and contributes to an efficient use of research evidence.

Improving Diagnostic Workup Following Traumatic Spinal Cord Injury: Advances in Biomarkers

PURPOSE OF REVIEW

Traumatic spinal cord injury (SCI) is a life-changing event with drastic implications for patients due to sensorimotor impairment and autonomous dysfunction. Current clinical evaluations focus on the assessment of injury level and severity using standardized neurological examinations. However, they fail to predict individual trajectories of recovery, which highlights the need for the development of advanced diagnostics. This narrative review identifies recent advances in the search of clinically relevant biomarkers in the field of SCI.

RECENT FINDINGS

Advanced neuroimaging and molecular biomarkers sensitive to the disease processes initiated by the SCI have been identified. These biomarkers range from advanced neuroimaging techniques, neurophysiological readouts, and molecular biomarkers identifying the concentrations of several proteins in blood and CSF samples. Some of these biomarkers improve current prediction models based on clinical readouts. Validation with larger patient cohorts is warranted. Several biomarkers have been identified-ranging from imaging to molecular markers-that could serve as advanced diagnostic and hence supplement current clinical assessments.

Clinical judgment is a cornerstone for validating and using clinical prediction rules: a head-to-head study on ambulation outcomes for spinal cord injured patients

STUDY DESIGN

Retrospective comparative study.

OBJECTIVE

Clinical prediction rules (CPRs) are an effervescent topic in the medical literature. Recovering ambulation after a traumatic spinal cord injury (tSCI) is a priority for patients and multiple CPRs have been proposed for predicting ambulation outcomes. Our objective is to confront clinical judgment to an established CPR developed for patients with tSCI.

SETTINGS

Level one trauma center specialized in tSCI and its affiliated rehabilitation center.

METHOD

In this retrospective comparative study, six physicians had to predict the ambulation outcome of 68 patients after a tSCI based on information from the acute hospitalization. Ambulation was also predicted according to the CPR of van Middendorp (CPR-vM). The success rate of the CPR-vM and clinicians to predict ambulation was compared using criteria of 5% for defining clinical significance, and a level of statistical significance of 0.05 for bilateral McNemar tests.

RESULTS

There was no statistical difference between the overall performance of physicians (success rate of 79%) and of the CPR-vM (81%) for predicting ambulation. The differences between the CPR-vM and physicians varied clinically and significantly with the level of experience, clinical setting, and field of expertise.

CONCLUSION

Confronting CPRs with the judgment of a group of clinicians should be an integral part of the design and validation of CPRs. Head-to-head comparison of CPRs with clinicians is also a cornerstone for defining the optimal strategy for translation into the clinical practice, and for defining which clinician and specific clinical context would benefit from using the CPR.

Toward Improving the Prediction of Functional Ambulation After Spinal Cord Injury Though the Inclusion of Limb Accelerations During Sleep and Personal Factors

OBJECTIVE

To determine if functional measures of ambulation can be accurately classified using clinical measures; demographics; personal, psychosocial, and environmental factors; and limb accelerations (LAs) obtained during sleep among individuals with chronic, motor incomplete spinal cord injury (SCI) in an effort to guide future, longitudinal predictions models.

DESIGN

Cross-sectional, 1-5 days of data collection.

SETTING

Community-based data collection.

PARTICIPANTS

Adults with chronic (>1 year), motor incomplete SCI (N=27).

INTERVENTIONS

Not applicable.

MAIN OUTCOME MEASURES

Ambulatory ability based on the 10-m walk test (10MWT) or 6-minute walk test (6MWT) categorized as nonambulatory, household ambulator (0.01-0.44 m/s, 1-204 m), or community ambulator (>0.44 m/s, >204 m). A random forest model classified ambulatory ability using input features including clinical measures of strength, sensation, and spasticity; demographics; personal, psychosocial, and environmental factors including pain, environmental factors, health, social support, self-efficacy, resilience, and sleep quality; and LAs measured during sleep. Machine learning methods were used explicitly to avoid overfitting and minimize the possibility of biased results.

RESULTS

The combination of LA, clinical, and demographic features resulted in the highest classification accuracies for both functional ambulation outcomes (10MWT=70.4%, 6MWT=81.5%). Adding LAs, personal, psychosocial, and environmental factors, or both increased the accuracy of classification compared with the clinical/demographic features alone. Clinical measures of strength and sensation (especially knee flexion strength), LA measures of movement smoothness, and presence of pain and comorbidities were among the most important features selected for the models.

CONCLUSIONS

The addition of LA and personal, psychosocial, and environmental features increased functional ambulation classification accuracy in a population with incomplete SCI for whom improved prognosis for mobility outcomes is needed. These findings provide support for future longitudinal studies that use LA; personal, psychosocial, and environmental factors; and advanced analyses to improve clinical prediction rules for functional mobility outcomes.

Clinical utility during inpatient rehabilitation of a clinical prediction rule for ambulation prognosis following spinal cord injury

OBJECTIVE

Mobility prognosis is a key focus during rehabilitation following spinal cord injury (SCI). The goal of this study was to prospectively evaluate the clinical utility of the van Middendorp clinical prediction rule (CPR).

DESIGN

Observational study.

SETTING

Inpatient rehabilitation unit.

PARTICIPANTS

Physical therapists and their patients with acute SCI and SCI disorders (SCI/D) for whom long-term ambulation prognosis was judged difficult to determine.

INTERVENTIONS

N/A.

OUTCOME MEASURES

CPR-determined probability of ambulation, therapist reported clinical utility (yes/no), shared with the patient (yes/no), useful for motivation/setting realistic expectations, and Functional Independence Measure (FIM) Locomotion walk score.

RESULTS

Five therapists and 52 patients (8 non-traumatic SCI/D) participated. 91% had lesions classified as AIS C or D. The median [IQR] for CPR probability of ambulation was 96.0 [86.5,99.0] for traumatic SCI and 80.0 [64.5, 94.5] for non-traumatic SCI/D. Clinical utility was reported for 45% of those with SCI and 88% with non-traumatic SCI/D. Therapists with less experience were more likely to report clinical utility and share with their patients. Ambulation probability was higher for patients who did not meet their FIM goal. CPR probability was correlated with discharge FIM only for non-traumatic SCI/D.

CONCLUSION

The CPR was not predictive of inpatient rehabilitation outcomes, in fact outcomes varied widely for individuals with similar probabilities emphasizing the importance of clinical judgement and continued need to identify individual factors that affect ambulation. However, greater utility in establishing prognosis and goal setting was noted for clinicians with less experience and for individuals with non-traumatic SCI/D.

Axial MRI biomarkers of spinal cord damage to predict future walking and motor function: a retrospective study

Study design

Retrospective.

Objectives

Primary: to assess if axial damage ratios are predictors of future walking after spinal cord injury (SCI), and if they add any predictive value if initial neurological impairment grades are available. Secondary: to determine if lateral spinal cord regions are predictors of future lower extremity motor scores (LEMS).

Setting

University/hospital.

Methods

Axial T₂-weighted MRIs were used. Axial damage ratios and non-damaged lateral cord volumes were calculated. Each participant answered at 1 year after SCI, “Are you able to walk for 150 feet? (45.72 meters)” For the secondary aim, right and left LEMS were used.

Results

In total, 145 participants were selected. Individuals that could walk had smaller ratios than those that were unable. Walking and axial damage ratios were negatively correlated. A 0.374 ratio cut-off showed optimal sensitivity/specificity. When initial neurological grades were used, axial damage ratios did not add predictive value. Forty-two participants had LEMS available and were included for the secondary aim. Right cord regions and right LEMS were positively correlated and left regions and left LEMS, but these variables were also correlated with each other.

Conclusions

Axial damage ratios were significant predictors of walking ability 1 year after SCI. However, this measure did not add predictive value over initial neurological grades. Lateral cord regions correlated with same-side LEMS, but the opposite was also found, calling this biomarker’s specificity into question. Axial damage ratios may be useful in predicting walking after SCI if initial neurological grades are unavailable.

Sponsorship

This research was funded by a National Institutes of Health award, National Institute of Child Health and Development—NIH R03HD094577.

The effect of age and injury severity on clinical prediction rules for ambulation among individuals with spinal cord injury

BACKGROUND CONTEXT

While several models for predicting independent ambulation early after traumatic spinal cord injury (SCI) based upon age and specific motor and sensory level findings have been published and validated, their accuracy, especially in individual American Spinal Injury Association [ASIA] Impairment Scale (AIS) classifications, has been questioned. Further, although age is widely used in prediction rules, its role and possible modifications have not been adequately evaluated until now.

PURPOSE

To evaluate the predictive accuracy of existing clinical prediction rules for independent ambulation among individuals at spinal cord injury model systems (SCIMS) Centers as well as the effect of modifying the age parameter from a cutoff of 65 years to 50 years.

STUDY DESIGN

Retrospective analysis of a longitudinal database.

PATIENT SAMPLE

Adult individuals with traumatic SCI.

OUTCOME MEASURES

The FIM locomotor score was used to assess independent walking ability at the 1-year follow-up.

METHODS

In all, 639 patients were enrolled in the SCIMS database between 2011 and 2015, with complete neurological examination data within 15 days following the injury and a follow-up assessment with functional independence measure (FIM) at 1-year post injury. Two previously validated logistic regression models were evaluated for their ability to predict independent walking at 1-year post injury with participants in the SCIMS database. Area under the receiver operating curve (AUC) was calculated for the individual AIS categories and for different age groups. Prediction accuracy was also calculated for a new modified LR model (with cut-off age of 50).

RESULTS

Overall AUC for each of the previous prediction models was found to be consistent with previous reports (0.919 and 0.904). AUCs for grouped AIS levels (A+D, B+C) were consistent with prior reports, moreover, prediction for individual AIS grades continued to reveal lower values. AUCs by different age categories showed a decline in prognostication accuracy with an increase in age, with statistically significant improvement of AUC when age-cut off was reduced to 50.

CONCLUSIONS

We confirmed previous results that former prediction models achieve strong prognostic accuracy by combining AIS subgroups, yet prognostication of the separate AIS groups is less accurate. Further, prognostication of persons with AIS B+C, for whom a clinical prediction model has arguably greater clinical utility, is less accurate than those with AIS A+D. Our findings emphasize that age is an important factor in prognosticating ambulation following SCI. Prediction accuracy declines for older individuals compared with younger ones. To improve prediction of independent ambulation, the age of 50 years may be a better cutoff instead of age of 65.

XGBoost, a Machine Learning Method, Predicts Neurological Recovery in Patients with Cervical Spinal Cord Injury

The accurate prediction of neurological outcomes in patients with cervical spinal cord injury (SCI) is difficult because of heterogeneity in patient characteristics, treatment strategies, and radiographic findings. Although machine learning algorithms may increase the accuracy of outcome predictions in various fields, limited information is available on their efficacy in the management of SCI. We analyzed data from 165 patients with cervical SCI, and extracted important factors for predicting prognoses. Extreme gradient boosting (XGBoost) as a machine learning model was applied to assess the reliability of a machine learning algorithm to predict neurological outcomes compared with that of conventional methodology, such as a logistic regression or decision tree. We used regularly obtainable data as predictors, such as demographics, magnetic resonance variables, and treatment strategies. Predictive tools, including XGBoost, a logistic regression, and a decision tree, were applied to predict neurological improvements in the functional motor status (ASIA [American Spinal Injury Association] Impairment Scale [AIS] D and E) 6 months after injury. We evaluated predictive performance, including accuracy and the area under the receiver operating characteristic curve (AUC). Regarding predictions of neurological improvements in patients with cervical SCI, XGBoost had the highest accuracy (81.1%), followed by the logistic regression (80.6%) and the decision tree (78.8%). Regarding AUC, the logistic regression showed 0.877, followed by XGBoost (0.867) and the decision tree (0.753). XGBoost reliably predicted neurological alterations in patients with cervical SCI. The utilization of predictive machine learning algorithms may enhance personalized management choices through pre-treatment categorization of patients.