Early Clinical Prediction of Independent Outdoor Functional Walking Capacity in a Prospective Cohort of Traumatic Spinal Cord Injury Patients

Data-driven prediction of spinal cord injury recovery: An exploration of current status and future perspectives

Spinal Cord Injury (SCI) presents a significant challenge in rehabilitation medicine, with recovery outcomes varying widely among individuals. Machine learning (ML) is a promising approach to enhance the prediction of recovery trajectories, but its integration into clinical practice requires a thorough understanding of its efficacy and applicability. We systematically reviewed the current literature on data-driven models of SCI recovery prediction. The included studies were evaluated based on a range of criteria assessing the approach, implementation, input data preferences, and the clinical outcomes aimed to forecast. We observe a tendency to utilize routinely acquired data, such as International Standards for Neurological Classification of SCI (ISNCSCI), imaging, and demographics, for the prediction of functional outcomes derived from the Spinal Cord Independence Measure (SCIM) III and Functional Independence Measure (FIM) scores with a focus on motor ability. Although there has been an increasing interest in data-driven studies over time, traditional machine learning architectures, such as linear regression and tree-based approaches, remained the overwhelmingly popular choices for implementation. This implies ample opportunities for exploring architectures addressing the challenges of predicting SCI recovery, including techniques for learning from limited longitudinal data, improving generalizability, and enhancing reproducibility. We conclude with a perspective, highlighting possible future directions for data-driven SCI recovery prediction and drawing parallels to other application fields in terms of diverse data types (imaging, tabular, sequential, multimodal), data challenges (limited, missing, longitudinal data), and algorithmic needs (causal inference, robustness).

Recommendations for clinical decision-making when offering exoskeletons for community use in individuals with spinal cord injury

Approved in 2014 by the Food and Drug Administration (FDA) for use with a trained companion, personal powered exoskeletons (PPE) for individuals with spinal cord injury (SCI) provide an opportunity for the appropriate candidate to ambulate in their home and community. As an adjunct to wheeled mobility, PPE use allows those individuals who desire to ambulate the opportunity to experience the potential physiological and psychosocial benefits of assisted walking outside of a rehabilitation setting. There exists, however, a knowledge gap for clinicians regarding appropriate candidate selection for use, as well as who might benefit from ambulating with a PPE. The purpose of this paper is to provide guidance for clinicians working with individuals living with SCI by outlining an expert consensus for a PPE decision-making algorithm, as well as a discussion of potential physiological and psychosocial benefits from PPE use based on early evidence in publication.

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.

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.

Predicting Outdoor Walking 1 Year After Spinal Cord Injury: A Retrospective, Multisite External Validation Study

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 ).