Prediction of bowel management independence after ischemic spinal cord injury

Systematic review and meta-analysis use in the field of spinal cord injury research: A bibliometric analysis

OBJECTIVE

To subvert issues of low sample sizes and high attrition rates and generate epidemiologically-sound evidence, collaborative research-through international consortia and multi-centric studies-and meta-analysis approaches are encouraged in spinal cord injury (SCI) research. We investigated the use of systematic reviews and meta-analyses (SRMA) methodology in SCI research and evaluated the quality of evidence across publications we identified.

METHODS

We searched the Web of Science Core Collection database by topic without time or language restrictions through 16 December 2022. We identified additional relevant articles through Embase.com. SRMA including human and animal SCI populations were eligible for inclusion. We analyzed data using Bibliometrix and VOSviewer. We used the JBI tool (former Joanna Briggs Institute) to assess methodological quality of a subset of 50 randomly selected articles.

RESULTS

We based our analysis on data from 1'224 documents authored by 5'237 scholars and published in 424 sources between 1985 and 2022. The use of SRMA methodology in the field gained momentum in 2009 and a steady increase followed with an annual growth rate of ≈15%. Our findings indicate major research themes in the field include recovery, SCI management, rehabilitation, and quality of life. Over the past 30 years there has been a shift from SRMA concerning functional recovery, secondary health complications, and quality of life toward biomarkers and neuro-regeneration. The major methodological issues across articles we evaluated included opaquely described search strategies, poorly reported critical appraisals, and insufficiently addressing publication bias. In addition, only one-fifth of articles reported review protocol registration.

CONCLUSIONS

Our bibliometric analysis clearly shows a rapid increase of SRMA applications in SCI research. We discuss the most important methodological concerns we identified among a randomly selected set of articles and provide guidance for improving adherence to methodological and reporting SRMA guidelines.

Development and Validation of a Novel Nomogram for Predicting Mechanical Ventilation After Cervical Spinal Cord Injury

OBJECTIVE

To investigate the risk factors relating to the need for mechanical ventilation (MV) in isolated patients with cervical spinal cord injury (cSCI) and to construct a nomogram prediction model.

DESIGN

Retrospective analysis study.

SETTING

National Spinal Cord Injury Model System Database (NSCID) observation data were initially collected during rehabilitation hospitalization.

PARTICIPANTS

A total of 5784 patients (N=5784) who had a cSCI were admitted to the NSCID between 2006 and 2021.

INTERVENTIONS

Not applicable.

MAIN OUTCOME MEASURE(S)

A univariate and multivariate logistic regression analysis was used to identify the independent factors affecting the use of MV in patients with cSCI, and these independent influencing factors were used to develop a nomogram prediction model. The area under the receiver operating characteristic curve (AUROC), calibration curve, and decision curve analysis (DCA) were used to evaluate the efficiency and the clinical application value of the model, respectively.

RESULTS

In a series of 5784 included patients, 926 cases (16.0%) were admitted to spinal cord model system inpatient rehabilitation with the need for MV. Logistic regression analysis demonstrated that associated injury, American Spinal Cord Injury Association Impairment Scale (AIS), the sum of unilateral optimal motor scores for each muscle segment of upper extremities (sUEM), and neurologic level of injury (NLI) were independent predictors for the use of MV (P<.05). The prediction nomogram of MV usage in patients with cSCI was established based on the above independent predictors. The AUROC of the training set, internal verification set, and external verification set were 0.871 (0.857-0.886), 0.867 (0.843-0.891), and 0.850 (0.824-0.875), respectively. The calibration curve and DCA results showed that the model had good calibration and clinical practicability.

CONCLUSIONS

The nomograph prediction model based on sUEM, NLI, associated injury, and AIS can accurately and effectively predict the risk of MV in patients with cSCI, to help clinicians screen high-risk patients and formulate targeted intervention measures.

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).

Prediction of early bladder outcomes after spinal cord injury: The HALT score

AIMS

Neurogenic bladder (NB) is a prevalent and debilitating consequence of spinal cord injury (SCI). Indeed, the accurate prognostication of early bladder outcomes is crucial for patient counseling, rehabilitation goal setting, and personalized intervention planning.

METHODS

A retrospective exploratory analysis was conducted on a cohort of consecutive SCI patients admitted to a rehabilitation facility in China from May 2016 to December 2022. Demographic, clinical, and electrophysiological data were collected within 40 days post-SCI, with bladder outcomes assessed at 3 months following SCI onset.

RESULTS

The present study enrolled 202 SCI patients with a mean age of 40.3 ± 12.3 years. At 3 months post-SCI, 79 participants exhibited complete bladder emptying. Least absolute shrinkage and selection operator (LASSO) and multivariate logistic regression analyses identified the H-reflex of the soleus muscle, the American Spinal Injury Association Lower Extremity Motor Score (ASIA-LEMS), and the time from lesion to rehabilitation facility (TLRF) as significant independent predictors for bladder emptying. A scoring system named HALT was developed, yielding a strong discriminatory performance with an area under the receiver operating characteristics curve (aROC) of 0.878 (95% CI: 0.823-0.933). A simplified model utilizing only the H-reflex exhibited excellent discriminatory ability with an aROC of 0.824 (95% CI: 0.766-0.881). Both models demonstrated good calibration via the Hosmer-Lemeshow test and favorable clinical net benefits through decision curve analysis (DCA). In comparison to ASIA-LEMS, both the HALT score and H-reflex showed superior predictive accuracy for bladder outcome. Notably, in individuals with incomplete injuries, the HALT score (aROC = 0.973, 95% CI: 0.940-1.000) and the H-reflex (aROC = 0.888, 95% CI: 0.807-0.970) displayed enhanced performance.

CONCLUSION

Two reliable models, the HALT score and the H-reflex, were developed to predict bladder outcomes as early as 3 months after SCI onset. Importantly, this study provides hitherto undocumented evidence regarding the predictive significance of the soleus H-reflex in relation to bladder outcomes in SCI patients.

Prediction of Lower Urinary Tract, Sexual, and Bowel Function, and Autonomic Dysreflexia after Spinal Cord Injury

Spinal cord injury (SCI) produces damage to the somatic and autonomic pathways that regulate lower urinary tract, sexual, and bowel function, and increases the risk of autonomic dysreflexia. The recovery of these functions has a high impact on health, functioning, and quality of life and is set as the utmost priority by patients. The application of reliable models to predict lower urinary tract, sexual, and bowel function, and autonomic dysreflexia is important for guiding counseling, rehabilitation, and social reintegration. Moreover, a reliable prediction is essential for designing future clinical trials to optimize patients' allocation to different treatment groups. To date, reliable and simple algorithms are available to predict lower urinary tract and bowel outcomes after traumatic and ischemic SCI. Previous studies identified a few risk factors to develop autonomic dysreflexia, albeit a model for prediction still lacks. On the other hand, there is an urgent need for a model to predict the evolution of sexual function. The aim of this review is to examine the available knowledge and models for the prediction of lower urinary tract, sexual, and bowel function, and autonomic dysreflexia after SCI, and critically discuss the research priorities in these fields.