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Biomarkers of acute human spinal cord injury (SCI) could provide a more objective measure of spinal cord damage and a better predictor of neurological outcome than current standardized neurological assessments. In SCI, there is growing interest in establishing biomarkers from cerebrospinal fluid (CSF) and from magnetic resonance imaging (MRI). Here, we compared the ability of CSF and MRI biomarkers to classify injury severity and predict neurological recovery in a cohort of acute cervical SCI patients. CSF samples and MRI scans from 36 acute cervical SCI patients were examined. From the CSF samples taken 24 h post-injury, the concentrations of inflammatory cytokines (interleukin [IL]-6, IL-8, monocyte chemotactic protein-1), and structural proteins (tau, glial fibrillary acidic protein, and S100β) were measured. From the pre-operative MRI scans, we measured intramedullary lesion length, hematoma length, hematoma extent, CSF effacement, cord expansion, and maximal spinal cord compression. Baseline and 6-month post-injury assessments of American Spine Injury Association Impairment Scale (AIS) grade and motor score were conducted. Both MRI measures and CSF biomarker levels were found to correlate with baseline injury grade, and in combination they provided a stronger model for classifying baseline AIS grade than CSF or MRI biomarkers alone. For predicting neurological recovery, the inflammatory CSF biomarkers best predicted AIS grade conversion, whereas structural biomarker levels best predicted motor score improvement. A logistic regression model utilizing CSF biomarkers alone had a 91.2% accuracy at predicting AIS conversion, and was not strengthened by adding MRI features or even knowledge of the baseline AIS grade. In a direct comparison of MRI and CSF biomarkers, the CSF biomarkers discriminate better between different injury severities, and are stronger predictors of neurological recovery in terms of AIS grade and motor score improvement. These findings demonstrate the utility of measuring the acute biological responses to SCI as biomarkers of injury severity and neurological prognosis.

To determine the influence of time from injury to surgery on neurological recovery and length of stay (LOS) in an observational cohort of individuals with traumatic spinal cord injury (tSCI), we analyzed the baseline and follow-up motor scores of participants in the Rick Hansen Spinal Cord Injury Registry to specifically assess the effect of an early (less than 24 h from injury) surgical procedure on motor recovery and on LOS. One thousand four hundred and ten patients who sustained acute tSCIs with baseline American Spinal Injury Association Impairment Scale (AIS) grades A, B, C, or D and were treated surgically were analyzed to determine the effect of the timing of surgery (24, 48, or 72 h from injury) on motor recovery and LOS. Depending on the distribution of data, we used different types of generalized linear models, including multiple linear regression, gamma regression, and negative binomial regression. Persons with incomplete AIS B, C, and D injuries from C2 to L2 demonstrated motor recovery improvement of an additional 6.3 motor points (SE=2.8 p<0.03) when they underwent surgical treatment within 24 h from the time of injury, compared with those who had surgery later than 24 h post-injury. This beneficial effect of early surgery on motor recovery was not seen in the patients with AIS A complete SCI. AIS A and B patients who received early surgery experienced shorter hospital LOS. While the issues of when to perform surgery and what specific operation to perform remain controversial, this work provides evidence that for an incomplete acute tSCI in the cervical, thoracic, or thoracolumbar spine, surgery performed within 24 h from injury improves motor neurological recovery. Early surgery also reduces LOS.

Study designCohort study.ObjectivesIt is widely accepted that the prediction of long-term neurologic outcome after traumatic spinal cord injury (SCI) can be done more accurately with neurological examinations conducted days to weeks post injury. However, modern clinical trials of neuroprotective interventions often require patients be examined and enrolled within hours. Our objective was to determine whether variability in timing of neurological examinations within 48 h after SCI is associated with differences in observations of follow-up neurologic recovery.SettingLevel I trauma hospital.MethodsAn observational analysis testing for differences in AIS conversion rates and changes in total motor scores by neurological examination timing, controlling for potential confounders with multivariate stepwise regression.ResultsWe included 85 patients, whose mean times from injury to baseline and follow-up examinations were 11.8 h (SD 9.8) and 208.2 days (SD 75.2), respectively. AIS conversion by 1+ grade was significantly more likely in patients examined at ≤4 h in comparison with later examination (78% versus 47%, RR = 1.66, p = 0.04), even after controlling for timing of surgery, age, and sex (OR 5.0, 95% CI 1.1–10, p = 0.04). We failed to identify any statistically significant associations for total motor score recovery in unadjusted or adjusted analyses.ConclusionsAIS grade conversion was significantly more likely in those examined ≤4 h of injury; the effect of timing on motor scores remains uncertain. Variability in neurological examination timing within hours after acute traumatic SCI may influence observations of long-term neurological recovery, which could introduce bias or lead to errors in interpretation of studies of therapeutic interventions.

During the last few years, the international community debated urinary tract infection and re-use of catheters when managing neurogenic lower urinary tract dysfunction (NLUTD) among individuals with spinal cord injury (SCI). In this respect, the 2014 Cochrane review by Prieto and colleagues, “Intermittent catheterisation for long-term bladder management,” became one of the leading documents that captured the minds and attention of clinicians around the world. Although numerous countries had switched to single-use catheters for management of NLUTD following SCI, the opinion that was expressed in the 2014 Cochrane review had a strong influence on healthcare providers and agencies to recommend re-use of catheters. However, many clinicians have expressed concern regarding the conclusions in the 2014 Cochrane review by Prieto and colleagues. We therefore conducted an independent appraisal of the data and analyses presented in the review. Our appraisal identified crucial discrepancies of data extraction and analyses within the review. In appraisal to that of Prieto and colleagues' review, our analysis revealed a trend to favor single over multiple use of catheters. After addressing our concerns to Cochrane's acting Editor-in-Chief, the most recent version of the 2014 Cochrane review was withdrawn from publication.

Background/Objective: Frailty, a geriatric syndrome characterized by decreased reserve and resistance to stressors in older adults, has been established as a robust predictor of health outcomes. Recently, the Neurocognitive Frailty Index (NFI) was introduced, including 42 physical and cognitive elements that collectively assess an individual’s vulnerability to age-related health decline. While this multidimensional approach improves predictive accuracy for cognitive decline, its high dimensionality might be a barrier to widespread adoption. Methods: We employed several machine learning techniques to reduce the dimensions of NFI while maintaining its predictive power. We trained five models: Network Analysis, neural networks, Least Absolute Shrinkage and Selection Operator Regression (LASSO), Random Forest, and eXtreme Gradient Boosting (XGBoost). Each model was calibrated using a dataset from the Canadian Study of Health and Aging, which included various cognitive, health, and functional variables. Results: Results indicated that six variables had minimal impact on outcome. This reduction in dimensionality resulted in a modified NFI scale including 36 elements, while maintaining good predictive performance for cognitive change similar to the original NFI. Conclusions: Our findings support the feasibility of applying machine learning techniques to modify predictive models in neurodegenerative diseases beyond frailty assessment. We recommend exploring the application of this scale using other data. The results also emphasize the potential of machine learning approaches for improving predictive models, highlighting their value as a tool for advancing our understanding of aging and its complexities.

Costs associated with initial hospitalization following spinal cord injury (SCI) are substantial, and a major driver of costs is the length of stay (LOS); that is, the time that the injured individual remains hospitalized prior to community reintegration. Our aim was to study the factors and variables that contribute to LOS following traumatic SCI. Modeling (process mapping of the SCI healthcare delivery system in Canada and discrete event simulation) and regression analysis using a national registry of individuals with acute traumatic SCI in Canada, existing databases, and peer-reviewed literature were used to examine the driver of LOS following traumatic SCI. In different jurisdictions, there is considerable variation in the definitions and methods used to determine LOS following SCI. System LOS can be subdivided into subcomponents, and progression through these is not unidirectional. Modeling reveals that healthcare organization and processes are important contributors to differences in LOS independent of patient demographics and injury characteristics. Future research is required to identify and improve understanding of contributors to LOS following traumatic SCI. This will help enhance system performance. Work in this area will be facilitated by the adoption of common terminology and definitions, as well as by the use of simulations and modeling.

Clinical trials of novel therapies for acute spinal cord injury (SCI) are challenging because variability in spontaneous neurologic recovery can make discerning actual treatment effects difficult. Unbiased Recursive Partitioning regression with Conditional Inference Trees (URP-CTREE) is a novel approach developed through analyses of a large European SCI database (European Multicenter Study about Spinal Cord Injury). URP-CTREE uses early neurologic impairment to predict achieved motor recovery, with potential to optimize clinical trial design by optimizing patient stratification and decreasing sample sizes. We performed external validation to determine how well a previously reported URP-CTREE model stratified patients into distinct homogeneous subgroups and predicted subsequent neurologic recovery in an independent cohort. We included patients with acute cervical SCI level C4–C6 from a prospective registry at a quaternary care center from 2004–2018 (n = 101) and applied the URP-CTREE model and evaluated Upper Extremity Motor Score (UEMS) recovery, considered correctly predicted when final UEMS scores were within a pre-specified threshold of 9 points from median; sensitivity analyses evaluated the effect of timing of baseline neurological examination. We included 101 patients, whose mean times from injury baseline and follow-up examinations were 6.1 days (standard deviation [SD] 17) and 235.0 days (SD 71), respectively. Median UEMS recovery was 7 points (interquartile range 2–12). One of the predictor variables was not statistically significant in our sample; one group did not fit progressively improving UEMS scores, and three of five groups had medians that were not significantly different from adjacent groups. Overall accuracy was 75%, but varied from 82% among participants whose examinations occurred at <12 h, to 64% at 12–24 h, and 58% at >24 h. A previous URP-CTREE model had limited ability to stratify an independent into homogeneous subgroups. Overall accuracy was promising, but may be sensitive to timing of baseline neurological examinations. Further evaluation of external validity in incomplete injuries, influence of timing of baseline examinations, and investigation of additional stratification strategies is warranted.

Adverse events (AEs) are common during care in patients with traumatic spinal cord injury (tSCI). Increased risk of AEs is linked to patient factors including pre-existing comorbidities. Our aim was to examine the relationships between patient factors and common post-injury AEs, and identify potentially modifiable comorbidities. Adults with tSCI admitted to a Level I acute specialized spine center between 2006 and 2014 who were enrolled in the Rick Hansen SCI Registry (RHSCIR) and had AE data collected using the Spine Adverse Events Severity system were included. Patient demographic, neurological injury, and comorbidities data were obtained from RHSCIR. Potentially modifiable comorbidities were grouped into health-related conditions, substance use/withdrawal, and psychiatric conditions. Negative binomial regression and multiple logistic regression were used to model the impact of patient factors on the number of AEs experienced and the occurrence of the five previously identified common AEs, respectively. Of the 444 patients included in the study, 24.8% reported a health-related condition, 15.3% had a substance use/withdrawal condition, 8% reported having a psychiatric condition; and 79.3% experienced one or more AEs. Older age (p = 0.004) and more severe injuries (p < 0.001) were nonmodifiable independent variables significantly associated with increased AEs. The AEs experienced by patients were urinary tract infections (42.8%), pneumonia (39.2%), neuropathic pain (31.5%), delirium (18.2%), and pressure ulcers (11.0%). Risk of delirium increased in those with substance use/withdrawal; and pneumonia risk increased with psychiatric comorbidities. Opportunity exists to develop clinical algorithms that include these types of risk factors to reduce the incidence and impact of AEs.

Concern has been expressed that preventive measures in older people might increase frailty by increasing survival without improving health. We investigated the impact of exercise on the probabilities of health improvement, deterioration and death in community-dwelling older people. In the Canadian Study of Health and Aging, health status was measured by a frailty index based on the number of health deficits. Exercise was classified as either high or low/no exercise, using a validated, self-administered questionnaire. Health status and survival were re-assessed at 5 years. Of 6297 eligible participants, 5555 had complete data. Across all grades of frailty, death rates for both men and women aged over 75 who exercised were similar to their peers aged 65 to 75 who did not exercise. In addition, while all those who exercised had a greater chance of improving their health status, the greatest benefits were in those who were more frail (e.g. improvement or stability was observed in 34% of high exercisers versus 26% of low/no exercisers for those with 2 deficits compared with 40% of high exercisers versus 22% of low/no exercisers for those with 9 deficits at baseline). In community-dwelling older people, exercise attenuated the impact of age on mortality across all grades of frailty. Exercise conferred its greatest benefits to improvements in health status in those who were more frail at baseline. The net effect of exercise should therefore be to improve health status at the population level.

On average, cognition declines with age but this average hides considerable variability, including the chance of improvement. Here, we investigate how exercise is associated with cognitive change and mortality in older people and, particularly, whether exercise might paradoxically increase the risk of dementia by allowing people to live longer. In the Canadian Study of Health and Aging (CSHA), of 8403 people who had baseline cognition measured and exercise reported at CSHA-1, 2219 had died and 5376 were re-examined at CSHA-2. We used a parametric Markov chain model to estimate the probabilities of cognitive improvement, decline, and death, adjusted for age and education, from any cognitive state as measured by the Modified Mini-Mental State Examination. High exercisers (at least three times per week, at least as intense as walking, n = 3264) had more frequent stable or improved cognition (42.3%, 95% confidence interval: 40.6-44.0) over 5 years than did low/no exercisers (all other exercisers and non exercisers, n = 4331) (27.8% (95% CI 26.4-29.2)). The difference widened as baseline cognition worsened. The proportion whose cognition declined was higher amongst the high exercisers but was more similar between exercise groups (39.4% (95% CI 37.7-41.1) for high exercisers versus 34.8% (95% CI 33.4-36.2) otherwise). People who did not exercise were also more likely to die (37.5% (95% CI 36.0-39.0) versus 18.3% (95% CI 16.9-19.7)). Even so, exercise conferred its greatest mortality benefit to people with the highest baseline cognition. Exercise is strongly associated with improving cognition. As the majority of mortality benefit of exercise is at the highest level of cognition, and declines as cognition declines, the net effect of exercise should be to improve cognition at the population level, even with more people living longer.