ATF3 is a neuron-specific biomarker for spinal cord injury and ischaemic stroke.

BACKGROUND: Although many molecules have been investigated as biomarkers for spinal cord injury (SCI) or ischemic stroke, none of them are specifically induced in central nervous system (CNS) neurons following injuries with low baseline expression. However, neuronal injury constitutes a major pathology associated with SCI or stroke and strongly correlates with neurological outcomes. Biomarkers characterized by low baseline expression and specific induction in neurons post-injury are likely to better correlate with injury severity and recovery, demonstrating higher sensitivity and specificity for CNS injuries compared to non-neuronal markers or pan-neuronal markers with constitutive expressions. METHODS: In animal studies, young adult wildtype and global Atf3 knockout mice underwent unilateral cervical 5 (C5) SCI or permanent distal middle cerebral artery occlusion (pMCAO). Gene expression was assessed using RNA-sequencing and qRT-PCR, while protein expression was detected through immunostaining. Serum ATF3 levels in animal models and clinical human samples were measured using commercially available enzyme-linked immune-sorbent assay (ELISA) kits. RESULTS: Activating transcription factor 3 (ATF3), a molecular marker for injured dorsal root ganglion sensory neurons in the peripheral nervous system, was not expressed in spinal cord or cortex of naïve mice but was induced specifically in neurons of the spinal cord or cortex within 1 day after SCI or ischemic stroke, respectively. Additionally, ATF3 protein levels in mouse blood significantly increased 1 day after SCI or ischemic stroke. Importantly, ATF3 protein levels in human serum were elevated in clinical patients within 24 hours after SCI or ischemic stroke. Moreover, Atf3 knockout mice, compared to the wildtype mice, exhibited worse neurological outcomes and larger damage regions after SCI or ischemic stroke, indicating that ATF3 has a neuroprotective function. CONCLUSIONS: ATF3 is an easily measurable, neuron-specific biomarker for clinical SCI and ischemic stroke, with neuroprotective properties. HIGHLIGHTS: ATF3 was induced specifically in neurons of the spinal cord or cortex within 1 day after SCI or ischemic stroke, respectively. Serum ATF3 protein levels are elevated in clinical patients within 24 hours after SCI or ischemic stroke. ATF3 exhibits neuroprotective properties, as evidenced by the worse neurological outcomes and larger damage regions observed in Atf3 knockout mice compared to wildtype mice following SCI or ischemic stroke.

Predictors of response in PROMIS-global in a chronic low back pain specialty clinic: STarTBack and chronic overlapping pain conditions.

BACKGROUND: Tools, such as the STarTBack Screening Tool (SBT), have been developed to identify risks of progressing to chronic disability in low back pain (LBP) patients in the primary care population. However, less is known about predictors of change in function after treatment in the specialty care population. OBJECTIVE: We pursued a retrospective observational cohort study involving LBP patients seen in a multidisciplinary specialty clinic to assess which features can predict change in function at follow-up. METHODS: The SBT was administered at initial visit, and a variety of patient characteristics were available in the chart including the presence of chronic overlapping pain conditions (COPCs). Patient Reported Outcomes Measurement Information System-10 (PROMIS-10) global physical health (PH) and global mental health (MH) were measured at baseline and at pragmatic time points during follow-up. Linear regression was used to estimate adjusted associations between available features and changes in PROMIS scores. RESULTS: 241 patients were followed for a mean of 17.0 ± 7.5 months. Mean baseline pain was 6.7 (SD 2.1), PROMIS-10 global MH score was 44.8 (SD 9.3), and PH score was 39.4 (SD 8.6). 29.7% were low-risk on the SBT, 41.8% were medium-risk, and 28.5% were high-risk. Mean change in MH and PH scores from baseline to the follow-up questionnaire were 0.86 (SD 8.11) and 2.39 (SD 7.52), respectively. Compared to low-risk patients, high-risk patients had a mean 4.35 points greater improvement in their MH score (p= 0.004) and a mean 3.54 points greater improvement in PH score (p= 0.006). Fewer COPCs also predicted greater improvement in MH and PH. CONCLUSIONS: SBT and the presence of COPC, which can be assessed at initial presentation to a specialty clinic, can predict change in PROMIS following treatment. Effort is needed to identify other factors that can help predict change in function after treatment in the specialty care setting.

Thresholding approaches for estimating paraspinal muscle fat infiltration using T1- and T2-weighted MRI: Comparative analysis using water-fat MRI.

Background: Paraspinal muscle fat infiltration is associated with spinal degeneration and low back pain, however, quantifying muscle fat using clinical magnetic resonance imaging (MRI) techniques continues to be a challenge. Advanced MRI techniques, including chemical-shift encoding (CSE) based water-fat MRI, enable accurate measurement of muscle fat, but such techniques are not widely available in routine clinical practice. Methods: To facilitate assessment of paraspinal muscle fat using clinical imaging, we compared four thresholding approaches for estimating muscle fat fraction (FF) using T1- and T2-weighted images, with measurements from water-fat MRI as the ground truth: Gaussian thresholding, Otsu's method, K-mean clustering, and quadratic discriminant analysis. Pearson's correlation coefficients (r), mean absolute errors, and mean bias errors were calculated for FF estimates from T1- and T2-weighted MRI with water-fat MRI for the lumbar multifidus (MF), erector spinae (ES), quadratus lumborum (QL), and psoas (PS), and for all muscles combined. Results: We found that for all muscles combined, FF measurements from T1- and T2-weighted images were strongly positively correlated with measurements from the water-fat images for all thresholding techniques (r = 0.70-0.86, p < 0.0001) and that variations in inter-muscle correlation strength were much greater than variations in inter-method correlation strength. Conclusion: We conclude that muscle FF can be quantified using thresholded T1- and T2-weighted MRI images with relatively low bias and absolute error in relation to water-fat MRI, particularly in the MF and ES, and the choice of thresholding technique should depend on the muscle and clinical MRI sequence of interest.

Data-driven distillation and precision prognosis in traumatic brain injury with interpretable machine learning.

Traumatic brain injury (TBI) affects how the brain functions in the short and long term. Resulting patient outcomes across physical, cognitive, and psychological domains are complex and often difficult to predict. Major challenges to developing personalized treatment for TBI include distilling large quantities of complex data and increasing the precision with which patient outcome prediction (prognoses) can be rendered. We developed and applied interpretable machine learning methods to TBI patient data. We show that complex data describing TBI patients' intake characteristics and outcome phenotypes can be distilled to smaller sets of clinically interpretable latent factors. We demonstrate that 19 clusters of TBI outcomes can be predicted from intake data, a ~ 6× improvement in precision over clinical standards. Finally, we show that 36% of the outcome variance across patients can be predicted. These results demonstrate the importance of interpretable machine learning applied to deeply characterized patients for data-driven distillation and precision prognosis.

Correction: Expert-augmented automated machine learning optimizes hemodynamic predictors of spinal cord injury outcome.

[This corrects the article DOI: 10.1371/journal.pone.0265254.].

Safety and comparative efficacy of initiating low-molecular-weight heparin within 24 hours of injury or surgery for venous thromboembolism prophylaxis in patients with spinal cord injury: a prospective TRACK-SCI registry study.

OBJECTIVE: Venous thromboembolism (VTE) following traumatic spinal cord injury (SCI) is a significant clinical concern. This study sought to determine the incidence of VTE and hemorrhagic complications among patients with SCI who received low-molecular-weight heparin (LMWH) within 24 hours of injury or surgery and identify variables that predict VTE using the prospective Transforming Research and Clinical Knowledge in SCI (TRACK-SCI) database. METHODS: The TRACK-SCI database was queried for individuals with traumatic SCI from 2015 to 2022. Primary outcomes of interest included rates of VTE (including deep vein thrombosis [DVT] and pulmonary embolism [PE]) and in-hospital hemorrhagic complications that occurred after LWMH administration. Secondary outcomes included intensive care unit and hospital length of stay, discharge location type, and in-hospital mortality. RESULTS: The study cohort consisted of 162 patients with SCI. Fifteen of the 162 patients withdrew from the study, leading to loss of data for certain variables for these patients. One hundred thirty patients (87.8%) underwent decompression and/or fusion surgery for SCI. DVT occurred in 11 (7.4%) of 148 patients, PE in 9 (6.1%) of 148, and any VTE in 18 (12.2%) of 148 patients. The analysis showed that admission lower-extremity motor score (p = 0.0408), injury at the thoracic level (p = 0.0086), admission American Spinal Injury Association grade (p = 0.0070), and younger age (p = 0.0372) were significantly associated with VTE. There were 3 instances of postoperative spine surgery-related bleeding (2.4%) in the 127 patients who had spine surgery with bleeding complication data available, with one requiring return to surgery (0.8%). Thirteen (8.8%) of 147 patients had a bleeding complication not related to spine surgery. There were 2 gastrointestinal bleeds associated with nasogastric tube placement, 3 cases of postoperative non-spine-related surgery bleeding, and 8 cases of other bleeding complications (5.4%) not related to any surgery. CONCLUSIONS: Initiation of LMWH within 24 hours was associated with a low rate of spine surgery-related bleeding. Bleeding complications unrelated to SCI surgery still occur with LMWH administration. Because neurosurgical intervention is typically the limiting factor in initializing chemical DVT prophylaxis, many of these bleeding complications would have likely occurred regardless of the protocol.

Improving rigor and reproducibility in western blot experiments with the blotRig analysis software.

Western blot is a popular biomolecular analysis method for measuring the relative quantities of independent proteins in complex biological samples. However, variability in quantitative western blot data analysis poses a challenge in designing reproducible experiments. The lack of rigorous quantitative approaches in current western blot statistical methodology may result in irreproducible inferences. Here we describe best practices for the design and analysis of western blot experiments, with examples and demonstrations of how different analytical approaches can lead to widely varying outcomes. To facilitate best practices, we have developed the blotRig tool for designing and analyzing western blot experiments to improve their rigor and reproducibility. The blotRig application includes functions for counterbalancing experimental design by lane position, batch management across gels, and analytics with covariates and random effects.

Multi-domain biopsychosocial postoperative recovery trajectories associate with patient outcomes following lumbar fusion.

PURPOSE: The purpose of this study is to describe and assess the impact of multi-domain biopsychosocial (BPS) recovery on outcomes following lumbar spine fusion. We hypothesized that discrete patterns of BPS recovery (e.g., clusters) would be identified, and then associated with postoperative outcomes and preoperative patient data. METHODS: Patient-reported outcomes for pain, disability, depression, anxiety, fatigue, and social roles were collected at multiple timepoints for patients undergoing lumbar fusion between baseline and one year. Multivariable latent class mixed models assessed composite recovery as a function of (1) pain, (2) pain and disability, and (3) pain, disability, and additional BPS factors. Patients were assigned to clusters based on their composite recovery trajectories over time. RESULTS: Using all BPS outcomes from 510 patients undergoing lumbar fusion, three multi-domain postoperative recovery clusters were identified: Gradual BPS Responders (11%), Rapid BPS Responders (36%), and Rebound Responders (53%). Modeling recovery from pain alone or pain and disability alone failed to generate meaningful or distinct recovery clusters. BPS recovery clusters were associated with number of levels fused and preoperative opioid use. Postoperative opioid use (p < 0.01) and hospital length of stay (p < 0.01) were associated with BPS recovery clusters even after adjusting for confounding factors. CONCLUSION: This study describes distinct clusters of recovery following lumbar spine fusion derived from multiple BPS factors, which are related to patient-specific preoperative factors and postoperative outcomes. Understanding postoperative recovery trajectories across multiple health domains will advance our understanding of how BPS factors interact with surgical outcomes and could inform personalized care plans.

Hypotension requiring vasopressor treatment and increased cardiac complications in elderly spinal cord injury patients: a prospective TRACK-SCI registry study.

OBJECTIVE: Increasing life expectancy has led to an older population. In this study, the authors analyzed complications and outcomes in elderly patients following spinal cord injury (SCI) using the established multi-institutional prospective study Transforming Research and Clinical Knowledge in SCI (TRACK-SCI) database collected in the Department of Neurosurgical Surgery at the University of California, San Francisco. METHODS: TRACK-SCI was queried for elderly individuals (≥ 65 years of age) with traumatic SCI from 2015 to 2019. Primary outcomes of interest included total hospital length of stay, perioperative complications, postoperative complications, and in-hospital mortality. Secondary outcomes included disposition location, and neurological improvement based on the American Spinal Injury Association Impairment Scale (AIS) grade at discharge. Descriptive analysis, Fisher's exact test, univariate analysis, and multivariable regression analysis were performed. RESULTS: The study cohort consisted of 40 elderly patients. The in-hospital mortality rate was 10%. Every patient in this cohort experienced at least 1 complication, with a mean of 6.6 separate complications (median 6, mode 4). The most common complication categories were cardiovascular, with a mean of 1.6 complications (median 1, mode 1), and pulmonary, with a mean of 1.3 (median 1, mode 0) complications, with 35 patients (87.5%) having at least 1 cardiovascular complication and 25 (62.5%) having at least 1 pulmonary complication. Overall, 32 patients (80%) required vasopressor treatment for mean arterial pressure (MAP) maintenance goals. The use of norepinephrine correlated with increased cardiovascular complications. Only 3 patients (7.5%) of the total cohort had an improved AIS grade compared with their acute level at admission. CONCLUSIONS: Given the increased frequency of cardiovascular complications associated with vasopressor use in elderly SCI patients, caution is warranted when targeting MAP goals in these patients. A downward adjustment of blood pressure maintenance goals and prophylactic cardiology consultation to select the most appropriate vasopressor agent may be advisable for SCI patients ≥ 65 years of age.

#OrthoTwitter: Relationship Between Author Twitter Utilization and Academic Impact in Orthopaedic Surgery.

Background #OrthoTwitter has evolved to disseminate findings and engage the public. However, the academic impact of Twitter utilization in orthopaedic surgery is unknown. Questions/purposes The purpose of the study was to evaluate relationships between the author and manuscript Twitter activity and citations. Methods Manuscripts in 17 orthopaedic journals from 2018 were identified. Citations, online mentions, impact factors, and subspecialties were obtained. H-index and Twitter account details for authors were obtained for a subset of manuscripts. Relationships between Twitter activity and citations were evaluated. Results 2,473/4,224 (58.5%) manuscripts were mentioned on Twitter (n=29,958 mentions), with Twitter manuscripts cited more frequently (median 10 vs. 7, p<0.0001). Twitter mentions, impact factors, non-open-access status, and subspecialties were associated with citation counts. Articles mentioned in 10, 100, and 1,000 Tweets were observed to have a 1.1-fold, 1.7-fold, and 245-fold increase in citations. In author-level analyses, 156 (20.0%) first and 216 (27.7%) senior authors had Twitter accounts. Citation count was associated with increasing senior author H-index (ß est=0.13, p<0.05), Twitter mentions (ß est=0.0043, p<0.0001), impact factors (ß est=0.13, p<0.0001), and having a first (ß est=0.20, p<0.05) or senior author (ß est=0.17, p<0.05) on Twitter. Articles published in arthroplasty (ß est=0.49, p<0.05), general interest (ß est=0.55, p<0.01), sports (ß est=0.63, p<0.01), and non-open access journals (ß est=0.41, p<0.001) were cited more. H-index correlated with followers for first (rho=0.31, p<0.0001) and senior authors (rho=0.44, p<0.0001). Conclusion Author Twitter utilization is independently associated with manuscript citations. Authors should be aware of the potential association between social media utilization and traditional academic impact. Understanding the relationship between social media utilization and academic impact is necessary to effectively disseminate research.

Combined space stressors induce independent behavioral deficits predicted by early peripheral blood monocytes.

Interplanetary space travel poses many hazards to the human body. To protect astronaut health and performance on critical missions, there is first a need to understand the effects of deep space hazards, including ionizing radiation, confinement, and altered gravity. Previous studies of rodents exposed to a single such stressor document significant deficits, but our study is the first to investigate possible cumulative and synergistic impacts of simultaneous ionizing radiation, confinement, and altered gravity on behavior and cognition. Our cohort was divided between 6-month-old female and male mice in group, social isolation, or hindlimb unloading housing, exposed to 0 or 50 cGy of 5 ion simplified simulated galactic cosmic radiation (GCRsim). We report interactions and independent effects of GCRsim exposure and housing conditions on behavioral and cognitive performance. Exposure to GCRsim drove changes in immune cell populations in peripheral blood collected early after irradiation, while housing conditions drove changes in blood collected at a later point. Female mice were largely resilient to deficits observed in male mice. Finally, we used principal component analysis to represent total deficits as principal component scores, which were predicted by general linear models using GCR exposure, housing condition, and early blood biomarkers.

Harmonization-Information Trade-Offs for Sharing Individual Participant Data in Biomedicine.

Biomedical practice is evidence-based. Peer-reviewed papers are the primary medium to present evidence and data-supported results to drive clinical practice. However, it could be argued that scientific literature does not contain data, but rather narratives about and summaries of data. Meta-analyses of published literature may produce biased conclusions due to the lack of transparency in data collection, publication bias, and inaccessibility to the data underlying a publication ('dark data'). Co-analysis of pooled data at the level of individual research participants can offer higher levels of evidence, but this requires that researchers share raw individual participant data (IPD). FAIR (findable, accessible, interoperable, and reusable) data governance principles aim to guide data lifecycle management by providing a framework for actionable data sharing. Here we discuss the implications of FAIR for data harmonization, an essential step for pooling data for IPD analysis. We describe the harmonization-information trade-off, which states that the level of granularity in harmonizing data determines the amount of information lost. Finally, we discuss a framework for managing the trade-off and the levels of harmonization. In the coming era of funder mandates for data sharing, research communities that effectively manage data harmonization will be empowered to harness big data and advanced analytics such as machine learning and artificial intelligence tools, leading to stunning new discoveries that augment our understanding of diseases and their treatments. By elevating scientific data to the status of a first-class citizen of the scientific enterprise, there is strong potential for biomedicine to transition from a narrative publication product orientation to a modern data-driven enterprise where data itself is viewed as a primary work product of biomedical research.

A transient inflammatory response contributes to oxaliplatin neurotoxicity in mice.

OBJECTIVES: Peripheral neuropathy is a relevant dose-limiting adverse event that can affect up to 90% of oncologic patients with colorectal cancer receiving oxaliplatin treatment. The severity of neurotoxicity often leads to dose reduction or even premature cessation of chemotherapy. Unfortunately, the limited knowledge about the molecular mechanisms related to oxaliplatin neurotoxicity leads to a lack of effective treatments to prevent the development of this clinical condition. In this context, the present work aimed to determine the exact molecular mechanisms involved in the development of oxaliplatin neurotoxicity in a murine model to try to find new therapeutical targets. METHODS: By single-cell RNA sequencing (scRNA-seq), we studied the transcriptomic profile of sensory neurons and satellite glial cells (SGC) of the Dorsal Root Ganglia (DRG) from a well-characterized mouse model of oxaliplatin neurotoxicity. RESULTS: Analysis of scRNA-seq data pointed to modulation of inflammatory processes in response to oxaliplatin treatment. In this line, we observed increased levels of NF-kB p65 protein, pro-inflammatory cytokines, and immune cell infiltration in DRGs and peripheral nerves of oxaliplatin-treated mice, which was accompanied by mechanical allodynia and decrease in sensory nerve amplitudes. INTERPRETATION: Our data show that, in addition to the well-described DNA damage, oxaliplatin neurotoxicity is related to an exacerbated pro-inflammatory response in DRG and peripheral nerves, and open new insights in the development of anti-inflammatory strategies as a treatment for preventing peripheral neuropathy induced by oxaliplatin.

Unsupervised Machine Learning on Motion Capture Data Uncovers Movement Strategies in Low Back Pain.

Chronic low back pain (LBP) is a leading cause of disability and opioid prescriptions worldwide, representing a significant medical and socioeconomic problem. Clinical heterogeneity of LBP limits accurate diagnosis and precise treatment planning, culminating in poor patient outcomes. A current priority of LBP research is the development of objective, multidimensional assessment tools that subgroup LBP patients based on neurobiological pain mechanisms, to facilitate matching patients with the optimal therapies. Using unsupervised machine learning on full body biomechanics, including kinematics, dynamics, and muscle forces, captured with a marker-less depth camera, this study identified a forward-leaning sit-to-stand strategy (STS) as a discriminating movement biomarker for LBP subjects. A forward-leaning STS strategy, as opposed to a vertical rise strategy seen in the control participants, is less efficient and results in increased spinal loads. Inefficient STS with the subsequent higher spinal loading may be a biomarker of poor motor control in LBP patients as well as a potential source of the ongoing symptomology.

Chondroitin sulfate proteoglycans prevent immune cell phenotypic conversion and inflammation resolution via TLR4 in rodent models of spinal cord injury.

Chondroitin sulfate proteoglycans (CSPGs) act as potent inhibitors of axonal growth and neuroplasticity after spinal cord injury (SCI). Here we reveal that CSPGs also play a critical role in preventing inflammation resolution by blocking the conversion of pro-inflammatory immune cells to a pro-repair phenotype in rodent models of SCI. We demonstrate that enzymatic digestion of CSPG glycosaminoglycans enhances immune cell clearance and reduces pro-inflammatory protein and gene expression profiles at key resolution time points. Analysis of phenotypically distinct immune cell clusters revealed CSPG-mediated modulation of macrophage and microglial subtypes which, together with T lymphocyte infiltration and composition changes, suggests a role for CSPGs in modulating both innate and adaptive immune responses after SCI. Mechanistically, CSPG activation of a pro-inflammatory phenotype in pro-repair immune cells was found to be TLR4-dependent, identifying TLR4 signalling as a key driver of CSPG-mediated immune modulation. These findings establish CSPGs as critical mediators of inflammation resolution failure after SCI in rodents, which leads to prolonged inflammatory pathology and irreversible tissue destruction.

Expert-augmented automated machine learning optimizes hemodynamic predictors of spinal cord injury outcome.

Artificial intelligence and machine learning (AI/ML) is becoming increasingly more accessible to biomedical researchers with significant potential to transform biomedicine through optimization of highly-accurate predictive models and enabling better understanding of disease biology. Automated machine learning (AutoML) in particular is positioned to democratize artificial intelligence (AI) by reducing the amount of human input and ML expertise needed. However, successful translation of AI/ML in biomedicine requires moving beyond optimizing only for prediction accuracy and towards establishing reproducible clinical and biological inferences. This is especially challenging for clinical studies on rare disorders where the smaller patient cohorts and corresponding sample size is an obstacle for reproducible modeling results. Here, we present a model-agnostic framework to reinforce AutoML using strategies and tools of explainable and reproducible AI, including novel metrics to assess model reproducibility. The framework enables clinicians to interpret AutoML-generated models for clinical and biological verifiability and consequently integrate domain expertise during model development. We applied the framework towards spinal cord injury prognostication to optimize the intraoperative hemodynamic range during injury-related surgery and additionally identified a strong detrimental relationship between intraoperative hypertension and patient outcome. Furthermore, our analysis captured how evolving clinical practices such as faster time-to-surgery and blood pressure management affect clinical model development. Altogether, we illustrate how expert-augmented AutoML improves inferential reproducibility for biomedical discovery and can ultimately build trust in AI processes towards effective clinical integration.

Decision tree-based machine learning analysis of intraoperative vasopressor use to optimize neurological improvement in acute spinal cord injury.

OBJECTIVE: Previous work has shown that maintaining mean arterial pressures (MAPs) between 76 and 104 mm Hg intraoperatively is associated with improved neurological function at discharge in patients with acute spinal cord injury (SCI). However, whether temporary fluctuations in MAPs outside of this range can be tolerated without impairment of recovery is unknown. This retrospective study builds on previous work by implementing machine learning to derive clinically actionable thresholds for intraoperative MAP management guided by neurological outcomes. METHODS: Seventy-four surgically treated patients were retrospectively analyzed as part of a longitudinal study assessing outcomes following SCI. Each patient underwent intraoperative hemodynamic monitoring with recordings at 5-minute intervals for a cumulative 28,594 minutes, resulting in 5718 unique data points for each parameter. The type of vasopressor used, dose, drug-related complications, average intraoperative MAP, and time spent in an extreme MAP range (< 76 mm Hg or > 104 mm Hg) were collected. Outcomes were evaluated by measuring the change in American Spinal Injury Association Impairment Scale (AIS) grade over the course of acute hospitalization. Features most predictive of an improvement in AIS grade were determined statistically by generating random forests with 10,000 iterations. Recursive partitioning was used to establish clinically intuitive thresholds for the top features. RESULTS: At discharge, a significant improvement in AIS grade was noted by an average of 0.71 levels (p = 0.002). The hemodynamic parameters most important in predicting improvement were the amount of time intraoperative MAPs were in extreme ranges and the average intraoperative MAP. Patients with average intraoperative MAPs between 80 and 96 mm Hg throughout surgery had improved AIS grades at discharge. All patients with average intraoperative MAP > 96.3 mm Hg had no improvement. A threshold of 93 minutes spent in an extreme MAP range was identified after which the chance of neurological improvement significantly declined. Finally, the use of dopamine as compared to norepinephrine was associated with higher rates of significant cardiovascular complications (50% vs 25%, p < 0.001). CONCLUSIONS: An average intraoperative MAP value between 80 and 96 mm Hg was associated with improved outcome, corroborating previous results and supporting the clinical verifiability of the model. Additionally, an accumulated time of 93 minutes or longer outside of the MAP range of 76-104 mm Hg is associated with worse neurological function at discharge among patients undergoing emergency surgical intervention for acute SCI.

Empowering Data Sharing and Analytics through the Open Data Commons for Traumatic Brain Injury Research.

Traumatic brain injury (TBI) is a major public health problem. Despite considerable research deciphering injury pathophysiology, precision therapies remain elusive. Here, we present large-scale data sharing and machine intelligence approaches to leverage TBI complexity. The Open Data Commons for TBI (ODC-TBI) is a community-centered repository emphasizing Findable, Accessible, Interoperable, and Reusable data sharing and publication with persistent identifiers. Importantly, the ODC-TBI implements data sharing of individual subject data, enabling pooling for high-sample-size, feature-rich data sets for machine learning analytics. We demonstrate pooled ODC-TBI data analyses, starting with descriptive analytics of subject-level data from 11 previously published articles (N = 1250 subjects) representing six distinct pre-clinical TBI models. Second, we perform unsupervised machine learning on multi-cohort data to identify persistent inflammatory patterns across different studies, improving experimental sensitivity for pro- versus anti-inflammation effects. As funders and journals increasingly mandate open data practices, ODC-TBI will create new scientific opportunities for researchers and facilitate multi-data-set, multi-dimensional analytics toward effective translation.

Using hierarchical unsupervised learning to integrate and reduce multi-level and multi-paraspinal muscle MRI data in relation to low back pain.

PURPOSE: The paraspinal muscles (PSM) are a key feature potentially related to low back pain (LBP), and their structure and composition can be quantified using MRI. Most commonly, quantifying PSM measures across individual muscles and individual spinal levels renders numerous separate metrics that are analyzed in isolation. However, comprehensive multivariate approaches would be more appropriate for analyzing the PSM within an individual. To establish and test these methods, we hypothesized that multivariate summaries of PSM MRI measures would associate with the presence of LBP symptoms (i.e., pain intensity). METHODS: We applied hierarchical multiple factor analysis (hMFA), an unsupervised integrative method, to clinical PSM MRI data from unique cohort datasets including a longitudinal cohort of astronauts with pre- and post-spaceflight data and a cohort of chronic LBP subjects and asymptomatic controls. Three specific use cases were investigated: (1) predicting longitudinal changes in pain using combinations of baseline PSM measures; (2) integrating baseline and post-spaceflight MRI to assess longitudinal change in PSM and how it relates to pain; and (3) integrating PSM quality and adjacent spinal pathology between LBP patients and controls. RESULTS: Overall, we found distinct complex relationships with pain intensity between particular muscles and spinal levels. Subjects with high asymmetry between left and right lean muscle composition and differences between spinal segments PSM quality and structure are more likely to increase in pain reported outcome after prolonged time in microgravity. Moreover, changes in PSM quality and structure between pre and post-spaceflight relate to increase in pain after prolonged microgravity. Finally, we show how unsupervised hMFA recapitulates previous research on the association of CEP damage and LBP diagnostic. CONCLUSION: Our analysis considers the spine as a multi-segmental unit as opposed to a series of discrete and isolated spine segments. Integrative and multivariate approaches can be used to distill large and complex imaging datasets thereby improving the clinical utility of MRI-based biomarkers, and providing metrics for further analytical goals, including phenotyping.

Appendicular Fracture and Polytrauma Correlate with Outcome of Spinal Cord Injury: A Transforming Research and Clinical Knowledge in Spinal Cord Injury Study.

Spinal cord injuries (SCIs) frequently occur in combination with other major organ injuries, such as traumatic brain injury (TBI) and injuries to the chest, abdomen, and musculoskeletal system (e.g., extremity, pelvic, and spine fractures). However, the effects of appendicular fractures on SCI recovery are poorly understood. We investigated whether the presence of SCI-concurrent appendicular fractures is predictive of a less robust SCI recovery. Patients enrolled in the Transforming Research and Clinical Knowledge in SCI (TRACK-SCI) prospective cohort study were identified and included in this secondary analysis study. Inclusion criteria resulted in 147 patients, consisting of 120 with isolated SCIs and 27 with concomitant appendicular fracture. The primary outcome was American Spinal Injury Association (ASIA) Impairment Scale (AIS) neurological grades at hospital discharge. Secondary outcomes included hospital length of stay, intensive care unit (ICU) length of stay, and AIS grade improvement during hospitalization. Multivariable binomial logistical regression analyses assessed whether SCI-concomitant appendicular fractures associate with SCI function and secondary outcomes. These analyses were adjusted for age, gender, injury severity, and non-fracture polytrauma. Appendicular fractures were associated with more severe AIS grades at hospital discharge, though covariate adjustments diminished statistical significance of this effect. Notably, non-fracture injuries to the chest and abdomen were influential covariates. Secondary analyses suggested that appendicular fractures also increased hospital length of stay. Our study indicated that SCI-associated polytrauma is important for predicting SCI functional outcomes. Further statistical evaluation is required to disentangle the effects of appendicular fractures, non-fracture solid organ injury, and SCI physiology to improve health outcomes among SCI patients.