Gender and sex hormone effects on neonatal innate immune function.

OBJECTIVES: Scientific evidence provides a widened view of differences in immune response between male and female neonates. The X-chromosome codes for several genes important in the innate immune response and neonatal innate immune cells express receptors for, and are inhibited by, maternal sex hormones. We hypothesized that sex differences in innate immune responses may be present in the neonatal population which may contribute to the increased susceptibility of premature males to sepsis. We aimed to examine the in vitro effect of pro-inflammatory stimuli and hormones in neutrophils and monocytes of male and female neonates, to examine the expression of X-linked genes involved in innate immunity and the miRNA profiles in these populations. METHODS: Preterm infants (n = 21) and term control (n = 19) infants were recruited from the Coombe Women and Infants University Hospital Dublin with ethical approval and explicit consent. The preterm neonates (eight female, 13 male) were recruited with a mean gestation at birth (mean ± SD) of 28 ± 2 weeks and corrected gestation at the time of sampling was 30 + 2.6 weeks. The mean birth weight of preterm neonates was 1084 ± 246 g. Peripheral blood samples were used to analyze immune cell phenotypes, miRNA human panel, and RNA profiles for inflammasome and inflammatory genes. RESULTS: Dividing neutrophil results by sex showed no differences in baseline CD11b between sexes among either term or preterm neonates. Examining monocyte CD11b by sex shows, that at baseline, total and classical monocytes have higher CD11b in preterm females than preterm males. Neutrophil TLR2 did not differ between sexes at baseline or following lipopolysaccharide (LPS) exposure. CD11b expression was higher in preterm male non-classical monocytes following Pam3CSK treatment when compared to females, a finding which is unique to our study. Preterm neonates had higher TLR2 expression at baseline in total monocytes, classical monocytes and non-classical monocytes than term. A sex difference was evident between preterm females and term females in TLR2 expression only. Hormone treatment showed no sex differences and there was no detectable difference between males and females in X-linked gene expression. Two miRNAs, miR-212-3p and miR-218-2-3p had significantly higher expression in preterm female than preterm male neonates. CONCLUSIONS: This study examined immune cell phenotypes and x-linked gene expression in preterm neonates and stratified according to gender. Our findings suggest that the responses of females mature with advancing gestation, whereas male term and preterm neonates have very similar responses. Female preterm neonates have improved monocyte activation than males, which likely reflects improved innate immune function as reflected clinically by their lower risk of sepsis. Dividing results by sex showed changes in preterm and term infants at baseline and following LPS stimulation, a difference which is reflected clinically by infection susceptibility. The sex difference noted is novel and may be limited to the preterm or early neonatal population as TLR2 expression on monocytes of older children does not differ between males and females. The differences shown in female and male innate immune cells likely reflect a superior innate immune defense system in females with sex differences in immune cell maturation. Existing human studies on sex differences in miRNA expression do not include preterm patients, and most frequently use either adult blood or cord blood. Our findings suggest that miRNA profiles are similar in neonates of opposite sexes at term but require further investigation in the preterm population. Our findings, while novel, provide only very limited insights into sex differences in infection susceptibility in the preterm population leaving many areas that require further study. These represent important areas for ongoing clinical and laboratory study and our findings represent an important contribution to exiting literature.

Predictors of social risk for post-ischemic stroke reintegration.

After stroke rehabilitation, patients need to reintegrate back into their daily life, workplace and society. Reintegration involves complex processes depending on age, sex, stroke severity, cognitive, physical, as well as socioeconomic factors that impact long-term outcomes post-stroke. Moreover, post-stroke quality of life can be impacted by social risks of inadequate family, social, economic, housing and other supports needed by the patients. Social risks and barriers to successful reintegration are poorly understood yet critical for informing clinical or social interventions. Therefore, the aim of this work is to predict social risk at rehabilitation discharge using sociodemographic and clinical variables at rehabilitation admission and identify factors that contribute to this risk. A Gradient Boosting modelling methodology based on decision trees was applied to a Catalan 217-patient cohort of mostly young (mean age 52.7), male (66.4%), ischemic stroke survivors. The modelling task was to predict an individual's social risk upon discharge from rehabilitation based on 16 different demographic, diagnostic and social risk variables (family support, social support, economic status, cohabitation and home accessibility at admission). To correct for imbalance in patient sample numbers with high and low-risk levels (prediction target), five different datasets were prepared by varying the data subsampling methodology. For each of the five datasets a prediction model was trained and the analysis involves a comparison across these models. The training and validation results indicated that the models corrected for prediction target imbalance have similarly good performance (AUC 0.831-0.843) and validation (AUC 0.881 - 0.909). Furthermore, predictor variable importance ranked social support and economic status as the most important variables with the greatest contribution to social risk prediction, however, sex and age had a lesser, but still important, contribution. Due to the complex and multifactorial nature of social risk, factors in combination, including social support and economic status, drive social risk for individuals.

Computable phenotype for real-world, data-driven retrospective identification of relapse in ANCA-associated vasculitis.

OBJECTIVE: ANCA-associated vasculitis (AAV) is a relapsing-remitting disease, resulting in incremental tissue injury. The gold-standard relapse definition (Birmingham Vasculitis Activity Score, BVAS>0) is often missing or inaccurate in registry settings, leading to errors in ascertainment of this key outcome. We sought to create a computable phenotype (CP) to automate retrospective identification of relapse using real-world data in the research setting. METHODS: We studied 536 patients with AAV and >6 months follow-up recruited to the Rare Kidney Disease registry (a national longitudinal, multicentre cohort study). We followed five steps: (1) independent encounter adjudication using primary medical records to assign the ground truth, (2) selection of data elements (DEs), (3) CP development using multilevel regression modelling, (4) internal validation and (5) development of additional models to handle missingness. Cut-points were determined by maximising the F1-score. We developed a web application for CP implementation, which outputs an individualised probability of relapse. RESULTS: Development and validation datasets comprised 1209 and 377 encounters, respectively. After classifying encounters with diagnostic histopathology as relapse, we identified five key DEs; DE1: change in ANCA level, DE2: suggestive blood/urine tests, DE3: suggestive imaging, DE4: immunosuppression status, DE5: immunosuppression change. F1-score, sensitivity and specificity were 0.85 (95% CI 0.77 to 0.92), 0.89 (95% CI 0.80 to 0.99) and 0.96 (95% CI 0.93 to 0.99), respectively. Where DE5 was missing, DE2 plus either DE1/DE3 were required to match the accuracy of BVAS. CONCLUSIONS: This CP accurately quantifies the individualised probability of relapse in AAV retrospectively, using objective, readily accessible registry data. This framework could be leveraged for other outcomes and relapsing diseases.

Current Topics in Technology-Enabled Stroke Rehabilitation and Reintegration: A Scoping Review and Content Analysis.

BACKGROUND: There is a worldwide health crisis stemming from the rising incidence of various debilitating chronic diseases, with stroke as a leading contributor. Chronic stroke management encompasses rehabilitation and reintegration, and can require decades of personalized medicine and care. Information technology (IT) tools have the potential to support individuals managing chronic stroke symptoms. OBJECTIVES: This scoping review identifies prevalent topics and concepts in research literature on IT technology for stroke rehabilitation and reintegration, utilizing content analysis, based on topic modelling techniques from natural language processing to identify gaps in this literature. ELIGIBILITY CRITERIA: Our methodological search initially identified over 14,000 publications of the last two decades in the Web of Science and Scopus databases, which we filter, using keywords and a qualitative review, to a core corpus of 1062 documents. RESULTS: We generate a 3-topic, 4-topic and 5-topic model and interpret the resulting topics as four distinct thematics in the literature, which we label as Robotics, Software, Functional and Cognitive. We analyze the prevalence and distinctiveness of each thematic and identify some areas relatively neglected by the field. These are mainly in the Cognitive thematic, especially for systems and devices for sensory loss rehabilitation, tasks of daily living performance and social participation. CONCLUSION: The results indicate that IT-enabled stroke literature has focused on Functional outcomes and Robotic technologies, with lesser emphasis on Cognitive outcomes and combined interventions. We hope this review broadens awareness, usage and mainstream acceptance of novel technologies in rehabilitation and reintegration among clinicians, carers and patients.

Delivery room glucose to reduce the risk of admission hypoglycemia in preterm infants: a systematic literature review.

AIMS: In order to mitigate early hypoglycemia in preterm infants, some clinicians have recently explored interventions such as delivery room commencement of dextrose infusions or delivery room administration of buccal dextrose gel. This review aimed to systematically investigate the literature regarding the provision of delivery room (prior to admission) parenteral glucose as a method to reduce the risk of initial hypoglycemia (measured at the time of NICU admission blood testing) in preterm infants. MATERIALS AND METHODS: Using PRISMA guidelines a literature search (May 2022) was conducted using PubMed, Embase, Scopus, Cochrane Library, OpenGrey, and Prospero databases. The clinicaltrials.gov database was searched for possible completed/ongoing clinical trials. Studies that included moderate preterm (≤33+6 weeks) or younger birth gestations or very low birth weight (or smaller) infants, and that administered parenteral glucose in the delivery room were included. The literature was appraised via data extraction, narrative synthesis, and critical review of the study data. RESULTS: A total of five studies (published 2014-2022) were eligible for inclusion (three before-after "quasi-experimental" studies, one retrospective cohort study, and one case-control study). Most included studies used intravenous dextrose as the intervention. Individual study effects (odds ratios) favored the intervention in all included studies. It was felt that the low number of studies, the variability in study design, and the nonadjustment for confounding co-interventions (co-exposures) precluded a meta-analysis. Quality assessment of the studies revealed a spectrum of bias from low to high risk, however, most studies had moderate to high risk of bias, and their direction of bias favored the intervention. CONCLUSIONS: This extensive search and systematic appraisal of the literature indicates that there exists few studies (these are low grade and at moderate to high risk of bias) for the interventions of either intravenous or buccal dextrose given in the delivery room. It is not clear if these interventions impact on rates of early (NICU admission) hypoglycemia in these preterm infants. Obtaining intravenous access in the delivery room is not guaranteed and can be difficult in these small infants. Future research should consider various routes for commencing delivery room glucose in these preterm infants and should take the form of randomized controlled trials.

Long-term trajectories of community integration: identification, characterization, and prediction using inpatient rehabilitation variables.

BACKGROUND: Community integration (CI) is often regarded as the foundation of rehabilitation endeavors after stroke; nevertheless, few studies have investigated the relationship between inpatient rehabilitation (clinical and demographic) variables and long-term CI. OBJECTIVES: To identify novel classes of patients having similar temporal patterns in CI and relate them to baseline features. METHODS: Retrospective observational cohort study analyzing (n = 287) adult patients with stroke admitted to rehabilitation between 2003 and 2018, including baseline Functional Independence Measure (FIM) at discharge, follow-ups (m = 1264) of Community Integration Questionnaire (CIQ) between 2006 and 2022. Growth mixture models (GMMs) were fitted to identify CI trajectories, and baseline predictors were identified using multivariate logistic regression (reporting AUC) with 10-fold cross validation. RESULTS: Each patient was assessed at 2.7 (2.2-3.7), 4.4 (3.7-5.6), and 6.2 (5.4-7.4) years after injury, 66% had a fourth assessment at 7.9 (6.8-8.9) years. GMM identified three classes of trajectories.Lowest CI (n=105, 36.6%): The lowest mean total CIQ; highest proportion of dysphagia (47.6%) and aphasia (46.7%), oldest at injury, largest length of stay (LOS), largest time to admission, and lowest FIM.Highest CI (n=63, 21.9%): The highest mean total CIQ, youngest, shortest LOS, highest education (27% university) highest FIM, and Intermediate CI (n=119, 41.5%): Intermediate mean total CIQ and FIM scores. Age at injury OR: 0.89 (0.85-0.93), FIM OR: 1.04 (1.02-1.07), hypertension OR: 2.86 (1.25-6.87), LOS OR: 0.98 (0.97-0.99), and high education OR: 3.05 (1.22-7.65) predicted highest CI, and AUC was 0.84 (0.76-0.93). CONCLUSION: Novel clinical (e.g. hypertension) and demographic (e.g. education) variables characterized and predicted long-term CI trajectories.

Determining the Proportionality of Ischemic Stroke Risk Factors to Age.

While age is an important risk factor, there are some disadvantages to including it in a stroke risk model: age can dominate the risk score and lead to over- or under-predictions in some age groups. There is evidence to suggest that some of these disadvantages are due to the non-proportionality of other risk factors with age, e.g., risk factors contribute differently to stroke risk based on an individual's age. In this paper, we present a framework to test if risk factors are proportional with age. We then apply the framework to a set of risk factors using Framingham heart study data from the NHLBI Biologic Specimen and Data Repository Information Coordinating Center to determine if we can find evidence of non-proportionality. Using our framework, we find that a number of risk factors (diastolic blood pressure, total cholesterol, BMI, sex, high blood pressure treatment) may be non-proportional to age. This suggests that testing for the proportionality of risk factors with age should be something that is considered in stroke risk prediction modelling and traditional modelling methods may need to be adjusted to capture this non-proportionality.

Telemedicine in a spine clinic setting: a large survey of patient preferences and experiences.

BACKGROUND: Coronavirus disease 2019 (COVID-19) has greatly disturbed healthcare and the practice of neurosurgery. As healthcare systems evolve in the face of COVID-19, the use of telehealth platforms has expanded. We presented the results of a large survey of patient perspectives on the role of telehealth in the care of spine clinic patients. METHODS: All patients at the spine clinic of a large, tertiary, academic medical center were surveyed at their clinic visit from May 30, 2020-June 30, 2020. All responses were anonymous, and results were analyzed with standard statistical techniques. RESULTS: One hundred and seventy-six surveys were returned and 164 were entirely completed. 56.8% of patients were new while 24.4% were postoperative follow-up visits: 54.9% had lumbar symptoms. 85% had pain while more than half also reported weakness; 58% traveled greater than twenty-five miles for their appointment; 96% had transportation readily available. Of all respondents, only 15.3% preferred the appointment via video telehealth while 4% had no preference between in-person appointment or virtual visit. Preference for telehealth appointment was not associated with factors such as new or established in the practice, spinal region of symptoms, pain, weakness, comfort with technology, age, or duration of symptoms. There was a significant difference between how far the patient traveled for the clinic appointment and their preference for a telehealth appointment, with patients traveling further distances favoring telehealth (P=0.04). This effect remained significant when stratifying based on 25 miles (P=0.03) or 50 miles (P=0.03) but not when stratifying based on 100 miles (P=0.32). However, the sample size of patients traveling >100 miles was small, limiting any inference regarding that subgroup. Access to transportation was associated with preference for telehealth (88.89% vs. 97.18%, P=0.08) but did not reach statistical significance. CONCLUSIONS: Most spine patients prefer in-person clinic appointments to virtual appointments. These preferences should be considered when arranging patient encounters.

A review of risk concepts and models for predicting the risk of primary stroke.

Predicting an individual's risk of primary stroke is an important tool that can help to lower the burden of stroke for both the individual and society. There are a number of risk models and risk scores in existence but no review or classification designed to help the reader better understand how models differ and the reasoning behind these differences. In this paper we review the existing literature on primary stroke risk prediction models. From our literature review we identify key similarities and differences in the existing models. We find that models can differ in a number of ways, including the event type, the type of analysis, the model type and the time horizon. Based on these similarities and differences we have created a set of questions and a system to help answer those questions that modelers and readers alike can use to help classify and better understand the existing models as well as help to make necessary decisions when creating a new model.

Anxiety as a Risk Factor for Postoperative Delirium in Elective Spine Deformity Surgeries: A National Database Study.

Introduction Generalized anxiety disorder has become one of the most prevalent mental health disorders in the United States. In addition, postoperative delirium has been shown to increase hospital stay, increase mortality, and increased healthcare costs. Few studies have looked at the prevalence of postoperative delirium in patients diagnosed with anxiety undergoing elective spinal deformity procedures. The purpose of this study was to determine if anxiety is a risk factor for postoperative delirium in elective spinal deformity surgeries. Methods The authors performed a retrospective analysis using the TriNetX Research Database. Patients diagnosed with kyphosis or lordosis who then underwent elective spinal correction surgeries were identified. This group was then separated based on the diagnosis of a generalized anxiety disorder before the operation versus no diagnosis. Propensity score adjustment, based on mental disorders and other risk factors, was then used to match cohorts on baseline demographics and characteristics. Analysis was performed on the primary outcome of postoperative delirium, with secondary outcomes of upper respiratory tract infections, surgical site infections, sepsis, ventilator dependence, convulsions, stroke, emergency department visits, myocardial infarction, pulmonary embolism, and urinary retention within 30 days after surgery. Results Our search included 1,211 patients with a diagnosis of anxiety and 8,055 patients without anxiety. After propensity score matching, 996 patients remained in each cohort. Statistical analysis showed significant outcomes between the matched cohorts in the anxiety group for postoperative delirium (OR 2.788; 1.587-4.899) and convulsions (OR 1.615; 1.006-2.592). All other outcomes were not significant after propensity score matching. Conclusion These results showed generalized anxiety disorder is a risk factor for postoperative delirium and convulsions after elective spine surgery. Further research is necessary on the effects of mental health disorders on postoperative delirium and other outcomes to better understand the risks in this population.

Understanding and Predicting Cognitive Improvement of Young Adults in Ischemic Stroke Rehabilitation Therapy.

Accurate early predictions of a patient's likely cognitive improvement as a result of a stroke rehabilitation programme can assist clinicians in assembling more effective therapeutic programs. In addition, sufficient levels of explainability, which can justify these predictions, are a crucial requirement, as reported by clinicians. This article presents a machine learning (ML) prediction model targeting cognitive improvement after therapy for stroke surviving patients. The prediction model relies on electronic health records from 201 ischemic stroke surviving patients containing demographic information, cognitive assessments at admission from 24 different standardized neuropsychology tests (e.g., TMT, WAIS-III, Stroop, RAVLT, etc.), and therapy information collected during rehabilitation (72,002 entries collected between March 2007 and September 2019). The study population covered young-adult patients with a mean age of 49.51 years and only 4.47% above 65 years of age at the stroke event (no age filter applied). Twenty different classification algorithms (from Python's Scikit-learn library) are trained and evaluated, varying their hyper-parameters and the number of features received as input. Best-performing models reported Recall scores around 0.7 and F1 scores of 0.6, showing the model's ability to identify patients with poor cognitive improvement. The study includes a detailed feature importance report that helps interpret the model's inner decision workings and exposes the most influential factors in the cognitive improvement prediction. The study showed that certain therapy variables (e.g., the proportion of memory and orientation executed tasks) had an important influence on the final prediction of the cognitive improvement of patients at individual and population levels. This type of evidence can serve clinicians in adjusting the therapeutic settings (e.g., type and load of therapy activities) and selecting the one that maximizes cognitive improvement.

The association between ambient UVB dose and ANCA-associated vasculitis relapse and onset.

BACKGROUND: The aetiology of ANCA-associated vasculitis (AAV) and triggers of relapse are poorly understood. Vitamin D (vitD) is an important immunomodulator, potentially responsible for the observed latitudinal differences between granulomatous and non-granulomatous AAV phenotypes. A narrow ultraviolet B spectrum induces vitD synthesis (vitD-UVB) via the skin. We hypothesised that prolonged periods of low ambient UVB (and by extension vitD deficiency) are associated with the granulomatous form of the disease and an increased risk of AAV relapse. METHODS: Patients with AAV recruited to the Irish Rare Kidney Disease (RKD) (n = 439) and UKIVAS (n = 1961) registries were studied. Exposure variables comprised latitude and measures of ambient vitD-UVB, including cumulative weighted UVB dose (CW-D-UVB), a well-validated vitD proxy. An n-of-1 study design was used to examine the relapse risk using only the RKD dataset. Multi-level models and logistic regression were used to examine the effect of predictors on AAV relapse risk, phenotype and serotype. RESULTS: Residential latitude was positively correlated (OR 1.41, 95% CI 1.14-1.74, p = 0.002) and average vitD-UVB negatively correlated (0.82, 0.70-0.99, p = 0.04) with relapse risk, with a stronger effect when restricting to winter measurements (0.71, 0.57-0.89, p = 0.002). However, these associations were not restricted to granulomatous phenotypes. We observed no clear relationship between latitude, vitD-UVB or CW-D-UVB and AAV phenotype or serotype. CONCLUSION: Our findings suggest that low winter ambient UVB and prolonged vitD status contribute to AAV relapse risk across all phenotypes. However, the development of a granulomatous phenotype does not appear to be directly vitD-mediated. Further research is needed to determine whether sufficient vitD status would reduce relapse propensity in AAV.

Shapley Idioms: Analysing BERT Sentence Embeddings for General Idiom Token Identification.

This article examines the basis of Natural Language Understanding of transformer based language models, such as BERT. It does this through a case study on idiom token classification. We use idiom token identification as a basis for our analysis because of the variety of information types that have previously been explored in the literature for this task, including: topic, lexical, and syntactic features. This variety of relevant information types means that the task of idiom token identification enables us to explore the forms of linguistic information that a BERT language model captures and encodes in its representations. The core of this article presents three experiments. The first experiment analyzes the effectiveness of BERT sentence embeddings for creating a general idiom token identification model and the results indicate that the BERT sentence embeddings outperform Skip-Thought. In the second and third experiment we use the game theory concept of Shapley Values to rank the usefulness of individual idiomatic expressions for model training and use this ranking to analyse the type of information that the model finds useful. We find that a combination of idiom-intrinsic and topic-based properties contribute to an expression's usefulness in idiom token identification. Overall our results indicate that BERT efficiently encodes a variety of information from topic, through lexical and syntactic information. Based on these results we argue that notwithstanding recent criticisms of language model based semantics, the ability of BERT to efficiently encode a variety of linguistic information types does represent a significant step forward in natural language understanding.

Age Specific Models to Capture the Change in Risk Factor Contribution by Age to Short Term Primary Ischemic Stroke Risk.

Age is one of the most important risk factors when it comes to stroke risk prediction. However, including age as a risk factor in a stroke prediction model can give rise to a number of difficulties. Age often dominates the risk score, and also not all risk factors contribute proportionally to stroke risk by age. In this study we investigate a number of common stroke risk factors, using Framingham heart study data from the NHLBI Biologic Specimen and Data Repository Information Coordinating Center to determine if they appear to contribute proportionally by age to a stroke risk score. As we find evidence that there is some non-proportionality by age, we then create a set of logistic regression risk models that each predict the 5 year stroke risk for a different age group. The age group models are shown to be better calibrated when compared to a model for all ages that includes age as a risk factor. This suggests that to get better predictions for stroke risk it may be necessary to consider alternative methods for including age in stroke risk prediction models that account for the non-proportionality of the other risk factors as age changes.

Long-term trajectories of motor functional independence after ischemic stroke in young adults: Identification and characterization using inpatient baseline assessments.

BACKGROUND: Stroke is a major worldwide cause of serious long-term disability. Most previous studies addressing functional independence included only inpatients with limited follow-up. OBJECTIVE: To identify novel classes of patients having similar temporal patterns in motor functional independence and relate them to baseline clinical features. METHODS: Retrospective observational cohort study, data were obtained for n = 428 adult patients with ischemic stroke admitted to rehabilitation (March 2005-March 2020), including baseline clinical features and follow-ups of motor Functional Independence Measure (mFIM) categorized as poor, fair or good. Growth mixture models (GMMs) were fitted to identify classes of patients with similar mFIM trajectories. RESULTS: GMM identified three classes of trajectories (1,664 mFIM assessments):C1 (11.2 %), 97.9% having poor admission mFIM, at 4.93 years 61.1% still poor, with the largest percentage of hypertension, neglect, dysphagia, diabetes and dyslipidemia of all three classes.C2 (23.1%), 99% had poor admission mFIM, 25% poor discharge mFIM, the largest percentage of aphasia and greatest mFIM gain, at 4.93 years only 6.2% still poor.C3 (65.7%) the youngest, lowest NIHSS, 37.7% poor admission mFIM, 73% good discharge mFIM, only 4.6% poor discharge mFIM, 90% good at 4.93 years. CONCLUSIONS: GMM identified novel motor functional classes characterized by baseline features.

Toward Personalized Web-Based Cognitive Rehabilitation for Patients With Ischemic Stroke: Elo Rating Approach.

BACKGROUND: Stroke is a worldwide cause of disability; 40% of stroke survivors sustain cognitive impairments, most of them following inpatient rehabilitation at specialized clinical centers. Web-based cognitive rehabilitation tasks are extensively used in clinical settings. The impact of task execution depends on the ratio between the skills of the treated patient and the challenges imposed by the task itself. Thus, treatment personalization requires a trade-off between patients' skills and task difficulties, which is still an open issue. In this study, we propose Elo ratings to support clinicians in tasks assignations and representing patients' skills to optimize rehabilitation outcomes. OBJECTIVE: This study aims to stratify patients with ischemic stroke at an early stage of rehabilitation into three levels according to their Elo rating; to show the relationships between the Elo rating levels, task difficulty levels, and rehabilitation outcomes; and to determine if the Elo rating obtained at early stages of rehabilitation is a significant predictor of rehabilitation outcomes. METHODS: The PlayerRatings R library was used to obtain the Elo rating for each patient. Working memory was assessed using the DIGITS subtest of the Barcelona test, and the Rey Auditory Verbal Memory Test (RAVLT) was used to assess verbal memory. Three subtests of RAVLT were used: RAVLT learning (RAVLT075), free-recall memory (RAVLT015), and recognition (RAVLT015R). Memory predictors were identified using forward stepwise selection to add covariates to the models, which were evaluated by assessing discrimination using the area under the receiver operating characteristic curve (AUC) for logistic regressions and adjusted R2 for linear regressions. RESULTS: Three Elo levels (low, middle, and high) with the same number of patients (n=96) in each Elo group were obtained using the 50 initial task executions (from a total of 38,177) for N=288 adult patients consecutively admitted for inpatient rehabilitation in a clinical setting. The mid-Elo level showed the highest proportions of patients that improved in all four memory items: 56% (54/96) of them improved in DIGITS, 67% (64/96) in RAVLT075, 58% (56/96) in RAVLT015, and 53% (51/96) in RAVLT015R (P<.001). The proportions of patients from the mid-Elo level that performed tasks at difficulty levels 1, 2, and 3 were 32.1% (3997/12,449), 31.% (3997/12,449), and 36.9% (4595/12,449), respectively (P<.001), showing the highest match between skills (represented by Elo level) and task difficulties, considering the set of 38,177 task executions. Elo ratings were significant predictors in three of the four models and quasi-significant in the fourth. When predicting RAVLT075 and DIGITS at discharge, we obtained R2=0.54 and 0.43, respectively; meanwhile, we obtained AUC=0.73 (95% CI 0.64-0.82) and AUC=0.81 (95% CI 0.72-0.89) in RAVLT075 and DIGITS improvement predictions, respectively. CONCLUSIONS: Elo ratings can support clinicians in early rehabilitation stages in identifying cognitive profiles to be used for assigning task difficulty levels.

Arachnoiditis Ossificans in the Thoracic Spine With Associated Cyst and Syringomyelia: A Rare, Intraoperative Finding Complicating Dural Opening.

Arachnoiditis ossificans (AO) is a rare spinal pathology that develops because of bony metaplasia secondary to chronic inflammation. AO may present with debilitating myelopathy secondary to nerve root compression, making it distinct from spinal calcification commonly seen with aging. AO is extremely rare, having been reported less than 100 times, most commonly in the thoracic spine. Even rarer still, AO has been associated with syringomyelia and arachnoid cyst because of associated cerebrospinal fluid (CSF) flow disruption. In this report, we describe a case of AO that presented with right shoulder pain, right-hand numbness, and bilateral lower extremity fatigue who had syringomyelia and arachnoid cyst discovered on MRI imaging. When brought to the operating room for syrinx shunting and cyst fenestration the dural opening was complicated by severe calcification and a diagnosis of AO was made. The patient was treated with partial resection of the calcified plaques. Syringomyelia shunting was abandoned due to low volume. Post-operatively, the patient had improvement in their myelopathy though syrinx was still visualized on follow-up imaging. This report reviews the pathology, clinical and radiographic diagnosis, and treatment strategies for arachnoiditis ossificans.

Self-expanding covered stent placement to treat a pseudoaneurysm caused by iatrogenic vertebral artery injury.

Vertebral artery injuries account for approximately 19% of cerebral vascular injuries and are typically managed conservatively. However, some patients require operative intervention to gain control of an active hemorrhage, either via surgical ligation or endovascular intervention. We present a case of iatrogenic vertebral artery injury occurring during cervical spine surgery which was treated emergently with a self-expanding covered stent. A 58-year-old male presented for cervical traction, C5 and C6 corpectomy, and possible C4 to T2 posterior fusion following a motor vehicle accident. Intraoperatively, following drilling the C5 endplate, copious bleeding was observed from injury to the right vertebral artery resulting in pseudoaneurysm formation. The patient was loaded with ticagrelor and a self-expanding covered stent was placed via a transfemoral approach, resulting in obliteration of the pseudoaneurysm prior to completion of his cervical spine surgery. Emergent self-expanding covered stent placement for iatrogenic vertebral artery injury in the setting of an intraoperative injury is a safe and effective option. Ticagrelor is a viable alternative to traditional dual antiplatelet therapy for preventing thromboembolic complications in this urgent setting.

Hybrid modelling for stroke care: Review and suggestions of new approaches for risk assessment and simulation of scenarios.

Stroke is an example of a complex and multi-factorial disease involving multiple organs, timescales, and disease mechanisms. To deal with this complexity, and to realize Precision Medicine of stroke, mathematical models are needed. Such approaches include: 1) machine learning, 2) bioinformatic network models, and 3) mechanistic models. Since these three approaches have complementary strengths and weaknesses, a hybrid modelling approach combining them would be the most beneficial. However, no concrete approach ready to be implemented for a specific disease has been presented to date. In this paper, we both review the strengths and weaknesses of the three approaches, and propose a roadmap for hybrid modelling in the case of stroke care. We focus on two main tasks needed for the clinical setting: a) For stroke risk calculation, we propose a new two-step approach, where non-linear mixed effects models and bioinformatic network models yield biomarkers which are used as input to a machine learning model and b) For simulation of care scenarios, we propose a new four-step approach, which revolves around iterations between simulations of the mechanistic models and imputations of non-modelled or non-measured variables. We illustrate and discuss the different approaches in the context of Precision Medicine for stroke.

Cervical Pediatric Spine Trauma Managed With Open Spinal Fixation and Instrumentation and a Review of the Literature.

Cervical spine injuries in the pediatric population are rare. Most injuries to the cervical spinal cord and vertebral column can be managed nonoperatively; however, surgical management may be required in certain clinical scenarios. A posterior surgical approach has been previously preferred; however, the utilization of anterior spinal fixation and instrumentation has been limited. We present a small case series of patients presenting with a traumatic cervical spine injury and detail the feasibility of craniocervical junction (CVJ) and subaxial spinal fixation in the pediatric population. We report four cases involving pediatric patients, all of whom presented with cervical spine injuries necessitating operative intervention using a combination of the anterior and posterior operative approaches. All four patients recovered well, did not require surgical revision, and were neurologically intact at the last follow-up.  Therefore, we conclude that spinal arthrodesis is a safe, effective way to manage spinal injuries in the cervical spine following traumatic injury.