Shunting for idiopathic normal pressure hydrocephalus.

BACKGROUND: Normal pressure hydrocephalus (NPH) occurs when the brain ventricles expand, causing a triad of gait, cognitive, and urinary impairment. It can occur after a clear brain injury such as trauma, but can also occur without a clear cause (termed idiopathic, or iNPH). Non-randomised studies have shown a benefit from surgically diverting ventricular fluid to an area of lower pressure by cerebrospinal fluid (CSF)-shunting in iNPH, but historically there have been limited randomised controlled trial (RCT) data to confirm this. OBJECTIVES: To determine the effect of CSF-shunting versus no CSF-shunting in people with iNPH and the frequency of adverse effects of CSF-shunting in iNPH. SEARCH METHODS: We searched the Cochrane Dementia and Cognitive Improvement Group's register, Cochrane Central Register of Controlled Trials (CENTRAL), MEDLINE (Ovid SP), Embase (Ovid SP), PsycINFO (Ovid SP), CINAHL (EBSCOhost), Web of Science Core Collection (Clarivate), LILACS (BIREME), ClinicalTrials.gov, and the World Health Organization International Clinical Trials Registry Platform on 15 February 2023. SELECTION CRITERIA: We included only RCTs of people who had symptoms of gait, cognitive, or urinary impairment with communicating hydrocephalus (Evans index of > 0.3) and normal CSF pressure. Control groups included those with no CSF shunts or those with CSF shunts that were in 'inactive' mode. DATA COLLECTION AND ANALYSIS: We used standard Cochrane methodological procedures. Where necessary, we contacted study authors requesting data not provided in the papers. We assessed the overall certainty of the evidence using GRADE. MAIN RESULTS: We included four RCTs, of which three were combined in a meta-analysis. The four RCTs included 140 participants (73 with immediate CSF-shunting and 67 controls who had delayed CSF-shunting) with an average age of 75 years. Risk of bias was low in all parallel-group outcomes evaluated apart from gait speed, cognitive function (general cognition and Symbol Digit Test) (some concerns) and adverse events, which were not blind-assessed. CSF-shunting probably improves gait speed at less than six months post-surgery (standardised mean difference (SMD) 0.62, 95% confidence interval (CI) 0.24 to 0.99; 3 studies, 116 participants; moderate-certainty evidence). CSF-shunting may improve qualitative gait function at less than six months post-surgery by an uncertain amount (1 study, 88 participants; low-certainty evidence). CSF-shunting probably results in a large reduction of disability at less than six months post-surgery (risk ratio 2.08, 95% CI 1.31 to 3.31; 3 studies, 118 participants; moderate-certainty evidence). The evidence is very uncertain about the effect of CSF-shunting on cognitive function at less than six months post-CSF-shunt surgery (SMD 0.35, 95% CI -0.04 to 0.74; 2 studies, 104 participants; very low-certainty evidence). The evidence is also very uncertain about the effect of CSF-shunt surgery on adverse events (1 study, 88 participants; very low-certainty evidence). There were no data regarding the effect of CSF-shunting on quality of life. AUTHORS' CONCLUSIONS: We found moderate-certainty evidence that CSF-shunting likely improves gait speed and disability in iNPH in the relative short term. The evidence is very uncertain regarding cognition and adverse events. There were no longer-term RCT data for any of our prespecified outcomes. More studies are required to improve the certainty of these findings. In addition, more information is required regarding patient ethnicity and the effect of CSF-shunting on quality of life.

Enhancing Asthma Self-Management with Environmental Passive-Monitoring Data and Machine Learning-Based Predictions.

Monitoring enables timely action which is critical in avoiding asthma attacks. With the abundance of local weather and pollution data, when augmented with machine learning, it is becoming possible to replace traditional tedious active monitoring in certain instances for some groups of patients. This analysis on the AAMOS-00 dataset, with 22 severe asthma patients, compared the performance of XGBoost to predict worsening asthma conditions using either passive or active monitoring on two groups of patients: individuals who were and were not triggered by outdoor environmental factors. On patients triggered by environmental factors, the performances of the passive- and the active-monitoring models were close to matching (passive: AUC = 0.83, 2.9-fold increase in precision; active: AUC = 0.89, 3.1-fold increase in precision). On the other hand, the active-monitoring model was significantly superior for patients not triggered by environmental factors (passive: AUC = 0.52, 1.2-fold increase in precision; active: AUC = 0.83, 3.7-fold increase in precision). Robustness evaluation showed it was possible to have good predictions of asthma attacks in the coming 5 to 7 days using 5 to 28 days of data.

Variation in care for inpatients with avoidant restrictive food intake disorder leads to development of a novel inpatient clinical pathway to standardize care.

INTRODUCTION: There is limited evidence to guide management of patients with avoidant restrictive food intake disorder (ARFID) admitted for medical stabilization. We describe variations in inpatient care which led to the development of a multidisciplinary inpatient clinical pathway (ICP) to provide standardized management and examine differences after the ICP was implemented. METHODS: A retrospective review of patients with ARFID admitted to Adolescent Medicine, Gastroenterology, and General Pediatrics at a single academic center was conducted. We compare hospital utilization and use of consulting services during the pre-ICP (2015-2017) and post-ICP (2018-2020) periods. RESULTS: 110 patients were admitted with ARFID (n = 57 pre- vs. n = 53 post-ICP). Most presented with moderate/severe malnutrition (63% pre vs. 81% post; p = 0.11) and co-morbid anxiety and/or depression (74% pre vs. 92% post; p = 0.01). There was some variation in use of enteral tube feeding by service in both periods (p = 0.76 and p = 0.38, respectively), although overall use was consistent between periods (46% pre vs. 58% post; p = 0.18). Pre-ICP, use of the restrictive eating disorder protocol differed across services (p < 0.001), with only AM using it. Overall, utilization of the restrictive eating disorder protocol decreased from 16% pre-ICP to 2% post-ICP (p = 0.02). There was variation by service in psychiatry/psychology (range 82-100% by service; p = 0.09) and social work consultations (range 17-71% by service; p = 0.001) during the pre-ICP period, though variation was reduced in the post-ICP period (p = 0.99 and p = 0.05, respectively). Implementation of the ICP led to improvements in these consultative services, with all patients in the post-ICP period receiving psychiatry/psychology consultation (p = 0.05) and an increase in social work consults from 44 to 64% (p = 0.03). Nutrition consults were consistently utilized in both periods (98% pre vs. 100% post; p = 0.33). CONCLUSION: The ICP was developed to standardize inpatient medical stabilization for patients with ARFID. In this single center study, implementation of the ICP increased standardized care for inpatients with ARFID with variation in care reduced: there were improvements in the use of consulting services and a reduction in the use of the restrictive eating disorder protocol. The ICP demonstrates the potential to further standardize and improve care over time.

There is limited evidence to guide management of children and adolescents with Avoidant Restrictive Food Intake Disorder (ARFID) admitted for medical stabilization. The study describes the variation in inpatient care for ARFID, which led to the development of a multidisciplinary standardized inpatient clinical pathway (ICP). The ICP centers the experience of the patient and family with an emphasis on biopsychosocial support. Implementation of the ICP increased standardized care for inpatients with ARFID with variation in care reduced: There were improvements in the use of psychiatry/psychology and social work consulting services and a reduction in the use of the restrictive eating disorder protocol. Future research is needed to better understand the impact of the inpatient clinical pathway to improve care over time.

DIGIPREDICT: physiological, behavioural and environmental predictors of asthma attacks-a prospective observational study using digital markers and artificial intelligence-study protocol.

INTRODUCTION: Asthma attacks are a leading cause of morbidity and mortality but are preventable in most if detected and treated promptly. However, the changes that occur physiologically and behaviourally in the days and weeks preceding an attack are not always recognised, highlighting a potential role for technology. The aim of this study 'DIGIPREDICT' is to identify early digital markers of asthma attacks using sensors embedded in smart devices including watches and inhalers, and leverage health and environmental datasets and artificial intelligence, to develop a risk prediction model to provide an early, personalised warning of asthma attacks. METHODS AND ANALYSIS: A prospective sample of 300 people, 12 years or older, with a history of a moderate or severe asthma attack in the last 12 months will be recruited in New Zealand. Each participant will be given a smart watch (to assess physiological measures such as heart and respiratory rate), peak flow meter, smart inhaler (to assess adherence and inhalation) and a cough monitoring application to use regularly over 6 months with fortnightly questionnaires on asthma control and well-being. Data on sociodemographics, asthma control, lung function, dietary intake, medical history and technology acceptance will be collected at baseline and at 6 months. Asthma attacks will be measured by self-report and confirmed with clinical records. The collected data, along with environmental data on weather and air quality, will be analysed using machine learning to develop a risk prediction model for asthma attacks. ETHICS AND DISSEMINATION: Ethical approval has been obtained from the New Zealand Health and Disability Ethics Committee (2023 FULL 13541). Enrolment began in August 2023. Results will be presented at local, national and international meetings, including dissemination via community groups, and submission for publication to peer-reviewed journals. TRIAL REGISTRATION NUMBER: Australian New Zealand Clinical Trials Registry ACTRN12623000764639; Australian New Zealand Clinical Trials Registry.

Heterozygote advantage at HLA class I and II loci and reduced risk of colorectal cancer.

Objective: Reduced diversity at Human Leukocyte Antigen (HLA) loci may adversely affect the host's ability to recognize tumor neoantigens and subsequently increase disease burden. We hypothesized that increased heterozygosity at HLA loci is associated with a reduced risk of developing colorectal cancer (CRC). Methods: We imputed HLA class I and II four-digit alleles using genotype data from a population-based study of 5,406 cases and 4,635 controls from the Molecular Epidemiology of Colorectal Cancer Study (MECC). Heterozygosity at each HLA locus and the number of heterozygous genotypes at HLA class -I (A, B, and C) and HLA class -II loci (DQB1, DRB1, and DPB1) were quantified. Logistic regression analysis was used to estimate the risk of CRC associated with HLA heterozygosity. Individuals with homozygous genotypes for all loci served as the reference category, and the analyses were adjusted for sex, age, genotyping platform, and ancestry. Further, we investigated associations between HLA diversity and tumor-associated T cell repertoire features, as measured by tumor infiltrating lymphocytes (TILs; N=2,839) and immunosequencing (N=2,357). Results: Individuals with all heterozygous genotypes at all three class I genes had a reduced odds of CRC (OR: 0.74; 95% CI: 0.56-0.97, p= 0.031). A similar association was observed for class II loci, with an OR of 0.75 (95% CI: 0.60-0.95, p= 0.016). For class-I and class-II combined, individuals with all heterozygous genotypes had significantly lower odds of developing CRC (OR: 0.66, 95% CI: 0.49-0.87, p= 0.004) than those with 0 or one heterozygous genotype. HLA class I and/or II diversity was associated with higher T cell receptor (TCR) abundance and lower TCR clonality, but results were not statistically significant. Conclusion: Our findings support a heterozygote advantage for the HLA class-I and -II loci, indicating an important role for HLA genetic variability in the etiology of CRC.

Thoraco-lumbar-sacral orthoses in older people - A narrative literature review.

Fractures in the thoracolumbar region have a bimodal distribution, with an increasing number of older people presenting with acute vertebral fractures after atraumatic or low energy mechanisms of injury. In the absence of neurological compromise and significant vertebral instability, thoracolumbar fractures are often managed conservatively and bracing is widely recommended. However, in older cohorts, bracing is often ill fitting and poorly tolerated with non-compliance leading to prolonged immobilization. Systematic reviews and meta-analyses have challenged the motive of bracing, but as evidence quality is low, the role of exploratory analysis has been limited. This descriptive review summarises and examines the current evidence that underpins the use of spinal orthoses, specific to older patients, in an effort to streamline its judicious use in clinical practice and identify scope to direct further research.

Systematic review and meta-analysis of predictive factors for spontaneous regression in lumbar disc herniation.

OBJECTIVE: Relatively little evidence exists on predictive factors for the spontaneous regression of lumbar disc herniation (LDH), although it is a well-documented phenomenon. Therefore, current care is not optimized to identify those who would benefit from early surgery versus those who could avoid surgical risks and pursue nonsurgical therapy. In this study, the authors aimed to analyze and summarize all literature to date on predictive factors for spontaneous LDH regression as well as suggest future research strategies to aid in the decision-making for this cohort. METHODS: A literature search was conducted of the Cochrane, Embase, and MEDLINE databases for articles that described LDH in terms of the North American Spine Society task force definitions: bulging, protruded, extruded, and sequestered disc morphologies. All articles described a nonsurgical primary symptomatic LDH cohort with at least two MR images to assess regression. Those with concomitant spinal disease were excluded. The primary outcome was to assess the probability of disc regression for each disc morphology, with a secondary analysis for any other predictive factors identified. The authors synthesized their results with the only previous review (examining articles published before March 2014) to comprehensively describe the literature. A qualitative analysis of the wider literature was also performed for those studies with differing definitions of LDH but meeting all remaining inclusion criteria. RESULTS: Sixteen articles describing 360 cases of LDH were identified. Participants tended to be younger and male and presented with radiculopathy and L4-5 or L5-S1 LDH. The mean time to follow-up imaging was 11.5 months. The probabilities of spontaneous regression with bulging, protruded, extruded, and sequestered discs were 13.3%, 52.5%, 70.4%, and 93.0%, respectively (χ2 = 126.01, p < 0.001). Extruded and sequestered discs were also significantly more likely to completely regress than smaller morphologies. Other predictors of regression were larger baseline herniation volume (1260.16 vs 1006.71 mm3, p < 0.002), transligamentous herniation (χ2 = 13.321, p < 0.001), and higher Komori types (χ2 = 14.5132, p < 0.001). The authors also found similar trends in qualitative data as well as confirmed that symptom improvement was associated with disc regression. CONCLUSIONS: This study shows further evidence of the influence of disc morphology on predicting LDH regression as well as provides the first meta-analysis of data indicating additional predictive factors. Further investigation of predictive factors for early (< 6 months) LDH regression is suggested to optimize clinical use.

Home monitoring with connected mobile devices for asthma attack prediction with machine learning.

Monitoring asthma is essential for self-management. However, traditional monitoring methods require high levels of active engagement, and some patients may find this tedious. Passive monitoring with mobile-health devices, especially when combined with machine-learning, provides an avenue to reduce management burden. Data for developing machine-learning algorithms are scarce, and gathering new data is expensive. A few datasets, such as the Asthma Mobile Health Study, are publicly available, but they only consist of self-reported diaries and lack any objective and passively collected data. To fill this gap, we carried out a 2-phase, 7-month AAMOS-00 observational study to monitor asthma using three smart-monitoring devices (smart-peak-flow-meter/smart-inhaler/smartwatch), and daily symptom questionnaires. Combined with localised weather, pollen, and air-quality reports, we collected a rich longitudinal dataset to explore the feasibility of passive monitoring and asthma attack prediction. This valuable anonymised dataset for phase-2 of the study (device monitoring) has been made publicly available. Between June-2021 and June-2022, in the midst of UK's COVID-19 lockdowns, 22 participants across the UK provided 2,054 unique patient-days of data.

Oral microbes and the formation of cerebral abscesses: A single-centre retrospective study.

OBJECTIVE: Intracranial abscesses are relatively uncommon, but can result in significant mortality and morbidity. Whilst many potential causes of brain abscesses are recognised, in many cases the origin of infection remains clinically unidentified. Our objective was to investigate the role of bacteria found in the oral cavity in the development of brain abscesses. METHODS: A retrospective analysis was performed using data from 87 patients admitted to a single UK neurosurgical unit with brain abscesses over a 16-year period. Using microbiological data obtained from abscess sampling and peripheral cultures, species of bacteria were categorised in patients where no primary source of infection was identified (NSI) for their brain abscess (n = 52), or where an infective source (ISI) was identified. The microbiological data was then screened to identify common oral bacteria in each group. RESULTS: Brain abscesses from the ISI group (n = 35) demonstrated a significantly lower preponderance of oral bacteria (n = 8), than the NSI group (n = 29) (p < 0.05). Brain abscesses from the NSI group also had significantly higher counts of Streptococcus anginosus compared to ISI (p < 0.05), with brain abscesses being most common in the frontal and parietal lobes for both ISI and NSI. CONCLUSIONS: These findings suggest that the oral cavity could be considered as a source of occult infection in cases of brain abscess where no clear cause has been identified. Future studies should include oral screening and microbiome analysis to better understand the mechanisms involved and develop approaches for prevention. CLINICAL SIGNIFICANCE STATEMENT: Oral bacteria may be an under-recognised cause of brain abscesses. Careful review of oral health in brain abscess patients may help establish causation, particularly in patients with no cause for their abscess identified. Good levels of oral health may help prevent the development of brain abscesses in some individuals.

Machine Learning-Based Asthma Attack Prediction Models From Routinely Collected Electronic Health Records: Systematic Scoping Review.

BACKGROUND: An early warning tool to predict attacks could enhance asthma management and reduce the likelihood of serious consequences. Electronic health records (EHRs) providing access to historical data about patients with asthma coupled with machine learning (ML) provide an opportunity to develop such a tool. Several studies have developed ML-based tools to predict asthma attacks. OBJECTIVE: This study aims to critically evaluate ML-based models derived using EHRs for the prediction of asthma attacks. METHODS: We systematically searched PubMed and Scopus (the search period was between January 1, 2012, and January 31, 2023) for papers meeting the following inclusion criteria: (1) used EHR data as the main data source, (2) used asthma attack as the outcome, and (3) compared ML-based prediction models' performance. We excluded non-English papers and nonresearch papers, such as commentary and systematic review papers. In addition, we also excluded papers that did not provide any details about the respective ML approach and its result, including protocol papers. The selected studies were then summarized across multiple dimensions including data preprocessing methods, ML algorithms, model validation, model explainability, and model implementation. RESULTS: Overall, 17 papers were included at the end of the selection process. There was considerable heterogeneity in how asthma attacks were defined. Of the 17 studies, 8 (47%) studies used routinely collected data both from primary care and secondary care practices together. Extreme imbalanced data was a notable issue in most studies (13/17, 76%), but only 38% (5/13) of them explicitly dealt with it in their data preprocessing pipeline. The gradient boosting-based method was the best ML method in 59% (10/17) of the studies. Of the 17 studies, 14 (82%) studies used a model explanation method to identify the most important predictors. None of the studies followed the standard reporting guidelines, and none were prospectively validated. CONCLUSIONS: Our review indicates that this research field is still underdeveloped, given the limited body of evidence, heterogeneity of methods, lack of external validation, and suboptimally reported models. We highlighted several technical challenges (class imbalance, external validation, model explanation, and adherence to reporting guidelines to aid reproducibility) that need to be addressed to make progress toward clinical adoption.

Predicting asthma attacks using connected mobile devices and machine learning: the AAMOS-00 observational study protocol.

INTRODUCTION: Supported self-management empowering people with asthma to detect early deterioration and take timely action reduces the risk of asthma attacks. Smartphones and smart monitoring devices coupled with machine learning could enhance self-management by predicting asthma attacks and providing tailored feedback.We aim to develop and assess the feasibility of an asthma attack predictor system based on data collected from a range of smart devices. METHODS AND ANALYSIS: A two-phase, 7-month observational study to collect data about asthma status using three smart monitoring devices, and daily symptom questionnaires. We will recruit up to 100 people via social media and from a severe asthma clinic, who are at risk of attacks and who use a pressurised metered dose relief inhaler (that fits the smart inhaler device).Following a preliminary month of daily symptom questionnaires, 30 participants able to comply with regular monitoring will complete 6 months of using smart devices (smart peak flow meter, smart inhaler and smartwatch) and daily questionnaires to monitor asthma status. The feasibility of this monitoring will be measured by the percentage of task completion. The occurrence of asthma attacks (definition: American Thoracic Society/European Respiratory Society Task Force 2009) will be detected by self-reported use (or increased use) of oral corticosteroids. Monitoring data will be analysed to identify predictors of asthma attacks. At the end of the monitoring, we will assess users' perspectives on acceptability and utility of the system with an exit questionnaire. ETHICS AND DISSEMINATION: Ethics approval was provided by the East of England - Cambridge Central Research Ethics Committee. IRAS project ID: 285 505 with governance approval from ACCORD (Academic and Clinical Central Office for Research and Development), project number: AC20145. The study sponsor is ACCORD, the University of Edinburgh.Results will be reported through peer-reviewed publications, abstracts and conference posters. Public dissemination will be centred around blogs and social media from the Asthma UK network and shared with study participants.

Relating Sound and Sight in Simulated Environments.

The auditory signals at the ear can be affected by components arriving both directly from a sound source and indirectly via environmental reverberation. Previous studies have suggested that the perceptual separation of these contributions can be aided by expectations of likely reverberant qualities. Here, we investigated whether vision can provide information about the auditory properties of physical locations that could also be used to develop such expectations. We presented participants with audiovisual stimuli derived from 10 simulated real-world locations via a head-mounted display (HMD; n = 44) or a web-based ( n = 60) delivery method. On each trial, participants viewed a first-person perspective rendering of a location before hearing a spoken utterance that was convolved with an impulse response that was from a location that was either the same as (congruent) or different to (incongruent) the visually-depicted location. We find that audiovisual congruence was associated with an increase in the probability of participants reporting an audiovisual match of about 0.22 (95% credible interval: [ 0.17 , 0.27 ]), and that participants were more likely to confuse audiovisual pairs as matching if their locations had similar reverberation times. Overall, this study suggests that human perceivers have a capacity to form expectations of reverberation from visual information. Such expectations may be useful for the perceptual challenge of separating sound sources and reverberation from within the signal available at the ear.

Application of Machine Learning Algorithms for Asthma Management with mHealth: A Clinical Review.

Background: Asthma is a variable long-term condition. Currently, there is no cure for asthma and the focus is, therefore, on long-term management. Mobile health (mHealth) is promising for chronic disease management but to be able to realize its potential, it needs to go beyond simply monitoring. mHealth therefore needs to leverage machine learning to provide tailored feedback with personalized algorithms. There is a need to understand the extent of machine learning that has been leveraged in the context of mHealth for asthma management. This review aims to fill this gap. Methods: We searched PubMed for peer-reviewed studies that applied machine learning to data derived from mHealth for asthma management in the last five years. We selected studies that included some human data other than routinely collected in primary care and used at least one machine learning algorithm. Results: Out of 90 studies, we identified 22 relevant studies that were then further reviewed. Broadly, existing research efforts can be categorized into three types: 1) technology development, 2) attack prediction, 3) patient clustering. Using data from a variety of devices (smartphones, smartwatches, peak flow meters, electronic noses, smart inhalers, and pulse oximeters), most applications used supervised learning algorithms (logistic regression, decision trees, and related algorithms) while a few used unsupervised learning algorithms. The vast majority used traditional machine learning techniques, but a few studies investigated the use of deep learning algorithms. Discussion: In the past five years, many studies have successfully applied machine learning to asthma mHealth data. However, most have been developed on small datasets with internal validation at best. Small sample sizes and lack of external validation limit the generalizability of these studies. Future research should collect data that are more representative of the wider asthma population and focus on validating the derived algorithms and technologies in a real-world setting.

Clonal Hematopoiesis and Mosaicism Revealed by a Multi-Tissue Analysis of Constitutional TP53 Status.

BACKGROUND: Though germline TP53 pathogenic/likely pathogenic variants (PV) are associated with Li-Fraumeni syndrome, many detected by multigene panels represent aberrant clonal expansion (ACE), most due to clonal hematopoiesis (CH). Discerning ACE/CH from germline variants and postzygotic mosaicism (PZM) is critically needed for risk assessment and management. METHODS: Participants in the Li-Fraumeni & TP53 Understanding & Progress (LiFT UP) study with a TP53 PV were eligible. Demographics, personal/family cancer history, and clinical laboratory test reports were obtained. DNA from multiple tissues was analyzed using a custom QIAseq assay (ACE panel) that included TP53 and other CH-associated genes; the ACE panel and eyebrow follicles were assessed in a workflow to discern TP53 PV clinical categories. RESULTS: Among 134 participants there was a significant difference for the age at diagnosis (P < 0.001), component cancers (P = 0.007), and clinical testing criteria (P < 0.001), comparing germline with PZM or ACE. ACE panel analysis of DNA from 55 sets of eyebrow follicles (mean 1.4 ug) and 36 formalin-fixed, paraffin imbedded tissues demonstrated low variance (SE, 3%; P = 0.993) for TP53 variant allele fraction, with no significant difference (P = 0.965) between tissue types, and detected CH gene PVs. Of 55 multi-tissue cases, germline status was confirmed for 20, PZM in seven, ACE for 25, and three were indeterminate. Additional CH variants were detected in six ACE and two germline cases. CONCLUSIONS: We demonstrated an effective approach and tools for discerning germline TP53 status. IMPACT: Discernment of PZM and TP53-driven CH increases diagnostic accuracy and enables risk-appropriate care.

Cross-sectional clinical cancer genomics community of practice survey analysis of provider attitudes and beliefs regarding the use of deceased family member tissue to guide living family member genetic cancer risk assessment.

Next-generation tumor tissue sequencing techniques may result in the detection of putative germline pathogenic variants (PVs), raising the possibility that germline cancer predisposition could be identified from archival medical tissue samples of deceased relatives. The approach, termed traceback, is designed to inform risk management recommendations for living family members. Provider perspectives regarding traceback testing have not yet been explored, so we conducted a cross-sectional survey of Clinical Cancer Genomics Community of Practice providers regarding their attitudes and beliefs toward traceback testing. Self-reported demographics, provider characteristics, attitudes and perceived barriers were collected. We evaluated responses in the context of whether providers had previous experience with traceback testing. Data were analyzed using chi-square and Fisher's exact testing. Among 207 respondents (of 816 eligible), most were women (89.4%), white (85.5%), and not Hispanic or Latino (89.7%). US-based providers represented the majority of respondents (87.4%). Relatively, few providers 32 of 207 (15.5%) had previous experience with traceback. Among the individuals without experience in traceback, 84.0% thought there would be barriers to implementation; however, only 68.8% of individuals with previous traceback experience agreed (p = .04). Respondents in both groups thought that traceback would be valuable in their practice (82.6%, p = .22) and that they would feel comfortable discussing the concept (83.6%, p = .83), interpreting the results (72.2%, p = .24), and discussing the results with their patients (80.7%, p = .38). Patient interest and cost were seen as less of a barrier by those with experience with traceback testing. Recurrent themes obtained in open-ended responses are also presented. Overall, providers believe that traceback would be a valuable tool in their practice. Individuals with previous experience identified less barriers with implementation of this testing, highlighting an area for future research and education.

Sudden perceived absence of foetal movement - a unique presentation of a vertebral haemangioma in pregnancy.

OBJECTIVE: Symptomatic vertebral haemangiomas that present during pregnancy are extremely uncommon with few cases reported in literature. Epidural haemangiomas are rarer still with few documented. METHODS: In this report, we describe the case of a 22-year-old pregnant patient who presented with apparent loss of foetal movement at 38 weeks' gestation. Clinical review demonstrated the foetus was well but neurological examination revealed lower limb paresthesia, paresis and evident uterine hypoesthesia. An MRI scan illustrated a haemangioma in the T1 vertebral body with an epidural component causing cord compression. RESULTS: The management of spinal haemangiomas that present during pregnancy is a complex clinical scenario, which requires careful multidisciplinary consideration to determine if surgical intervention is appropriate. In this case, the patient had an emergency caesarean section followed by posterior decompression and laminectomy of the T1 vertebra with excellent post-operative recovery. CONCLUSION: Gestational increase in the size of vertebral haemangiomas is well documented. We discuss a rare case in which a young pregnant patient presents with an atypical symptom of a vertebral haemangioma (uterine hypoesthesia). This case highlights the importance of prompt imaging in these scenarios and a cohesive multidisciplinary approach in order to provide optimal treatment for the patient.

Use of a modified Delphi process to develop research priorities in major trauma.

PURPOSE: The burden of major trauma within the UK is ever increasing. There is a need to establish research priorities within the field. Delphi methodology can be used to develop consensus opinion amongst a group of stakeholders. This can be used to prioritise clinically relevant, patient-centred research questions to guide future funding allocations. The aim of our study was to identify key future research priorities pertaining to the management of major trauma in the UK. METHODS: A three-phased modified Delphi process was undertaken. Phase 1 involved the submission of research questions by members of the trauma community using an online survey (Phase 1). Phases 2 and 3 involved two consecutive rounds of prioritisation after questions were subdivided into 6 subcategories: Brain Injury, Rehabilitation, Trauma in Older People, Pre-hospital, Interventional, and Miscellaneous (Phases 2 and 3). Cut-off points were agreed by consensus amongst the steering subcommittees. This established a final prioritised list of research questions. RESULTS: In phase 1, 201 questions were submitted by 65 stakeholders. After analysis and with consensus achieved, 186 questions were taken forward for prioritisation in phase 2 with 114 included in phase 3. 56 prioritised major trauma research questions across the 6 categories were identified with a clear focus on long-term patient outcomes. Research priorities across the patient pathway from roadside to rehabilitation were deemed of importance. CONCLUSIONS: Consensus within the major trauma community has identified 56 key research questions across 6 categories. Dissemination of these questions to funding bodies to allow for the development of high-quality research is now required. There is a clear indication for targeted multi-centre multi-disciplinary research in major trauma.

Genetic epidemiology of BRCA1- and BRCA2-associated cancer across Latin America.

The prevalence and contribution of BRCA1/2 (BRCA) pathogenic variants (PVs) to the cancer burden in Latin America are not well understood. This study aims to address this disparity. BRCA analyses were performed on prospectively enrolled Latin American Clinical Cancer Genomics Community Research Network participants via a combination of methods: a Hispanic Mutation Panel (HISPANEL) on MassARRAY; semiconductor sequencing; and copy number variant (CNV) detection. BRCA PV probability was calculated using BRCAPRO. Among 1,627 participants (95.2% with cancer), we detected 236 (14.5%) BRCA PVs; 160 BRCA1 (31% CNVs); 76 BRCA2 PV frequency varied by country: 26% Brazil, 9% Colombia, 13% Peru, and 17% Mexico. Recurrent PVs (seen ≥3 times), some region-specific, represented 42.8% (101/236) of PVs. There was no ClinVar entry for 14% (17/125) of unique PVs, and 57% (111/196) of unique VUS. The area under the ROC curve for BRCAPRO was 0.76. In summary, we implemented a low-cost BRCA testing strategy and documented a significant burden of non-ClinVar reported BRCA PVs among Latin Americans. There are recurrent, population-specific PVs and CNVs, and we note that the BRCAPRO mutation probability model performs adequately. This study helps address the gap in our understanding of BRCA-associated cancer in Latin America.

Fusion rates for conservative and surgical management of type II odontoid fractures and its impact.

BACKGROUND: Type II odontoid fractures are known to have low fusion rates following conservative management with a hard collar. However, most patients are elderly with comorbidities and are not fit for surgery. The present study identified the rates of bony fusion, complications, and clinical outcomes following conservative management of type II odontoid fractures. METHODS: We included consecutive patients referred with a suspected odontoid fracture to a Major Trauma Centre in the UK between March 2015 and December 2017. Data including patient demographics, fracture management, complications and outcomes. Bony fusion was assessed by two neurosurgeons and one neuroradiologists. Results were analysed with simple statistics and chi-squared test. RESULTS: 102 patients were included in the study (mean age = 80.4 ± 15.3). 10 (9.8%) were managed surgically and 92 (90.2%) were managed conservatively with a hard collar, for a mean of 87 days. Patients were followed up for a mean of 28.1 months (range 1-855 days) until discharge. 37% developed collar complications, namely pain, stiffness and non-tolerance. Bony union was achieved in 37.3% of patients treated with a hard collar (versus 80% in the surgical group, p = 0.0096). Increasing age was an independent risk factor for non-union (p < 0.001). Of the patients without bony union, none reported symptoms, and 90% were discharged without a collar. CONCLUSION: The management of type II odontoid fractures are difficult in an elderly, co-morbid population. With conservative management fusion rates are low, and collar complications are not insignificant. However, outcomes are good regardless of union.