Vertebral Osteonecrosis

Vertebral osteonecrosis (ON) is a rare, underdiagnosed disease, also called pseudarthrosis due to ischemia following a compression fracture (CF). The main features include the air-occupied intravertebral cleft visualized as a radiolucent shade of linear or semilunar X-ray, namely an intravertebral vacuum cleft (IVC) sign. Usually, this phenomenon shows low signal intensity with all magnetic resonance imaging (MRI) sequences. Another feature of ON of the vertebral body is the intravertebral fluid analogous to edema and fibrosis in histological sections. This appears as low signal intensity on T1-weighted MRI, with high signal intensity on T2-weighted images. The risk factors for vertebral ON are multivariate, and the pathophysiological mechanisms are still unknown with certainty

Prediction-based classification for longitudinal biomarkers

Assessment of circulating CD4 count change over time in HIV-infected subjects on antiretroviral therapy (ART) is a central component of disease monitoring. The increasing number of HIV-infected subjects starting therapy and the limited capacity to support CD4 count testing within resource-limited settings have fueled interest in identifying correlates of CD4 count change such as total lymphocyte count, among others. The application of modeling techniques will be essential to this endeavor due to the typically nonlinear CD4 trajectory over time and the multiple input variables necessary for capturing CD4 variability. We propose a prediction-based classification approach that involves first stage modeling and subsequent classification based on clinically meaningful thresholds. This approach draws on existing analytical methods described in the receiver operating characteristic curve literature while presenting an extension for handling a continuous outcome. Application of this method to an independent test sample results in greater than 98% positive predictive value for CD4 count change. The prediction algorithm is derived based on a cohort of $n=270$ HIV-1 infected individuals from the Royal Free Hospital, London who were followed for up to three years from initiation of ART. A test sample comprised of $n=72$ individuals from Philadelphia and followed for a similar length of time is used for validation. Results suggest that this approach may be a useful tool for prioritizing limited laboratory resources for CD4 testing after subjects start antiretroviral therapy.Comment: Published in at http://dx.doi.org/10.1214/10-AOAS326 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Increased Risk of Fragility Fractures among HIV Infected Compared to Uninfected Male Veterans

BACKGROUND: HIV infection has been associated with an increased risk of fragility fracture. We explored whether or not this increased risk persisted in HIV infected and uninfected men when controlling for traditional fragility fracture risk factors. METHODOLOGY/PRINCIPAL FINDINGS: Cox regression models were used to assess the association of HIV infection with the risk for incident hip, vertebral, or upper arm fracture in male Veterans enrolled in the Veterans Aging Cohort Study Virtual Cohort (VACS-VC). We calculated adjusted hazard ratios comparing HIV status and controlling for demographics and other established risk factors. The sample consisted of 119,318 men, 33% of whom were HIV infected (34% aged 50 years or older at baseline, and 55% black or Hispanic). Median body mass index (BMI) was lower in HIV infected compared with uninfected men (25 vs. 28 kg/m²; p<0.0001). Unadjusted risk for fracture was higher among HIV infected compared with uninfected men [HR: 1.32 (95% CI: 1.20, 1.47)]. After adjusting for demographics, comorbid disease, smoking and alcohol abuse, HIV infection remained associated with an increased fracture risk [HR: 1.24 (95% CI: 1.11, 1.39)]. However, adjusting for BMI attenuated this association [HR: 1.10 (95% CI: 0.97, 1.25)]. The only HIV-specific factor associated with fragility fracture was current protease inhibitor use [HR: 1.41 (95% CI: 1.16, 1.70)]. CONCLUSIONS/SIGNIFICANCE: HIV infection is associated with fragility fracture risk. This risk is attenuated by BMI

Point-of-Care Ultrasound Assessment of Tropical Infectious Diseases—A Review of Applications and Perspectives

The development of good quality and affordable ultrasound machines has led to the establishment and implementation of numerous point-of-care ultrasound (POCUS) protocols in various medical disciplines. POCUS for major infectious diseases endemic in tropical regions has received less attention, despite its likely even more pronounced benefit for populations with limited access to imaging infrastructure. Focused assessment with sonography for HIV-associated TB (FASH) and echinococcosis (FASE) are the only two POCUS protocols for tropical infectious diseases, which have been formally investigated and which have been implemented in routine patient care today. This review collates the available evidence for FASH and FASE, and discusses sonographic experiences reported for urinary and intestinal schistosomiasis, lymphatic filariasis, viral hemorrhagic fevers, amebic liver abscess, and visceral leishmaniasis. Potential POCUS protocols are suggested and technical as well as training aspects in the context of resource-limited settings are reviewed. Using the focused approach for tropical infectious diseases will make ultrasound diagnosis available to patients who would otherwise have very limited or no access to medical imaging

Hard hitting facts on childhood head trauma: an epidemiological analysis

Background: According to the World Health Organization (WHO), Traumatic Brain Injury (TBI) will become the third largest cause of global disease by the year 2020. Despite its astonishing numbers, TBI remains a silent or even forgotten epidemic with significant paucity in epidemiological data. TBI in developing countries represents a disproportionate burden of disease and data are lacking regarding the unique demographics in South Africa to design and implement focused prevention programmes. A valuable tool to assess the severity of TBI is the use of Computer tomography (CT). CT also is the main imaging modality to provide rapid identification and information for the management of children with TBI. CT scanning utilises ionising radiation and as an imaging modality poses risk to the patient. In order to guide decision protocol/algorithm, various Clinical Decision Rules (CDRs) have been established in High Income Countries. These protocols, including the need for CT scan might differ in a Medium/Low Income setting. Methodology: This is a prospective, single centre cohort study. Data were collected over an 18-month period (1 August 2015 - 31 January 2017). Children under the age of 13 years (n=3007) presenting to RCWCH after sustaining a head injury were included. Various epidemiological data were collected. A Road Safety Questionnaire was also used to evaluate safety knowledge of health care workers. Three different CDRs were compared to the standard of practice in RCWCH. A final analysis of demographics, mechanism of injury, radiology outcome, safety analysis and evaluation of a comparison of local protocol compared to the other CDRs was performed using descriptive statistics. Results: The mean age of paediatric patients presenting after a head injury was 4.6 years. There was a significant male predominance (66%) and almost two thirds of all children were of pre-school age. Falls (53%; n=1601) represented the most common mechanism of injury across all age groups, followed by road traffic related injuries (RTI) (29%; n=864), struck by or against an object (9%; n=279) and injuries as a result of interpersonal violence (8%; n=230). Within the subset of RTI (n=864) only 6 passengers were appropriately restrained, with 142 unrestrained and 56 passengers transported on the back of a goods vehicle. In the under 3-yearold age group, only 1 patient was appropriately transported in a car seat, with 51 unrestrained and 6 transported on the back of a goods vehicle. Pedestrian related injuries were by far the largest group of RTI (70%) with 50% of these under the age of 5 years. Intentional injuries inflicted by an adult were most common (34%) in the pre-verbal (under 2 years old) group. Interpersonal violence among minors (assault with a brick or stone) constituted 52% of intentional injuries. Eight firearm related injuries were recorded. Appliances and iron gates that were not correctly installed were additional causes of injury. CT scans were obtained according to the RCWCH protocol in 59% of cases and 34% showed an abnormal result. The sensitivity (98%) and specificity (93%) while using the standard of practice protocol was better than the 3 CDRs developed in High Income Countries. Analysing our Road Safety Questionnaire there appears great room for improvement regarding awareness of road safety guidelines and legislation. Conclusion: The performance of the current RCWCH CT scan protocol appears appropriate in our setting although there is some room for improvement using the strengths of the other CDRs. Valuable insight regarding the epidemiology of TBI in our setting has been highlighted. Of specific importance is the large proportion of very young children at risk of injury by all mechanisms of injury, particularly pedestrian-related injuries, unrestrained passengers and interpersonal violence among minors. Important gaps in knowledge about current recommendations for road safety were identified by the questionnaire. As long as these issues are not appropriately addressed through enhanced injury prevention programmes, children will continue to carry the heavy burden of TBI morbidity and mortality

Clinical text data in machine learning: Systematic review

Background: Clinical narratives represent the main form of communication within healthcare providing a personalized account of patient history and assessments, offering rich information for clinical decision making. Natural language processing (NLP) has repeatedly demonstrated its feasibility to unlock evidence buried in clinical narratives. Machine learning can facilitate rapid development of NLP tools by leveraging large amounts of text data. Objective: The main aim of this study is to provide systematic evidence on the properties of text data used to train machine learning approaches to clinical NLP. We also investigate the types of NLP tasks that have been supported by machine learning and how they can be applied in clinical practice. Methods: Our methodology was based on the guidelines for performing systematic reviews. In August 2018, we used PubMed, a multi-faceted interface, to perform a literature search against MEDLINE. We identified a total of 110 relevant studies and extracted information about the text data used to support machine learning, the NLP tasks supported and their clinical applications. The data properties considered included their size, provenance, collection methods, annotation and any relevant statistics. Results: The vast majority of datasets used to train machine learning models included only hundreds or thousands of documents. Only 10 studies used tens of thousands of documents with a handful of studies utilizing more. Relatively small datasets were utilized for training even when much larger datasets were available. The main reason for such poor data utilization is the annotation bottleneck faced by supervised machine learning algorithms. Active learning was explored to iteratively sample a subset of data for manual annotation as a strategy for minimizing the annotation effort while maximizing predictive performance of the model. Supervised learning was successfully used where clinical codes integrated with free text notes into electronic health records were utilized as class labels. Similarly, distant supervision was used to utilize an existing knowledge base to automatically annotate raw text. Where manual annotation was unavoidable, crowdsourcing was explored, but it remains unsuitable due to sensitive nature of data considered. Beside the small volume, training data were typically sourced from a small number of institutions, thus offering no hard evidence about the transferability of machine learning models. The vast majority of studies focused on the task of text classification. Most commonly, the classification results were used to support phenotyping, prognosis, care improvement, resource management and surveillance. Conclusions: We identified the data annotation bottleneck as one of the key obstacles to machine learning approaches in clinical NLP. Active learning and distant supervision were explored as a way of saving the annotation efforts. Future research in this field would benefit from alternatives such as data augmentation and transfer learning, or unsupervised learning, which does not require data annotation

The reporting quality of natural language processing studies - systematic review of studies of radiology reports

Abstract Background Automated language analysis of radiology reports using natural language processing (NLP) can provide valuable information on patients’ health and disease. With its rapid development, NLP studies should have transparent methodology to allow comparison of approaches and reproducibility. This systematic review aims to summarise the characteristics and reporting quality of studies applying NLP to radiology reports. Methods We searched Google Scholar for studies published in English that applied NLP to radiology reports of any imaging modality between January 2015 and October 2019. At least two reviewers independently performed screening and completed data extraction. We specified 15 criteria relating to data source, datasets, ground truth, outcomes, and reproducibility for quality assessment. The primary NLP performance measures were precision, recall and F1 score. Results Of the 4,836 records retrieved, we included 164 studies that used NLP on radiology reports. The commonest clinical applications of NLP were disease information or classification (28%) and diagnostic surveillance (27.4%). Most studies used English radiology reports (86%). Reports from mixed imaging modalities were used in 28% of the studies. Oncology (24%) was the most frequent disease area. Most studies had dataset size > 200 (85.4%) but the proportion of studies that described their annotated, training, validation, and test set were 67.1%, 63.4%, 45.7%, and 67.7% respectively. About half of the studies reported precision (48.8%) and recall (53.7%). Few studies reported external validation performed (10.8%), data availability (8.5%) and code availability (9.1%). There was no pattern of performance associated with the overall reporting quality. Conclusions There is a range of potential clinical applications for NLP of radiology reports in health services and research. However, we found suboptimal reporting quality that precludes comparison, reproducibility, and replication. Our results support the need for development of reporting standards specific to clinical NLP studies

The association between headache presentation, normal examination and neuroimaging findings: a retrospective analysis of patients presenting to a tertiary referral centre

Background: There is a high worldwide burden of headaches. Selection of patients with headaches for neuroimaging, in the absence of traditional red flags, is imperative in guiding further management. Objectives: Determine the yield of neuroimaging findings in patients with headache and normal examination; and potentially identifying additional red flags. Methods: A retrospective consecutive chart review of patients with a main complaint of headaches and normal clinical examination were assessed at a tertiary hospital, over a 10-year period. Results: Cohort consisted of 114 patients. Unexpected or normal variants found in 20.2% of patients (23/114) and 11.4% (13/114) required change in management. The absence of nausea and vomiting (p=0.009) and absence of sharp type headaches in unexpected or normal variants group (p=0.03) were statistically significant. There was a higher chance of an abnormal neuroimaging study in men and HIV seropositive patients. Conclusions: Decision to neuroimage should be determined on an individual basis (demographic factors, history of headache and examination) as normal examination cannot preclude patients from unexpected findings on neuroimaging. Headache with nausea and vomiting in isolation may be associated with normal neuroimaging reflecting primary type headaches. Findings support a lower threshold to neuroimage men and HIV seropositive patients with headaches despite normal clinical examination. Keywords: Headache; normal clinical examination; neuroimaging; headache red flags

A systematic review of natural language processing applied to radiology reports

NLP has a significant role in advancing healthcare and has been found to be key in extracting structured information from radiology reports. Understanding recent developments in NLP application to radiology is of significance but recent reviews on this are limited. This study systematically assesses recent literature in NLP applied to radiology reports. Our automated literature search yields 4,799 results using automated filtering, metadata enriching steps and citation search combined with manual review. Our analysis is based on 21 variables including radiology characteristics, NLP methodology, performance, study, and clinical application characteristics. We present a comprehensive analysis of the 164 publications retrieved with each categorised into one of 6 clinical application categories. Deep learning use increases but conventional machine learning approaches are still prevalent. Deep learning remains challenged when data is scarce and there is little evidence of adoption into clinical practice. Despite 17% of studies reporting greater than 0.85 F1 scores, it is hard to comparatively evaluate these approaches given that most of them use different datasets. Only 14 studies made their data and 15 their code available with 10 externally validating results. Automated understanding of clinical narratives of the radiology reports has the potential to enhance the healthcare process but reproducibility and explainability of models are important if the domain is to move applications into clinical use. More could be done to share code enabling validation of methods on different institutional data and to reduce heterogeneity in reporting of study properties allowing inter-study comparisons. Our results have significance for researchers providing a systematic synthesis of existing work to build on, identify gaps, opportunities for collaboration and avoid duplication