Impact of COVID-19 national lockdown on asthma exacerbations: interrupted time-series analysis of English primary care data

Background: The impact of Covid-19 and ensuing national lockdown on asthma exacerbations is unclear. Methods: We conducted an interrupted time-series (lockdown on 23rd March as point of interruption) analysis in asthma cohort identified using a validated algorithm from a national-level primary care database, the Optimum Patient Care Database (OPCRD). We derived asthma exacerbation rates for every week and compared exacerbation rates in the period: January-August 2020 with a pre-Covid-19 period; January-August 2016-2019). Exacerbations were defined as asthma-related hospital attendance/admission (including accident and emergency visit), or an acute course of oral corticosteroids with evidence of respiratory review, as recorded in primary care. We used a generalised least squares modelling approach and stratified the analyses by age, sex, English region, and healthcare setting. Results: From a database of 9,949,487 patients, there were 100,165 asthma patients who experienced at least one exacerbation during 2016-2020. Of 278,996 exacerbation episodes, 49,938 (17.1%) required hospital visit. Comparing pre-lockdown to post-lockdown period, we observed a statistically significant reduction in the level (-0.196 episodes per person-year; p-value<0.001; almost 20 episodes for every 100 asthma patients per year) of exacerbation rates across all patients. The reductions in level in stratified analyses were: 0.005-0.244 (healthcare setting, only those without hospital attendance/admission were significant), 0.210-0.277 (sex), 0.159-0.367 (age), 0.068-0.371 (region). Conclusions: There has been a significant reduction in attendance to primary care for asthma exacerbations during the pandemic. This reduction was observed in all age groups, both sexes, and across most regions in England

Application of Machine Learning Using Decision Trees for Prognosis of Deep Brain Stimulation of Globus Pallidus Internus for Children With Dystonia

Background: While Deep Brain Stimulation (DBS) of the Globus pallidus internus is a well-established therapy for idiopathic/genetic dystonia, benefits for acquired dystonia are varied, ranging from modest improvement to deterioration. Predictive biomarkers to aid DBS prognosis for children are lacking, especially in acquired dystonias, such as dystonic Cerebral Palsy. We explored the potential role of machine learning techniques to identify parameters that could help predict DBS outcome. Methods: We conducted a retrospective study of 244 children attending King's College Hospital between September 2007 and June 2018 for neurophysiological tests as part of their assessment for possible DBS at Evelina London Children's Hospital. For the 133 individuals who underwent DBS and had 1-year outcome data available, we assessed the potential predictive value of six patient parameters: sex, etiology (including cerebral palsy), baseline severity (Burke-Fahn-Marsden Dystonia Rating Scale-motor score), cranial MRI and two neurophysiological tests, Central Motor Conduction Time (CMCT) and Somatosensory Evoked Potential (SEP). We applied machine learning analysis to determine the best combination of these features to aid DBS prognosis. We developed a classification algorithm based on Decision Trees (DTs) with k-fold cross validation for independent testing. We analyzed all possible combinations of the six features and focused on acquired dystonias. Results: Several trees resulted in better accuracy than the majority class classifier. However, the two features that consistently appeared in top 10 DTs were CMCT and baseline dystonia severity. A decision tree based on CMCT and baseline severity provided a range of sensitivity and specificity, depending on the threshold chosen for baseline dystonia severity. In situations where CMCT was not available, a DT using SEP alone provided better than the majority class classifier accuracy. Conclusion: The results suggest that neurophysiological parameters can help predict DBS outcomes, and DTs provide a data-driven, highly interpretable decision support tool that lends itself to being used in clinical practice to help predict potential benefit of DBS in dystonic children. Our results encourage the introduction of neurophysiological parameters in assessment pathways, and data collection to facilitate multi-center evaluation and validation of these potential predictive markers and of the illustrative decision support tools presented here

Study protocol and design for the assessment of paediatric pneumonia from X-ray images using deep learning

Introduction In low-income and middle-income countries, pneumonia remains the leading cause of illness and death in children&lt;5 years. The recommended tool for diagnosing paediatric pneumonia is the interpretation of chest X-ray images, which is difficult to standardise and requires trained clinicians/radiologists. Current automated computational tools have primarily focused on assessing adult pneumonia and were trained on images evaluated by a single specialist. We aim to provide a computational tool using a deep-learning approach to diagnose paediatric pneumonia using X-ray images assessed by multiple specialists trained by the WHO expert X-ray image reading panel.Methods and analysis Approximately 10 000 paediatric chest X-ray images are currently being collected from an ongoing WHO-supported surveillance study in Bangladesh. Each image will be read by two trained clinicians/radiologists for the presence or absence of primary endpoint pneumonia (PEP) in each lung, as defined by the WHO. Images whose PEP labels are discordant in either lung will be reviewed by a third specialist and the final assignment will be made using a majority vote. Convolutional neural networks will be used for lung segmentation to align and scale the images to a reference, and for interpretation of the images for the presence of PEP. The model will be evaluated against an independently collected and labelled set of images from the WHO. The study outcome will be an automated method for the interpretation of chest radiographs for diagnosing paediatric pneumonia.Ethics and dissemination All study protocols were approved by the Ethical Review Committees of the Bangladesh Institute of Child Health, Bangladesh. The study sponsor deemed it unnecessary to attain ethical approval from the Academic and Clinical Central Office for Research and Development of University of Edinburgh, UK. The study uses existing X-ray images from an ongoing WHO-coordinated surveillance. All findings will be published in an open-access journal. All X-ray labels and statistical code will be made openly available. The model and images will be made available on request

Compliance and Usability of an Asthma Home Monitoring System

Asthma monitoring is an important aspect of patient self-management. However, due to its repetitive nature, patients can find long-term monitoring te-dious. Mobile health can provide an avenue to monitor asthma without needing high levels of active engagement, and instead rely on passive monitoring. In our recent AAMOS-00 study, we collected mobile health data over six months from 22 asthma patients using passive and active monitoring technology, including smartwatch, peak flow measurements, and daily asthma diaries. Compliance to smartwatch monitoring was found to lie between the compliance to complete daily asthma diaries and measuring daily peak flow. However, some study participants faced technical issues with the devices which could have af-fected the relative compliance of the monitoring tasks. Moreover, as evidenced by standard usability questionnaires, we found that the AAMOS-00 study’s data collection system was similar in quality to other studies and published apps

Immunization status of students of Nishtar medical university against hepatitis B

Background: Hepatitis B virus (HBV) infection causes significant morbidity and mortality worldwide. Occupational exposure of health care workers and medical students increase their risk of acquiring HBV infection, and many authorities recommend vaccination. However, significant proportions of health care workers do not receive HBV immunization, and remain at increased risk to HBV infection. The present study was conducted on medical students to evaluate their knowledge regarding HBV and to know their vaccination status.Methods: This cross sectional, randomized, observational study was done at Nishtar medical university, Multan from November 2017 to July 2018. A pre-structured and tested questionnaire was given to 150 medical students from first year to final year. Out of these students 75 were males and 75 were females. The students were also interviewed about age, gender, year of study, screening before vaccination, history of vaccination, completion of all 3 doses and reasons for not getting vaccinated.Results: Out of 150 participants, 117 (78%) were vaccinated against HBV. In the vaccinated group, 90 (77%) completed all the three doses of their vaccination schedule and remaining 27 (23%) students were incompletely vaccinated. Rate of vaccine uptake was higher in females; 63 (84%) than in males: 53 (71%). Reasons of not being vaccinated were lack of knowledge about consequences (15.5%), casual behaviour (36%), not knowing from where to get vaccine (12%), fear of injection (10%), busy in studies (10%) and financial problems (8%). Prior screening was done in 74 (63%) students before the vaccination.Conclusions: Despite the availability and accessibility of a cost-effective hepatitis B vaccine since mid80's, the vaccination coverage among medical students is low. Health education needs to be improved in all medical students. The orientation and awareness programmes should be held to create awareness regarding HBV infection