Tetanus severity classification in low-middle income countries through ECG wearable sensors and a 1D-vision transformer

Tetanus, a life-threatening bacterial infection prevalent in low- and middle-income countries like Vietnam, impacts the nervous system, causing muscle stiffness and spasms. Severe tetanus often involves dysfunction of the autonomic nervous system (ANS). Timely detection and effective ANS dysfunction management require continuous vital sign monitoring, traditionally performed using bedside monitors. However, wearable electrocardiogram (ECG) sensors offer a more cost-effective and user-friendly alternative. While machine learning-based ECG analysis can aid in tetanus severity classification, existing methods are excessively time-consuming. Our previous studies have investigated the improvement of tetanus severity classification using ECG time series imaging. In this study, our aim is to explore an alternative method using ECG data without relying on time series imaging as an input, with the aim of achieving comparable or improved performance. To address this, we propose a novel approach using a 1D-Vision Transformer, a pioneering method for classifying tetanus severity by extracting crucial global information from 1D ECG signals. Compared to 1D-CNN, 2D-CNN, and 2D-CNN + Dual Attention, our model achieves better results, boasting an F1 score of 0.77 ± 0.06, precision of 0.70 ± 0. 09, recall of 0.89 ± 0.13, specificity of 0.78 ± 0.12, accuracy of 0.82 ± 0.06 and AUC of 0.84 ± 0.05

Automatic Detection of B-lines in Lung Ultrasound Videos From Severe Dengue Patients

Lung ultrasound (LUS) imaging is used to assess lung abnormalities, including the presence of B-line artefacts due to fluid leakage into the lungs caused by a variety of diseases. However, manual detection of these artefacts is challenging. In this paper, we propose a novel methodology to automatically detect and localize B-lines in LUS videos using deep neural networks trained with weak labels. To this end, we combine a convolutional neural network (CNN) with a long short-term memory (LSTM) network and a temporal attention mechanism. Four different models are compared using data from 60 patients. Results show that our best model can determine whether one-second clips contain B-lines or not with an F1 score of 0.81, and extracts a representative frame with B-lines with an accuracy of 87.5%.Comment: 5 pages, 2 figures, 2 table

Association of Genetic Variants With Primary Open-Angle Glaucoma Among Individuals With African Ancestry.

Importance:Primary open-angle glaucoma presents with increased prevalence and a higher degree of clinical severity in populations of African ancestry compared with European or Asian ancestry. Despite this, individuals of African ancestry remain understudied in genomic research for blinding disorders. Objectives:To perform a genome-wide association study (GWAS) of African ancestry populations and evaluate potential mechanisms of pathogenesis for loci associated with primary open-angle glaucoma. Design, Settings, and Participants:A 2-stage GWAS with a discovery data set of 2320 individuals with primary open-angle glaucoma and 2121 control individuals without primary open-angle glaucoma. The validation stage included an additional 6937 affected individuals and 14 917 unaffected individuals using multicenter clinic- and population-based participant recruitment approaches. Study participants were recruited from Ghana, Nigeria, South Africa, the United States, Tanzania, Britain, Cameroon, Saudi Arabia, Brazil, the Democratic Republic of the Congo, Morocco, Peru, and Mali from 2003 to 2018. Individuals with primary open-angle glaucoma had open iridocorneal angles and displayed glaucomatous optic neuropathy with visual field defects. Elevated intraocular pressure was not included in the case definition. Control individuals had no elevated intraocular pressure and no signs of glaucoma. Exposures:Genetic variants associated with primary open-angle glaucoma. Main Outcomes and Measures:Presence of primary open-angle glaucoma. Genome-wide significance was defined as P < 5 × 10-8 in the discovery stage and in the meta-analysis of combined discovery and validation data. Results:A total of 2320 individuals with primary open-angle glaucoma (mean [interquartile range] age, 64.6 [56-74] years; 1055 [45.5%] women) and 2121 individuals without primary open-angle glaucoma (mean [interquartile range] age, 63.4 [55-71] years; 1025 [48.3%] women) were included in the discovery GWAS. The GWAS discovery meta-analysis demonstrated association of variants at amyloid-β A4 precursor protein-binding family B member 2 (APBB2; chromosome 4, rs59892895T>C) with primary open-angle glaucoma (odds ratio [OR], 1.32 [95% CI, 1.20-1.46]; P = 2 × 10-8). The association was validated in an analysis of an additional 6937 affected individuals and 14 917 unaffected individuals (OR, 1.15 [95% CI, 1.09-1.21]; P < .001). Each copy of the rs59892895*C risk allele was associated with increased risk of primary open-angle glaucoma when all data were included in a meta-analysis (OR, 1.19 [95% CI, 1.14-1.25]; P = 4 × 10-13). The rs59892895*C risk allele was present at appreciable frequency only in African ancestry populations. In contrast, the rs59892895*C risk allele had a frequency of less than 0.1% in individuals of European or Asian ancestry. Conclusions and Relevance:In this genome-wide association study, variants at the APBB2 locus demonstrated differential association with primary open-angle glaucoma by ancestry. If validated in additional populations this finding may have implications for risk assessment and therapeutic strategies

The A's, G's, C's, and T's of health disparities

In order to eliminate health disparities in the United States, more efforts are needed to address the breadth of social issues directly contributing to the healthy divide observed across racial and ethnic groups. Socioeconomic status, education, and the environment are intimately linked to health outcomes. However, with the tremendous advances in technology and increased investigation into human genetic variation, genomics is poised to play a valuable role in bolstering efforts to find new treatments and preventions for chronic conditions and diseases that disparately affect certain ethnic groups. Promising studies focused on understanding the genetic underpinnings of diseases such as prostate cancer or beta-blocker treatments for heart failure are illustrative of the positive contribution that genomics can have on improving minority health

Sepsis mortality prediction using wearable monitoring in low-middle income countries

Sepsis is associated with high mortality-particularly in low-middle income countries (LMICs). Critical care management of sepsis is challenging in LMICs due to the lack of care providers and the high cost of bedside monitors. Recent advances in wearable sensor technology and machine learning (ML) models in healthcare promise to deliver new ways of digital monitoring integrated with automated decision systems to reduce the mortality risk in sepsis. In this study, firstly, we aim to assess the feasibility of using wearable sensors instead of traditional bedside monitors in the sepsis care management of hospital admitted patients, and secondly, to introduce automated prediction models for the mortality prediction of sepsis patients. To this end, we continuously monitored 50 sepsis patients for nearly 24 h after their admission to the Hospital for Tropical Diseases in Vietnam. We then compared the performance and interpretability of state-of-the-art ML models for the task of mortality prediction of sepsis using the heart rate variability (HRV) signal from wearable sensors and vital signs from bedside monitors. Our results show that all ML models trained on wearable data outperformed ML models trained on data gathered from the bedside monitors for the task of mortality prediction with the highest performance (area under the precision recall curve = 0.83) achieved using time-varying features of HRV and recurrent neural networks. Our results demonstrate that the integration of automated ML prediction models with wearable technology is well suited for helping clinicians who manage sepsis patients in LMICs to reduce the mortality risk of sepsis

Effectiveness of educational interventions for healthcare workers on vaccination dialogue with older adults: a systematic review

Abstract Background Healthcare workers (HCW) significantly influence older adults’ vaccine acceptance. This systematic review aimed to identify effective educational interventions for HCWs that could enhance their ability to engage in a dialogue with older adults on vaccination. Methods Medline, Scopus, Cochrane library and grey literature were searched for comparative studies investigating educational interventions concerning older adult vaccinations. The search encompassed all languages and publication years. Analysis was performed on the outcomes ‘vaccines offered or ordered’ and ‘vaccination rates’. Whenever feasible, a sub-analysis on publication year was conducted. Methodological limitations were assessed using the RoB 2 for RCTs and the GRADE checklist for non-randomized studies. Study outcomes were categorized according to the four-level Kirkpatrick model (1996) for effectiveness: reaction, learning, behaviour, and results. Results In total, 48 studies met all inclusion criteria. Most studies included reminder systems signalling HCWs on patients due for vaccination. Other interventions included seminars, academic detailing and peer-comparison feedback. Four articles reporting on the reaction-level indicated that most HCWs had a favourable view of the intervention. Two of the six articles reporting on the learning-level observed positive changes in attitude or knowledge due to the intervention. Seventeen studies reported on the behaviour-level. An analysis on eleven out of seventeen studies focusing on vaccines ‘ordered’ or ‘offered’ outcomes suggested that tailored reminders, particularly those implemented before 2000, were the most effective. Out of 34 studies reporting on the result-level, 24 were eligible for analysis on the outcome ‘vaccination rate’, which showed that compared to usual care, multicomponent interventions were the most effective, followed by tailored reminders, especially those predating 2000. Nonetheless, tailored reminders often fell short compared to other interventions like standing orders or patient reminders. In both the behaviour-level and result-level ‘education only’ interventions frequently underperformed relative to other interventions. Seventeen out of the 27 RCTs, and seven of the 21 non-randomized studies presented a low-to-medium risk for bias in the studies’ findings. Conclusions Tailored reminders and multicomponent interventions effectively assist HCWs in addressing vaccines with older adults. However, education-only interventions appear to be less effective compared to other interventions