Perceptions, challenges and experiences of frontline healthcare providers in Emergency Departments regarding Workplace Violence during the COVID-19 pandemic: A protocol for an exploratory qualitative study from an LMIC.

INTRODUCTION: Workplace violence (WPV) against Healthcare Workers (HCWs) has emerged as a global issue. Emergency Department (ED) HCWs as front liners are more vulnerable to it due to the nature of their work and exposure to unique medical and social situations. COVID-19 pandemic has led to a surge in the number of cases of WPV against HCWs, especially against ED HCWs. In most cases, the perpetrators of these acts of violence are the patients and their attendants as families. The causes of this rise are multifactorial; these include the inaccurate spread of information and rumours through social media, certain religious perspectives, propaganda and increasing anger and frustration among the general public,ED overcrowding, staff shortages etc. We aim to conduct a qualitative exploratory study among the ED frontline care providers at the two major EDs of Karachi city. The purpose of this study is to determine the perceptions, challenges and experiences regarding WPV faced by ED healthcare providers during the COVID-19 pandemic. METHODS AND ANALYSIS: For this research study, a qualitative exploratory research design will be employed using in-depth interviews and a purposive sampling approach. Data will be collected using in-depth interviews from study participants working at the EDs of Jinnah Postgraduate Medical Centre (JPMC) and the Aga Khan University Hospital(AKUH) Karachi, Pakistan. Thestudy data will be analysed thematically using NVivo V.12 Plus software. ETHICS AND DISSEMINATION: The ethical approval for this study was obtained from the Aga Khan University Ethical Review Committee and from Jinnah postgraduate Medical Center (JPMC). The results of the study will be disseminated to the scientific community and to the research subjects participating in the study.The findings of this study will help to explore the perceptions of ED healthcare providers regarding WPV during the COVID-19 pandemic and provide a better understanding of study participant's' challenges concerning WPV during the COVID-19 pandemic.

Predicting mortality in SARS-COV-2 (COVID-19) positive patients in the inpatient setting using a novel deep neural network.

BACKGROUND: The nextwave of COVID-19 pandemic is anticipated to be worse than the initial one and will strain the healthcare systems even more during the winter months. Our aim was to develop a novel machine learning-based model to predict mortality using the deep learning Neo-V framework. We hypothesized this novel machine learning approach could be applied to COVID-19 patients to predict mortality successfully with high accuracy. METHODS: We collected clinical and laboratory data prospectively on all adult patients (≥18 years of age) that were admitted in the inpatient setting at Aga Khan University Hospital between February 2020 and September 2020 with a clinical diagnosis of COVID-19 infection. Only patients with a RT-PCR (reverse polymerase chain reaction) proven COVID-19 infection and complete medical records were included in this study. A Novel 3-phase machine learning framework was developed to predict mortality in the inpatients setting. Phase 1 included variable selection that was done using univariate and multivariate Cox-regression analysis; all variables that failed the regression analysis were excluded from the machine learning phase of the study. Phase 2 involved new-variables creation and selection. Phase 3 and final phase applied deep neural networks and other traditional machine learning models like Decision Tree Model, k-nearest neighbor models, etc. The accuracy of these models were evaluated using test-set accuracy, sensitivity, specificity, positive predictive values, negative predictive values and area under the receiver-operating curves. RESULTS: After application of inclusion and exclusion criteria (n=)1214 patients were selected from a total of 1228 admitted patients. We observed that several clinical and laboratory-based variables were statistically significant for both univariate and multivariate analyses while others were not. With most significant being septic shock (hazard ratio [HR], 4.30; 95% confidence interval [CI], 2.91-6.37), supportive treatment (HR, 3.51; 95% CI, 2.01-6.14), abnormal international normalized ratio (INR) (HR, 3.24; 95% CI, 2.28-4.63), admission to the intensive care unit (ICU) (HR, 3.24; 95% CI, 2.22-4.74), treatment with invasive ventilation (HR, 3.21; 95% CI, 2.15-4.79) and laboratory lymphocytic derangement (HR, 2.79; 95% CI, 1.6-4.86). Machine learning results showed our deep neural network (DNN) (Neo-V) model outperformed all conventional machine learning models with test set accuracy of 99.53%, sensitivity of 89.87%, and specificity of 95.63%; positive predictive value, 50.00%; negative predictive value, 91.05%; and area under the receiver-operator curve of 88.5. CONCLUSION: Our novel Deep-Neo-V model outperformed all other machine learning models. The model is easy to implement, user friendly and with high accuracy.

Perfecting temporary pacemakers in a developing country.

INTRODUCTION: Transvenous pacemakers are used to temporarily pace heart in emergent situations. This study was conducted to analyze the current success rate of temporary pacemaker insertion in our institution and discover causes for failure to improve the technique. METHODOLOGY: A retrospective cohort study was conducted of 263 patients from 2006 to 2016 who underwent TPM insertion at Aga Khan University Hospital, Karachi. RESULTS: The success rate for the procedure was 97.7%, with one mortality caused by the pacemaker. No significant risk factor was found for the failure of TPM. CONCLUSION: There was no significant effect of anatomical site or technique on the failure of TPM insertion. However, with better training and higher experience of the residents, the complications and rate of failures can be reduced.

Exploring the Potential of Artificial Intelligence and Machine Learning to Combat COVID-19 and Existing Opportunities for LMIC: A Scoping Review.

BACKGROUND: In the face of the current time-sensitive COVID-19 pandemic, the limited capacity of healthcare systems resulted in an emerging need to develop newer methods to control the spread of the pandemic. Artificial Intelligence (AI), and Machine Learning (ML) have a vast potential to exponentially optimize health care research. The use of AI-driven tools in LMIC can help in eradicating health inequalities and decrease the burden on health systems. METHODS: The literature search for this Scoping review was conducted through the PubMed database using keywords: COVID-19, Artificial Intelligence (AI), Machine Learning (ML), and Low Middle-Income Countries (LMIC). Forty-three articles were identified and screened for eligibility and 13 were included in the final review. All the items of this Scoping review are reported using guidelines for PRISMA extension for scoping reviews (PRISMA-ScR). RESULTS: Results were synthesized and reported under 4 themes. (a) The need of AI during this pandemic: AI can assist to increase the speed and accuracy of identification of cases and through data mining to deal with the health crisis efficiently, (b) Utility of AI in COVID-19 screening, contact tracing, and diagnosis: Efficacy for virus detection can a be increased by deploying the smart city data network using terminal tracking system along-with prediction of future outbreaks, (c) Use of AI in COVID-19 patient monitoring and drug development: A Deep learning system provides valuable information regarding protein structures associated with COVID-19 which could be utilized for vaccine formulation, and (d) AI beyond COVID-19 and opportunities for Low-Middle Income Countries (LMIC): There is a lack of financial, material, and human resources in LMIC, AI can minimize the workload on human labor and help in analyzing vast medical data, potentiating predictive and preventive healthcare. CONCLUSION: AI-based tools can be a game-changer for diagnosis, treatment, and management of COVID-19 patients with the potential to reshape the future of healthcare in LMIC.

School-based interventions to promote personal and environmental hygiene practices among children in Pakistan: protocol for a mixed methods study.

BACKGROUND: Poor personal hygiene and inadequate sanitation practices among young children leads to communicable diseases. There remains a gap in the holistic assessment of school children's hygiene literacy, practices and effectiveness of school-based hygiene interventions in Pakistan. Therefore, a school-based intervention protocol has been designed to promote personal and environmental hygiene practices for primary school children. Towards improving children's hygiene behaviors, the study will also focus on enhancing mothers' hygiene knowledge and practices. METHODS: Using quasi-experimental design with mixed methods data collection approaches, this study will be conducted in schools located in an urban squatter settlements in Pakistan. To assess primary grade children and their mothers' hygiene status, a survey will be held in the pre-intervention phase. This phase also includes qualitative exploration of key stakeholders (mothers, teachers, health & education authorities representatives') perceptions about the factors facilitating and impeding the adaption of hygiene behaviors among school children. In-depth guides and focus group discussion tools will be used for this purpose. This will be followed by multi-component intervention phase with behavior change strategies to improve children's and mothers' hygiene literacy and behaviors. The post-intervention phase will assess the intervention effectiveness in terms of enhancing hygiene knowledge and practices among school children and mothers, alongside exploration of mothers and teachers' insights into whether or not the intervention has been effective in improving hygiene practices among children. Paired t-test will be applied pre and post-intervention to measure the differences between the mothers and children's hygiene literacy and knowledge scores. Similar test will be performed to assess the differences in children's hygiene knowledge and practice scores, pre and post-intervention (< 50 = poor, 50-75 = good and > 75 = excellent). Thematic analysis will be carried out for qualitative data. DISCUSSION: Multi-component intervention aimed at improving personal and environmental hygiene among primary school children offers an opportunity to design and test various behavioral change strategies at school and in home settings. The study findings will be significant in assessing the intervention's effectiveness in improving children's overall hygiene. TRIAL REGISTRATION: Retrospectively registered with ClinicalTrials.gov (NCT03942523) on 5th May 2019.