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

Risk factors for intensive care unit admission and mortality in hospitalized COVID-19 patients

Background: This study investigated the clinical features and outcome of hospitalized coronavirus disease 2019 (COVID-19) patients admitted to our quaternary care hospital.Methods: In this retrospective cohort study, we included all adult patients with COVID-19 infection admitted to a quaternary care hospital in Pakistan from March 1 to April 15, 2020. The extracted variables included demographics, comorbidities, presenting symptoms, laboratory tests and radiological findings during admission. Outcome measures included in-hospital mortality and length of stay.Results: Sixty-six COVID-19 patients were hospitalized during the study period. Sixty-one percent were male and 39% female; mean age was 50.6±19.1 years. Fever and cough were the most common presenting symptoms. Serial chest X-rays showed bilateral pulmonary opacities in 33 (50%) patients. The overall mortality was 14% and mean length of stay was 8.4±8.9 days. Ten patients (15%) required intensive care unit (ICU) care during admission, of which six (9%) were intubated. Age ≥60 years, diabetes, ischemic heart disease, ICU admission, neutrophil to lymphocyte ratio ≥3.3, and INR ≥1.2 were associated with increased risk of mortality.Conclusion: We found a mortality rate of 14% in hospitalized COVID-19 patients. COVID-19 cases are still increasing exponentially around the world and may overwhelm healthcare systems in many countries soon. Our findings can be used for early identification of patients who may require intensive care and aggressive management in order to improve outcomes

Cadmium toxicity alleviation through exogenous application of gibberellic acid (GA3) in mustard (Brassica juncea (L.) czern.) and rapeseed (Brassica rapa L.)

An experiment was carried out by considering adverse impact of heavy metals on human health through consumption of crops. To alleviate the adverse effects of cadmium (Cd) toxicity through foliar application of gibberellic acid (GA3), two varieties of Brassica including Indian mustard (Brassica juncea (L.) Czern.) commonly known as ‘Raya’ and rapeseed (Brassica rapa L.) as ‘Toria’ were studied. The Completely Randomized Design (CRD) was used with eight treatments including control in four replicates. Treatments were as following, T0 (control), T1 (150 μM CdCl2), T2 (50 mg/L GA3), T3 (75 mg/L GA3), T4 (100 mg/L GA3), T5 (150 μM CdCl2 + 50 mg/L GA3), T6 (150 μM CdCl2 + 75 mg/L GA3), and T7 (150 μM CdCl2 + 100 mg/L GA3). Gibberellic acid (GA3), a plant growth regulator applied exogenously. The concentration of cadmium (150 μM CdCl2) resulted in Cd toxicity affected adversely the morphological and biochemical parameters. Foliar application of GA3 (50 mg, 75 mg and 100 mg) positively influenced the various growth parameters as root length (30 cm), shoot length (129.75 cm), number of leaves (14.5), pods per plant (88) and biochemical parameters like total chlorophyll (0.19 mg/g), protein content (0.70 mg/mL), carbohydrates (0.37 mg/mL) and CAT (0.56 units/mg). Outcome indicated that GA3 reduces the harmful effects of Cd stress in both varieties. It was concluded that all growth and yield parameters of variety ‘Raya’ were better as compared to variety ‘Toria’, hence Raya recommended for large scale cultivation with GA3 under Cd stress

Impact of a school-based intervention to address iodine deficiency disorder in adolescent girls in Gilgit, Pakistan

Background: Gilgit Baltistan has long been recognized as an endemic region for severe iodine deficiency disorder (IDD) and hence it is still a major public health problem in this high mountain province of Pakistan. In this study we aimed to evaluate the impact of iodine supplementation, dietary modification coupled with iodine nutrition education on IDD in adolescent girls. Aims: This study aimed to have a latest review of iodine profile / effects of iodization and diet modification among the affected population. This is the first ever quasi-experimental study in Gilgit-Baltistan for assessing the magnitude of IDD with multiple impact indicators in short span of 6 months. Methods: We conducted a pre- and post intervention study in a stratified random sample of 152 girls aged 10 to 19 years from four schools of Gilgit town in 2011. IDD was defined as having a goiter, and urinary iodine deficiency (\u3c100 mcg/l). Five trained female research assistants conducted iodine nutrition education for 6 months. Results: Out of 152 participants, 125 (83%) completed the study. Use of iodized salt increased from 91.4% to 94.9% (p= 0.006) at household level.Knowledge about the role of iodine in health increased from 30.9% to 53.3% of the study population. Practice about use of iodized salt before cooking declined from 89.5 % to 77.5%. Consumption of iodine poor food decreased from 74.7% to 59.6% after the intervention. Median urinary iodine concentration increased from 31 mcg/L to 108mcg/L, while mean urinary iodine concentration increased from 33.2mcg/L (SD±14.9) to 119.1 mcg/L (SD±65.8) over the study period and the change was statistically significant (p\u3c0.05). At the end of the study 82.8% of the adolescents had no goiter compared to 72.4% at the baseline. Conclusion: This study showed improvement in iodized salt consumption and decreased IDD among the study adolescents. Keywords: IDD, Urinary Iodine, Goiter, IDD prevalence in adolescent female

074 Perceptions, challenges, and experiences of healthcare providers in emergency departments regarding workplace violence during the COVID-19 pandemic: An exploratory qualitative study from an LMIC (a qualitative study protocol)

Statement of Purpose To determine the perceptions, challenges and experiences regarding Workplace Violence faced by ED healthcare providers (Doctors, nurses, and frontline staff) during the COVID-19 pandemic.Methods/Approach We aim to conduct a qualitative exploratory study at two major ED’s of the city namely Aga Khan University Hospital (AKUH) & Jinnah Postgraduate Medical Center (JPMC) involving emergency doctors, nurses, paramedics, admin staff and pharmacists. In-depth interviews will be conducted online using a structured IDI guide. Data will be analyzed using a thematic approach on NVivo computer software.Results This is an abstract for a study protocol. The results will be reported based on the consolidated criteria for reporting qualitative research (COREQ) guidelines.Significance Workplace violence (WPV) against healthcare workers (HCWs) has emerged out as a global issue. Emergency Department (ED) HCWs as front liners are more vulnerable to it due to their nature of work and their profound exposure to medical and social situations. The pandemic has not only brought up a health ordeal, alongside it serves as a challenge in social perspective to HCW’s; ever since fighting the stigmas related to the current pandemic which brought up a wave of antagonism from the patients and their attendants. Thus, an ED’s outcry not yet spoken of. Behind closed doors, what had been an infuriating factor for the population were outraged, irrational religious and social perspectives being quoted and referred to, repeatedly on all prominent medium. Thus, the vulnerable and already exposed population to pandemic, was hovered by societal stigmas to bring down their outrage on HCW’s and already battling ED. We anticipate that through this study we can establish basis of WPV amidst pandemic situation and evidence for future interventions to combat such issues

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

Clinical characteristics and outcomes of patients presenting with hip fractures at a tertiary care hospital in Pakistan

Osteoporosis remains under-recognized and sub-optimally managed in Pakistan, with a lack of awareness that minimal impact hip fracture is a manifestation of low bone mineral density (BMD).Purpose: Hip fracture is often the first clinical presentation of osteoporosis and an opportunity to intervene and reduce future fracture risk. Our aim was to understand the current practices in Pakistan related to bone health in patients presenting with a hip fracture.Methods: This is a retrospective study at a tertiary care center in Pakistan of patients admitted with a hip fracture. Data collected includes previous fracture history, known preceding diagnosis of low BMD medication details, comorbidities, and DXA results.Results: Two hundred ten patients were studied. The mean age of patients was 73.1 years, with 112 (53.3%) women. Most (195 (92.9%)) had presented with a low-impact hip fracture, with 17 (8.1%) reporting previous history of fracture. None had been treated with osteoporosis medications prior to fracture. Nineteen (9%) were on calcium and vitamin D supplements prior to fracture; of the minority who were screened, all were vitamin D deficient and subsequently discharged on vitamin D supplements. No one was prescribed medications to reduce fracture risk at discharge.Conclusion: This study reveals that patients admitted with minimal impact hip fractures in Pakistan are rarely evaluated for low BMD and not started on osteoporosis medications even after presenting with a typical osteoporosis-related fracture. This underscores the need for health provider education about osteoporosis as a major cause for hip fractures and the need to intervene for future fracture risk reduction

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\u27s\u27 challenges concerning WPV during the COVID-19 pandemic