Analysis of Pharmacist Interventions in Adult COVID-19 Patients Admitted to a Tertiary Care Hospital.

Background: Pharmacists are integral members of the multidisciplinary healthcare team who, with their skills, knowledge, and training, are well positioned to prevent, identify, and manage medication-related issues. Many published articles related to COVID-19 management have highlighted the important role of the pharmacists in assuring the safe, effective, and cost-effective use of medications. During such challenging times of COVID-19 pandemic that resulted in a high demand on medical resources and healthcare providers, pharmacists are well positioned to contribute and add more efforts to the healthcare system to achieve best use of the available resources including medications and providing high quality pharmaceutical care to help the patients and support the healthcare providers. Methods: This is a retrospective chart review included all admitted adult patients with confirmed COVID-19 diagnosis from 1 March 2020 till 30 June 2020. The documented clinical pharmacist interventions were extracted from the EMR and reviewed by multiple clinical pharmacists to identify type, number, frequency, outcome, and physician's acceptance rate of documented interventions. Results: A total of 484 pharmacist interventions included in the final analysis. Antimicrobial stewardship interventions were the most reported (149, 30.8%) and antibiotics were the most reported class of medication, constituting 31.1% of the total interventions. "Optimized therapy" was the most commonly reported outcome (58.8%). Overall, 50.8% (246) of the interventions were rated as having "moderate" clinical significance using the clinical significance scoring tool. The physicians' acceptance rate was 94.7%.Conclusion: Pharmacist interventions are associated with improved communication and medication use in admitted adult patients with COVID-19. Clinical pharmacists can play a crucial role in optimizing medication use in patients with COVID-19 through prevention, identification, and resolving existing or potential drug-related problems.

Non-arteritic Anterior Ischemic Optic Neuropathy with Macular Star following COVID-19 Vaccination.

PURPOSE: To report a case of non-arteritic anterior ischemic optic neuropathy (NAAION) with macular star after receiving the second dose of SARS-CoV-2 vaccination. METHOD: Case report. OBSERVATION: A 51-year-old male presented with acute visual disturbances one day after the second dose of BNT162b2 mRNA SARS-CoV-2 vaccination. At presentation, best corrected visual acuity (BCVA) was 20/25 right eye (OD) and counting fingers at 3 feet left eye (OS). Anterior segment examination was normal in both eyes. Dilated fundoscopy was unremarkable OD, however, it disclosed optic nerve swelling and subretinal fluid OS. Patient was treated with a gradual tapering dose of oral prednisone over 1 month. At the five-week follow-up visit, optic disc swelling and subretinal fluid resolved with minimal improvement in BCVA to 20/400 OS. CONCLUSION: It is unclear whether COVID-19 vaccination was the triggering agent to the NAAION or just a coincidence, yet ophthalmologists should be aware of such a possible association.

COVID-WideNet-A capsule network for COVID-19 detection.

Ever since the outbreak of COVID-19, the entire world is grappling with panic over its rapid spread. Consequently, it is of utmost importance to detect its presence. Timely diagnostic testing leads to the quick identification, treatment and isolation of infected people. A number of deep learning classifiers have been proved to provide encouraging results with higher accuracy as compared to the conventional method of RT-PCR testing. Chest radiography, particularly using X-ray images, is a prime imaging modality for detecting the suspected COVID-19 patients. However, the performance of these approaches still needs to be improved. In this paper, we propose a capsule network called COVID-WideNet for diagnosing COVID-19 cases using Chest X-ray (CXR) images. Experimental results have demonstrated that a discriminative trained, multi-layer capsule network achieves state-of-the-art performance on the COVIDx dataset. In particular, COVID-WideNet performs better than any other CNN based approaches for diagnosis of COVID-19 infected patients. Further, the proposed COVID-WideNet has the number of trainable parameters that is 20 times less than that of other CNN based models. This results in fast and efficient diagnosing COVID-19 symptoms and with achieving the 0.95 of Area Under Curve (AUC), 91% of accuracy, sensitivity and specificity respectively. This may also assist radiologists to detect COVID and its variant like delta.

Workforce planning and safe workload in sterile compounding hospital pharmacy services.

PURPOSE: A prospective observational study was conducted to assess sterile compounding time and workforce requirements in a hospital pharmacy, resulting in development of staff benchmarking metrics. METHODS: The study was conducted in the IV room of a quaternary hospital over 2 periods totalling 7 weeks. Compounding was directly observed and timing data collected for each compounded sterile preparation (CSP). The primary objective was to assess CSP workload, compounding time requirements, and workforce requirements to enable development of a data-driven staffing benchmark. RESULTS: A total of 320 sterile product preparations were directly observed during the study. Overall, the average time to compound 1 CSP (including small- and large-volume parenteral solutions, chemotherapy CSPs, batched CSPs, and syringes) was 3.25 minutes. Chemotherapy CSPs had the longest average preparation time (17.74 minutes); batched CSPs had the shortest preparation time, at 1.90 minutes per unit. A safe workload analysis indicated that in an 8-hour shift, 1 pharmacy technician can safely prepare 253 batched CSPs; 148 preparations of SVP solutions, LVP solutions, and syringes combined; 31 parenteral nutrition solutions prepared using an automated device; or 29 chemotherapy preparations. Through extrapolation of these results, it was calculated that a hospital with a capacity of 100 beds would require 1.4 pharmacist full-time equivalents (FTEs) and 2.7 technician FTEs to meet its sterile compounding needs, with proportionate increases in those estimates for a 300-bed hospital. CONCLUSION: Organizations wishing to use external benchmarking information need to understand data characterization, pharmacy services offered, automation, workflows, and workload before utilizing that information for workforce planning.

Application of Deep Learning on Student Engagement in e-learning environments.

The drastic impact of COVID-19 pandemic is visible in all aspects of our lives including education. With a distinctive rise in e-learning, teaching methods are being undertaken remotely on digital platforms due to COVID-19. To reduce the effect of this pandemic on the education sector, most of the educational institutions are already conducting online classes. However, to make these digital learning sessions interactive and comparable to the traditional offline classrooms, it is essential to ensure that students are properly engaged during online classes. In this paper, we have presented novel deep learning based algorithms that monitor the student's emotions in real-time such as anger, disgust, fear, happiness, sadness, and surprise. This is done by the proposed novel state-of-the-art algorithms which compute the Mean Engagement Score (MES) by analyzing the obtained results from facial landmark detection, emotional recognition and the weights from a survey conducted on students over an hour-long class. The proposed automated approach will certainly help educational institutions in achieving an improved and innovative digital learning method.

A randomised trial of Mycobacterium w in critically ill patients with COVID-19: ARMY-1.

PURPOSE: We investigated whether Mycobacterium w (Mw), an immunomodulator, would improve clinical outcomes in coronavirus disease 2019 (COVID-19). METHODS: We conducted an exploratory, randomised, double-blind, placebo-controlled trial of hospitalised subjects with severe COVID-19 (pulmonary infiltrates and oxygen saturation ≤94% on room air) conducted at four tertiary care centres in India. Patients were randomised 1:1 to receive either 0.3 mL·day-1 of Mw intradermally or a matching placebo for three consecutive days. The primary outcome of the study was the distribution of clinical status assessed on a seven-point ordinal scale ranging from discharged (category 1) to death (category 7) on study days 14, 21, and 28. The co-primary outcome was a change in SOFA (sequential organ failure assessment) score on days 7 and 14 compared to the baseline. The secondary outcomes were 28-day mortality, time to clinical recovery, time to reverse transcription PCR negativity, adverse events, and others. RESULTS: We included 42 subjects (22 Mw, 20 placebo). On days 14 (OR 30.4 (95% CI 3.3-276.4)) and 21 (OR 14.9 (95% CI 1.8-128.4)), subjects in the Mw arm had a better clinical status distribution than placebo. There was no difference in the SOFA score change on days 7 and 14 between the two groups. We did not find any difference in the mortality, or other secondary outcomes. We observed no adverse events related to the use of Mw. CONCLUSIONS: The use of Mw results in better clinical status distribution on days 14 and 21 compared to placebo in critically ill patients with COVID-19.

Increased Morbidity and Mortality in COVID-19 Patients with Liver Injury.

BACKGROUND: There is a high prevalence of liver injury (LI) in patients with coronavirus disease 2019 (COVID-19); however, few large-scale studies assessing risk factors and clinical outcomes in these patients have been done. AIMS: To evaluate the risk factors and clinical outcomes associated with LI in a large inpatient cohort of COVID-19 patients. METHODS: Adult patients with COVID-19 between March 1 and April 30, 2020, were included. LI was defined as peak levels of alanine aminotransferase/aspartate aminotransferase that were 3 times the ULN or peak levels in alkaline phosphatase/total bilirubin that were 2 times the ULN. Mild elevation in liver enzymes (MEL) was defined as abnormal peak liver enzyme levels lower than the threshold for LI. Patients with MEL and LI were compared to a control group comprising patients with normal liver enzymes throughout hospitalization. RESULTS: Of 1935 hospitalized COVID-19 patients, 1031 (53.2%) had MEL and 396 (20.5%) had LI. Compared to control patients, MEL and LI groups contained proportionately more men. Patients in the MEL cohort were older compared to control, and African-Americans were more highly represented in the LI group. Patients with LI had an increased risk of mortality (relative risk [RR] 4.26), intensive care unit admission (RR, 5.52), intubation (RR, 11.01), 30-day readmission (RR, 1.81), length of hospitalization, and intensive care unit stay (10.49 and 10.06 days, respectively) compared to control. CONCLUSION: Our study showed that patients with COVID-19 who presented with LI had a significantly increased risk of mortality and poor clinical outcomes.

Association of obesity with illness severity in hospitalized patients with COVID-19: A retrospective cohort study.

BACKGROUND: Although recent studies have shown an association between obesity and adverse coronavirus disease 2019 (COVID-19) patient outcomes, there is a paucity in large studies focusing on hospitalized patients. We aimed to analyze outcomes associated with obesity in a large cohort of hospitalized COVID-19 patients. METHODS: We performed a retrospective study at a tertiary care health system of adult patients with COVID-19 who were admitted between March 1 and April 30, 2020. Patients were stratified by body mass index (BMI) into obese (BMI ≥ 30 kg/m 2) and non-obese (BMI < 30 kg/m 2) cohorts. Primary outcomes were mortality, intensive care unit (ICU) admission, intubation, and 30-day readmission. RESULTS: A total of 1983 patients were included of whom 1031 (51.9%) had obesity and 952 (48.9%) did not have obesity. Patients with obesity were younger (P < 0.001), more likely to be female (P < 0.001) and African American (P < 0.001) compared to patients without obesity. Multivariable logistic models adjusting for differences in age, sex, race, medical comorbidities, and treatment modalities revealed no difference in 60-day mortality and 30-day readmission between obese and non-obese groups. In these models, patients with obesity had increased odds of ICU admission (adjusted OR, 1.37; 95% CI, 1.07-1.76; P = 0.012) and intubation (adjusted OR, 1.37; 95% CI, 1.04-1.80; P = 0.026). CONCLUSIONS: Obesity in patients with COVID-19 is independently associated with increased risk for ICU admission and intubation. Recognizing that obesity impacts morbidity in this manner is crucial for appropriate management of COVID-19 patients.

A deep learning and grad-CAM based color visualization approach for fast detection of COVID-19 cases using chest X-ray and CT-Scan images.

The world is suffering from an existential global health crisis known as the COVID-19 pandemic. Countries like India, Bangladesh, and other developing countries are still having a slow pace in the detection of COVID-19 cases. Therefore, there is an urgent need for fast detection with clear visualization of infection is required using which a suspected patient of COVID-19 could be saved. In the recent technological advancements, the fusion of deep learning classifiers and medical images provides more promising results corresponding to traditional RT-PCR testing while making detection and predictions about COVID-19 cases with increased accuracy. In this paper, we have proposed a deep transfer learning algorithm that accelerates the detection of COVID-19 cases by using X-ray and CT-Scan images of the chest. It is because, in COVID-19, initial screening of chest X-ray (CXR) may provide significant information in the detection of suspected COVID-19 cases. We have considered three datasets known as 1) COVID-chest X-ray, 2) SARS-COV-2 CT-scan, and 3) Chest X-Ray Images (Pneumonia). In the obtained results, the proposed deep learning model can detect the COVID-19 positive cases in  ≤  2 seconds which is faster than RT-PCR tests currently being used for detection of COVID-19 cases. We have also established a relationship between COVID-19 patients along with the Pneumonia patients which explores the pattern between Pneumonia and COVID-19 radiology images. In all the experiments, we have used the Grad-CAM based color visualization approach in order to clearly interpretate the detection of radiology images and taking further course of action.

Correlation Between Temperature and COVID-19 (Suspected, Confirmed and Death) Cases based on Machine Learning Analysis

Currently, the whole world is struggling with the biggest health problem COVID-19 name coined by the World Health Organization (WHO). This was raised from China in December 2019. This pandemic is going to change the world. Due to its communicable nature, it is contagious to both medically and economically. Though different contributing factors are not known yet. Herein, an effort has been made to find the correlation between temperature and different cases situation (suspected, confirmed, and death cases). For a said purpose, k-means clustering-based machine learning method has been employed on the data set from different regions of China, which has been obtained from the WHO. The novelty of this work is that we have included the temperature field in the original WHO data set and further explore the trends. The trends show the effect of temperature on each region in three different perspectives of COVID-19 - suspected, confirmed and death.

Application of deep learning for fast detection of COVID-19 in X-Rays using nCOVnet.

Presently, COVID-19 has posed a serious threat to researchers, scientists, health professionals, and administrations around the globe from its detection to its treatment. The whole world is witnessing a lockdown like situation because of COVID-19 pandemic. Persistent efforts are being made by the researchers to obtain the possible solutions to control this pandemic in their respective areas. One of the most common and effective methods applied by the researchers is the use of CT-Scans and X-rays to analyze the images of lungs for COVID-19. However, it requires several radiology specialists and time to manually inspect each report which is one of the challenging tasks in a pandemic. In this paper, we have proposed a deep learning neural network-based method nCOVnet, an alternative fast screening method that can be used for detecting the COVID-19 by analyzing the X-rays of patients which will look for visual indicators found in the chest radiography imaging of COVID-19 patients.

Pharmacy leadership during emergency preparedness: Insights from the Middle East and South Asia

Disclaimer: In an effort to expedite the publication of articles related to the COVID-19 pandemic, AJHP is posting these manuscripts online as soon as possible after acceptance. Accepted manuscripts have been peer-reviewed and copyedited, but are posted online before technical formatting and author proofing. These manuscripts are not the final version of record and will be replaced with the final article (formatted per AJHP style and proofed by the authors) at a later time.