Liquid Chromatographic Approach for Analysis of Favipiravir–A Repurpose Drug for COVID-19

Favipiravir is a potential repurpose moiety to treat COVID-19 by depletion of virus load in infectious patients. To analyze and separate Favipiravir with remarkable efficiency, X-Bridge C8 column (150 x 4.6 mm, 5 µ) and a solvent phase of 0.1% TEA and acetonitrile (40:60 v/v) with 1-mL/min flow rate were used. The eluted favipiravir and possible degradants were detected at 225 nm. Further, the process was validated by using ICH (Q2R1) guidelines to ensure the method's suitability in the pharmaceutical sector. The RT of Favipiravir was observed at 3.7 min with good linearity of 2 to 30 µg/mL. %RSD of both system and method precision was assessed in the series of 0.32 to 0.98. The mean percentage recovery of Favipiravir was in the range of 99.0–100.4%. The limit of detection (LoD) and limit of quantification (LoQ) were assessed to be 0.024 and 0.084 μg/mL for favipiravir. The outcomes confirmed that the projected approach was economical, insightful, simple and precise with better sensitivity. Investigation of Favipiravir in the incidence of a variety of stressed or forced degradation environments ensures stability indicating quality of the developed approach. © 2023, Dr. Yashwant Research Labs Pvt. Ltd.. All rights reserved.

MCMARS: Hybrid Multi-criteria Decision-Making Algorithm for Recommender Systems of Mobile Applications

A mobile application (app) recommender system needs to support both developers and users. Existing recommender systems in the literature are based on single-criterion analysis, which is insufficient for producing better recommendations. Moreover, recommendations do not reflect the user's perspectives. To address these issues, in this paper, we present a Multi-Criteria Mobile App Recommender System (MCMARS) that assists developers in improving their apps and recommends the top-performing apps to users. We define the performance score of an app based on four criteria attributes: risk assessment score, functionality score, user rating, and the app's memory size. We define the risk assessment score for each app using multi-perspective analysis and the functionality score by assigning preference weights to the services of apps in the same category. We evaluate optimal weights of the criteria by integrating the entropy method and the extended Best-Worst method (BWM) using Hesitant-Triangular-Fuzzy information with group-decisions. Finally, the TOPSIS uses these weights to assess the app's performance. To validate our MCMARS, we prepared a dataset of 124 government-approved COVID-19 Android apps from 80 countries and made it available on GitHub for the research community. Finally, we perform a fine-grained analysis of the app's performance based on the criteria attributes that help the developers to improve their apps. The experimental results show that two independent attributes, "risk assessment score” and "functionality score”, significantly measure the app's performance. According to our findings, only 12.5% of the apps in the experimental dataset provide high-performance, high-functionality, and low-risk. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Experiences of acquired brain injury one-month post-discharge from acute hospitalisation.

Background: Healthcare professionals may have a preconceived idea about life after an acquired brain injury (ABI). Understanding lived experiences of individuals with ABI and their significant others, post-hospitalisation, may improve communication between healthcare professionals and individuals directly influenced by the ABI. Objective: To describe perceived experiences of individuals with ABI, and their significant others, regarding rehabilitation services and returning to daily activities, one-month post-discharge from acute hospitalisation. Method: Semi-structured interviews, via an online platform, expanded on the experiences of six dyads (individuals with an ABI and their significant others). Data were thematically analysed. Results: Six main themes emerged that best described participants' experiences; two of which were shared between individuals with ABI and their significant others (SO). Individuals with an ABI acknowledged recovery as their priority and highlighted the importance of patience. The need for counselling and additional support from healthcare professionals and peers arose. The SO expressed a need for written information, improved communication from healthcare professionals, and education regarding the implications of an ABI. The coronavirus disease 2019 (COVID-19) pandemic negatively influenced all participants' overall experiences, mainly because of termination of visiting hours. Psychosocial intervention would have been beneficial to all participants. Faith influenced most participants' attitudes towards recovery and adapting post-ABI. Conclusion: Most participants accepted their new reality but required additional support to cope emotionally. Individuals with an ABI would benefit from opportunities to share experiences with and learn from others in a similar situation. Streamlined services and improved communication may alleviate anxiety among families during this crucial transitional period. Contribution: This article provides valuable information on the perspectives and experiences of individuals with ABI and their significant others during the transition from acute hospitalisation. The findings can assist with the continuity of care, integrative health and supportive strategies during the transition period post-ABI.

Predictors of High-Flow Nasal Cannula Failure in Covid-19

INTRODUCTION: Severe coronavirus disease-19 (COVID-19) is characterized by progressive hypoxemia and patients may require advanced oxygen therapy, including high-flow nasal cannula (HFNC) therapy and mechanical ventilation. Previous data has suggested that the ROX index, IL-6 levels, thrombocytopenia, and kidney injury may predict failure of high-flow nasal cannula therapy. Our study aims to evaluate risk factors that predict HFNC failure in our patient population. METHOD(S): Retrospective cohort study of patients treated for COVID-19 across 4 hospitals in Atlanta, Georgia between February 2020 and February 2021. Patients placed on high-flow nasal cannula within the first 24 hours of admission and who remained on high-flow nasal cannula for at least 6 consecutive hours were identified. Patients that met our cohort criteria were followed for the first seven days of admission and transition across oxygen therapy modalities were examined. Demographic and comorbidity data of patients who failed high-flow nasal cannula therapy within the first 7 days, defined as need for mechanical ventilation or death, were compared to patients who did not fail. RESULT(S): There were 1205 patients placed on high-flow nasal cannula oxygen therapy in our hospitals between February 2020 and February 2021. In total, 465 patients met inclusion criteria. Of the cohort, 35.9% remained on highflow, 32.0 % transitioned to low-flow or room air, and 31.6% failed high-flow nasal cannula therapy within the first week of hospitalization (26.2% failed due to requiring intubation and 5.4% failed due to death). When comparing demographics and comorbidities, patients who failed were older (median age 67.5 vs 62 years, p=0.01) and more frequently had renal disease (28.8% vs 18.5%, p=0.02). There were no significant differences in sex, race, congestive heart failure, pulmonary disease, hypertension, diabetes mellitus, liver disease, or metastatic cancer. CONCLUSION(S): In our patient population, 31.6% of patients failed high-flow nasal cannula therapy within the first week of admission due to mechanical ventilation or death. Age and renal disease were significant risk factors for highflow nasal cannula therapy failure in COVID-19 patients.

A Time-Dependent SEIRD Model for Forecasting the Transmission Dynamics in Infectious Diseases: COVID-19 a Case Study

The spread of a disease caused by a virus can happen through human-to-human contact or from the environment. An estimation is crucial to make policy decisions and plan for the medical emergencies that may arise. Many mathematical models extend the standard SIR model to capture disease spread and estimate the infections, recoveries, and fatalities that may result from the disease. One major factor important in the forecasts using the models is the dynamic nature of the disease spread. Unless we can develop a way to guide this dynamic spread, estimating the parameters may not give accurate forecasts. To capture the transmission dynamics, we implement a time-dependent SEIRD model. In this data-driven model, we try to estimate parameters from the equations derived from the traditional SEIRD model. The main principle is to keep the model generic while making minimal assumptions. In this work, we have derived a data-driven model from SEIRD, where we attempt to forecast infected, recovered, and deceased rates of COVID-19 for the next 21 days. A method for estimating the dynamic change in the parameters of the model is the crucial contribution of this work. The model has been tested for India at the district level and the USA at the state level. The mean absolute percentage error (MAPE) obtained for predicting confirmed/deceased for day 7 is between 4–5%, by day 14 is about 8–10%,and 12–15% for day 21. A dashboard has been developed based on the proposed model showing the predictions for active, recovered, and deaths at the district level in India [1]. We believe that these forecasts can help the governments in planning for emergencies such as ICU requirements, PPEs, and hospitalizations during the spread of infectious diseases. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

THE EFFECT OF AMBIENT TEMPERATURE ON THE SENSITIVITY OF 100.4°F FEVER CUT-OFF IN PATIENTS WITH COVID-19

TOPIC: Chest Infections TYPE: Original Investigations PURPOSE: Body temperature is an important clinical marker used to screen for infections such as COVID-19. Previous studies have demonstrated individual variation in core body temperature with factors such as age, gender, circadian rhythm, menstruation, and energy expenditure;however, it is unknown whether ambient temperature affects the host ability to mount a fever. The possibility of a systematic change in body temperature during different seasons of the year has implications throughout healthcare. Using 100.4°F as the cut-off for fever regardless of ambient temperature may result in poor sensitivity in screening for infections. METHODS: We performed a retrospective chart review of patients admitted to four different hospitals for COVID-19 from 03/01/2020-02/28/2021. The 24-hour mean ambient temperature as well as the 72-hour mean ambient temperature was correlated with the percentage of patients who presented with fever. Fever was defined as maximum oral temperature greater than or equal to 100.4°F within the first 24 hours of hospitalization. Ambient temperature was stratified into deciles. Logistic regression was used to evaluate the association of ambient temperature with fever, controlling for demographics and comorbidities (congestive heart failure, pulmonary disease, hypertension, diabetes mellitus, renal disease, and liver disease). RESULTS: 5,275 patients admitted to the hospital with COVID-19 were included in the study. The mean age of patients was 61 years, 49.7% (2622) were female, and the mortality rate was 8.7%. There was a linear relationship between the ambient temperature and the sensitivity of the 100.4°F fever cut-off (i.e., the sensitivity to detect COVID-19 increased with increasing ambient temperature). In the coldest decile of ambient temperatures (<42.6°F), only 13% of COVID-19 patients presented with a fever compared to 25% in the highest decile of ambient temperature (>79.8°F). When controlling for demographics and comorbidities, the odds ratio of presenting with fever increased by 13% for every 10°F increase in ambient temperature (OR 1.13, p<0.001). CONCLUSIONS: Ambient temperature affects the sensitivity of fever in detecting COVID-19, with increased sensitivity at higher ambient temperature. The one-size-fits-all fever cut-off may not adequately detect viral infections in different locations and climates. CLINICAL IMPLICATIONS: This study shows that ambient temperature exposure should be taken into consideration when screening for infection. Lower cut-offs for fever may be required in screening patients during the winter season or in colder climates. DISCLOSURES: No relevant relationships by Sivasubramanium Bhavani, source=Admin input No relevant relationships by Neethu Edathara, source=Web Response no disclosure on file for Chad Robichaux;no disclosure on file for Philip Verhoef;

Predictors of hospital outcomes and clinical subphenotypes differ between COVID-19 and influenza pneumonia

Introduction: Pneumonia due to SARS-CoV-2 (Coronavirus Disease 2019, COVID-19) has frequently been compared to other viral pneumonias, including influenza. While some data suggest significant differences in biological responses, dissimilarities in the clinical course and characteristics between SARS-COV-2 and influenza pneumonia remain unknown. We evaluated differences in clinical predictors of outcomes and early clinical subphenotypes in COVID-19 and influenza pneumonia. Methods: We performed a retrospective cohort study of all patients hospitalized for > 24 hours, requiring oxygen support, at Barnes-Jewish Hospital with COVID-19 (March-July 2020) or influenza (Jan 2012-Dec 2018). In-hospital mortality or hospice discharge was the primary outcome. First, supervised machine learning classifier models (XGBoost) were trained using bootstrap replications of each viral cohort to predict the primary outcome. 28 candidate predictor variables among the most extreme vital signs and laboratory values within 24 hours of hospitalization were preselected, excluding highly correlated variables. We compared each model's internal discrimination to its performance in the alternate cohort and evaluated differences in variable importance between the two viral pneumonia models. Next, we evaluated differences in clinical subphenotypes in two ways: 1) a previously-validated algorithm to group patients into four distinct subphenotypes based on temperature trajectories within 72 hours of hospitalization;2) latent class analysis (LCA) to identify unmeasured subgroups within each viral cohort based on the predictor variables described above. In both analyses, we compared frequency of subphenotype membership and each subphenotype's primary outcome between viral cohorts. Results: We evaluated 321 unique hospitalizations with COVID-19 and 535 with influenza. The primary outcome was experienced in 23% and 9.5% of patients, respectively. Influenza predictor model discriminated outcomes worse in COVID-19 than on internal evaluation (Panel A), suggesting prognostic variables differ between the viral pneumonias. Only one of the top five contributory variables was shared between the two models (Panel B). Prevalences of temperature trajectory subphenotype also differed significantly between viral pneumonias. All COVID-19 temperature trajectory subphenotypes experienced the primary outcome more frequently than their influenza counterparts (Panel C). LCA identified two distinct classes in each cohort, with each viral pneumonia's minority class experiencing worse outcomes than the majority class. Of each model's top 5 classdefining variables, only 2 were shared (Panel D). Conclusions: COVID-19 and influenza pneumonia differ markedly in predictors of outcome and in clinical subphenotypes. These findings emphasize observable pathogen-specific differential responses in viral pneumonias and suggest that distinct management approaches should be investigated for these diseases. (Table Presented).

Body temperature trajectory associated with venous thromboembolism in COVID-19 patients

Rationale: COVID-19 is associated with significant morbidity and presents unique challenges, including an increased risk of venous thromboembolism (VTE). In a single-center study early in the pandemic, we identified four distinct COVID-19 subphenotypes using longitudinal body temperature (i.e., temperature trajectory subphenotypes). Importantly, these subphenotypes had significant differences in hematological labs such as platelets and d-dimer, suggesting a relationship between temperature and coagulopathy. In this study, we aim to validate the temperature trajectory subphenotypes in a multi-center cohort of COVID-19 patients and evaluate whether temperature trajectory can identify patients at higher risk for VTEs. Methods: We included all patients hospitalized with laboratory-confirmed diagnosis of COVID-19 across four hospitals in the greater Atlanta area. For the trajectory analyses, we included patients' temperature measurements from the first 72 hours of hospitalization. We compared the temperature measurements from the study patients to each of the four trajectories from the published model to calculate the “trajectory distance” (i.e., the distance the patient is away from each trajectory). The patients were classified into the trajectory subphenotype from which they were the smallest distance away. We used ICD-10 codes at discharge to identify patients who had documented diagnoses of acute VTEs and evaluated the association between VTEs and trajectory subphenotype. Then, we used logistic regression to evaluate whether trajectory distance could predict VTE when controlling for demographics and ddimer levels. Results: The 2,107 hospitalized patients who met study criteria had a median age of 59 years (IQR 47-71 years), were 51% female, 65% Black, 21% White, and 10% Hispanic. The incidence of VTE was 12% and the inpatient mortality rate was 11.6%. By temperature trajectory subphenotype: 12% were Group 1, 31% Group 2, 48% Group 3, and 8.1% Group 4 (“hypothermic”). Temperature trajectory had significant association with mortality (p<0.001), with Groups 1 and 4 having the highest mortality rates (17 and 18%, respectively). Temperature trajectory subphenotype was significantly associated with VTE (p=0.004), with “hypothermic” patients having twice the incidence of other subphenotypes. On logistic regression, trajectory distance was significantly associated with VTEs even controlling for d-dimer (Figure). Conclusions: We validated our temperature trajectory subphenotypes in a multi-center cohort of hospitalized patients with COVID-19. We found that temperature trajectory could have utility in identifying patients at higher risk for VTEs who may require more aggressive anticoagulation. (Table Presented).