PCOM Library / Archive for "Hot Topics in Research"

Category: Hot Topics in Research

Hot Topics – Remdesivir for Compassionate Use in Severe Covid

Jackie Werner Covid-19, Hot Topics in Research, Pharmaceutical Sciences

Grein J, Ohmagari N, Shin D, et al. Compassionate use of remdesivir for patients with severe covid-19. N Engl J Med. 2020;382(24):2327-2336. https://doi.org/10.1056/NEJMoa2007016.

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BACKGROUND

Remdesivir, a nucleotide analogue prodrug that inhibits viral RNA polymerases, has shown in vitro activity against SARS-CoV-2.

METHODS

We provided remdesivir on a compassionate-use basis to patients hospitalized with Covid-19, the illness caused by infection with SARS-CoV-2. Patients were those with confirmed SARS-CoV-2 infection who had an oxygen saturation of 94% or less while they were breathing ambient air or who were receiving oxygen support. Patients received a 10-day course of remdesivir, consisting of 200 mg administered intravenously on day 1, followed by 100 mg daily for the remaining 9 days of treatment. This report is based on data from patients who received remdesivir during the period from January 25, 2020, through March 7, 2020, and have clinical data for at least 1 subsequent day.

RESULTS

Of the 61 patients who received at least one dose of remdesivir, data from 8 could not be analyzed (including 7 patients with no post-treatment data and 1 with a dosing error). Of the 53 patients whose data were analyzed, 22 were in the United States, 22 in Europe or Canada, and 9 in Japan. At baseline, 30 patients (57%) were receiving mechanical ventilation and 4 (8%) were receiving extracorporeal membrane oxygenation. During a median follow-up of 18 days, 36 patients (68%) had an improvement in oxygen-support class, including 17 of 30 patients (57%) receiving mechanical ventilation who were extubated. A total of 25 patients (47%) were discharged, and 7 patients (13%) died; mortality was 18% (6 of 34) among patients receiving invasive ventilation and 5% (1 of 19) among those not receiving invasive ventilation.

CONCLUSIONS

In this cohort of patients hospitalized for severe Covid-19 who were treated with compassionate-use remdesivir, clinical improvement was observed in 36 of 53 patients (68%). Measurement of efficacy will require ongoing randomized, placebo-controlled trials of remdesivir therapy.

Hot Topics: Asymptomatic Covid Carriers May Not Infect

Jackie Werner Covid-19, Hot Topics in Research

Gao M, Yang L, Chen X, et al. A study on infectivity of asymptomatic SARS-CoV-2 carriers. Respir Med. 2020;169:106026. https://doi.org/10.1016/j.rmed.2020.106026.

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Abstract:

Background: An ongoing outbreak of coronavirus disease 2019 (COVID-19) has spread around the world. It is debatable whether asymptomatic COVID-19 virus carriers are contagious. We report here a case of the asymptomatic patient and present clinical characteristics of 455 contacts, which aims to study the infectivity of asymptomatic carriers.

Material and methods: 455 contacts who were exposed to the asymptomatic COVID-19 virus carrier became the subjects of our research. They were divided into three groups: 35 patients, 196 family members and 224 hospital staffs. We extracted their epidemiological information, clinical records, auxiliary examination results and therapeutic schedules.

Results: The median contact time for patients was four days and that for family members was five days. Cardiovascular disease accounted for 25% among original diseases of patients. Apart from hospital staffs, both patients and family members were isolated medically. During the quarantine, seven patients plus one family member appeared new respiratory symptoms, where fever was the most common one. The blood counts in most contacts were within a normal range. All CT images showed no sign of COVID-19 infection. No severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections was detected in 455 contacts by nucleic acid test.

Conclusion: In summary, all the 455 contacts were excluded from SARS-CoV-2 infection and we conclude that the infectivity of some asymptomatic SARS-CoV-2 carriers might be weak.

Hot Topics: Ivermectin Inhibits Covid in Vitro

Jackie Werner Covid-19, Hot Topics in Research, Pharmaceutical Sciences

Caly L, Druce JD, Catton MG, Jans DA, Wagstaff KM. The FDA-approved drug ivermectin inhibits the replication of SARS-CoV-2 in vitro. Antiviral Research. 2020;178:104787. https://doi.org/10.1016/j.antiviral.2020.104787.

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Abstract:

Although several clinical trials are now underway to test possible therapies, the worldwide response to the COVID-19 outbreak has been largely limited to monitoring/containment. We report here that Ivermectin, an FDA-approved anti-parasitic previously shown to have broad-spectrum anti-viral activity in vitro, is an inhibitor of the causative virus (SARS-CoV-2), with a single addition to Vero-hSLAM cells 2 h post infection with SARS-CoV-2 able to effect ~5000-fold reduction in viral RNA at 48 h. Ivermectin therefore warrants further investigation for possible benefits in humans.

Hot Topics: Social Distancing + PPE Prevent Covid Transmission

Jackie Werner Covid-19, Hot Topics in Research

Chu DK, Akl EA, Duda S, et al. Physical distancing, face masks, and eye protection to prevent person-to-person transmission of SARS-CoV-2 and COVID-19: A systematic review and meta-analysis. Lancet. 2020;395(10242):1973-1987. https://doi.org/S0140-6736(20)31142-9.

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Background Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) causes COVID-19 and is spread personto-person through close contact. We aimed to investigate the effects of physical distance, face masks, and eye
protection on virus transmission in health-care and non-health-care (eg, community) settings.

Methods We did a systematic review and meta-analysis to investigate the optimum distance for avoiding person-toperson virus transmission and to assess the use of face masks and eye protection to prevent transmission of viruses. We obtained data for SARS-CoV-2 and the betacoronaviruses that cause severe acute respiratory syndrome, and Middle East respiratory syndrome from 21 standard WHO-specific and COVID-19-specific sources. We searched these data sources from database inception to May 3, 2020, with no restriction by language, for comparative studies and for contextual factors of acceptability, feasibility, resource use, and equity. We screened records, extracted data, and assessed risk of bias in duplicate. We did frequentist and Bayesian meta-analyses and random-effects metaregressions. We rated the certainty of evidence according to Cochrane methods and the GRADE approach. This study is registered with PROSPERO, CRD42020177047.

Findings Our search identified 172 observational studies across 16 countries and six continents, with no randomised controlled trials and 44 relevant comparative studies in health-care and non-health-care settings (n=25697 patients). Transmission of viruses was lower with physical distancing of 1 m or more, compared with a distance of less than 1 m
(n=10736, pooled adjusted odds ratio [aOR] 0·18, 95% CI 0·09 to 0·38; risk difference [RD] –10·2%, 95% CI –11·5 to –7·5; moderate certainty); protection was increased as distance was lengthened (change in relative risk
[RR] 2·02 per m; pinteraction=0·041; moderate certainty). Face mask use could result in a large reduction in risk of infection (n=2647; aOR 0·15, 95% CI 0·07 to 0·34, RD –14·3%, –15·9 to –10·7; low certainty), with stronger
associations with N95 or similar respirators compared with disposable surgical masks or similar (eg, reusable 12–16-layer cotton masks; pinteraction=0·090; posterior probability >95%, low certainty). Eye protection also was associated with less infection (n=3713; aOR 0·22, 95% CI 0·12 to 0·39, RD –10·6%, 95% CI –12·5 to –7·7; low certainty). Unadjusted studies and subgroup and sensitivity analyses showed similar findings.

Interpretation The findings of this systematic review and meta-analysis support physical distancing of 1 m or more and provide quantitative estimates for models and contact tracing to inform policy. Optimum use of face masks, respirators, and eye protection in public and health-care settings should be informed by these findings and contextual
factors. Robust randomised trials are needed to better inform the evidence for these interventions, but this systematic appraisal of currently best available evidence might inform interim guidance.

Hot Topics: Covid-19 Outbreak Linked to Air Conditioning

Jackie Werner Covid-19, Hot Topics in Research, Infectious Disease

COVID-19 Outbreak Associated with Air Conditioning in Restaurant, Guangzhou, China, 2020

Lu J, Gu J, Li K, Xu C, Su W, Lai Z, et al. COVID-19 outbreak associated with air conditioning in restaurant, Guangzhou, China, 2020. Emerg Infect Dis. 2020 Jul. Accessed 2020 June 3. https://doi.org/10.3201/eid2607.200764

During January 26–February 10, 2020, an outbreak of 2019 novel coronavirus disease in an air-conditioned restaurant in Guangzhou, China, involved 3 family clusters. The airflow direction was consistent with droplet transmission. To prevent the spread of the virus in restaurants, we recommend increasing the distance between tables and improving ventilation.

Hot Topics: Computer Models Could Predict Suicide Risk

Jackie Werner Hot Topics in Research, Psychology and Psychiatry

Validation of an Electronic Health Record–Based Suicide Risk Prediction Modeling Approach Across Multiple Health Care Systems

Barak-Corren Y, Castro VM, Nock MK, et al. Validation of an electronic health Record–Based suicide risk prediction modeling approach across multiple health care systems. JAMA Netw Open. 2020;3(3):e201262. https://doi.org/10.1001/jamanetworkopen.2020.1262.

Importance  Suicide is a leading cause of mortality, with suicide-related deaths increasing in recent years. Automated methods for individualized risk prediction have great potential to address this growing public health threat. To facilitate their adoption, they must first be validated across diverse health care settings.

Objective  To evaluate the generalizability and cross-site performance of a risk prediction method using readily available structured data from electronic health records in predicting incident suicide attempts across multiple, independent, US health care systems.

Design, Setting, and Participants  For this prognostic study, data were extracted from longitudinal electronic health record data comprising International Classification of Diseases, Ninth Revision diagnoses, laboratory test results, procedures codes, and medications for more than 3.7 million patients from 5 independent health care systems participating in the Accessible Research Commons for Health network. Across sites, 6 to 17 years’ worth of data were available, up to 2018. Outcomes were defined by International Classification of Diseases, Ninth Revision codes reflecting incident suicide attempts (with positive predictive value >0.70 according to expert clinician medical record review). Models were trained using naive Bayes classifiers in each of the 5 systems. Models were cross-validated in independent data sets at each site, and performance metrics were calculated. Data analysis was performed from November 2017 to August 2019.

Main Outcomes and Measures  The primary outcome was suicide attempt as defined by a previously validated case definition using International Classification of Diseases, Ninth Revision codes. The accuracy and timeliness of the prediction were measured at each site.

Results  Across the 5 health care systems, of the 3 714 105 patients (2 130 454 female [57.2%]) included in the analysis, 39 162 cases (1.1%) were identified. Predictive features varied by site but, as expected, the most common predictors reflected mental health conditions (eg, borderline personality disorder, with odds ratios of 8.1-12.9, and bipolar disorder, with odds ratios of 0.9-9.1) and substance use disorders (eg, drug withdrawal syndrome, with odds ratios of 7.0-12.9). Despite variation in geographical location, demographic characteristics, and population health characteristics, model performance was similar across sites, with areas under the curve ranging from 0.71 (95% CI, 0.70-0.72) to 0.76 (95% CI, 0.75-0.77). Across sites, at a specificity of 90%, the models detected a mean of 38% of cases a mean of 2.1 years in advance.

Conclusions and Relevance  Across 5 diverse health care systems, a computationally efficient approach leveraging the full spectrum of structured electronic health record data was able to detect the risk of suicidal behavior in unselected patients. This approach could facilitate the development of clinical decision support tools that inform risk reduction interventions.

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Hot Topics: Reports of Drug Side Effects Inconsistent

Jackie Werner Hot Topics in Research, Lung, Oncology, Pharmaceutical Sciences

Variation in toxicity reporting methods for early phase lung cancer treatment trials at oncology conferences

Simons EA, Smith DE, Gao D, Camidge DR. Variation in toxicity reporting methods for early phase lung cancer treatment trials at oncology conferences. Journal of Thoracic Oncology. https://doi.org/10.1016/j.jtho.2020.04.020.

Introduction

Phase I and II trials provide the initial human safety and tolerability data for new drugs. However, the methods for presenting toxicity data are not standardized. Clinicians often first encounter these data at professional conferences. We sought to characterize how the burden of adverse events (AE) is reported at the largest professional conference in clinical oncology.

Methods

We collected toxicity data from all lung cancer-associated phase I and II trial presentations and posters at the American Society for Clinical Oncology annual meetings 2017-2019. We captured AE features including the minimum incidence utilized for reporting; whether AEs shown were treatment-emergent or treatment-related, grouped by organ system or separated by individual descriptors; whether combined or separated across dose levels when a dose escalation component was included; and whether dose-limiting toxicities, serious AE, dose reduction rules and denominators for laboratory tests were described.

Results

209 trials were analyzed. There was wide variability in toxicity reporting practices. Six different thresholds for reporting AE of any grade were used. Treatment-related AEs were reported twice as frequently as treatment-emergent AEs. Toxicities were as likely to be reported across dose level as by dose level. Terms such as dose-limiting toxicity and serious AE were rarely defined. Dose reduction rules and denominators for laboratory tests were never defined.

Conclusion

Standardization of methods for reporting toxicities could improve the quality and ease of comparability of data on adverse effects in early phase therapeutic trials. A minimal AE data disclosure template is proposed.

Hot Topics: Loss of Smell May Mean Less Severe COVID-19

Jackie Werner Hot Topics in Research, Infectious Disease

Self‐reported olfactory loss associates with outpatient clinical course in Covid‐19

Yan CH, Faraji F, Prajapati DP, Ostrander BT, DeConde AS. Self-reported olfactory loss associates with outpatient clinical course in covid-19. Int Forum Allergy Rhinol. 2020. https://doi.org/10.1002/alr.22592.

Background

Rapid spread of the SARS‐CoV‐2 virus has left many health systems around the world overwhelmed, forcing triaging of scarce medical resources. Identifying indicators of hospital admission for Covid‐19 patients early in the disease course could aid the efficient allocation of medical interventions. Self‐reported olfactory impairment has recently been recognized as a hallmark of Covid‐19 and may be an important predictor of clinical outcome.

Methods

A retrospective review of all patients presenting to a San Diego Hospital system with laboratory‐confirmed positive Covid‐19 infection was conducted with evaluation of olfactory and gustatory function and clinical disease course. Univariable and multivariable logistic regression were performed to identify risk factors for hospital admission and anosmia.

Results

A total of 169 patients tested positive for Covid‐19 disease between March 3 and April 8, 2020. Olfactory and gustatory data were obtained for 128/169 (75.7%) subjects of which 26/128 (20.1%) required hospitalization. Admission for Covid‐19 was associated with intact sense of smell and taste, increased age, diabetes, as well as subjective and objective parameters associated with respiratory failure. On adjusted analysis, anosmia was strongly and independently associated with outpatient care (aOR 0.09 95% CI: 0.01‐0.74) while positive findings of pulmonary infiltrates and/or pleural effusion on chest radiograph (aOR 8.01 95% CI: 1.12‐57.49) was strongly and independently associated with admission.

Conclusions

Normosmia is an independent predictor of admission in Covid‐19 cases. Smell loss in Covid‐19 may associate with a milder clinical course.

Hot Topics: AI Could Assist Radiology Interpretation

Jackie Werner Hot Topics in Research, Radiology

Comparison of Artificial Intelligence–Based Fully Automatic Chest CT Emphysema Quantification to Pulmonary Function Testing

Fischer AM, Varga-Szemes A, van Assen M, et al. Comparison of artificial Intelligence–Based fully automatic chest CT emphysema quantification to pulmonary function testing. Am J Roentgenol. 2020;214(5):1065-1071. https://doi.org/10.2214/AJR.19.21572.

OBJECTIVE. The purpose of this study was to evaluate an artificial intelligence (AI)-based prototype algorithm for fully automated quantification of emphysema on chest CT compared with pulmonary function testing (spirometry).

MATERIALS AND METHODS. A total of 141 patients (72 women, mean age ± SD of 66.46 ± 9.7 years [range, 23–86 years]; 69 men, mean age of 66.72 ± 11.4 years [range, 27–91 years]) who underwent both chest CT acquisition and spirometry within 6 months were retrospectively included. The spirometry-based Tiffeneau index (TI; calculated as the ratio of forced expiratory volume in the first second to forced vital capacity) was used to measure emphysema severity; a value less than 0.7 was considered to indicate airway obstruction. Segmentation of the lung based on two different reconstruction methods was carried out by using a deep convolution image-to-image network. This multilayer convolutional neural network was combined with multilevel feature chaining and depth monitoring. To discriminate the output of the network from ground truth, an adversarial network was used during training. Emphysema was quantified using spatial filtering and attenuation-based thresholds. Emphysema quantification and TI were compared using the Spearman correlation coefficient.

RESULTS. The mean TI for all patients was 0.57 ± 0.13. The mean percentages of emphysema using reconstruction methods 1 and 2 were 9.96% ± 11.87% and 8.04% ± 10.32%, respectively. AI-based emphysema quantification showed very strong correlation with TI (reconstruction method 1, ρ = −0.86; reconstruction method 2, ρ = −0.85; both p < 0.0001), indicating that AI-based emphysema quantification meaningfully reflects clinical pulmonary physiology.

CONCLUSION. AI-based, fully automated emphysema quantification shows good correlation with TI, potentially contributing to an image-based diagnosis and quantification of emphysema severity.

Hot Topics: Race and Class Affect COVID-19 Risk

Jackie Werner Hot Topics in Research, Infectious Disease, Public Health

Disparities in the Population at Risk of Severe Illness From COVID-19 by Race/Ethnicity and Income

Raifman M, Raifman J. Disparities in the population at risk of severe illness from COVID-19 by race/ethnicity and income. American Journal of Preventive Medicine. 2020. https://doi.org/10.1016/j.amepre.2020.04.003.

Identifying those at heightened risk of severe illness from novel coronavirus disease 2019 (COVID-19) is essential for modeling disease, designing return-to-work criteria, allocating economic assistance, advancing health equity, and limiting morbidity and mortality. The U.S. Centers for Disease Control and Prevention has identified criteria associated with risk of severe complications from COVID-19 infection. Structural inequities have shaped racial, ethnic, and income disparities for many of these criteria. To date, there has been limited analysis of the proportion of the population at risk in the U.S. based on these criteria, or risk factors by race/ethnicity or income. Preliminary national data on cases by race/ethnicity suggest that disparities in hospitalization are already developing. Quantifying disparities in risk is important for allocating resources to prevent, identify, and treat COVID-19-related severe illness and limit diverging outcomes for already vulnerable subgroups.