title: Machine Learning and Radiomics for Osteoporosis Risk Prediction Using X-ray Imaging

abstract: Osteoporosis is a significant health and economic issue, as it predisposes patients to a higher risk of bone fracture. Measuring bone mineral density has been shown to be an accurate way to assess the risk for osteoporosis. The most common way for bone density testing is a dual-energy X-ray absorptiometry (DEXA) scan, which may be recommended for patients with increased risk of osteoporosis. Radiograph imaging is widely available in clinical settings and acquired for many reasons, such as trauma or pain. The goal of this project is to extract radiomics information from pelvic X-rays (both the hip and femoral neck regions) to assess the risk of osteoporosis (triaging patients into "normal" vs. "at-risk", or "low risk" vs. "high risk" categories). The motivation here is not to replace the DEXA scan but to proactively identify patients at risk for osteoporosis and appropriately refer them to management options. We apply machine learning-based radiomics techniques on a study cohort of 565 patients. Our preliminary results show that a correlation between the radiomics features extracted from pelvic X-rays and the level of osteoporosis risk derived from the DEXA test results.

category: geriatric medicine


title: SARS-CoV-2 Omicron variant, lineage BA.1, is associated with lower viral load in nasopharyngeal samples compared to Delta variant.

abstract: High viral load in upper respiratory tract specimens observed for Delta cases may contributed to its increased infectivity compared to the Alpha variant. Herein, we showed that the RT-PCR Ct values in Health Care Workers sampled within five days after symptom onset were significantly higher for Omicron cases than Delta cases (+2.84 Ct, p=0.008). This result comfort the studies showing that the increased transmissibility of Omicron is related to other mechanisms than higher virus excretion.

category: infectious diseases


title: At-Home Testing Study to Assess Risk Factors for Acquisition of SARS-CoV-2 Infection in a Major US Metropolitan Area

abstract:
ImportanceUnbiased assessment of risks associated with acquisition of SARS-CoV-2 is critical to informing mitigation efforts during pandemics.

ObjectiveUnderstand risk factors for acquiring COVID-19 in a large, prospective cohort of adult residents recruited to be representative of a large US metropolitan area.

DesignFully remote longitudinal cohort study launched in October 2020 and ongoing; Study data reported through June 15, 2021.

SettingBrigham and Womens Hospital, Boston MA.

ParticipantsAdults within 45 miles of Boston, MA.

InterventionMonthly at-home SARS-CoV-2 viral and antibody testing.

Main OutcomesBetween October 2020 and January 2021, we enrolled 10,289 adults reflective of Massachusetts census data. At study entry, 567 (5.5%) participants had evidence of current or prior SARS-CoV-2 infection. This increased to 13.4% by June 15, 2021. Compared to whites, Black non-Hispanic participants had a 2.2 fold greater risk of acquiring COVID-19 (HR 2.19, 95% CI 1.91-2.50; p=<0.001) and Hispanics had a 1.5 fold greater risk (HR 1.52, 95% CI 1.32-1.71; p=<0.016). Individuals aged 18-29, those who worked outside the home, and those living with other adults and children were at an increased risk. Individuals in the second and third lowest disadvantaged neighborhood communities, as measured by the area deprivation index as a marker for socioeconomic status by census block group, were associated with an increased risk in developing COVID-19. Individuals with medical risk factors for severe COVID-19 disease were at a decreased risk of SARS-CoV-2 acquisition.

ConclusionsThese results demonstrate that race/ethnicity and socioeconomic status are not only risk factors for severity of disease but are also the biggest determinants of acquisition of infection. Importantly, this disparity is significantly underestimated if based on PCR data alone as noted by the discrepancy in serology vs. PCR detection for non-white participants, and points to persistent disparity in access to testing. Meanwhile, medical conditions and advanced age that increase the risk for severity of SARS-CoV-2 disease were associated with a lower risk of acquisition of COVID-19 suggesting the importance of behavior modifications. These findings highlight the need for mitigation programs that overcome challenges of structural racism in current and future pandemics.

Trial RegistrationN/A

KEY POINTS

QuestionWhat population and occupational groups in the United States are at increased risk for acquiring COVID-19?

FindingsIn this remote, longitudinal cohort study involving monthly PCR and serology self-testing of 10,289 adult residents of the Boston metropolitan area, 9257 (90.0%) of TestBoston participants acquired evidence of immunity to SARS-CoV-2 through vaccination, infection, or both as of June 15, 2021. Residents identifying as Black, Hispanic/Latinx had an increased risk of acquisition of COVID-19. Healthcare workers were not at increased risk of SARS-CoV-2 acquisition. Individuals with medical risk factors for severe COVID-19 disease were at a decreased risk of SARS-CoV-2 acquisition.

MeaningThese results demonstrate that race/ethnicity and socioeconomic status are not only risk factors for severity of disease but also are the biggest determinants of acquisition of infection. These findings highlight the need to address the consequences of structural racism during the development of mitigation programs for current and future pandemics.


title: Concordance of B and T cell responses to SARS-CoV-2 infection, irrespective of symptoms suggestive of COVID-19.

abstract:
ObjectivesTo assess T cell responses in individuals with and without a positive antibody response to SARS-CoV-2, in symptomatic and asymptomatic individuals during the COVID-19 pandemic.

MethodsParticipants were drawn from the TwinsUK cohort, selected according to a) presence or absence of COVID-associated symptoms (S+, S-), logged prospectively through the COVID Symptom Study app, and b) Anti-IgG Spike and anti-IgG Nucleocapsid antibodies measured by ELISA (Ab+, Ab-), during the first wave of the UK pandemic. T cell helper and regulatory responses after stimulation with SARS-CoV-2 peptides were assessed.

Results32 participants were included in final analysis. 14 of 15 with IgG Spike antibodies had a T cell response to SARS-CoV-2-specific peptides; none of 17 participants without IgG Spike antibodies had a T cell response (Chi-squared 28.2, p<0.001). Quantitative T cell responses correlated strongly with fold-change in IgG Spike antibody titre (rho=0.79, p<0.0001) but not to symptom score (rho=0.17, p=0.35).

ConclusionsHumoral and cellular immune responses to SARS-CoV-2 are highly correlated, with no evidence that cellular immunity differs from antibody status four months after acute illness.


title: MACHINE LEARNING IMPACT ASSESSMENT OF CLIMATE FACTORS ON DAILY COVID-19 CASES

abstract: Coronavirus disease 2019 (also known as COVID-19) is a vastly infectious virus instigated by the coronavirus-2, which causes severe acute respiratory illness (SARS-Cov-2). Scientists and researchers are conducting a number of studies to better understand the COVID-19 pandemics behavioral nature and spread, and machine learning provides useful tools. We used machine learning techniques to study the effect of climate conditions on daily instances of COVID-19 in this study. The study has three main objectives: first, to investigate the most climatic features that could affect the spread of novel COVID-19 cases; second, to assess the influence of government strategies on COVID-19 using our dataset; third, to do a comparative analysis of two different machine learning models, and develop a model to predict accurate response to the most features on COVID-19 spread. The goal of this research is to assist health-care facilities and governments with planning and decision-making. The study compared random forest and artificial neural network models for analysis. In addition, feature importance among the independent variables (climate variables) were identified with the random forest. The study used publicly available datasets of COVID-19 cases from the World Health Organization and climate variables from National Aeronautics and Space Administration websites respectively. Our results showed that relative humidity and solar had significant impact as a feature of weather variables on COVID-19 recorded cases; and that random forest predicted accurate response to the most climatic features on COVID-19 spread. Based on this, we propose the random forest model to predict COVID-19 cases using weather variables.


title: Impact of SARS-CoV-2 variants on inpatient clinical outcome

abstract:
BackgroundPrior observation has shown differences in COVID-19 hospitalization rates between SARS-CoV-2 variants, but limited information describes differences in hospitalization outcomes.

MethodsPatients admitted to 5 hospitals with COVID-19 were included if they had hypoxia, tachypnea, tachycardia, or fever, and data to describe SARS-CoV-2 variant, either from whole genome sequencing, or inference when local surveillance showed [≥]95% dominance of a single variant. The average effect of SARS-CoV-2 variant on 14-day risk of severe disease, defined by need for advanced respiratory support, or death was evaluated using models weighted on propensity scores derived from baseline clinical features.

ResultsSevere disease or death within 14 days occurred for 950 of 3,365 (28%) unvaccinated patients and 178 of 808 (22%) patients with history of vaccination or prior COVID-19. Among unvaccinated patients, the relative risk of 14-day severe disease or death for Delta variant compared to ancestral lineages was 1.34 (95% confidence interval [CI] 1.13-1.55). Compared to Delta variant, this risk for Omicron patients was 0.78 (95% CI 0.62-0.97) and compared to ancestral lineages was 1.04 (95% CI 0.84-1.24). Among Omicron and Delta infections, patients with history of vaccination or prior COVID-19 had one-half the 14-day risk of severe disease or death (adjusted hazard ratio 0.46, IQR 0.34-0.62) but no significant outcome difference between Delta and Omicron infections.

ConclusionsAlthough the risk of severe disease or death for unvaccinated patients with Omicron was lower than Delta, it was similar to ancestral lineages. Severe outcomes were less common in vaccinated patients, but there was no difference between Delta and Omicron infections.


title: BNT162b2, mRNA-1273, and Sputnik V vaccines induce comparable immune responses on a par with severe course of COVID-19

abstract: Vaccines against the severe acute respiratory syndrome coronavirus 2, which have been in urgent need and development since the beginning of 2020, are aimed to induce a prominent immune system response capable of recognizing and fighting future infection. Here we analyzed the levels of IgG antibodies against the receptor-binding domain (RBD) of the viral spike protein after the administration of three types of popular vaccines, BNT162b2, mRNA-1273, or Sputnik V, using the same ELISA assay to compare their effects. An efficient immune response was observed in the majority of cases. The obtained ranges of signal values were wide, presumably reflecting specific features of the immune system of individuals. At the same time, these ranges were comparable among the three studied vaccines. The anti-RBD IgG levels after vaccination were also similar to those in the patients with moderate/severe course of the COVID-19, and significantly higher than in the individuals with asymptomatic or light symptomatic courses of the disease. No significant correlation was observed between the levels of anti-RBD IgG and sex or age of the vaccinated individuals. The signals measured at different time points for several individuals after full Sputnik V vaccination did not have a significant tendency to lower within many weeks. The rate of neutralization of the interaction of the RBD with the ACE2 receptor after vaccination with Sputnik V was on average slightly higher than in patients with a moderate/severe course of COVID-19. The importance of the second dose administration of the two-dose Sputnik V vaccine was confirmed: while several individuals had not developed detectable levels of the anti-RBD IgG antibodies after the first dose of Sputnik V, after the second dose the antibody signal became positive for all tested individuals and raised on average 5.4 fold. Finally, we showed that people previously infected with SARS-CoV-2 developed high levels of antibodies, efficiently neutralizing interaction of RBD with ACE2 after the first dose of Sputnik V, with almost no change after the second dose.

category: allergy and immunology


title: Device-assessed sleep and physical activity in individuals recovering from a hospital admission for COVID-19: a prospective, multicentre study

abstract:
ObjectivesTo describe physical behaviours following hospital admission for COVID-19 including associations with acute illness severity and ongoing symptoms.

Methods1077 patients with COVID-19 discharged from hospital between March and November 2020 were recruited. Using a 14-day wear protocol, wrist-worn accelerometers were sent to participants after a five-month follow-up assessment. Acute illness severity was assessed by the WHO clinical progression scale, and the severity of ongoing symptoms was assessed using four previously reported data-driven clinical recovery clusters. Two existing control populations of office workers and type 2 diabetes were comparators.

ResultsValid accelerometer data from 253 women and 462 men were included. Women engaged in a mean{+/-}SD of 14.9{+/-}14.7 minutes/day of moderate-to-vigorous physical activity (MVPA), with 725.6{+/-}104.9 minutes/day spent inactive and 7.22{+/-}1.08 hours/day asleep. The values for men were 21.0{+/-}22.3 and 755.5{+/-}102.8 minutes/day and 6.94{+/-}1.14 hours/day, respectively. Over 60% of women and men did not have any days containing a 30-minute bout of MVPA. Variability in sleep timing was approximately 2 hours in men and women. More severe acute illness was associated with lower total activity and MVPA in recovery. The very severe recovery cluster was associated with fewer days/week containing continuous bouts of MVPA, longer sleep duration, and higher variability in sleep timing. Patients post-hospitalisation with COVID-19 had lower levels of physical activity, greater sleep variability, and lower sleep efficiency than a similarly aged cohort of office workers or those with type 2 diabetes.

ConclusionsPhysical activity and regulating sleep patterns are potential treatable traits for COVID-19 recovery programmes.

category: epidemiology


title: COVID-19 Vaccination is Associated with Decreasing Cases, Hospitalizations, and Deaths Across Age Groups and Variants over 9 months in Switzerland

abstract: Recent studies assessing COVID-19 vaccine efficacy at the population level found counterintuitive results, such as positive associations between vaccination and infections or deaths. These ecological studies have limitations, including too short observation periods, focusing on infections, and not controlling for age groups and dominant variants. The current study addresses these limitations by investigating the relations between vaccination and COVID-19 cases, hospitalizations, and deaths over a longer period (9[1/2] months) while also considering age groups (from 10 to 80+ years old) and variants (Alpha and Delta), utilizing data from Switzerland. Results suggest that vaccination is negatively related to cases overall and in all cantons of Switzerland, and that vaccination is negatively related to hospitalizations and deaths from 50 years old. Furthermore, vaccination is a significant predictor of cases, hospitalizations, and deaths while holding the effects of age and dominant variant constant.


title: Population Estimates of People with Spina Bifida in the United States in 2020

abstract:
IntroductionSpina bifida is a birth defect that results in deficits of neurological function. Individuals diagnosed with spina bifida often require a lifetime of medical care to manage this condition. Currently, the number of people living with spina bifida in the United States is unclear. The purpose of this study is to provide estimates of the magnitude of this condition, and its distribution by gender and age.

MethodsTotal births affected by spina bifida were calculated using rates from the Birth Defects Monitoring Program (BDMP) and state-based birth defects tracking systems supported by the Centers for Disease Control (CDC), over an 80-year period (1940-2020). Spina bifida mortality rates were determined using death certificate data available through the Centers for Disease Control and Prevention, National Center for Health Statistics. Life tables were created for each year of birth between 1940 and 2020 to estimate the total number of people with spina bifida alive in 2020 in the United States.

ResultsIn 2020, the estimated number of people in the U.S. living with spina bifida (0-80 years of age) was 124,150 (67,662 female; 56,488 male). The majority were adults aged 30 to 80 years (66.6%), compared to children and young adults (33.4%).

ConclusionUnderstanding the approximate size and distribution by age and gender may assist health care providers in planning services for this changing population.


title: Mental health in a diverse sample of healthcare workers during the COVID-19 pandemic: cross-sectional analysis of the UK-REACH study

abstract:
ObjectivesTo investigate how ethnicity and other sociodemographic, work, and physical health factors are related to mental health in UK healthcare and ancillary workers (HCWs), and how structural inequities in these factors may contribute to differences in mental health by ethnicity.

DesignCross-sectional analysis of baseline data from the UK-REACH national cohort study

SettingHCWs across UK healthcare settings.

Participants11,695 HCWs working between December 2020-March 2021.

Main outcome measuresAnxiety or depression symptoms (4-item Patient Health Questionnaire, cut-off >3), and Post-Traumatic Stress Disorder (PTSD) symptoms (3-item civilian PTSD Checklist, cut-off >5).

ResultsAsian, Black, Mixed/multiple and Other ethnic groups had greater odds of PTSD than the White ethnic group. Differences in anxiety/depression were less pronounced. Younger, female HCWs, and those who were not doctors had increased odds of symptoms of both PTSD and anxiety/depression. Ethnic minority HCWs were more likely to experience the following work factors that were also associated with mental ill-health: workplace discrimination, feeling insecure in raising workplace concerns, seeing more patients with COVID-19, reporting lack of access to personal protective equipment (PPE), and working longer hours and night shifts. Ethnic minority HCWs were also more likely to live in a deprived area and have experienced bereavement due to COVID-19. After adjusting for sociodemographic and work factors, ethnic differences in PTSD were less pronounced and ethnic minority HCWs had lower odds of anxiety/depression compared to White HCWs.

ConclusionsEthnic minority HCWs were more likely to experience PTSD and disproportionately experienced work and sociodemographic factors associated with PTSD, anxiety and depression. These findings could help inform future work to develop workplace strategies to safeguard HCWs mental health. This will only be possible with adequate investment in staff recruitment and retention, alongside concerted efforts to address inequities due to structural discrimination.

Summary boxO_ST_ABSWhat is already known on this topicC_ST_ABSO_LIThe pandemic is placing healthcare workers under immense pressure, and there is currently a mental health crisis amongst NHS staff
C_LIO_LIEthnic inequities in health outcomes are driven by structural discrimination, which occurs inside and outside the workplace
C_LIO_LIInvestigating ethnic inequities in the mental health of healthcare workers requires large diverse studies, of which few exist
C_LI

What this study addsO_LIIn UK-REACH (N=11,695), ethnic minority staff had higher odds of Post-Traumatic Stress Disorder symptoms; we report many other factors associated with mental-ill health, including those experienced disproportionately by ethnic minority staff, such as workplace discrimination, contact with more patients with COVID-19, and bereavement due to COVID-19
C_LIO_LIThese findings underline the moral and practical need to care for staff mental health and wellbeing, which includes tackling structural inequities in the workplace; improving staff mental health may also reduce workforce understaffing due to absence and attrition
C_LI


title: The Longitudinal Aging Study Amsterdam COVID-19 exposure index: a cross-sectional analysis of the impact of the pandemic on daily functioning of older adults

abstract:
ObjectivesThe aim of this study was to develop an index to measure older adults exposure to the COVID-19 pandemic, and to study its association with various domains of functioning.

DesignCross-sectional study.

SettingThe Longitudinal Aging Study Amsterdam (LASA), a cohort study in the Netherlands.

ParticipantsCommunity dwelling older adults aged 62-102 years (n=1089) who participated in the LASA COVID-19 study (June-September 2020), just after the first wave of the pandemic.

Primary outcome measuresA 35-item COVID-19 exposure index with a score ranging between 0 and 1 was developed, including items that assess the extent to which the COVID-19 situation affected daily lives of older adults. Descriptive characteristics of the index were studied, stratified by several socio-demographic factors. Logistic regression analyses were performed to study associations between the exposure index and several indicators of functioning (functional limitations, anxiety, depression, and loneliness).

ResultsThe mean COVID-19 exposure index score was 0.20 (SD 0.10). Scores were relatively high among women and in the southern region of the Netherlands. In models adjusted for socio-demographic factors and pre-pandemic functioning (2018-2019), those with scores in the highest tertile of the exposure index were more likely to report functional limitations (OR: 2.23; 95% CI: 1.48 to 3.38), anxiety symptoms (OR: 3.87; 95% CI: 2.27 to 6.61), depressive symptoms (OR: 2.45; 95% CI: 1.53 to 3.93) and loneliness (OR: 2.97; 95% CI: 2.08-4.26) than those in the lowest tertile.

ConclusionsAmong older adults in the Netherlands, those with higher scores on a COVID-19 exposure index reported worse functioning in the physical, mental and social domain. The index may be used to identify persons for whom targeted interventions are needed to maintain or improve functioning during the pandemic or post-pandemic.

Strengths and limitations of this studyO_LIThis study was based on a representative sample of older adults from three culturally different regions in the Netherlands.
C_LIO_LIThe Longitudinal Aging Study Amsterdam COVID-19 study provides unique data on functioning of older adults in various life domains during the COVID-19 pandemic.
C_LIO_LIThe items of the COVID-19 exposure index that was developed were based on self-report, more objective sources such as medical records were lacking.
C_LIO_LIThe study covered the first months of the pandemic in the Netherlands, longitudinal data is needed to monitor functioning of older adults in later stages of the pandemic.
C_LI


title: Occurrence of relative bradycardia and relative tachycardia in individuals diagnosed with COVID-19

abstract:
BackgroundThe COVID-19 disease caused by the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) has become one of the worst global pandemics of the century causing tremendous human and economic suffering worldwide. While considered a respiratory disease, COVID-19 is known to cause cardiac complications. Wearable devices are well equipped to measure heart rate continuously and their popularity makes them valuable devices in the field of digital health. In this article, we use Fitbit devices to examine resting heart rate from individuals diagnosed with COVID-19 a

MethodsThe Fitbit COVID-19 survey was conducted from May 2020 - June 2021. We collected resting heart rate data from 7,200 individuals (6,606 symptomatic, 594 asymptomatic) diagnosed with COVID-19 between March 2020 - December 2020, as well as from 463 individuals diagnosed with influenza between January 2020 - December 2020. Data from healthy individuals served as a control, in order to model the seasonal variation. We also computed heart rate variability and respiratory rate data for symptomatic COVID-19.

FindingsResting Heart Rate is elevated during COVID-19 symptom onset, with average peak increases relative to the baseline of 1.8%{+/-}0.1% (3.4%{+/-}0.2%) for females (males), where the quoted numbers are mean and standard error of the mean. After the initial peak, the resting heart rate decreased and reached a minimum on average{approx} 13 days after symptom onset. The minimum value relative to the baseline is more negative for females (-1.75% {+/-} 0.1%) compared to males (0.08% {+/-} 0.2%). The resting heart rate then increased, reaching a second peak on average{approx} 28 days from symptom onset, before falling back to the baseline{approx} 112 days from symptom onset. All estimates vary with disease severity.

InterpretationThe resting heart rate is modified for several months following a COVID-19 diagnosis. Interestingly, this effect is seen with seasonal influenza also, although the bradycardia minimum and the second tachycardia peak are often more pronounced in the case of symptomatic COVID-19. By computing resting heart rate daily, wearable devices can contribute to monitoring wellness during recovery from COVID-19, and seasonal influenza.

FundingA.N., H.-W.S., and C.H. are supported by Fitbit Research, Google LLC.

Research in ContextO_ST_ABSEvidence before this studyC_ST_ABSWe searched PubMed, Google, and Google Scholar for research articles published in English up to Oct 31, 2021, using common search terms such as "bradycardia and COVID-19", "cardiac complications and COVID-19", etc. Articles were also retrieved by searching through citations of known literature. It is known that COVID-19 can cause cardiac complications such as bradycardia and arrhythmias. Using data from commercially available wearable devices, it has been shown previously that the resting heart is elevated during symptom onset, then decreases reaching a minimum, before rising again to attain a second peak, before finally returning to the baseline.

Added value of this studyWe present results from the largest (to our knowledge) dataset considered to-date, involving 7200 participants (6606 symptomatic and 594 asymptomatic) diagnosed with COVID-19. We also present results from 463 individuals diagnosed with influenza. Our large dataset allows us to perform more detailed examinations by age, disease severity, and sex. We also discuss the time evolution of heart rate variability and respiratory rate. The heart rate variability shows a similar time evolution as the resting heart rate but with opposite phase, while the respiratory rate decreases monotonously following the peak at symptom onset.

Implications of all the available evidenceThe results presented in this work show that commercially available trackers and smart-watches can help in monitoring heart health in the weeks and months following a COVID-19 diagnosis. An estimate of the amplitude of the bradycardia dip may provide information valuable to critical care.

category: public and global health


title: Predicting personalised risk of disability worsening in multiple sclerosis with machine learning

abstract:
Multiple sclerosis is a heterogeneous disease with an unpredictable course. We applied machine learning to generate individualised risk scores of disability worsening and stratify patients into subgroups with different prognosis.

Clinical data and MRI scans from published randomised clinical trials in patients with relapsing-remitting and progressive MS were divided into training (n=5,483) and external validation data sets (n=2,668). We processed brain MRI scans to obtain 18 measures for lobar grey matter, deep grey matter and lesion volumes, and T1-/T2-weighted ratio of the normal-appearing white matter regions. We developed a machine learning model, called subpopulation risk stratification (SunRiSe), that combines multi-parametric clinical and MRI data to estimate individualised risk scores and stratify patients into subgroups on the basis of this risk; in particular, we entered MRI measures, the Expanded Disability Status Scale, age and gender to generate risk scores of disability worsening (i.e., the time to confirmed disability worsening). Based on SunRiSe risk scores, high-, medium-, and low-risk subpopulations were defined at study entry. We assessed whether selecting patients at high risk of disability worsening reduces sample size compared to when all risk groups were sampled together.

In both the training and external validation data sets, SunRiSe-stratified patients in three groups associated with different levels of risk of disability worsening. In the external validation data set, patients at high risk were mainly progressive MS and had more disability events compared to those at medium-risk (hazard ratio [HR]=1.34, p<0.0001) and low-risk (HR=1.51, p<0.0001). At study entry, male gender, older age, higher lesion load, higher disability, lower lobar cortical grey matter, lower normal-appearing white matter T1/T2 ratio and lower deep grey matter volumes, were the most important variables in defining the SunRiSe risk score.

The inclusion of patients predicted to be at high risk, reduced (i) duration of an event-driven trial by an average of 4.5 months ({+/-}2.1 months); (ii) the number of participants in a randomised trial by approximately 200, with 80% statistical power to detect a 30% treatment effect.

Machine learning provides a personalised risk score that can identify patients who have the greatest risk of disability worsening and therefore should be treated with the most effective medications and monitored more closely. Risk stratification allows the enrichment of clinical trials with patients more likely to worsen, and thereby reduces trial duration and sample size.

category: neurology


title: Private Equity Acquisition in Ophthalmology and Optometry: A Time Series Analysis of the Pre-COVID, COVID Pre-Vaccine, and COVID Post-Vaccine Eras

abstract:
ObjectiveTo identify temporal and geographic trends in private equity (PE) backed acquisitions of ophthalmology and optometry practices in the United States from 2012 to 2021.

DesignCross-sectional time series analysis using acquisition data from 10/21/2019 to 9/1/2021 compared to previously published data from 1/1/2012-10/20/2019. Acquisition data was compiled from 6 financial databases, 5 industry news outlets, and publicly available press releases. Linear regression models were used to compare rates of acquisition.

Subjects245 PE acquisitions of ophthalmology and optometry practices in the United States between 10/21/2019 and 9/1/2021.

MeasuresNumber of total acquisitions, practice type, locations, provider details, and geographic footprint.

Results245 practices associated with 614 clinical locations and 948 ophthalmologists or optometrists were acquired by 30 PE-backed platform companies. 18 of 30 platform companies were new compared to our prior study. Of these acquisitions from 10/21/2019 - 9/1/2021, 127, 29, and 89 were comprehensive, retina, and optometry practices, respectively. From 2012 to 2021, monthly acquisitions increased by 0.947 acquisitions per year (p<0.001*). Texas, Florida, Michigan, and New Jersey were the states with the greatest number of PE acquisitions with 55, 48, 29, 28 clinic acquisitions, respectively. Average monthly PE acquisitions were 5.71 per month from 1/1/2019 - 2/29/2020 (pre-COVID), 5.30 per month 3/1/2020-12/31/2020 (COVID pre-vaccine, p=0.8072), and were 8.78/month 1/1/2021-9/1/2021 (COVID post-vaccine, p=0.1971).

ConclusionPE acquisitions increased from 2012-2021 as companies continue to utilize both regionally focused and multi-state models of add-on acquisitions.

category: ophthalmology


title: Biologic correlates of beneficial convalescent plasma therapy in a COVID-19 patient reveal disease resolution mechanisms

abstract:
BackgroundWhile the biomarkers of COVID-19 severity have been thoroughly investigated, the key biological dynamics associated with COVID-19 resolution are still insufficiently understood.

Main bodyWe report a case of full resolution of severe COVID-19 due to convalescent plasma transfusion in a patient with underlying multiple autoimmune syndrome. Following transfusion, the patient showed fever remission, improved respiratory status, and rapidly decreased viral burden in respiratory fluids and SARS-CoV-2 RNAemia. Longitudinal unbiased proteomic analysis of plasma and single-cell transcriptomics of peripheral blood cells conducted prior to and at multiple times after convalescent plasma transfusion identified the key biological processes associated with the transition from severe disease to disease-free state. These included (i) temporally ordered upward and downward changes in plasma proteins reestablishing homeostasis and (ii) post-transfusion disappearance of a particular subset of dysfunctional monocytes characterized by hyperactivated Interferon responses and decreased TNF- signaling.

ConclusionsMonitoring specific subsets of innate immune cells in peripheral blood may provide prognostic keys in severe COVID-19. Moreover, understanding disease resolution at the molecular and cellular level should contribute to identify targets of therapeutic interventions against severe COVID-19.


title: QUALITY ASSESSMENT AND COMPARATIVE ANALYSIS ON THE RECOMMENDATIONS OF CURRENT GUIDELINES ON SCREENING AND DIAGNOSIS OF PERIPHERAL ARTERIAL DISEASE; A SYSTEMATIC REVIEW.

abstract:
IntroductionPeripheral Artery Disease (PAD) is a major atherosclerotic disease, and there are several clinical practice guidelines available for it. The paucity of strong evidence is known to give room for variations in recommendations across guidelines with attendant confusion amongst clinicians in clinical practice. This study aims to conduct a quality assessment and comparative analysis on PAD screening and diagnostic recommendations in the management of PAD.

MethodsWe conducted a systematic review of CPGs written after 2010 and on or before 2020. An exhaustive search was conducted through the major medical databases and websites of specialist international organisations of interest and using our inclusion criteria, the appropriate guidelines were extracted. The AGREE-II instrument was used for quality assessment, while the recommendations across screening and diagnosis were extracted and then comparatively analysed.

ResultsWe found nine guidelines that fit our criteria. The guidelines had the lowest scores across the applicability and stakeholder involvement domains. The highest scores were recorded in the Clarity of presentation, Scope and purpose and Editorial independence in order of decreasing magnitude. Also, the trend was the guideline quality scores improved over time. The guidelines were unanimous in offering to screen to  high-risk  patients, although there were some discrepancies in the appropriate age range and unavailability of strong evidence across the guidelines backing this recommendation. The guidelines also showed harmony in adopting the Ankle-Brachial index as the initial diagnostic investigation of choice. However, concerning further diagnostic investigations and imaging, we found several discrepancies among the recommendations in the absence of strong evidence.

ConclusionThough the quality of the guidelines is shown to be improving over time, they display poor scores in the stakeholder involvement and applicability domains, which could be influencing low interest in research that can improve screening and diagnostic recommendations.

STRENGHTS AND LIMITATIONS- This review, unlike previous studies, focused on Peripheral Arterial Diseases(PAD) guidelines written after 2010 and reflects a synthesis of the current state of guideline quality and the most recent recommendations in PAD management regarding screening and diagnosis.
- Complex data has been aggregated, comparatively assessed using thematic analysis and the results presented in concise and straightforward forms using texts, charts and tables to satisfy the needs of all kinds of readers alike from the medical research community to the patients and public reader.
- By utilising rigorous systematic review methodology and a mixed qualitative and quantitative approach to the data analysis, this study has revealed the current areas of strengths and weaknesses of the quality of the PAD guidelines, which is inadvertently related to the reason behind the persisting absence of high-level evidence in screening and diagnostic recommendations.
- Qualitative analyses are inherently challenging to process, especially when dealing with clinical practise guidelines (CPGs) that contain large amounts of information; as such, the process was cumbersome and time-consuming with the inevitable loss of data during the thematic classification process.
- During the literature search, the search strategies were executed exclusively in English Language labouring under the auspices that the major PAD CPGs will have an English language translation, so it is possible that some guidelines written within the study timeframe were not captured due to this limitation.

RegistrationRegistrated in PROSPERO; ID; CRD42020219176

category: cardiovascular medicine


title: Cross-sectional equity analysis of accessibility by automobile to tertiary care emergency services in Cali, Colombia in 2020

abstract:
This study provides data on equity in accessibility to tertiary care emergency services in Cali, accounting for traffic congestion, in two separate weeks in 2020.

This cross-sectional study builds on a proof-of-concept, the AMORE Project(1) and provides a baseline assessment of accessibility to urgent tertiary care at peak and free flow traffic times in Cali.1 It makes the case for assessing travel time over distance, and accounting for traffic congestion.

This study indicates that people in vulnerable situations have to travel longer and therefore invest more of their personal direct and indirect resources to access tertiary care emergency departments than the average population. This study emphasizes the added value of integrating new data sources that can inform health services and urban planning. These new data sources merit future testing by concerned stakeholders.1

This study used the digital AMORE Platform to show the effects of traffic congestion on equitable access to tertiary care emergency departments. The data shows which populations take longer to reach a facility within a time threshold under different traffic congestion levels.

The broader proof-of-concept assesses the value of new data obtained by integrating secondary data from publicly available sources. These sources combine geospatial analysis with census microdata, health services location data, and bigdata for travel times.

The analysis covered the city of Cali, which has 2.258 million residents and is the third-largest city in Colombia. The analysis shows the projected accessibility assessments for two weeks during the COVID-19 pandemic, 6 - 12 July 2020, and 23 - 29 November 2020. Restrictions on car travel had been lifted before the July assessment, but stay-at-home orders were in place during the November assessment, which showed substantially less traffic.

This assessment found that traffic congestion sharply reduces accessibility to tertiary emergency care. Reduced access has the greatest impact on people with less education, those living in low-income households or on the periphery of Cali, and specific ethnic groups (e.g., nomadic people like the Rrom, and Afro-descendants). This assessment also identifies the concentration of tertiary care emergency departments in areas of lower population density, leaving large swaths of the population with poor accessibility.

Data was reported in dashboards that used simple univariate and bivariate analyses. In July 2020, the estimated overall accessibility at peak traffic hours was 37% and in November 2020 it increased to 57% due to reduced traffic congestion. These results illustrate the value of the proposed tools in monitoring and adjusting to changing conditions.

category: health systems and quality improvement


title: A Systems Biology Workflow to Support the Diagnosis of Pyrimidine and Urea Cycle Disorders

abstract:
BackgroundPyrimidine (and purine) metabolism provides essential high-energy vehicles, universal throughout all life, which carry both activated metabolites that serve as fuel and building blocks for countless metabolic processes as well as the messenger molecules that steer these processes. Furthermore, these nitrogen-containing structures encode the alphabet needed to construct the genetic code. The pyrimidine pathway overlaps with the Urea Cycle through a common metabolite (carbamoyl phosphate), with the urea cycle being responsible for the production of several amino acids and removing ammonia. Various Inherited Metabolic Disorders (IMDs) disrupt these pathways, with considerably large phenotypic variation in affected patients, hindering diagnosis. Moreover, the biomarkers for these disorders overlap substantially between the IMDs and their underlying biological pathways, interfering with identifying the deficient protein. We developed a framework that allows us to combine clinical and theoretical biomarkers with pathway models through network approaches and semantic web technologies to support the diagnosis of pyrimidine and urea cycle IMDs.

MethodsWe integrated literature and expert knowledge into machine-readable pathway models for pyrimidine and urea cycle disorders, including disease information and relevant downstream biomarkers. The theoretical change for each biomarker per disease was compiled based on a manual database search. The top three pathways of interest were retrieved through semantic web technologies, by selecting pathways that covered most unique markers as well as showing overlap with the theoretical marker data. These pathways and the corresponding clinical data were visualized through network analysis. Two expert laboratory scientists evaluated our approach by diagnosing a cohort of sixteen previously diagnosed patients with various pyrimidine and urea cycle disorders, based on a visualization of theoretical biomarker overlap with patient data, as well as top three pathways displaying the relationships between the individual biomarkers.

ResultsThe number of relevant biomarkers for each patient varies greatly (five to 48), and likewise the pathways covering most unique biomarkers differ for equivalent disorders. The two experts reached similar conclusions regarding the diagnosis of nine patient samples without knowledge about clinical symptoms or sex. For the remaining seven cases, four interpretations pointed in the direction of a subset of disorders, which could be prioritized for further investigation. Three cases were found to be undiagnosable with the data available.

ConclusionThe presented workflow supports the diagnosis of several IMDs of pyrimidine and urea cycle pathways, by directly linking biological pathway knowledge and theoretical biomarker data to clinical cases. This workflow is adaptable to analyze different types of IMDs, difficult patient cases and functional assays in the future. Furthermore, the pathway models can be used as a basis to perform various other types of (omics) data analysis, e.g. transcriptomics, metabolomics, fluxomics.

Data AvailabilityThe data used in this study have been deposited at https://github.com/BiGCAT-UM/IMD-PUPY.

Highlights- Machine readable pathway models suitable for purine, pyrimidine, and urea cycle omics data analysis
- Visualizing clinically validated biomarkers with pathway knowledge and theoretical biomarker data
- Utilizing semantic web technology and network approaches to precisely represent the biochemistry underlying pyrimidine and urea cycle disorders
- Opens up the possibility to visualize the interactions between metabolic pathways, which can be used in the diagnostic workflow
- Approach is extendable to other inherited metabolic disorders

category: genetic and genomic medicine


title: Integrating Human Brain Proteomic Data with Genome-Wide Association Study Findings Identifies Novel Brain Proteins in Substance Use Traits

abstract:
BackgroundDespite the growing number of genetic risk loci identified for substance use traits (SUTs), the impact of these loci on protein abundance and their potential as therapeutic targets are unknown.

MethodsTo address this, we performed a proteome-wide association study (PWAS) by integrating human brain proteomes from discovery (Banner; N = 152) and validation (ROSMAP; N = 376) datasets with genome-wide association study (GWAS) summary statistics for 4 SUTs. The sample comprised 4 GWAS of European-ancestry individuals for smoking initiation [Smk] (N = 1,232,091), alcohol use disorder [AUD] (N = 313,959), cannabis use disorder [CUD] (N = 384,032), and opioid use disorder [OUD] (N = 302,585). We conducted transcriptome-wide association studies (TWAS) with human brain transcriptomic data to examine the overlap of genetic effects at the proteomic and transcriptomic levels and tested significant genes for causality through Colocalization analysis.

ResultsTwenty-seven genes (Smk=21, AUD=3, CUD=2, OUD=1) were significantly associated with cis-regulated brain protein abundance. There was evidence for causality in 6 genes (Smk: NT5C2, GMPPB, NQO1, SRR, and ACTR1B; AUD: CTNND1), which act by regulating brain protein abundance. Cis-regulated transcript levels for 8 genes (Smk=6, CUD=1, OUD=1) were associated with SUTs, indicating that genetic loci could confer risk for these SUTs by modulating both gene expression and proteomic abundance.

ConclusionsFunctional studies of the high-confidence risk proteins (SRR for Smk and CTNND1 for AUD) identified here are needed to determine whether they are modifiable targets and useful in developing medications and biomarkers for these SUTs.


title: Liability-scale heritability estimation for biobank studies of low prevalence disease

abstract: Theory for liability-scale models of the underlying genetic basis of complex disease provides an important way to interpret, compare and understand results generated from biological studies. In particular, liability models facilitate an understanding and comparison of the relative importance of genetic and environmental risk factors that shape different clinically important disease outcomes, through estimation of the liability-scale heritability (LSH). Increasingly, large-scale biobank studies that link genetic information to electronic health records are becoming available, containing hundreds of disease diagnosis indicators that mostly occur infrequently within the sample. Here, we propose an extension of the existing liability-scale model theory suitable for estimating LSH in biobank studies of low-prevalence disease. In a simulation study, we find that our derived expression yields lower MSE and is less sensitive to prevalence misspecification as compared to previous transformations, for diseases with [≤] 2% population prevalence and LSH of [≤] 0.45, especially if the biobank sample prevalence is less than that of the wider population. Applying our expression to 13 diagnostic outcomes of [≤] 3% prevalence in the UK Biobank study, revealed important differences in LSH obtained from the different theoretical expressions, that impact the conclusions made when comparing LSH across disease outcomes. This demonstrates the importance of careful consideration for estimation and prediction of low prevalence disease outcomes, and facilitates improved inference of the underlying genetic basis of [≤] 2% population prevalence diseases, especially where biobanking sample ascertainment results in a healthier sample population.


title: Artificial intelligence-driven meta-analysis of brain gene expression data identifies novel gene candidates in Alzheimer's Disease

abstract: Microarrays have identified thousands of dysregulated genes in the brains of patients with Alzheimers disease (AD); yet identifying the best gene candidates to both model and treat AD remains a challenge. To this end, we performed a meta-analysis of microarray data from the frontal cortex and cerebellum of AD patients, followed by an artificial-intelligence driven approach to identify the top AD gene candidates. In the frontal cortex, gene candidates included mitochondrial complex V subunits: ATP5J, ATP5L and ATP5H. In the cerebellum, the top candidate was SC5D, which is involved in cholesterol biosynthesis and catabolism. An important finding was that there was no overlap in dysregulated pathways between the frontal cortex and cerebellum, suggesting that pathophysiological mechanisms in AD are brain-region specific. Combined, these results have significant implications for future models and therapeutic strategies in AD.


title: The HR Challenges: Building Workplace Resilience in the Healthcare Industry During the COVID-19 Pandemic - A Systematic Review

abstract:
The COVID-19 outbreak has wreaked havoc worldwide,disrupting industries and life, posing hitches for stakeholders, organizations, and common people. The viable workplace and smooth living have been challenged and forced the organizations to introduce an innovative, imaginative, and comprehensive approach to respond to the challenges. The companies have been striving hard to adjust to the new normal. At the same time, the government, on the other hand, is trying to normalize the situation for the general public.Industries have maintained their services by implementing a work-from-home strategy. However, forthe healthcare industry, it was not an option. Though telemedicine/consultation was hailed as a solution, but in low-income countries, it was met with skepticism, forcing health personnel to contact patients personally. However, this has jeopardized health workers lives and resulted in a human resource shortage.Nevertheless, the study aimed to identify HR challenges and interventionsto createa resilient workplace.

Nonetheless, following a systematic review from17 databases, the HR challenges and strategic responses for a workable workplace have been highlighted. The results identified are shown in the table enlisting as challenges, manifestation, strategy, and different levels of the interventions. Furthermore, HR professionals devised a plan for the employees to rejuvenate and refresh themselves from stress and weariness by doing meditation yoga sessions that gave physical and mental support.All of these techniques contributed significantly to a stable working environment.


title: SurvMaximin: Robust Federated Approach to Transporting Survival Risk Prediction Models

abstract:
ObjectiveFor multi-center heterogeneous Real-World Data (RWD) with time-to-event outcomes and high-dimensional features, we propose the SurvMaximin algorithm to estimate Cox model feature coefficients for a target population by borrowing summary information from a set of health care centers without sharing patient-level information.

Materials and MethodsFor each of the centers from which we want to borrow information to improve the prediction performance for the target population, a penalized Cox model is fitted to estimate feature coefficients for the center. Using estimated feature coefficients and the covariance matrix of the target population, we then obtain a SurvMaximin estimated set of feature coefficients for the target population. The target population can be an entire cohort comprised of all centers, corresponding to federated learning, or can be a single center, corresponding to transfer learning.

ResultsSimulation studies and a real-world international electronic health records application study, with 15 participating health care centers across three countries (France, Germany, and the U.S.), show that the proposed SurvMaximin algorithm achieves comparable or higher accuracy compared with the estimator using only the information of the target site and other existing methods. The SurvMaximin estimator is robust to variations in sample sizes and estimated feature coefficients between centers, which amounts to significantly improved estimates for target sites with fewer observations.

ConclusionsThe SurvMaximin method is well suited for both federated and transfer learning in the high-dimensional survival analysis setting. SurvMaximin only requires a one-time summary information exchange from participating centers. Estimated regression vectors can be very heterogeneous. SurvMaximin provides robust Cox feature coefficient estimates without outcome information in the target population and is privacy-preserving.


title: Evaluating the Association and Predictability of Complex Medication Regimen Scores with Clinical Outcomes Among the Critically Ill

abstract:
IntroductionMedication Regimen Complexity (MRC) refers to the combination of medication classes, dosages, and frequencies. The relationship between MRC and clinical outcomes in the intensive care unit (ICU) has not been examined. The objective of this study is to examine the association between MRC scoring tools and their utility in predicting clinical outcomes.

MethodsWe conducted a retrospective cohort study that includes 322 adult patients admitted and stayed (>24 hours) to the ICU between February 1, 2020, and August 30, 2020 in a community-based, teaching hospital in Rhode Island. Medication complexity was assessed using two MRC scoring tools: MRC Index (MRCI) and the MRC in ICU (MRCICU). We used a multivariable logistic regression model to identify the association between MRC scores and clinical outcomes and to predict the clinical outcomes.

RESULTSAmong the 317 patients included in the study (55.2% men with a median age of 62 [IQR: 51-75] years). Higher MRC scores (i.e., > 63 MRCI or > 6 MRCICU) were associated with increased mortality (14% and 15%), longer ICU length of stay (LOS)(30% and 34%), and need for MV (24% and 28%). MRCICU scores at 24 hours were found to be a significant risk factor in all clinical outcomes (ICU mortality, LOS, and MV) with Odds Ratio (ORs) of 1.12 (95% CI: 1.06-1.19), 1.17 (1.1-1.24), and 1.21 (1.14-1.29), respectively. In the prediction models, both MRCI and MRCICU models performed similarly (AUC: 0.88 [0.75-0.97] and 0.88 [0.76-0.97] in predicting mortality. The Medication Model included 15 medication classes outperformed others (AUC: 0.82 [0.71-0.93] in predicting ICU LOS and the MRCICU model outperformed others (AUC: 0.87 [0.77-0.96]) in predicting the need for MV.

CONCLUSIONMRC scores are associated with poorer clinical outcomes and improves the prediction of poorer clinical outcomes which will support clinicians to prescribe safer therapies.

category: intensive care and critical care medicine


title: A longitudinal investigation of blood neurofilament light chain levels in chronic cocaine users

abstract:
ObjectivesTo explore the hypothesis that plasma levels of neurofilament light chain (NfL), a marker of neuroaxonal pathology, are elevated in chronic cocaine users (CU) and longitudinally associated with changes in cocaine use.

MethodsAs part of the Social Stress Cocaine Study (SSCP), we assessed 35 CU and 35 stimulant-naive healthy controls (HC) at baseline and at a 4-month follow-up. Plasma NfL levels were determined from blood samples using single molecule array (SIMOA) technology. Substance use was subjectively assessed with an extensive interview and objectively measured via toxicological analysis of urine and 4-month hair samples.

ResultsIn a generalized linear model corrected for sex, age, and body mass index, NfL plasma levels were elevated in CU compared to HC (p<0.05). A moderate positive correlation between cocaine hair concentration and NfL levels was also found in CU (r(s)=0.36, p=0.03). Changes in cocaine hair concentration (group analysis of increasers vs. decreasers) over the 4-month interval predicted NfL levels at follow-up (p=0.002), indicating a rise in NfL with increased cocaine use and a reduction with decreased use. No associations between use or change of use of other substances (including the cocaine adulterant levamisole) and NfL levels were found (r(s)[≤]{+/-}0.27, p>0.05).

ConclusionsOur findings demonstrate that NfL is a sensitive marker for assessing cocaine-related brain pathology, supporting the utility of blood NfL analysis in addiction research. The results also suggest that cocaine use should be considered a potential confounder in diagnostic applications and clinical studies using NfL.

category: addiction medicine


title: Identification and Functional Characterization of Bicaudal-D2 As A Candidate Disease Gene in Autosomal Recessive Consanguineous Family with Dilated Cardiomyopathy

abstract: Familial Dilated cardiomyopathy (DCM) is a genetic cardiomyopathy with reduced left ventricle function or systolic function. Fifty-one DCM genes have been reported, most of which are inherited in an autosomal dominant manner, while those caused by a recessive manner are rarely observed. Here we identified an autosome recessive and evolutionarily conserved missense variant, c.2429G>A: p.Arg810His, in bicaudal D homolog 2 (BICD2), which was segregated with the disease phenotype in a consanguineous family with DCM. Furthermore, we confirmed the presence of BICD2 variants in 3 of 210 sporadic cases, highlighting its candidate causative role. Interestingly, we discovered that BICD2 expressed higher in cardiomyocytes from DCM than that from control by reanalyzing published scRNA-seq dataset, implicating its possible involvement in cardiac function. We next explored BICD2 function in zebrafish model at both embryonic and adult stages. Knockout of bicd2 resulted in partial embryonic lethality in homozygotes, suggesting a vital role for bicd2 in embryogenesis. We performed zebrafish echocardiography to detect indices of ventricular size at the adult zebrafish stage. Intriguingly, dilated hearts, decreased ejection fraction, cardiac output and stroke volume were observed, suggesting a phenotype similar to human DCM in bicd2-knockout homozygotes. Furthermore, RNA-seq confirmed the largest transcriptome shift in bicd2 homozygotes. Gene Set Enrichment Analysis(GSEA) of bicd2-deficient fish showed altered gene expression enriched in cardiac pathways and mitochondrial energy metabolism. In conclusion, we found for the first time that an autosomal recessive BICD2 variant is associated with familial DCM, providing further insight into the molecular pathological mechanism of DCM.


title: RISK STRATIFICATION OF PATIENTS WITH COVID-19 DISEASE THROUGH THE USE OF CLINICAL SCORES IN AN EMERGENCY DEPARTMENT. A review of the literature

abstract:
ObjectiveTo analyze various clinical scores and to understand which of these are able to predict mortality and clinical worsening in patients with Covid-19 disease admitted to an ED.

Material and methodsThe research question was: What are the best clinical scores used to predict hospital mortality or worsening of clinical conditions with hospitalization in intensive care in adult patients with Covid-19 who present in EDa The research of the articles and the consequent review of the literature were performed using databases such as Pubmed, Cochrane Database of Systematic Re-view and CINAHL Database (Cumulative Index to Nursing and Allied Health Literature) until November 4, 2021. The keywords used were clinical scores, risk stratifications, Covid patients and Emergency Department. Inclusion criteria were patients > 18 years with symptoms and diagnosed Covid-19 disease admitted to the ED or directly to the hospital, prospective and retrospective cohort studies, randomized studies, meta-analyses, use or analysis of a clinical score and articles published in English between January 2020 and August 2021 without countries restrictions. Exclusion criteria were unpublished articles, articles without abstracts, articles with a population different from the selected one, articles with outcomes different from those selected and articles that did not mention or did not analyze the effectiveness of a score or a scale. The studies selected compared NEWS, NEWS2, SIRS, q-SOFA, CURB-65, PSI, REMS, MEWS, RISE UP, 4C MORTALITY SCORE, TRPNCLP and STPCAL scores for patients with confirmed Covid-19 disease admitted to Emergency Department. A meta-analysis was carried out by using fixed effects and random effects models to evaluate mortality and Intensive Care Unit (ICU) admission in patients presenting in ED.

ResultsA total of 39318 patients from 8 studies, 5 retrospective observational and 3 prospective, were included. Likelihood ratio was 9.1 (95% CI, 4.76-17.5) for NEWS and 3.3 (95% CI, 2.5-4.3) for NEWS2 in two different studies. The Area Under the Curve values were regarding to intensive care unit admission 0.94 (95% CI, 0.88-0.99) and 0.8 (95% CI, 0.72-0.8) for NEWS and NEWS2 respectively. Considering mortality AUC values were 0.87 (95% CI, 0.72-1.00) and 0.8 (95% CI, 0.72-0.88) for NEWS and NEWS2 respectively. q-SOFA is probably the second most reliable score, analyzed and studied by most of the articles and in one study AUC value was 0.78 (95% CI, 0.60-0.96) for mortality and 0.78 (95% CI, 0.64-0.93) for ICU admission. Considering the scores created and studied specifically for Covid-19 disease the most accurate is the TRPNCLP score with a AUC value of 0.88 (95% CI, 0.85-0.91) and an LR + of 4.86 (95% CI, 4.01 - 5.88). Also RISE UP score and 4C mortality score had excellent results with a positive LR +> 1 and AUC values very close to the 0.80.

ConclusionsThe clinical scores that are more reliable in predicting hospital mortality and admission to intensive care units are NEWS and NEWS2. At the beginning Covid-19 disease management is based on home monitoring so that the use of clinical scores NEWS and NEWS2 could be an effective to predict mortality and worsening of clinical conditions.

category: emergency medicine


title: Fatality assessment and variant risk monitoring for COVID-19 using three new hospital occupancy related metrics

abstract:
BackgroundThough case fatality rate (CFR) is widely used to reflect COVID-19 fatality risk, its use is limited by large temporal and spatial variation. Hospital mortality rate (HMR) is also used to assess the severity of COVID-19, but HMR data is not directly available except 35 states of USA. Alternative metrics are needed for COVID-19 severity and fatality assessment.

MethodsNew metrics and their applications in fatality measurements and risk monitoring are proposed here. We also introduce a new mathematical model to estimate average hospital length of stay for death (Ldead) and discharges (Ldis). Multiple data sources were used for our analysis.

FindingsWe propose three new metrics, hospital occupancy mortality rate (HOMR), ratio of total deaths to hospital occupancy (TDHOR) and ratio of hospital occupancy to cases (HOCR), for dynamic assessment of COVID-19 fatality risk. Estimated Ldead and Ldis for 501,079 COVID-19 hospitalizations in US 34 states between Aug 7, 2020 and Mar 1, 2021 were 14.0 and 18.2 days, respectively. We found that TDHOR values of 27 countries are less spatially and temporally variable and more capable of detecting changes in COVID-19 fatality risk. The dramatic changes in COVID-19 CFR observed in 27 countries during early stages of the pandemic were mostly caused by undiagnosed cases. Compared to the first week of November, 2021, the week mean HOCRs (mimics hospitalization-to-case ratio) for Omicron variant decreased 34.08% and 65.16% in the United Kingdom and USA respectively as of Jan 16, 2022.

InterpretationThese new and reliable measurements for COVID-19 that could be expanded as a general index to other fatal infectious diseases for disease fatality risk and variant-associated risk monitoring.

Research in contextO_ST_ABSEvidence before this studyC_ST_ABSWe searched PubMed, medRxiv, and bioRxiv for peer-reviewed articles, preprints, and research reports on risk and health care evaluation for COVID-19 using the search terms "hospital occupancy mortality rate", "ratio of total deaths to hospital occupancy", "ratio of hospital occupancy to case" up to Jan 20, 2022. No similar concepts or studies were found. No similar mathematical models based on "hospital occupancy mortality rate" for the estimation of hospital length of stay for deaths and discharges have been identified to date.

Added value of the studyOur new metrics, HOMR and TDHOR, mimic HMR for COVID-19 fatality risk assessment but utilize readily available data for many US states and countries around the world. HOCR mimics hospitalization-to-case ratio for COVID-19. We also provide evidence that explains why COVID-19 CFR has such dramatic changes at the beginning of a COVID-19 outbreak. We have additionally provided new metrics for COVID-19 fatality risk dynamic monitoring including Omicron variant and showed that these metrics provided additional information.

Implications of all the available evidenceThe results of this study, including average hospital length of stay for deaths and discharges for over 500,000 COVID-19 hospitalizations in the US, can aid county, state, and national leaders in making informed public health decisions related to the ongoing COVID-19 pandemic. This is the first study to provide quantitative evidence to address why CFR has a such a large variation at the beginning of the COVID-19 pandemic in most countries and will hopefully encourage more countries to release hospital occupancy data, which we show is both useful and easy information to collect. The new metrics introduced by our study are effective indicators for monitoring COVID-19 fatality risk, as well as potentially fatal COVID-19 variants, and could also be expanded to other fatal infectious diseases.


title: Modeling Vaccinations, Virus Variants and Lockdown: Early guidance for Sars-Cov-2 health policies in India

abstract:
In this paper we consider long range models that help in predicting the impact of virus transmission over a period of a few months. This is applied in the context of the SARS-CoV2 virus for India. Long range models are useful for preemptive policy actions. Our models utilize a dynamic transmission factor, dependent on the populations knowledge and tendency to be careful when the case load of virus transmission rises. Our model incorporates the state of vaccination and the virus variants that form part of the transmission dynamics.

We also present the impact of lockdown policies and illustrate the advantage of adopting partial lockdown policies in the early period of 2022.


title: Influence of meteorological factors and drought on coccidioidomycosis incidence in California, 2000-2020

abstract:
BackgroundCoccidioidomycosis is an emerging infection in the southwestern United States. We examined the effects of precipitation and temperature on the incidence of coccidioidomycosis in California during 2000-2020, and estimated incident cases attributable to the California droughts of 2007-09 and 2012-15.

MethodsWe analyzed monthly California coccidioidomycosis surveillance data from 2000-2020 at the census tract-level using generalized additive models. Models included distributed lags of precipitation and temperature within each endemic county, pooled using fixed-effects meta-analysis. An ensemble prediction algorithm of incident cases per census tract was developed to estimate the impact of drought on expected cases.

ResultsAcross 14 counties examined, coccidioidomycosis was strongly suppressed during, and amplified following, the 2007-2009 and 2012-2015 droughts. An estimated excess of 1,358 and 2,461 drought-attributable cases were observed in California in the two years following the 2007-2009 and 2012-2015 droughts, respectively. These post-drought excess cases more than offset the drought-attributable declines of 1,126 and 2,192 cases, respectively, that occurred during the 2007-2009 and 2012-2015 droughts. Across counties, a temperature increase from the 25th to 75th percentile (interquartile range) in the summer was associated with a doubling of incidence in the following fall (incidence rate ratio (IRR): 2.02, 95% CI: 1.84, 2.22), and a one IQR increase in precipitation in the winter was associated with 1.45 (95% CI: 1.36, 1.55) times higher incidence in the fall. The effect of winter precipitation was stronger (interaction coefficient representing ratio of IRRs: 1.36, 95% CI: 1.25, 1.48) when preceded by two dry rather than average winters. Incidence in arid lower San Joaquin Valley counties was most sensitive to winter precipitation fluctuations, while incidence in wetter coastal counties was most sensitive to summer temperature fluctuations.

ConclusionsIn California, wet winters along with hot summers, particularly those following previous dry years, increased risk of coccidioidomycosis in California. Drought conditions may suppress incidence, then amplify incidence in subsequent years. With anticipated increasing frequency of drought in California, continued expansion of incidence, particularly in wetter, coastal regions, is expected.


title: Effectiveness of the inactivated COVID-19 vaccine (CoronaVac) in adult population in Indonesia

abstract:
BackgroundInactivated SARS-CoV-2 vaccine has been included in the national COVID-19 vaccination program in Indonesia since January 2021. The study aims to assess the impacts of inactivated COVID-19 vaccine on infection, hospitalization, and death among adult population aged [≥]18 years in Bali, Indonesia.

MethodsTest-negative, case control study was conducted by linking SARS-CoV-2 laboratory records, vaccination, and health administrative data for the period of January 13 to June 30, 2021. Case-subjects were defined as individuals who had a positive RT-PCR test for SARS-CoV-2 during the period; they were matched with controls (tested negative) (1:1) based on age, sex, district of residence, and week of testing. We estimated the odds of vaccination in PCR confirmed, hospitalization and death due to COVID-19, accounting for the presence of comorbidities and prior infection. Vaccine effectiveness was estimated as (1-odds ratio) x 100%.

ResultsTotal 109,050 RT-PCR test results were retrieved during the January 13 to June 30, 2021. Of these, 14,168 subjects were eligible for inclusion in the study. Total 5518 matched case-control pairs were analyzed. Adjusted vaccine effectiveness (VE) against laboratory-confirmed SARS-CoV-2 infection was 14.5% (95% confidence interval -11 to 34.2) at 0-13 days after the first dose; 66.7% (95% CI: 58.1-73.5) at [≥]14 days after the second dose. The adjusted effectiveness against hospitalization and COVID-19-associated death was 71.1% (95% CI: 62.9-77.6) and 87.4% (95% CI: 65.1-95.4%) at [≥]14 days after receiving the second dose, respectively.

ConclusionsTwo-dose of inactivated CoronaVac vaccine showed high effectiveness against laboratory confirmed COVID-19 infection, hospitalization, and death associated with COVID-19 among adults aged [≥]18 years.


title: Real world performance of inactivated SARS-CoV-2 vaccine (CoronaVac) against infection, hospitalization and death due to COVID-19 in adult population in Indonesia


title: Risk of severe COVID-19 from the Delta and Omicron variants in relation to vaccination status, sex, age and comorbidities: surveillance results from southern Sweden

abstract: The risk of severe COVID-19 disease requiring hospitalization with extensive oxygen supply was compared among infected cases during two calendar periods when Delta and Omicron, respectively, were the dominating virus variants in Scania county, Sweden. Adjustments were made for differences among cases in comorbidities, prior infection, vaccination status, age and sex. Markedly lower risks were observed from Omicron among the vaccinated in the present study. The risk of severe disease was also lower for unvaccinated during Omicron than during Delta, but remained high among older people and middle-aged males with comorbidities. Efforts to increase vaccination uptake across countries, populations and subgroups should thus remain a public health priority.


title: Equitable COVID-19 vaccine prioritization: front-line workers or 65-74 year olds?

abstract:
1BackgroundThe COVID-19 epidemic in the United States has been characterized by two stark disparities. COVID-19 burden has been unequally distributed among racial and ethnic groups and at the same time the mortality rates have been sharply higher among older age groups.

These disparities have led some to suggest that higher equity could be attained by vaccinating front-line workers before vaccinating older individuals, who in the US are disproportionately Non-Hispanic White.

MethodsWe compare the performance of two distribution policies, one allocating vaccines to front-line workers and another to older individuals aged 65-74-year-old. We estimate both the number of lives saved and the number of years of life saved under each of the policies, overall and in every race/ethnicity groups, in the United States and every state.

FindingsWe show that prioritizing COVID-19 vaccines for 65-74-year-olds saves both more lives and more years of life than attributing vaccines front-line workers in each racial/ethnic group, in the United States as a whole and in nearly every state.

InterpretationWhen evaluating equity in vaccine allocation policies, the overall benefit to impact of each population subgroup should be considered, not only the proportion of doses that is distributed to each subgroup. Further work can identify prioritization schemes that perform better on multiple equity metrics.

FundingThe funding sources played no role in this research.


title: Evaluating binary classifiers: extending the Efficiency Index

abstract: An "efficiency index" (EI) for the evaluation of binary classifiers was recently characterised, where EI is the ratio of classifier accuracy to inaccuracy. The purpose of this study was to further develop EI by substituting balanced accuracy and unbiased accuracy in place of accuracy, and their respective complements in place of inaccuracy, to construct balanced EI and unbiased EI measures. Additional investigations, using the dataset of a prospective pragmatic test accuracy study of a cognitive screening instrument, explored use of the log method to calculate confidence intervals for the various EI formulations; the dependence of EI formulations on prevalence; and comparison of EI formulations with analogous formulations based on the Identification Index (II), a previously described metric which is also based on accuracy and inaccuracy, where II is accuracy minus inaccuracy. EI formulations are shown to have advantages over II formulations, in particular their boundary values (0 and {infty}) mean that negative values never occur, unlike the case for II, and the inflection point of 1 demarcates likelihood of correct versus incorrect classification.


title: Corticospinal suppression underlying intact movement preparation fades in late Parkinson's disease

abstract:
In Parkinsons disease, neurophysiological abnormalities within the primary motor cortex have been shown to contribute to cardinal symptoms such as bradykinesia, but the exact modalities are still uncertain. Here, we propose that such impairment could involve alterations of mechanisms shaping motor activity specifically during voluntary movement preparation. Indeed, several past studies in healthy populations have suggested that a strong suppression of corticospinal excitability called "preparatory suppression" - propels movement execution by increasing motor neural gain. Thus, we hypothesized that a gradual alteration to this mechanism may contribute to progressive motor slowness in Parkinsons disease. We also predicted a benefit of dopamine medication in restoring correct motor neural activity during action preparation.

To test these hypotheses, we investigated preparatory suppression on two consecutive days in 29 Parkinsons disease patients (ON and OFF medication) and 29 matched healthy controls. Single-pulse transcranial magnetic stimulation was applied over both primary motor cortices, eliciting concurrent motor-evoked potentials in the two hands, while subjects were either at rest or prepared a left- or right-hand response in an instructed-delay choice reaction time task. Preparatory suppression was assessed by expressing the amplitude of motor potentials evoked during movement preparation relative to those obtained at rest. These neurophysiological measures were cross-analysed with task behaviour and clinical data.

Contrary to healthy controls, Parkinsons disease patients showed a lack of preparatory suppression, which appeared to depend on disease progression, but not on dopamine medication. Indeed, in relatively early disease stages, patients still exhibited partial preparatory suppression, while in later stages, they lacked it completely and even exhibited a tendency for corticospinal facilitation in the hand selected for movement execution. As expected, patients also showed increasing motor handicap with disease progression as well as a decreased movement velocity during the task, but such findings did not directly correlate with levels of preparatory suppression in our cohort. While dopamine medication had no effect on the latter, it did however globally reduce raw corticospinal excitability in the dominant hand.

Taken together, our results are in line with the idea that a lack of corticospinal suppression during movement preparation in Parkinsons disease slows down response execution and illustrate the importance of considering disease stages in such investigations; they also suggest differential roles of dopamine in shaping corticospinal output in those patients. Our findings thus support the use of task-related functional markers such as preparatory suppression in future studies on motor impairment in Parkinsons disease.


title: Inference of causal relationships based on the genetics of cardiometabolic traits and conditions unique to females in >50,000 participants

abstract:
BackgroundCardiometabolic diseases are highly comorbid and associated with poor health outcomes. However, the investigation of the relationship between the genetic predisposition to cardiometabolic diseases with the risk of conditions unique to females such as breast cancer, endometriosis and pregnancy-related complications is highly understudied. This study aimed to estimate the cross-trait genetic overlap and influence of genetic burden of cardiometabolic traits on health conditions unique to females.

MethodsWe obtained data for female participants in the Penn Medicine BioBank (PMBB; 21,837 samples) and the electronic MEdical Records and GEnomics (eMERGE; 49,171 samples) network. We examined the relationship between four cardiometabolic phenotypes (body mass index (BMI), coronary artery disease (CAD), type 2 diabetes (T2D) and hypertension (through blood pressure measurements)) and 23 female health conditions by performing four analyses: 1) Cross-trait genetic correlation analyses to compare genetic architecture. 2) Polygenic risk scores (PRS)-based association tests to characterize shared genetic effects on disease risk. 3) Mendelian randomization (MR) for significant associations to assess cross-trait causal relationships. 4) Chronology analyses to visualize the timeline of events unique to groups of females with high and low genetic burden for cardiometabolic traits and highlight the disease prevalence in risk groups by age.

ResultsWe observed high genetic correlation among cardiometabolic and female health conditions. PRS meta-analysis identified 29 significant associations reflecting potential shared biology among common cardiometabolic phenotypes and female health conditions. Significant associations include PRSBMI with endometrial cancer and polycystic ovarian syndrome (PCOS), PRSCAD with breast cancer, and the PRST2D with gestational diabetes and PCOS. Mendelian randomization provided additional evidence of independent causal effects between T2D and gestational diabetes and CAD and with breast cancer. Our results reflected inverse association between PRSCAD and breast cancer. Lastly, as visualized from chronology analyses, individuals with high PRS are also more likely to develop conditions such as PCOS and gestational hypertension at earlier ages.

ConclusionsPolygenic susceptibility to cardiometabolic traits is associated with conditions unique to females. Several of these associations are likely to result from the complex pathophysiology of cardiometabolic risk, and others may reflect potential pleiotropic effects that go beyond cardiometabolic health in females.

category: obstetrics and gynecology


title: The Macroscopic Growth Laws of Brain Metastases

abstract: Tumor growth is the result of the interplay of complex biological processes in a huge number of individual cells in a changing environment. Effective simple mathematical laws have been shown to describe tumor growth in vitro, or in animal models with bounded-growth dynamics accurately. However, results for human cancers in patients are scarce. The study mined a dataset of 1133 brain metastases (BMs) with longitudinal imaging follow-up, treated with radiosurgery (SRS) to find growth laws for untreated BMs, relapsing treated BMs, and radiation necrosis (RN). Untreated BMs showed sustained growth acceleration, most likely related to the underlying evolutionary dynamics. Relapsing BM growth was slower, most probably due to a reduction in tumor heterogeneity after SRS, which may limit the evolutionary possibilities of the tumor. RN lesions had significantly larger growth exponents than relapsing BMs, providing a way to differentiate them from true progression. This may help in solving a problem of clinical relevance, since the first condition may resolve spontaneously, and not require further work-up, while the second requires therapeutic action.

category: oncology


title: DNA methylation-based age acceleration observed in IDH wild-type glioblastoma is associated with better outcome - including in elderly patients

abstract:
Elderly patients represent a growing proportion of individuals with glioblastoma, who however, are often excluded from clinical trials owing to poor expected prognosis. Little is known about molecular features that would justify different treatment modalities, not related to frailty and co-morbidities.

We investigated the DNA methylome (450k) for age related associations using 4 different glioblastoma datasets (mean age, years: DKFZ, 60; TCGA, 62; Nordic, 71; EORTC-26981/NCIC-CE.3 & Lausanne-Pilot, 54), including two are cohorts from clinical trials. We excluded patient samples not compatible with glioblastoma grade 4 WHO-CNS-5 classification 2021. The combined methylome was interrogated for differences based on age, DNA methylation (DNAm) age acceleration (DNAm age "Horvath-clock" minus patient age), methylation-based classification (Heidelberg), entropy, and functional methylation of DNA damage response (DDR) genes.

Age dependent methylation included 19 CpGs (p-value[≤]0.1, Bonferroni corrected), comprising ELOVL2 that is part of a 13-gene forensic age predictor. Three CpGs met our criteria of functional methylation defined as correlation of methylation with expression (r[≤]-0.3) of the corresponding gene. Most of the age related CpGs (n=16) were also associated with age acceleration that itself was associated with a large number of CpGs (n=50551). Over 70% age acceleration-associated CpGs (n=36348) overlapped with those associated with the methylation based tumor classification (n=170759). Functional methylation of few DDR genes was associated with age acceleration (n=8), tumor classification (n=12), or both (n=4), the latter including MGMT. DNAm age acceleration was significantly associated with better outcome in the trial cohorts of the EORTC/NCIC & Lausanne Pilot, and Nordic (treating elderly patients). Multivariate analysis included treatment (RT, RT/TMZ[->]TMZ; TMZ, RT), MGMT promoter methylation status, and interaction with treatment.

In conclusion, DNA methylation features of age acceleration are an integrative part of the methylation-based tumor classification (RTK I, RTK II, MES), while age seems hardly reflected in the glioblastoma DNA methylome.


title: TEXT MINING OF THE PEOPLE's PHARMACY RADIO SHOW TRANSCRIPTS CAN IDENTIFY NOVEL DRUG REPURPOSING HYPOTHESES

abstract:
ObjectiveSocial media mining may provide surprising information about unknown effects of drugs. We endeavored to uncover such unknown drug-disease relationships by text mining of audio record transcripts from the popular NPR show, The Peoples Pharmacy.

Materials and MethodsWe used Google Cloud to transcribe episodes of the NPR podcast into textual documents. We then built a pipeline for systematically pre-processing the text to ensure quality input to the core classification model. Finally, results of the model were filtered by a series of post-processing steps. Our classification model itself uses the FLAIR language model pre-trained on PubMed abstracts. The modular nature of our pipeline allows for ease of future developments in this area by substituting higher quality components at each stage of the pipeline. To validate the drug-disease relating assertions extracted from the podcast, we utilized the DrugCentral database and ROBOKOP biomedical knowledge graph, which capture drug-disease relationships for FDA approved medications.

ResultsOur model identified 128 drug-disease pairs that were found in DrugCentral and 112 novel candidate pairs requiring expert review. To demonstrate the expert review process, we found literature evidence supporting the assertions for novel drug-disease pairs.

Discussion and ConclusionText mining of social media is increasingly used to uncover novel relationships between semantic concepts corresponding to biomedical concepts. However, mining audio transcripts of specialized podcast shows has not been explored previously for this purpose. Using this approach, we have identified several unknown drug-disease relationships with support in biomedical literature. The proposed approach can extend beyond radio podcasts and could be applied to any source of audio and textual data.

category: health informatics


title: Melanoma Skin Cancer Detection using Deep Learning

abstract:
Data from the World Health Organization (WHO) indicate a worldwide occurrence of 2 to 3 million cases of non-melanoma skin cancer annually. The American Cancer Society estimates that the incidence reaches 5.4 million in the United States alone. In cases of fatal diseases, early detection received great attention from the population and the media due to the premise that the earlier a cancer is identified, the greater the chances of cure. It is to be believed that the application of automated methods will help in early diagnosis, especially with the set of images with a variety of diagnoses. Thus, this article presents a system for recognizing dermatological diseases through images with lesions, a machine intervention in contrast to conventional detection based on medical personnel. Our model is designed in three phases, committing to data collection and augmentation, model development, and finally, prediction. We used various AI algorithms such as ANN with image processing tools to form a better structure, leading to higher accuracy of 89%.

Contactdheiver.santos@ictbridge.org, dheiver.santos@gmail.com


title: Using a Computational Cognitive Model to Simulate the Effects of Personal and Social Network Experiences on Seasonal Influenza Vaccination Decisions

abstract: The substantial societal costs of seasonal influenza include illness, loss of lives, and loss of work productivity. Vaccination is the most effective means for averting the disease, yet fewer than half of adults in the United States are vaccinated annually. In this research, we focus on how personal experience and the experiences of close social contacts contribute to vaccination decisions. The results of a multi-year longitudinal survey study revealed the significant effects of personal and social network experiences on vaccination. We develop a memory-based model of vaccination decisions using the Adaptive Control of Thought - Rational (ACT-R) integrated cognitive architecture. The model accounts for the effects of personal and social experience on vaccination, and suggests interventions that may be effective in increasing vaccination uptake.

category: health policy


title: OraSure InteliSwab Rapid Antigen Test performance with the SARS-CoV-2 Variants of Concern Alpha, Beta, Gamma, Delta, and Omicron

abstract: The emergence of SARS-CoV-2 in the human population and the resulting COVID-19 pandemic has led to the development of various diagnostic tests. The OraSure InteliSwab(R) COVID-19 Rapid Test is a recently developed and FDA emergency use authorized rapid antigen-detecting test that functions as a lateral flow device targeting the nucleocapsid protein. Due to SARS-CoV-2 evolution, there is a need to evaluate the sensitivity of rapid antigen-detecting tests for new variants, especially variants of concern like Omicron. In this study, the sensitivity of the OraSure InteliSwab(R) Test was investigated using cultured strains of the known variants of concern (VOCs, Alpha, Beta, Gamma, Delta, and Omicron) and the ancestral lineage (lineage A). Based on dilution series in cell culture medium, an approximate limit of detection for each variant was determined. The OraSure InteliSwab(R) Test showed an overall comparable performance using recombinant nucleocapsid protein and different cultured variants with recorded limits of detection ranging between 3.77 x 105 and 9.13 x 105 RNA copies/mL. Finally, the sensitivity was evaluated using oropharyngeal swabs from Syrian golden hamsters inoculated with the 6 VOCs. Ultimately, the OraSure InteliSwab(R) COVID-19 Rapid Test showed no decrease in sensitivity between the ancestral SARS-CoV-2 strain and any VOCs including Omicron.


title: Efficacy and safety of nitazoxanide combined with ritonavir-boosted atazanavir for the treatment of mild to moderate COVID-19

abstract:
BackgroundFinding effective therapeutics for COVID-19 continues to be an urgent need, especially considering use context limitations and high cost of currently approved agents. The NACOVID trial investigated the efficacy and safety of repurposed antiprotozoal and antiretroviral drugs, nitazoxanide and atazanavir/ritonavir, used in combination for COVID-19.

MethodsIn this pilot, randomized, open-label trial conducted in Nigeria, patients diagnosed with mild to moderate COVID-19 were randomly assigned to receive standard of care (SoC) or SoC plus a 14-day course of nitazoxanide (1000 mg b.i.d.) and atazanavir/ritonavir (300/100 mg od) and followed through day 28. Study endpoints included time to clinical improvement, SARS-CoV-2 viral load change, and time to complete symptom resolution. Safety and pharmacokinetics of nitazoxanide active metabolite, tizoxanide, were also evaluated. This trial was registered with ClinicalTrials.gov (NCT04459286).

FindingsThere was no difference in time to clinical improvement between the SoC (n = 26) and SoC plus intervention arms (n = 31; Cox proportional hazards regression analysis adjusted hazard ratio, aHR = 0.898, 95% CI: 0.492-1.638, p = 0.725). No difference was observed in the pattern of saliva SARS-CoV-2 viral load changes from days 2 to 28 in the 35% of patients with detectable virus at baseline (20/57) between the two arms (aHR = 0.948, 95% CI: 0.341-2.636, p = 0.919). There was no significant difference in time from enrolment to complete symptom resolution (aHR = 0.535, 95% CI: 0.251 - 1.140, p = 0.105). Atazanavir/ritonavir increased tizoxanide plasma exposure by 68% and median trough plasma concentration was 1546 ng/ml (95% CI: 797-2557), above its putative EC90 in 54% of patients. Tizoxanide was not detectable in saliva.

InterpretationThese findings should be interpreted in the context of incomplete enrolment (64%) and the limited number of patients with detectable SARS-CoV-2 in saliva at baseline in this trial.

FundingThe University of Liverpool.

Research in contextO_ST_ABSEvidence before this studyC_ST_ABSThe potential efficacy of nitazoxanide as a repurposed drug for COVID-19 is being investigated in a number of studies due to confirmed in vitro activity against SARS-CoV-2. Available data from completed randomised controlled trials in which clinical improvement, effect on viral load, and symptom resolution were evaluated as outcomes do not offer conclusive evidence.

Added value of this studyIn the NACOVID trial, we sought to take advantage of a model-informed strategy and known interaction between nitazoxanide and atazanavir/ritonavir to achieve optimal concentration of tizoxanide in plasma, and possibly in respiratory tracts of patients with mild to moderate COVID-19. While this strategy significantly enhanced tizoxanide exposure in the plasma of patients, our data indicated poor penetration into the respiratory tracts. Specifically, there were no differences in time to clinical improvement, viral load changes, and symptom resolutions between patients who were given standard of care alone and those who combined it with study intervention.

Implications of all the available evidenceThe clinical benefit of nitazoxanide remains uncertain. The present study highlights the need for early insight into target site biodistribution of potential COVID-19 therapeutics to better inform candidate selection for clinical trials.


title: Transparency and reporting characteristics of COVID-19 randomized controlled trials.

abstract:
Randomized controlled trials (RCTs) are essential to support clinical decision making. We assessed the transparency, completeness and consistency of reporting of 244 reports (120 peer-reviewed journal publications; 124 preprints) of RCTs assessing pharmacological interventions for the treatment of COVID-19 published the first 17 months of the pandemic (up to May 31, 2021). Transparency was poor. Only 55% of trials were prospectively registered; 39% made their full protocols available and 29% provided access to their statistical analysis plan. Only 6% completely reported the most important information. Primary outcome(s) reported in trial registries and published reports were inconsistent in 47% of trials. Of the 124 RCTs published as preprint, 76 were secondarily published in a peer-reviewed journal. There was no major improvement after the peer-review process.

Lack of transparency, completeness and consistency of reporting is an important barrier to trust, interpretation and synthesis in COVID-19 clinical trials.


title: Alpha globin gene copy number and hypertension risk among Black Americans

abstract:
BACKGROUNDAlpha globin is expressed in the endothelial cells of human resistance arteries where it binds to endothelial nitric oxide synthase and limits release of the vasodilator nitric oxide. Genomic deletion of the alpha globin gene (HBA) is common among Black Americans and could lead to increased endothelial nitric oxide signaling and reduced risk of hypertension.

METHODSCommunity-dwelling US adults aged 45 years or older were enrolled and examined from 2003 to 2007, followed by telephone every 6 months, and reexamined from 2013 to 2016. At both visits, trained personnel performed standardized, in-home blood pressure measurements and pill bottle review. Prevalent hypertension was defined as systolic blood pressure [≥] 140mmHg or diastolic blood pressure [≥] 90mmHg or anti-hypertensive medication use. Droplet digital PCR was used to determine HBA copy number. The associations of HBA copy number with prevalent hypertension, resistant hypertension, and incident hypertension were estimated using multivariable regression.

RESULTSAmong 9,684 Black participants, 7,439 (77%) had hypertension at baseline and 1,044 of those had treatment-resistant hypertension. 1,000 participants were not hypertensive at baseline and participated in a follow up visit; 517 (52%) developed hypertension over median 9.2 years follow-up. Increased HBA copy number was not associated with prevalent hypertension (PR=1.00; 95%CI 0.98,1.02), resistant hypertension (PR=0.95; 95%CI 0.86,1.05), or incident hypertension (RR=0.96; 95%CI 0.86,1.07).

CONCLUSIONSThere were no associations between increased HBA copy number and risk of hypertension. These findings suggest that genetic variation in alpha globin gene expression does not modify the risk of hypertension among Black American adults.