Clinical features and acute management in adults

COVID-19 is a multisystem disease that requires holistic management. Most patients will experience mild symptoms including cough, fever and mild dyspnoea. A small proportion of patients will have severe manifestations including respiratory failure, ARDS and multiorgan failure. Extrapulmonary features are common and include gastrointestinal, thromboembolic, neurological, cardiac, renal, endocrine and dermatological manifestations. The care of COVID-19 patients requires close attention to these features. This includes respiratory support (such as supplemental oxygen, NIV and awake proning);fluid, electrolyte and nutrition management;prevention, detection and treatment of thrombotic events;management of diabetic complications;review of medications;appropriate use of antibiotics;and evidence-based use of therapeutic agents such as corticosteroids, antivirals such as remdesivir and other emerging therapies such as immunomodulating agents. Early planning for treatment escalation and decision making around the appropriateness of cardiopulmonary resuscitation are crucial as deterioration can be rapid. Prolonged symptoms occur in a minority of patients and longitudinal follow-up is required.Copyright © ERS 2021.

Validation of dried blood spot sampling for detecting SARS-CoV-2 antibodies and total immunoglobulins in a large cohort of asymptomatic young adults.

BACKGROUND: Detecting antibody responses following infection with SARS-CoV-2 is necessary for sero-epidemiological studies and assessing the role of specific antibodies in disease, but serum or plasma sampling is not always viable due to logistical challenges. Dried blood spot sampling (DBS) is a cheaper, simpler alternative and samples can be self-collected and returned by post, reducing risk for SARS-CoV-2 exposure from direct patient contact. The value of large-scale DBS sampling for the assessment of serological responses to SARS-CoV-2 has not been assessed in depth and provides a model for examining the logistics of using this approach to other infectious diseases. The ability to measure specific antigens is attractive for remote outbreak situations where testing may be limited or for patients who require sampling after remote consultation. METHODS: We compared the performance of SARS-CoV-2 anti-spike and anti-nucleocapsid antibody detection from DBS samples with matched serum collected by venepuncture in a large population of asymptomatic young adults (N = 1070) living and working in congregate settings (military recruits, N = 625); university students, N = 445). We also compared the effect of self-sampling (ssDBS) with investigator-collected samples (labDBS) on assay performance, and the quantitative measurement of total IgA, IgG and IgM between DBS eluates and serum. RESULTS: Baseline seropositivity for anti-spike IgGAM antibody was significantly higher among university students than military recruits. Strong correlations were observed between matched DBS and serum samples in both university students and recruits for the anti-spike IgGAM assay. Minimal differences were found in results by ssDBS and labDBS and serum by Bland Altman and Cohen kappa analyses. LabDBS achieved 82.0% sensitivity and 98.2% specificity and ssDBS samples 86.1% sensitivity and 96.7% specificity for detecting anti-spike IgGAM antibodies relative to serum samples. For anti-SARS-CoV-2 nucleocapsid IgG there was qualitatively 100% agreement between serum and DBS samples and weak correlation in ratio measurements. Strong correlations were observed between serum and DBS-derived total IgG, IgA, and IgM. CONCLUSIONS: This is the largest validation of DBS against paired serum for SARS-CoV-2 specific antibody measurement and we have shown that DBS retains performance from prior smaller studies. There were no significant differences regarding DBS collection methods, suggesting that self-collected samples are a viable sampling collection method. These data offer confidence that DBS can be employed more widely as an alternative to classical serology.

A Bayesian predictive analytics model for improving long range epidemic forecasting during an infection wave

Following the outbreak of the coronavirus epidemic in early 2020, municipalities, regional governments and policymakers worldwide had to plan their Non-Pharmaceutical Interventions (NPIs) amidst a scenario of great uncertainty. At this early stage of an epidemic, where no vaccine or medical treatment is in sight, algorithmic prediction can become a powerful tool to inform local policymaking. However, when we replicated one prominent epidemiological model to inform health authorities in a region in the south of Brazil, we found that this model relied too heavily on manually predetermined covariates and was too reactive to changes in data trends. Our four proposed models access data of both daily reported deaths and infections as well as take into account missing data (e.g., the under-reporting of cases) more explicitly, with two of the proposed versions also attempting to model the delay in test reporting. We simulated weekly forecasting of deaths from the period from 31/05/2020 until 31/01/2021, with first week data being used as a cold-start to the algorithm, after which we use a lighter variant of the model for faster forecasting. Because our models are significantly more proactive in identifying trend changes, this has improved forecasting, especially in long-range predictions and after the peak of an infection wave, as they were quicker to adapt to scenarios after these peaks in reported deaths. Assuming reported cases were under-reported greatly benefited the model in its stability, and modelling retroactively-added data (due to the "hot” nature of the data used) had a negligible impact on performance. © 2022 The Author(s)

The 2022 monkeypox outbreak: A UK military perspective.

With the emergence of SARS-CoV-2 and now monkeypox, the UK Defence Medical Services have been required to provide rapid advice in the management of patients with airborne high consequence infectious diseases (A-HCID). The Defence Public Health Network (DPHN) cadre, consisting of closely aligned uniformed and civilian public health specialists have worked at pace to provide evidence-based recommendations on the clinical management, public health response and policy for monkeypox, with military medicine and pathology clinicians (primarily infectious disease physicians and medical microbiologists). Military environments can be complicated and nuanced requiring specialist input and advice to non-specialists as well as unit commanders both in the UK and overseas. DPHN and military infection clinicians have close links with the UK National Health Service (NHS) and the UK Health Security Agency (UKHSA), allowing for a dynamic two-way relationship that encompasses patient management, public health response, research and development of both UK military and national guidelines. This is further demonstrated with the Royal Air Force (RAF) Air Transport Isolator (ATI) capability, provided by Defence to support the UK Government and UKHSA. Military infectious disease clinicians are also embedded within NHS A-HCID units. In this manuscript we provide examples of the close interdisciplinary working of the DPHN and Defence clinicians in managing military monkeypox patients, co-ordinating the public health response, advising the Command and developing monkeypox policy for Defence through cross-government partnership. We also highlight the co-operation between civilian and military medical authorities in managing the current outbreak.

Time to biopsy of screening mammography-detected abnormalities: Evaluating the impact of same-day services implemented during the COVID-19 pandemic

Background: Screening mammography programs often require patients undergo multiple visits (screening exam, diagnostic exam, and biopsy) before tissue diagnoisis of screen-detected abnormalities. During the COVID-19 pandemic, same-day breast imaging services were leveraged to decrease the number of visits following abnormal screening exams. Specifically, in May 2020, we implemented an immediate-read screening mammography program to synergize with our pre-existing same-day breast biopsy program, such that every effort was made to perform diagnostic imaging during the same visit after an abnormal screening mammogram. This study aims to evaluate the impact of these same-day breast imaging services on time and number of patient visits to undergo breast biopsy after an abnormal screening mammogram. Methods: Consecutive screening mammograms performed during normal business hours pre- (6/1/16 to 5/30/17) and post-implementation (6/1/20 to 5/30/21) of same-day services were identified. Patient demographics, imaging and biopsy results, and visit dates were extracted from the medical record. Multivariable logistic, linear, and ordinal regression models estimated with generalized estimating equations were fit to assess the association of period (pre- versus post-implementation), patient age, and race and ethnicity (White versus races other than White) with having a same-day biopsy (biopsy on the same day as the abnormal screening exam), number of days to biopsy, and number of visits. Adjusted odds ratios (aOR) and beta estimates (aBeta) of each covariate and corresponding 95% confidence intervals (CI) were estimated. Results: A total of 409/25,922 (1.6%) of patients (median age 61, IQR 50-70) pre-implementation and 221/20,452 (1.1%) patients (median age 62, IQR 49-71) post-implementation had screen-detected abnormalities leading to diagnostic breast imaging and biopsy. Median number of days from screening to biopsy decreased from 16 days pre-implementation to 5 days post-implementation (p < 0.001). Pre-implementation, 86.8% of patients required 3 visits between screening and biopsy, while post-implementation only 23.1% required 3 visits (p < 0.001). Compared to pre-implementation, the post-implementation period was associated with increased odds of undergoing same-day biopsy (aOR 20.7, 95% CI 8.3-51.7), p < 0.001), fewer days from abnormal screening mammogram to biopsy (aBeta -13.3, 95% CI -15.7 to -10.9, p < 0.001), and fewer visits (aOR 0.05, 95% CI 0.02-0.09), p < 0.001), controlling for age and race and ethnicity. Conclusions: Same-day breast imaging services decreased time and patient visits between abnormal screening mammogram and breast biopsy. Same-day services implemented out of necessity during the COVID-19 pandemic should be continued after the pandemic has subsided to improve timeliness of care.

A COST AND TIME ANALYSIS OF PARTICIPANT RECRUITMENT AND URINE SAMPLE COLLECTION THROUGH SOCIAL MEDIA OPTIMIZATION

INTRODUCTION AND OBJECTIVE: Clinical research study can be expensive and time consuming due to high cost and/or long duration of a study. We hypothesized that research studies using online recruitment and engaging patients via social medial channels have the potential to reach a large population in a small timeframe, at a reasonable cost. This is an especially appropriate adaptation during COVID-19, with limited hospital and clinic access. To our knowledge, no prior study has performed a detailed cost analysis of the use of online recruitment of clinical sample collection. We sought to address this knowledge gap by comparing cost and efficiency of the recruitment of urine samples online compared to recruitment of urine samples at clinical research sites. METHODS: We performed a retrospective cost analysis of a cohort study comparing cost per sample and time per sample for both online and clinically recruited participants. The retrospective review was conducted from August 2020 to September 2021. During this time, cost data was collected based on patient surveys, urine sample analyses, invoices, and budget spreadsheets. The data was subsequently subjected to statistical analysis. This study was performed with full institutional IRB approval. RESULTS: Each sample collection kit contained 3 urine cups, 1 for the IC sample and 2 for control samples. Out of the 3576 (1192 IC + 2384 control) total sample cups mailed, 1,211 (677 control) samples were returned, with a return rate of 44.8% for IC samples and 28.4% for controls. Comparatively, the two clinical sites collected 305 samples in the same period. Men and women of all age groups (18+) and ethnicities enrolled in the study, representing all 50 states. Although the initial startup cost of online recruitment was higher, cost per sample for online recruited was found to be $147.06 compared to $398.14 for clinic sample. CONCLUSIONS: We conducted a nationwide, contactless, urine sample drive through online participant recruitment, in the midst of the COVID-19 pandemic. Results were compared with the samples collected in traditional clinical setting. Online patient recruitment can be utilized to collect urine sample rapidly, efficiently and at a cost per sample that was 37% of in an in-person clinic and without risk of COVID-19 exposure.

CONDUCTING UROLOGY RESEARCH DURING COVID-19: SOCIAL MEDIA-BASED CROWDSOURCING OF URINE SAMPLES FOR BIOMARKER DEVELOPMENT

INTRODUCTION AND OBJECTIVE: Conductingresearch during the COVID-19 pandemic remains a challenge for the entireurologic community. We hereby report our effort to use social mediabasedcrowdsourcing methodology to recruit participants from the entire UnitedStates, including Alaska and Hawaii. We developed a novel research model thatcan engage multiple stakeholders and allows for fast and broad participationfor urine biomarker development towards a diagnostic tool for InterstitialCystitis (IC). METHODS: We partneredwith a patient advocacy group, the Interstitial Cystitis Association (ICA), toaid in recruitment efforts. A website with study information and links to theHIPAA-compliant enrollment questionnaire was created. The study was advertisedthough Google, and Beaumont's and ICA's social media using Twitter, emails,YouTube videos, Facebook posts, websites, and TikTok. Enrolled participants wereshipped at-home collection kits containing urine cups with preservative, a prepaidreturn mailing envelope, instructions, and survey materials. RESULTS: National publicparticipation was strong, and enrollment was closed within three months. Menand women of all age groups and ethnicities enrolled in the study, representingall 50 United States. Currently, 1,211 crowdsourced samples have been returned,including 677 asymptomatic control samples. Surprisingly, most participantsaccessed the website via mobile devices, as opposed to via desktop computer ortablet. Potential participants were referred to the website primarily by directlinks (i.e. email links), with other referring sites being Facebook, GoogleAdvertisements, the ICA's website, and Beaumont Hospital's website. CONCLUSIONS: Following COVID-19 safety guidelines, we conducted a nationwide crowdsource collection ofurine samples. Social media and web-based recruitment tools were used torecruit participants. Having a website user-friendly on mobile devices wasessential. Nontraditional recruitment methods, such as TikTok videos,encouraged study participation. Utilizing various social media platforms, it ispossible to crowdsource urine samples from across the United States quickly,economically, and most importantly during the COVID-19 pandemic, safely. (Figure Presented).

Sustained Control and Prevention of COVID-19 Outbreaks in Detroit Skilled Nursing Facilities

Background. Nursing home residents, a vulnerable population, experienced an extraordinary surge of COVID-19 cases and deaths at the beginning of the pandemic. Multidisciplinary collaboration from the Detroit Health Department (DHD), academic centers, along with interim guidance from the CDC provided a structured approach to control SARS-CoV-2 in Detroit skilled nursing facilities (SNF). We aim to describe this model. Methods. There were 26 SNF prioritized by the DHD over a 13-month period from 3/2020 - 4/2021. Testing for SARS-CoV-2 occurred biweekly, on average, at each facility for staff and residents. Any staff or resident cases were investigated by a specialized investigations team to determine outbreak status. Any resident that was identified as positive for SARS-CoV-2 was moved to a designated in-house quarantine unit or specific COVID-19 designated nursing homes within the City of Detroit, and cohorting guidance was provided. Facilities were evaluated for environmental controls, PPE provided as needed and infection prevention guidance was provided. COVID-19 vaccination was conducted by pharmaceutical chains or the DHD and vaccine education sessions were conducted for nursing home staff and residents. Results. On average, SNF facilities served a total of 2,262 residents (2031-2367 range) and employed a total of 2,965 staff (1034-3124 range) during the period from 7/2020 - 4/2021. SARS-CoV-2 cases overall for Michigan and Detroit are shown in Figure 1. In SNF facilities, cases ranged from zero to 279 cases in residents and zero to 115 cases per week in staff (Figure 1). Beginning 3/2020, the majority of cases were residents, whereas after 10/2020, staff cases exceeded resident cases. Immunization rates were 63% (partial) and 58% (complete) for residents, and 26% and 23% for staff, respectively. Measures to reduce vaccine hesitancy included organized education sessions, messaging from trusted leaders and organized mass vaccination schedules. Conclusion. We describe the effectiveness of multidisciplinary interventions to control dissemination, morbidity and mortality of SARS-CoV-2 amongst SNF residents in Detroit. We emphasize the continued need to address vaccine hesitancy and importance of this model as successful interventions to decrease infection rates.

COVID-19 associated cystitis (CAC): increased urinary symptoms and biomarkers of inflammation in urine in patients with acute COVID-19

INTRODUCTION AND OBJECTIVE: Emerging evidence suggests that the bladder is one of many organs are targeted by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), which contributes significantly to Coronavirus Disease 2019 (COVID-19) morbidity and leads to organ-specific complications. We were the first U.S. group to identify severe and bothersome de novo genitourinary symptoms in patients with confirmed SARS-CoV-2 infection, termed COVID-19 Associate Cystitis (CAC). We then sought to determine if this was associated with pro-inflammatory cytokines in the urine of patients. METHODS: With IRB approval, hospital discharged COVID-19 patients with normal renal function (N[53) and asymptomatic controls (N=12) completed an AUA Urology Care Foundation Overactive Bladder (OAB) Assessment Tool to determine their current urinary symptoms (score of 0 to 25, increasing severity) and provided a urine sample. Urine samples were tested for SARS-CoV-2 using PCR, CRP by ELISA, and pro-inflammatory cytokines using Luminex assays following manufacturer's instructions. RESULTS: The median total OAB symptom score in both men and women with COVID-19 was 18 (range 4-21). The median total QOL score for both men and women with COVID-19 was 19 (range 8-24). Median age was 64.5 (range 47-82). Symptoms of CAC include increased urinary frequency, urgency, nocturia, and pain or pressure upon voiding. These symptoms were not due to urinary tract infection, acute renal injury, prostatitis, or urinary retention. These symptoms have caused worsening QOL in CAC patients, and nocturia the most bothersome symptom. The majority of COVID-19 patients did not have virus in their urine. The levels of GRO/CXCL-1, IL-6, IP-10, and CRP were significantly increased in urine samples from COVID-19 patients compared to controls. CONCLUSIONS: COVID-19 patients reported severe de novo genitourinary symptoms, most notably an increase in urgency, frequency, and nocturia. SARS-CoV-2 infection results in increased expression of proinflammatory biomarkers in the urine. The variance between individuals may reflect the severity and duration of their disease, and/or the presence of comorbidities. This data supports that COVID-19 patients, especially those with CAC, have increased inflammatory cytokines in the urine.

Impact of early fluid administration on inpatient mortality and acute kidney injury in patients with COVID-19 disease

BACKGROUND AND AIMS: Initial WHO guidance advised cautious fluid administration for patients with COVID-19 due to concern about the development of acute respiratory distress syndrome (ARDS). However, as the pandemic unfolded it became apparent that patients who were admitted to hospital had high rates of AKI and this initiated a change in local clinical guidelines during early April 2020. We aimed to ascertain the impact of judicious intravenous fluid use on mortality, length of hospitalisation and AKI. METHOD: An observational cohort study of 158 adults admitted with confirmed SARS-Cov-2 between 18th March and 9th May 2020 was conducted in a teaching hospital and designated centre for infectious diseases, London, UK. Key clinical and demographic data collected included clinical severity markers on admission, biochemical and haematological parameters as well as radiological findings. Primary outcomes were inpatient mortality, mortality at 6-weeks post discharge, length of hospitalisation and intensive care (ICU) admission. We also measured requirement for kidney replacement therapy (KRT) and AKI recovery rate at discharge. Using tests of difference, we compared key outcomes between patients treated with varying fluid regimens and then identified risk factors for AKI and mortality using multivariate logistic regression with results expressed as odds ratios (OR) with corresponding 95% confidence interval (CI). RESULTS: The median age was 74.4 (IQR 59.90 - 84.35) years, 66% were male, 53% white with hypertension and diabetes being the commonest co-morbidities. The median duration of illness prior to admission was 7 days (IQR 2 - 10) with respiratory symptoms and fever most prevalent. The people who presented with AKI on admission were more likely to receive fluids (34% vs 15%, p=0.02). 118 patients (75%) received fluids within 24-hours of admission with no difference in volume administered after local guidance change (p=0.78). Comparing patients receiving fluids with those who did not, we observed no difference in mortality (p=0.97), duration of hospital stays (p=0.26) or requirement for ICU admission (p=0.70). 18% died as an inpatient, and 52 patients were either admitted with or developed AKI. Of these 52 patients, 43 received fluids and 9 did not with no difference in KRT requirement (p=0.34), mortality (p=0.50) or AKI recovery (p=0.63). Peak AKI stage was greater among participants who received fluids though stage of AKI at presentation was also greater (p=0.04). Mortality rate in patients with an AKI is higher compared to overall inpatient mortality (31% vs 18%). Of the 36 patients with AKI (Figure Presnted) who were discharged home, 25 patients (69.4%) had renal recovery by the time of discharge. Increasing age and clinical severity on admission were associated with higher mortality (see Figure 1). Older age was associated with 34 - 53 times higher risk of death compared with those aged ≥ 65 years (age 76 - 85 years: OR 34.26, 95% CI: 3.94 - 297.48, p=0.001;age > 85 years: OR 53.07, 95% CI: 5.23 - 539.03, p=0.001). Patients with NEWS2 >4 on admission has 5-fold increased risk of death than those with a score ≥4 (OR 5.26, 95% CI: 1.32 - 20.92). Black ethnicity was associated with a 16-fold increased risk of developing AKI (OR 15.86, 95% CI: 1.67 - 150.99). CONCLUSION: To our knowledge, this is the first study to examine the impact of fluid management on inpatient mortality as well as on renal-associated outcomes of COVID-19 admission. Fluid administration regimen did not have an impact on mortality, length of hospitalisation or ICU admission, nor did it affect renal outcomes. Given the high rates of AKI and KRT in COVID-19 disease, early fluid administration is likely to be an important cornerstone of future management. Further adequately powered prospective studies are required to identify whether early fluid administration can reduce renal injury.

COVID-19 and exacerbation of screening mammography inequities

Background: After state-mandated cessation of screening mammography (SM) in Spring 2020 due to COVID-19, centers were urged to resume screening, particularly of patients at increased risk. As our tertiary-care medical center's screening program provides SM at four sites across our metropolitan area, we examined whether sites that historically served more patients from more disadvantaged areas returned slower to pre-COVID volumes. Methods: Patient records were linked by ZIP code of residence to ZIP Code Tabulation Area (ZCTA)-level area-based social metrics (ABSMs) from the 2014-2018 American Community Survey. We compared baseline pre-COVID (MayOctober, 2015-2019) SM population ABSMs between our four imaging sites for: % persons below poverty (≥ vs < 10%), % persons of color (POC) (quintiles: top 2 vs bottom 3), index of racialized economic segregation (quintiles: bottom 2 [more POC low-income households] vs top 3 [more white non-Hispanic (WNH) highincome households]);and race/ethnicity (% WNH vs POC). We modeled weekly SM volumes per screening day by site using Poisson regression and tested for weekly differences at each site, COVID-era (May-October 2020) vs preCOVID;and tested for monthly differences in SM population composition by logistic regression modeling. Results: There were 89,082 pre-COVID and 16,220 COVID-era SM exams. At pre-COVID baselines the four sites differed in population composition by ABSMs and race/ethnicity (all chi-square P values <.001) (Table). The two sites that served more disadvantaged populations (A, B) returned slower to pre-COVID volumes (site-specific weekly screening volume no longer different [P >.05] vs pre-COVID) (Table). As a result, compositions of the aggregate SM population across all sites showed a smaller proportion of patients from the most disadvantaged ZCTAs by ABSMs (all P values <.001) before returning to pre-COVID compositions three months after SM resumption. Conclusions: SM was slower to return to pre-COVID volumes at imaging sites that historically served lower-income communities of color. As a result, our COVIDera SM population skewed away from patients in disadvantaged ZCTAs. Our findings highlight the need to monitor for emergent disparities in the pandemic era. Future work will focus on understanding causes of inequitable SM engagement across our imaging sites to mitigate care disparities for our most vulnerable patients. (Table Presented).

Reducing racial disparities in time to breast cancer diagnosis: Impact of immediate screening mammogram reads during the COVID pandemic

Background: During the COVID-19 pandemic, barriers to access screening mammography along with goals to reduce visits supported immediate reading of screening mammograms. Typically, screening mammograms are reported after patients have left the facility. If imaging is abnormal, then an additional visit is needed for diagnostic imaging, introducing delays and potential disparities.Thus, we implemented an immediate-read screening mammography program and measured its impact on racial/ethnic disparities in time to diagnostic imaging after an abnormal screening mammogram. Methods: Responding to the COVID-19 pandemic, we implemented an immediate read screening program in late May 2020. Patients were provided imaging results before discharge and if the exam was abnormal, efforts were made to perform diagnostic imaging during that visit. We identified consecutive screening mammograms performed weekdays 8:00am-4:30pm and Saturdays 9:00am-4:00pm pre-implementation (6/1/19-10/31/19) and post-implementation (6/1/2020-10/31/2020). Exams left unread while awaiting comparison studies, due to technical factors, or for more than 10 days were excluded. Patient demographics and time from screening exam completion to report finalized were obtained from the electronic medical record. Cancer detection rate (CDR), abnormal interpretation rate (AIR), and positive predictive value (PPV) were calculated. Multivariable linear and logistic regression models were used to compare time from screening exam to report, same-day diagnostic imaging, and screening performance metrics pre- and postimplementation overall and by patient subgroups. Results: After 963 exams met exclusion criteria, a total of 8,222 pre- and 7,235 post-implementation exams were included. Median time to report finalization decreased from 61minutes (interquartile range [IQR]:24, 152) to 4 minutes (IQR:2, 7) for pre- and postimplementation periods (p < 0.001). During the pre-implementation period, non-white patients had lower odds of having same-day diagnostic imaging after an abnormal screening exam (age-adjusted odds ratio: 0.28;95% CI: 0.10, 0.78 p = 0.015). There was no evidence of this disparity post-implementation. AIR was higher in the pre- versus post-implementation period (6.3% versus 5.0%;p < 0.001). There was no evidence of a difference in CDR (5.8 versus 4.2 cancers/1,000 exams) and PPV (9.2% versus 8.4%) for pre- versus post-implementation periods. Conclusions: An immediate read screening mammography program reduces racial/ethnic disparities in time to diagnostic imaging after an abnormal screening mammogram, thus promoting equity in access to care. (Table Presented).

Hidden clues in the mammogram: How AI can improve early breast cancer detection

Innovative methods of risk assessment that leverage the strength of Artificial Intelligence (AI) are essential to propelthe goals of precision prevention forward. Since the creation of the Gail model in 1989, risk models have supportedrisk-adjusted screening and prevention, and their continued evolution has been a central pillar of breast cancerresearch. Prior research has explored multiple risk factors related to hormonal and genetic information. One factorthat has received substantial attention is mammographic breast density. Incorporating mammographic breastdensity into clinically used models such as the Gail and Tyrer-Cuzick risk models significantly improves prediction and discrimination. However, current risk models are limited in that they incorporate only a small fraction of dataavailable on any given patient. Using breast density as a proxy for the detailed information embedded in themammogram is extremely limited, as breast density assessment is subjective, varies widely across radiologists, andrestricts the rich information contained in the digital images to a single crude value. Patients of the same ageassigned the same density score can have mammogram images that appear drastically different and can have verydifferent future risk profiles. While previous studies have explored automated methods to assess breast density,these efforts reduce the complex data contained in the mammogram into a few statistics, which are not sufficientlyrich to distinguish patients who will and will not develop breast cancer. Deep learning models can operate over fullresolution mammogram images to assess a patient's future breast cancer risk. Rather than manually identifyingdiscriminative image patterns, machine learning models can discover these patterns directly from the data.Specifically, models are trained with full resolution mammograms and the outcome of interest, namely whether the patient developed breast cancer with in five years from the date of the examination. Our recent work demonstratesthat application of novel artificial intelligence applications to imaging data can significantly improve breast cancerrisk prediction. In addition, unlike traditional models, our DL model performs equally well across varied races, ages,and family histories and we have built a clinical platform which is currently in use to support implementation of ourrisk model into clinical care. The COVID-19 pandemic has revealed severe inequities in healthcare while providingopportunities for essential reform. In breast cancer care, preliminary, conservative estimates predict the disruption of breast cancer screening due to the COVID-19 pandemic will result in a significant upward stage shift of cancersdiagnosed and more than 5,000 breast cancer deaths in the U.S. alone. Due to severely limited healthcare resources during pandemics, and to protect patients and healthcare workers,state governments urge providers to focus cancer screening efforts on those patients at higher risk. Thesemandates are necessary responses to support fair allocation of scarce resources to maximize benefits for allpatients across the full spectrum of healthcare needs. AI-based breast cancer risk models have the potential tosupport more effective and more equitable mammographic screening for breast cancer during these times of severely restricted access to screening.

Does vitamin D supplementation prevent SARS-CoV-2 infection in military personnel? Review of the evidence.

For most individuals residing in Northwestern Europe, maintaining replete vitamin D status throughout the year is unlikely without vitamin D supplementation and deficiency remains common. Military studies have investigated the association with vitamin D status, and subsequent supplementation, with the risk of stress fractures particularly during recruit training. The expression of nuclear vitamin D receptors and vitamin D metabolic enzymes in immune cells additionally provides a rationale for the potential role of vitamin D in maintaining immune homeostasis. One particular area of interest has been in the prevention of acute respiratory tract infections (ARTIs). The aims of this review were to consider the evidence of vitamin D supplementation in military populations in the prevention of ARTIs, including SARS-CoV-2 infection and consequent COVID-19 illness. The occupational/organisational importance of reducing transmission of SARS-CoV-2, especially where infected young adults may be asymptomatic, presymptomatic or paucisymptomatic, is also discussed.