Limited Availability Of Tests Research Articles

#1640 Alternative complement pathway disorders in the transplant setting—experience from a large volume center

Abstract Background and Aims Complement alternative pathway disorders, atypical hemolytic-uremic syndrome (aHUS) and C3 glomerulopathy (C3G), are rare diseases with a high rate of recurrence after kidney transplantation. Due to limited availability of testing, patients may go undiagnosed, indicating testing during pretransplant evaluation. Posttransplant thrombotic microangiopathy (TMA) is a rare complication of transplantation. A large number of post transplant TMAs is regarded as secondary, either to immunosuppressive drugs or transplant itself. In select cases, complement alternative pathway disorders analysis is warranted. Method We retrospectively reviewed all patients in whom complement system diagnostic work-up was performed in the transplant setting in our center, from 2017 to 2022. Complement system analysis in isolated histological TMA of the allograft, without extrarenal manifestations and with known cause of native kidneys ESRD, was not performed routinely. Patients in whom alternative pathway disorders were confirmed were compared with those with no abnormalities detected by complement system analysis. Results 12 patients were identified, 75% male, median age 39.5 years (IQR 28-60.25). 8 patients were evaluated before transplantation. 4 of the 8 patients were referred for transplantation with diagnosed aHUS. Genetic testing was used for recurrence risk stratification. In living donor transplantations 2 donors were tested as well. One patient was referred from a neighboring resource poor country and was not on eculizumab therapy. He was homozygous for a high risk causative mutation. He was declined for living related donor kidney transplant, due to risk to the donor and unavailability of eculizumab. Compassionate use request was initiated. One patient was referred as possible C3G and repeat complement analysis revealed elevated C3Nef. Out of 3 patients, previously diagnosed as CKD of unknown origin or immune complex-mediated membranoproliferative glomerulonephritis, 2 were diagnosed during pretransplant evaluation: one as C3G and the other as aHUS. 4 patients were evaluated posttransplant. One analysis was performed due to recurrent C3G of the allograft, with confirmed competent dysregulation. 2 analyses were in liver transplant recipients, both with systemic TMA. Severe overactivation with consumption of complement was proven in one and treated with eculizumab. The characteristics of patients with diagnosed disorders of the complement system and those without are shown in Table 1. Conclusion Transplant physicians should keep a high volume of suspicion for complement dysregulation disorders, both during the pretransplant work-up and when evaluating TMA and recurrent GN posttransplant. Special caution should be advised when a patient is referred as chronic GN, MPGN or unknown cause ESKD at a young age.

Data-driven adaptive testing resource allocation strategies for real-time monitoring of infectious diseases

Since 2002, with the SARS outbreak, infectious diseases, including the ongoing COVID-19 pandemic, have continued to be a major global public health threat. It is critical to develop effective data science methods to quickly detect disease outbreaks and contain their rapid globalized spread. However, in practice, limited testing availability, and thus insufficient testing data poses significant challenges in effective analysis and real-time monitoring of infectious diseases, especially during early stages of a novel disease outbreak. To tackle these challenges, this article proposes adaptive testing resource allocation strategies integrated with a physics-informed model to dynamically allocate limited testing resources across different communities. The physics-informed model accounts for transmission dynamics and health disparities, enabling effective health risk assessment despite limited data. By integrating nonstationary Multi-Armed Bandit techniques that strike superior balance between exploring the communities with high uncertain risks and exploiting those with high risk levels, the proposed methodology facilitates test allocation to collect high-quality testing data for early outbreak detection. Theoretical analysis is carried out to evaluate the performance of the proposed allocation strategies, ensuring either sublinear or linear dynamic pseudo-regret under regularity assumptions. A comprehensive simulation study is conducted under three transmission scenarios to thoroughly evaluate the proposed methodology.

A Natural Language Processing Model for COVID-19 Detection Based on Dutch General Practice Electronic Health Records by Using Bidirectional Encoder Representations From Transformers: Development and Validation Study.

Natural language processing (NLP) models such as bidirectional encoder representations from transformers (BERT) hold promise in revolutionizing disease identification from electronic health records (EHRs) by potentially enhancing efficiency and accuracy. However, their practical application in practice settings demands a comprehensive and multidisciplinary approach to development and validation. The COVID-19 pandemic highlighted challenges in disease identification due to limited testing availability and challenges in handling unstructured data. In the Netherlands, where general practitioners (GPs) serve as the first point of contact for health care, EHRs generated by these primary care providers contain a wealth of potentially valuable information. Nonetheless, the unstructured nature of free-text entries in EHRs poses challenges in identifying trends, detecting disease outbreaks, or accurately pinpointing COVID-19 cases. This study aims to develop and validate a BERT model for detecting COVID-19 consultations in general practice EHRs in the Netherlands. The BERT model was initially pretrained on Dutch language data and fine-tuned using a comprehensive EHR data set comprising confirmed COVID-19 GP consultations and non-COVID-19-related consultations. The data set was partitioned into a training and development set, and the model's performance was evaluated on an independent test set that served as the primary measure of its effectiveness in COVID-19 detection. To validate the final model, its performance was assessed through 3 approaches. First, external validation was applied on an EHR data set from a different geographic region in the Netherlands. Second, validation was conducted using results of polymerase chain reaction (PCR) test data obtained from municipal health services. Lastly, correlation between predicted outcomes and COVID-19-related hospitalizations in the Netherlands was assessed, encompassing the period around the outbreak of the pandemic in the Netherlands, that is, the period before widespread testing. The model development used 300,359 GP consultations. We developed a highly accurate model for COVID-19 consultations (accuracy 0.97, F1-score 0.90, precision 0.85, recall 0.85, specificity 0.99). External validations showed comparable high performance. Validation on PCR test data showed high recall but low precision and specificity. Validation using hospital data showed significant correlation between COVID-19 predictions of the model and COVID-19-related hospitalizations (F1-score 96.8; P<.001; R2=0.69). Most importantly, the model was able to predict COVID-19 cases weeks before the first confirmed case in the Netherlands. The developed BERT model was able to accurately identify COVID-19 cases among GP consultations even preceding confirmed cases. The validated efficacy of our BERT model highlights the potential of NLP models to identify disease outbreaks early, exemplifying the power of multidisciplinary efforts in harnessing technology for disease identification. Moreover, the implications of this study extend beyond COVID-19 and offer a blueprint for the early recognition of various illnesses, revealing that such models could revolutionize disease surveillance.

Factors Contributing to SARS-CoV-2 Vaccine Hesitancy of Hispanic Population in Rio Grande Valley.

Hispanic communities have been disproportionately affected by economic disparities. These inequalities have put Hispanics at an increased risk for preventable health conditions. In addition, the CDC reports Hispanics to have 1.5× COVID-19 infection rates and low vaccination rates. This study aims to identify the driving factors for COVID-19 vaccine hesitancy of Hispanic survey participants in the Rio Grande Valley. Our analysis used machine learning methods to identify significant associations between medical, economic, and social factors impacting the uptake and willingness to receive the COVID-19 vaccine. A combination of three classification methods (i.e., logistic regression, decision trees, and support vector machines) was used to classify observations based on the value of the targeted responses received and extract a robust subset of factors. Our analysis revealed different medical, economic, and social associations that correlate to other target population groups (i.e., males and females). According to the analysis performed on males, the Matthews correlation coefficient (MCC) value was 0.972. An MCC score of 0.805 was achieved by analyzing females, while the analysis of males and females achieved 0.797. Specifically, several medical, economic factors, and sociodemographic characteristics are more prevalent in vaccine-hesitant groups, such as asthma, hypertension, mental health problems, financial strain due to COVID-19, gender, lack of health insurance plans, and limited test availability.

Inferring the true number of SARS-CoV-2 infections in Japan

IntroductionIn Japan, as of December 31, 2021, more than 1.73 million laboratory-confirmed cases have been reported. However, the actual number of infections is likely to be under-ascertained due to the epidemiological characteristics such as mild and subclinical infections and limited testing availability in the early days of the pandemic. In this study, we infer the true number of infections in Japan between January 16, 2020, and December 31, 2021, using a statistical modelling framework that combines data on reported cases and fatalities. MethodsWe used reported COVID-19 deaths and age-specific infection fatality ratios (IFR) to impute the true number of infections. Estimates of IFR were informed from published studies and were adjusted to reflect the effects of pharmaceutical interventions, mass vaccination, and evolving variants. To account for the uncertainty in IFR, we sampled values from relevant distributions. ResultsWe estimated that as of December 31, 2021, 3.07 million (CrI: 2.05–4.24 million) people had been infected in Japan, which is 1.77 times higher than the 1.73 million reported cases. Our meta-analysis confirmed that these findings were consistent with the intermittent seroprevalence studies conducted in Japan. ConclusionsWe have estimated that a substantial number of COVID-19 infections in Japan were unreported, particularly in adults. Our approach provides a more realistic assessment of the true underlying burden of COVID-19. The results of this study can be used as fundamental components to strengthen population health control and surveillance measures.

Implementation of School-Based COVID-19 Testing Programs in Underserved Populations.

Evidence suggests that coronavirus disease 2019 (COVID-19) testing in schools can add a layer of protection to reduce the spread of Severe Acute Respiratory Syndrome Coronavirus 2 and facilitate a safer return to in-person learning. Despite this evidence, implementation of testing in school settings has been challenging initially because of a lack of funding and limited availability of testing, but, as the pandemic has progressed and more funding and resources have been devoted to testing, other implementation challenges have arisen. We describe key implementation barriers and strategies that have been operationalized across 5 projects working to help schools with predominantly underserved populations who have faced significant COVID-19-related health disparities. We leveraged a key framework from the implementation science field to identify the challenges and used a matching tool to align implementation strategies to these challenges. Our findings suggest that the biggest obstacles to COVID-19 testing were the perceived relative advantages versus burden of COVID-19 testing, limited engagement with the target beneficiaries (eg, families, students, staff), and innovation complexity. Common strategies to overcome these challenges included identifying and preparing testing champions, altering incentive and allowance structures, assessing for readiness, and identifying barriers and facilitators. We aim to augment existing implementation guidance for schools by describing common barriers and recommended solutions from the implementation science field. Our results indicate a clear need to provide implementation support to schools to facilitate COVID-19 testing as an added layered mitigation strategy.

Predicting daily COVID-19 case rates from SARS-CoV-2 RNA concentrations across a diversity of wastewater catchments.

We assessed the relationship between municipality COVID-19 case rates and SARS-CoV-2 concentrations in the primary sludge of corresponding wastewater treatment facilities. Over 1700 daily primary sludge samples were collected from six wastewater treatment facilities with catchments serving 18 cities and towns in the State of Connecticut, USA. Samples were analyzed for SARS-CoV-2 RNA concentrations during a 10 month time period that overlapped with October 2020 and winter/spring 2021 COVID-19 outbreaks in each municipality. We fit lagged regression models to estimate reported case rates in the six municipalities from SARS-CoV-2 RNA concentrations collected daily from corresponding wastewater treatment facilities. Results demonstrate the ability of SARS-CoV-2 RNA concentrations in primary sludge to estimate COVID-19 reported case rates across treatment facilities and wastewater catchments, with coverage probabilities ranging from 0.94 to 0.96. Lags of 0 to 1 days resulted in the greatest predictive power for the model. Leave-one-out cross validation suggests that the model can be broadly applied to wastewater catchments that range in more than one order of magnitude in population served. The close relationship between case rates and SARS-CoV-2 concentrations demonstrates the utility of using primary sludge samples for monitoring COVID-19 outbreak dynamics. Estimating case rates from wastewater data can be useful in locations with limited testing availability, testing disparities, or delays in individual COVID-19 testing programs.

State Policy Responses to COVID-19 in Nursing Homes

Context: COVID-19 has a high case fatality rate in high-risk populations and can cause severe morbidity and high healthcare resource use. Nursing home residents are a high-risk population; they live in congregate settings, often with shared rooms, and require hands-on care.Objectives: To assess state responses to the coronavirus pandemic related to nursing homes in the first half of 2020.Methods: An in-depth examination of 12 states’ responses to the COVID-19 pandemic in nursing homes through June 2020, using publicly reported information such as government decrees, health department guidance, and news reports.Findings: No state emerged as a model of care. All states faced difficulty with limited availability of testing and Personal Protective Equipment (PPE). State-level efforts to increase pay and benefits as a strategy to enable infected staff to quickly physically separate from residents were minimal, and other separation strategies depended on the ability to obtain test results rapidly and on state rules regarding accepting discharged COVID-19 patients into nursing homes. Visitor restrictions to reduce risk were ubiquitous, though based on a slim evidence-base.Limitations: The information used was limited to that which was publicly available.Implications: Overall, the results suggest that the states that handle the ongoing pandemic in nursing homes best will be those that find ways to make sure nursing homes have the resources to follow best practices for testing, PPE, separation, and staffing. Evidence is needed on visitor restrictions and transmission, as states and their citizens would benefit from finding safe ways to relax visitor restrictions.

Impartially Validated Multiple Deep-Chain Models to Detect COVID-19 in Chest X-ray Using Latent Space Radiomics.

The COVID-19 pandemic continues to spread globally at a rapid pace, and its rapid detection remains a challenge due to its rapid infectivity and limited testing availability. One of the simply available imaging modalities in clinical routine involves chest X-ray (CXR), which is often used for diagnostic purposes. Here, we proposed a computer-aided detection of COVID-19 in CXR imaging using deep and conventional radiomic features. First, we used a 2D U-Net model to segment the lung lobes. Then, we extracted deep latent space radiomics by applying deep convolutional autoencoder (ConvAE) with internal dense layers to extract low-dimensional deep radiomics. We used Johnson–Lindenstrauss (JL) lemma, Laplacian scoring (LS), and principal component analysis (PCA) to reduce dimensionality in conventional radiomics. The generated low-dimensional deep and conventional radiomics were integrated to classify COVID-19 from pneumonia and healthy patients. We used 704 CXR images for training the entire model (i.e., U-Net, ConvAE, and feature selection in conventional radiomics). Afterward, we independently validated the whole system using a study cohort of 1597 cases. We trained and tested a random forest model for detecting COVID-19 cases through multivariate binary-class and multiclass classification. The maximal (full multivariate) model using a combination of the two radiomic groups yields performance in classification cross-validated accuracy of 72.6% (69.4–74.4%) for multiclass and 89.6% (88.4–90.7%) for binary-class classification.

Strategic testing approaches for targeted disease monitoring can be used to inform pandemic decision-making.

More than 1.6 million Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) tests were administered daily in the United States at the peak of the epidemic, with a significant focus on individual treatment. Here, we show that objective-driven, strategic sampling designs and analyses can maximize information gain at the population level, which is necessary to increase situational awareness and predict, prepare for, and respond to a pandemic, while also continuing to inform individual treatment. By focusing on specific objectives such as individual treatment or disease prediction and control (e.g., via the collection of population-level statistics to inform lockdown measures or vaccine rollout) and drawing from the literature on capture–recapture methods to deal with nonrandom sampling and testing errors, we illustrate how public health objectives can be achieved even with limited test availability when testing programs are designed a priori to meet those objectives.

P625 Characteristics of SARS-CoV-2 infection in IBD patients in the second and third wave compared with the first wave and with the data of general population

BackgroundOur aims were: 1.) to compare the characteristics of SARS-CoV-2 infection in IBD patients in the second and third wave with respect to the results published in our site in the first wave (I. Guerra et al. Inflamm Bowel Dis. 2021 Jan 1;27(1):25–33) and 2.) to compare the date of the second and third wave with data of general population from the Autonomous Community of MadridMethodsCohort, observational study in IBD patients followed in our IBD Unit with SARS-CoV-2 infection between March 2020 and May 2020 (first wave) and between July 2020 to March 2021 (second-third wave). All data were collected by telephone interview and reviewing the electronical medical recordsResultsThe demographic characteristics of the patients included are shown in Table 1.Table 1.First Wave(Nº of cases 28/805)Second-Third wave(Nº of cases 51/805)P-ValueMean age55±14 y45±13 y 0.004 Gender (female)13 (46.4%)29 (56.9%)0.61Type of IBD (Crohn’s disease)11 (39.3%)29 (56.9%)0.38Smoking (Yes)1 (3.6%)12 (23.5%) 0.048 Cormobilities (Yes)17 (60.7%)15 (29.4%)0.09IMM treatment (Yes)10 (35.7%)19 (37.3%)0.92Anti-TNF treatment (Yes)5 (17.8%)15 (29.4%)0.27Regarding clinical features of SARS-CoV-2 infection in both group of patients are represented in Table 2.Table 2.First Wave(Nº of cases 28/805)Second-Third wave(Nº of cases 51/805)P-ValueDigestive symptoms14 (50.0%)22 (43.1%)0.64Cough21 (75.0%)13 (25.5%) 0.0096 Fever24 (85.7%)22 (43.1%)0.07Dyspnoea13 (46.4%)9 (17.6%) 0.045 Dysosmia/dysgeusia17 (60.7%)20 (39.2%)0.11Headache17 (60.7%)13 (25.5%) 0.04 Myalgia11(36.3%)10 (19.6%)0.15Severity (moderate-severe)15 (53.6%)6 (11.7%) 0.031 IUC admission1 (3.5%)0 (0%)0.18Mortality1 (3.5%)1 (2.0%)0.61Any treatment for COVID-19 infection (Yes)15 (53.6%)5 (9.8%) 0.0014 Temporary withdrawal of IBD treatment (Yes)13 (46.4%)6 (11.7%) 0.009 The severity of disease was not related to immunomodulators and/or biological treatments in both cohorts.The number of COVID-19 cases by date in our IBD Unit in comparison with the cases in the Community of Madrid are shown in the Figure 1. ConclusionThe patients diagnosed in the first wave were older and more symptomatic. Although the number of severe cases was higher in the first wave, influenced by the limited availability of tests in that period, no difference was found in mortality or in the percentage of ICU admissions.Severity of SARS-CoV-2 infection was not related to immunosuppression and in the second-third wave the IBD treatment was maintained more frequently.The distribution of cases in our series was in concordance with the data obtained in the general population.

Spring 2020 COVID-19 Surge: Prospective Relations between Demographic Factors, Personality Traits, Social Cognitions and Guideline Adherence, Mask Wearing, and Symptoms in a U.S. Sample.

BackgroundTo date, much of the research on individual difference correlates of coronavirus guideline adherence is cross-sectional, leaving prospective associations between these factors unaddressed. Additionally, investigations of prospective predictors of mask-wearing, COVID-19 symptoms, and viral testing remain wanting.PurposeThe present study examined prospective relations between demographic factors, personality traits, social cognitions and guideline adherence, mask-wearing, symptoms, and viral testing in a U.S. sample (N = 500) during the initial surge of COVID-19 deaths in the United State between late March and early May 2020.MethodsGuided by a disposition-belief-motivation framework, correlational analyses, and path models tested associations among baseline personality traits, guideline adherence social cognitions, health beliefs, guideline adherence and follow-up guideline adherence, mask-wearing, symptom counts, and 30-day viral testing.ResultsModeling results showed greater baseline agreeableness, conscientiousness, and extraversion were associated with more frequent baseline guideline adherence. More liberal political beliefs, greater guideline adherence intentions, and more frequent guideline adherence at baseline predicted more frequent mask-wearing at follow-up. Sex (female), lower perceived health, and greater neuroticism at baseline predicted greater symptom counts at follow-up. Reports of viral testing were quite low (1.80%), yet were consistent with concurrent national reporting and limited availability of testing.ConclusionsResults show how inconsistencies and politicization of health policy communication were concomitant with the effects of individual-level political beliefs on mask-wearing during the initial surge. The results further clarify how personality traits related to social responsibility (i.e., agreeableness, conscientiousness) are associated with following new norms for prescribed behaviors and how symptom reporting can be as much a marker of perceived health as emotional stability.

National Early Warning Scores And COVID-19 Deaths In Care Homes: An Ecological Time Series Study

Tracking COVID-19 infections in the care home population has been challenging, because of the limited availability of testing and varied disease presentation. We consider whether National Early Warning Scores (NEWS/NEWS2) could contribute to COVID-19 surveillance in care homes. We analysed NEWS measurements from care homes in England (December 2019 to May 2020). We estimated pre-COVID (baseline) levels for NEWS and NEWS components using 80th and 20th centile scores for measurements before March 2020. We used time-series to compare the proportion of above-baseline NEWS to area-matched reports of registered deaths in care home residents from the Office for National Statistics We analysed 29,656 anonymised NEWS from 6,464 people in 480 care home units across 46 local authority areas. From March 23rd to May 20th, there were 5,753 deaths (1,532 involving COVID-19, 4,221 other causes) in corresponding geographical areas. A rise in the proportion of above-baseline NEWS was observed from March 16th 2020. The proportion of above-baseline oxygen saturation, respiratory rate and temperature measurements also increased approximately two weeks before peaks in deaths. We conclude that NEWS could contribute to disease surveillance in care homes during the COVID-19 pandemic. Oxygen saturation, respiratory rate and temperature could be prioritised as they appear to signal rise in mortality almost as well as total NEWS. This study reinforces the need to collate data from care homes, to monitor and protect residents’ health. Further work using individual level outcome data is needed to evaluate the role of NEWS in the early detection of resident illness.

High prevalence of SARS-CoV-2 infection among symptomatic healthcare workers in a large university tertiary hospital in S\xe3o Paulo, Brazil

BackgroundBrazil became the epicenter of the COVID-19 pandemic in Latin America since May 2020, reporting the highest number of cases and deaths in the region. Healthcare workers (HCWs) are at increased risk of SARS-CoV-2 infection, experiencing a significant burden from COVID-19. Identifying and understanding the clinical characteristics and risk factors associated with infection are of paramount importance to inform screening strategies and infection control practices in this scenario. The aims of this study were to investigate the prevalence and clinical characteristics of HCWs with COVID-19 symptoms.MethodsBetween March 21st and May 22nd, 2020 a cross-sectional study was performed in a tertiary university hospital in São Paulo. Prevalence of SARS-CoV-2 infection among HCWs with COVID-19 symptoms was determined by RT-PCR testing on nasopharyngeal and oropharyngeal samples. Participants were asked to complete an electronic structured questionnaire including clinical and demographic data.ResultsOverall, 125 (42.37%) of 295 symptomatic HCWs tested positive for SARS-CoV-2. Over the 10-week study period, positivity rates varied from 22.2% (95% CI 15.9–60.3%) in the second week to 55.9% (95% CI 43.2–68.6%) in the sixth week, reaching a plateau (38–46%) thereafter. Median (SD) age was 34.2 (9.9) years and 205 (69.5%) were female. We did not find significant differences in the prevalence of the most commonly reported underlying medical condition among healthcare workers that tested positive or negative for SARS-CoV-2 infection. After multivariable analysis, using logistic regression, anosmia (adjusted OR 4.4 95% CI 2.21–8.74) and ocular pain (adjusted OR 1.95 95% CI 1.14–3.33) were the only symptoms independently associated with positivity for SARS-CoV-2 infection. Follow-up information on clinical outcomes showed that 9 (7.2%) HCWs were hospitalized (seven were male) and 2 (1.6%) died.ConclusionsThe findings of this study confirmed the high burden of SARS-CoV-2 infection among healthcare workers in the hardest hit city by the pandemic in Latin America. Anosmia and ocular pain were symptoms independently associated with COVID-19 diagnosis. In low and middle-income countries, where limited availability of tests is frequent, these findings may contribute to optimize a targeted symptom-oriented screening strategy.

Myelodysplastic Syndromes (MDS) & COVID-19: Clinical Experience from the US Epicenter of the Pandemic

Background:The COVID-19 (SARS-CoV-2) pandemic has affected cancer patients (pts) in a myriad of ways, including diagnostic & treatment delays, scarcity of blood products, and most importantly, higher risks of morbidity and mortality from the viral infection itself. Though COVID-19’s effects on many specific cancers has previously been described, there has been little reported on its effects on pts with MDS.Methods:We prospectively reviewed the records of all pts seen in the MDS clinic of a large New York City tertiary academic medical center between March 12 and May 07 2020. A confirmed case of COVID-19 was defined by a positive (+) result on a real-time reverse-transcriptase polymerase chain reaction (PCR) assay of a specimen collected on a nasopharyngeal swab, or detectable COVID-19 antibodies. COVID-19 antibodies were determined via an IgG assay developed at Mount Sinai with the ability to detect antibody titers to a dilution of 1:2880. Initially, only symptomatic pts were screened due to limited testing availability. However, after April 7, all clinic pts were screened by PCR.Results:Among 85 pts seen in the clinic, 23 were found to have COVID-19 (27.1%). The median age of all pts was 72 years (range 20-90); racial breakdown included 58.8% Caucasian, 12.9% Hispanic, 10.6% African-American, 7.1% Asian, and 10.6% other. Of note, 65.2% of COVID-19+ pts were Caucasian, 13.0% Hispanic, and 17.4% other. The most common diagnoses were MDS (n=61), AML (n=10), multiple myeloma (MM) (n=9), large granular lymphocytic leukemia (LGL) (n=6), ALL (n=6), MPN (n=4), NHL (n=3), and CML (n=2). Forty-two of the MDS patients had no other malignancies; co-diagnoses among the remaining MDS patients included MM (n=8), LGL (n=6), & T-cell dyscrasias (n=5).Of the 23 COVID-19+ pts, 11 (47.8%) were hospitalized and 4 (17.4%) were asymptomatic. Common symptoms were URI symptoms (15) and fever (14). ARDS and pneumonia occurred in 4 pts. The most common treatments were hydroxychloroquine (n=4), steroids (n=3), and azithromycin (n=2). Eighteen of the 23 + pts (78.3%) had MDS; the others included pts with MPN, APL, ALL, AML, or NHL. Among the COVID-19+ MDS pts, IPSS-R was very low (n=5), low (n=3), intermediate (n=1), high (n=2), and very high risk (n=5) in evaluable patients. MDS directed treatment included azacitidine (8) (2 with venetoclax), erythropoietin stimulating agents (3), best-supportive care (4), intravenous immunoglobulin (3), and lenalidomide (1). Disease status at diagnosis was stable disease (SD; n=9), hematologic improvement (HI; n=6), progressive disease (PD; n=1), and complete response (CR; n=1). The median number of co-morbidities per pt was 2. Major co-morbidities included cardiac disease (n=5), hypertension (n=5), diabetes (n=5), and dementia (n=2). COVID-19+ pts remained PCR+ for a range of 14-42 days in those with available serial testing (n=8). Of the PCR+ pts, 14 were tested for the presence of COVID-19 antibodies; 12 were +, with a range of titers from <1:80 to 1:2880. Three patients were PCR negative at least once but tested antibody positive.Nine of 23 (39%) COVID-19+ pts died; mortality among MDS patients included 6/18 (33%) COVID-19+ and 6/61 (9.8%) from the overall cohort. Among the 9 who died, the median number of co-morbidities was 3 and the median age was 75 years-old. Disease status in these patients was SD (2), CR (1), HI (1), and PD (1). IPSS-R was very high (n=4), high (n=1), and intermediate (n=1) in the MDS pts who died.Conclusions:This represents the first reported large case series regarding the risks of developing COVID-19 and its effects at an MDS clinic in the initial US epicenter of the pandemic. Overall, 27.1% of the pt population was diagnosed with COVID-19; 39.1% of these pts died, or 10.6% of the overall cohort. A retrospective study across the US reported a 16% mortality rate of COVID-19 in cancer pts (Dr Rivera et al, 2020). The mortality rate reported here is higher, but with a smaller sample size. The clinical significance of persistently + PCR tests up to 6 weeks is unclear. COVID-19 antibodies were found in 85.7% of COVID-19 PCR+ pts tested, showing MDS pts can mount a humoral response. Likely factors contributing to the high mortality of MDS pts were co-morbidities and age. The majority of pts recovered and have resumed MDS directed therapy. Protecting MDS pts from COVID-19 infection must be a primary overall therapeutic approach until there are more effective COVID-19 treatment strategies.DisclosuresNavada:Onconova Therapeutics Inc: Research Funding. Silverman:Medimmune: Research Funding; Onconova Therapeutics Inc: Patents & Royalties, Research Funding; Celgene: Research Funding.

Detection and genetic characterization of community-based SARS-CoV-2 infections – New York City, March 2020

To limit SARS-CoV-2 introduction, the United States restricted travel from China on February 2 and from Europe on March 13, 2020 By March 15, community transmission was widespread in New York City (NYC) The NYC Department of Health and Mental Hygiene conducted sentinel surveillance of influenza-like symptoms (ILS) and genetic sequencing to characterize community transmission and determine the geographic origin of SARS-CoV-2 infections Among 544 specimens tested from persons with ILS and negative influenza test results, 36 (6 6%) were positive Genetically sequenced positive specimens most closely resembled sequences circulating in Europe Partnering with health care facilities and establishing systems for sentinel surveillance with capacity for genetic sequencing before an outbreak can inform timely public health response strategies

Effect of Timing of and Adherence to Social Distancing Measures on COVID-19 Burden in the United States : A Simulation Modeling Approach.

Across the United States, various social distancing measures were implemented to control the spread of coronavirus disease 2019 (COVID-19). However, the effectiveness of such measures for specific regions with varying population demographic characteristics and different levels of adherence to social distancing is uncertain. To determine the effect of social distancing measures in unique regions. An agent-based simulation model. Agent-based model applied to Dane County, Wisconsin; the Milwaukee metropolitan (metro) area; and New York City (NYC). Synthetic population at different ages. Different times for implementing and easing social distancing measures at different levels of adherence. The model represented the social network and interactions among persons in a region, considering population demographic characteristics, limited testing availability, "imported" infections, asymptomatic disease transmission, and age-specific adherence to social distancing measures. The primary outcome was the total number of confirmed COVID-19 cases. The timing of and adherence to social distancing had a major effect on COVID-19 occurrence. In NYC, implementing social distancing measures 1 week earlier would have reduced the total number of confirmed cases from 203261 to 41366 as of 31 May 2020, whereas a 1-week delay could have increased the number of confirmed cases to 1407600. A delay in implementation had a differential effect on the number of cases in the Milwaukee metro area versus Dane County, indicating that the effect of social distancing measures varies even within the same state. The effect of weather conditions on transmission dynamics was not considered. The timing of implementing and easing social distancing measures has major effects on the number of COVID-19 cases. National Institute of Allergy and Infectious Diseases.

Substantial underestimation of SARS-CoV-2 infection in the United States

Accurate estimates of the burden of SARS-CoV-2 infection are critical to informing pandemic response. Confirmed COVID-19 case counts in the U.S. do not capture the total burden of the pandemic because testing has been primarily restricted to individuals with moderate to severe symptoms due to limited test availability. Here, we use a semi-Bayesian probabilistic bias analysis to account for incomplete testing and imperfect diagnostic accuracy. We estimate 6,454,951 cumulative infections compared to 721,245 confirmed cases (1.9% vs. 0.2% of the population) in the United States as of April 18, 2020. Accounting for uncertainty, the number of infections during this period was 3 to 20 times higher than the number of confirmed cases. 86% (simulation interval: 64–99%) of this difference is due to incomplete testing, while 14% (0.3–36%) is due to imperfect test accuracy. The approach can readily be applied in future studies in other locations or at finer spatial scale to correct for biased testing and imperfect diagnostic accuracy to provide a more realistic assessment of COVID-19 burden.

A Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) Prediction Model From Standard Laboratory Tests.

BackgroundWith the limited availability of testing for the presence of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus and concerns surrounding the accuracy of existing methods, other means of identifying patients are urgently needed. Previous studies showing a correlation between certain laboratory tests and diagnosis suggest an alternative method based on an ensemble of tests.MethodsWe have trained a machine learning model to analyze the correlation between SARS-CoV-2 test results and 20 routine laboratory tests collected within a 2-day period around the SARS-CoV-2 test date. We used the model to compare SARS-CoV-2 positive and negative patients.ResultsIn a cohort of 75 991 veteran inpatients and outpatients who tested for SARS-CoV-2 in the months of March through July 2020, 7335 of whom were positive by reverse transcription polymerase chain reaction (RT-PCR) or antigen testing, and who had at least 15 of 20 lab results within the window period, our model predicted the results of the SARS-CoV-2 test with a specificity of 86.8%, a sensitivity of 82.4%, and an overall accuracy of 86.4% (with a 95% confidence interval of [86.0%, 86.9%]).ConclusionsAlthough molecular-based and antibody tests remain the reference standard method for confirming a SARS-CoV-2 diagnosis, their clinical sensitivity is not well known. The model described herein may provide a complementary method of determining SARS-CoV-2 infection status, based on a fully independent set of indicators, that can help confirm results from other tests as well as identify positive cases missed by molecular testing.

Simulation of Pool Testing to Identify Patients With Coronavirus Disease 2019 Under Conditions of Limited Test Availability

This decision analytical model study examines the feasibility of using pool testing to identify patients with coronavirus disease 2019 (COVID-19) in a setting with limited testing availability.