Implications of COVID-19 for the busy gastroenterologist.

Coronavirus disease 2019 (COVID-19) is an infection caused by a novel coronavirus (SARS-CoV-2) originated in China in December 2020 and declared pandemic by WHO. This coronavirus mainly spreads through the respiratory tract and enters cells through angiotensin-converting enzyme 2 (ACE2). The clinical symptoms of COVID-19 patients include fever, cough, and fatigue. Gastrointestinal symptoms (diarrhea, anorexia, and vomiting) may be present in 50% of patients and may be associated with worst prognosis. Other risk factors are older age, male gender, and underlying chronic diseases. Mitigation measures are essential to reduce the number of people infected. Hospitals are a place of increased SARS-CoV-2 exposure. This has implications in the organization of healthcare services and specifically endoscopy departments. Patients and healthcare workers safety must be optimized in this new reality. Comprehension of COVID-19 gastrointestinal manifestations and implications of SARS-CoV-2 in the management of patients with gastrointestinal diseases, under or not immunosuppressant therapies, is essential. In this review, we summarized the latest research progress and major societies recommendations regarding the implications of COVID-19 in gastroenterology, namely the adaptations that gastroenterology/endoscopy departments and professionals must do in order to optimize the provided assistance, as well as the implications that this infection will have, in particularly vulnerable patients such as those with chronic liver disease and inflammatory bowel disease under or not immunosuppressant therapies.

Development of a Choice-framework for Covid vaccines in India using a multi-criteria decision analysis approach.

BACKGROUND: Vaccines were crucial in controlling the Covid-19 pandemic. As more vaccines receive regulatory approval, stakeholders will be faced with several options and must make an appropriate choice for themselves. We proposed a multi-criteria decision analysis (MCDA) framework to guide decision-makers in comparing vaccines for the Indian context. METHODS: We adhered to the ISPOR guidance for the MCDA process. Seven vaccine options were compared under ten criteria. Through three virtual workshops, we obtained opinions and weights from citizens, private-sector hospitals, and public health organisations. Available evidence was rescaled and incorporated into the performance matrix. The final score for each vaccine was calculated for the different groups. We performed different sensitivity analyses to assess the consistency of the rank list. RESULTS: The cost, efficacy and operational score of the vaccines had the highest weights among the stakeholders. From the six scenario groups, Janssen had the highest score in four. This was driven by the advantage of having a single dose of vaccination. In the probabilistic sensitivity analysis for the overall group, Covaxin, Janssen, and Sputnik were the first three options. The participants expressed that availability, WHO approvals and safety, among others, would be crucial when considering vaccines. CONCLUSIONS: The MCDA process has not been capitalised on in healthcare decision-making in India and LMICs. Considering the available data and stakeholder preference at the time of the study, Covaxin, Janssen, and Sputnik were preferred options. The choice framework with the dynamic performance matrix is a valuable tool that could be adapted to different population groups and extended based on increasing vaccine options and emerging evidence. *ISPOR - The Professional Society for Health Economics and Outcomes Research.

Knowledge Mapping of Multicriteria Decision Analysis in Healthcare: A Bibliometric Analysis.

Objective: Multicriteria decision analysis (MCDA) is a useful tool in complex decision-making situations, and has been used in medical fields to evaluate treatment options and drug selection. This study aims to provide valuable insights into MCDA in healthcare through examining the research focus of existing studies, major fields, major applications, most productive authors and countries, and most common journals in the domain. Methods: A bibliometric analysis was conducted on the publication related to MCDA in healthcare from the Web of Science Core Collection (WoSCC) database on 14 July 2021. Three bibliometric software (VOSviewer, R-bibliometrix, and CiteSpace) were used to conduct the analysis including years, countries, institutes, authors, journals, co-citation references, and keywords. Results: A total of 410 publications were identified with an average yearly growth rate of 32% (1999-2021), from 196 academic journals with 23,637 co-citation references by 871 institutions from 70 countries/regions. The United States was the most productive country (n = 80). Universiti Pendidikan Sultan Idris (n = 16), Université de Montréal (n = 13), and Syreon Research Institute (n = 12) were the top productive institutions. A A Zaidan, Mireille Goetghebeur and Zoltan Kalo were the biggest nodes in every cluster of authors' networks. The top journals in terms of the number of articles (n = 17) and citations (n = 1,673) were Value in Health and Journal of Medical Systems, respectively. The extant literature has focused on four aspects, including the analytic hierarchy process (AHP), decision-making, health technology assessment, and healthcare waste management. COVID-19 and fuzzy TOPSIS received careful attention from MCDA applications recently. MCDA in big data, telemedicine, TOPSIS, and fuzzy AHP is well-developed and an important theme, which may be the trend in future research. Conclusion: This study uncovers a holistic picture of the performance of MCDA-related literature published in healthcare. MCDA has a broad application on different topics and would be helpful for practitioners, researchers, and decision-makers working in healthcare to advance the wheel of medical complex decision-making. It can be argued that the door is still open for improving the role of MCDA in healthcare, whether in its methodology (e.g., fuzzy TOPSIS) or application (e.g., telemedicine).

Evaluation of Vulnerability Status of the Infection Risk to COVID-19 Using Geographic Information Systems (GIS) and Multi-Criteria Decision Analysis (MCDA): A Case Study of Addis Ababa City, Ethiopia.

COVID-19 is a disease caused by a new coronavirus called SARS-CoV-2 and is an accidental global public health threat. Because of this, WHO declared the COVID-19 outbreak a pandemic. The pandemic is spreading unprecedently in Addis Ababa, which results in extraordinary logistical and management challenges in response to the novel coronavirus in the city. Thus, management strategies and resource allocation need to be vulnerability-oriented. Though various studies have been carried out on COVID-19, only a few studies have been conducted on vulnerability from a geospatial/location-based perspective but at a wider spatial resolution. This puts the results of those studies under question while their findings are projected to the finer spatial resolution. To overcome such problems, the integration of Geographic Information Systems (GIS) and Multi-Criteria Decision Analysis (MCDA) has been developed as a framework to evaluate and map the susceptibility status of the infection risk to COVID-19. To achieve the objective of the study, data like land use, population density, and distance from roads, hospitals, bus stations, the bank, markets, COVID-19 cases, health care units, and government offices are used. The weighted overlay method was used; to evaluate and map the susceptibility status of the infection risk to COVID-19. The result revealed that out of the total study area, 32.62% (169.91 km2) falls under the low vulnerable category (1), and the area covering 40.9% (213.04 km2) under the moderate vulnerable class (2) for infection risk of COVID-19. The highly vulnerable category (3) covers an area of 25.31% (132.85 km2), and the remaining 1.17% (6.12 km2) is under an extremely high vulnerable class (4). Thus, these priority areas could address pandemic control mechanisms like disinfection regularly. Health sector professionals, local authorities, the scientific community, and the general public will benefit from the study as a tool to better understand pandemic transmission centers and identify areas where more protective measures and response actions are needed at a finer spatial resolution.

Cultural adaptation of a community-based advance serious illness planning decision aid to the Quebec context involving end-users.

INTRODUCTION: Traditional advance care planning focuses on end-of-life planning in the context of a certain or imminent death. It is not tailored for serious illness planning, where the 'death' outcome is uncertain. The Plan Well Guide™ (PWG) is a decision aid that empowers lay persons to better understand different types of care and prepares them, and their substitute decision-makers, to express both their authentic values and informed treatment preferences in anticipation of serious illness. A cultural adaptation was necessary to make the material suitable to the context of Quebec, a French-speaking Canadian province. METHODS: We engaged lay collaborators and experts in a panel, involving three phases of consultation and data collection. These included an online questionnaire, focused interviews and virtual focus groups that identified elements within the francophone PWG affecting its feasibility, adaptation and integration, as well as items that should be modified. RESULTS: We engaged 22 collaborators between April and September 2021. The majority (82%) ranked the first translation as good or very good; most (70%) stated that they would recommend the final adaptation. Both lay and expert panel members suggested simplifying the language and framing the tool better within the context of other advance medical planning processes in Quebec. Translation was considered in a cultural context; the challenges identified by the research team or by collaborators were addressed during the focus group. Examples of wording that required discussion include translating 'getting the medical care that's right for you' when referring to the PWG's goal. An equivalent expression in the French translation was believed to invoke religious associations. Using the term 'machines' to describe life-sustaining treatments was also deliberated. CONCLUSION: Our collaborative iterative adaptation process led to the first French advanced serious illness planning tool. How acceptable and user-friendly this French adaptation of the PWG is in various Canadian French-speaking environments requires further study. CONTRIBUTION: We organized a focus group inviting both lay collaborators and experts to contribute to the interpretation of the results of the previous phases. This choice allowed us to add more value to our results and to the final PWG in French.

Modelling the impact of the coronavirus pandemic on bowel cancer screening outcomes in England: A decision analysis to prepare for future screening disruption.

The English Bowel Cancer Screening Programme invites people between the ages of 60 and 74 to take a Faecal Immunochemical Test every two years. This programme was interrupted during the coronavirus pandemic. The research aimed: (1) to estimate the impact of colorectal cancer (CRC) Faecal Immunochemical Test screening pauses of different lengths and the actual coronavirus-related screening pause in England, and (2) to analyse the most effective and cost-effective strategies to re-start CRC screening to prepare for future disruptions. The analysis used the validated Microsimulation Model in Cancer of the Bowel built in the R programming language. The model simulated the life course of a representative English screening population from 2019, by age, sex, socio-economic deprivation, and prior screening history. The modelling scenarios were based on assumptions and data from screening centres in England. Pausing bowel screening in England due to coronavirus pandemic is predicted to increase CRC deaths by 0.73% within 10 years and 0.13% over the population's lifetime, with excess deaths due to peak in 2023. More deaths are expected in men and people aged over 70. Pausing screening for longer would result in greater additional CRC cases and deaths. Postponing screening for everyone would be the most cost-effective strategy to minimise the impact of screening disruption without any additional endoscopy capacity. If endoscopy capacity can be increased, temporarily raising the Faecal Immunochemical Test threshold to 190 µg/g may help to minimise CRC deaths, particularly if screening programmes start from age 50 in the future.

Quantitative comparison of SARS-CoV-2 nucleic acid amplification test and antigen testing algorithms: a decision analysis simulation model.

BACKGROUND: Antigen tests for SARS-CoV-2 offer advantages over nucleic acid amplification tests (NAATs, such as RT-PCR), including lower cost and rapid return of results, but show reduced sensitivity. Public health organizations recommend different strategies for utilizing NAATs and antigen tests. We sought to create a framework for the quantitative comparison of these recommended strategies based on their expected performance. METHODS: We utilized a decision analysis approach to simulate the expected outcomes of six testing algorithms analogous to strategies recommended by public health organizations. Each algorithm was simulated 50,000 times in a population of 100,000 persons seeking testing. Primary outcomes were number of missed cases, number of false-positive diagnoses, and total test volumes. Outcome medians and 95% uncertainty ranges (URs) were reported. RESULTS: Algorithms that use NAATs to confirm all negative antigen results minimized missed cases but required high NAAT capacity: 92,200 (95% UR: 91,200-93,200) tests (in addition to 100,000 antigen tests) at 10% prevalence. Selective use of NAATs to confirm antigen results when discordant with symptom status (e.g., symptomatic persons with negative antigen results) resulted in the most efficient use of NAATs, with 25 NAATs (95% UR: 13-57) needed to detect one additional case compared to exclusive use of antigen tests. CONCLUSIONS: No single SARS-CoV-2 testing algorithm is likely to be optimal across settings with different levels of prevalence and for all programmatic priorities. This analysis provides a framework for selecting setting-specific strategies to achieve acceptable balances and trade-offs between programmatic priorities and resource constraints.

Spatiotemporal sentiment variation analysis of geotagged COVID-19 tweets from India using a hybrid deep learning model.

India is a hotspot of the COVID-19 crisis. During the first wave, several lockdowns (L) and gradual unlock (UL) phases were implemented by the government of India (GOI) to curb the virus spread. These phases witnessed many challenges and various day-to-day developments such as virus spread and resource management. Twitter, a social media platform, was extensively used by citizens to react to these events and related topics that varied temporally and geographically. Analyzing these variations can be a potent tool for informed decision-making. This paper attempts to capture these spatiotemporal variations of citizen reactions by predicting and analyzing the sentiments of geotagged tweets during L and UL phases. Various sentiment analysis based studies on the related subject have been done; however, its integration with location intelligence for decision making remains a research gap. The sentiments were predicted through a proposed hybrid Deep Learning (DL) model which leverages the strengths of BiLSTM and CNN model classes. The model was trained on a freely available Sentiment140 dataset and was tested over manually annotated COVID-19 related tweets from India. The model classified the tweets with high accuracy of around 90%, and analysis of geotagged tweets during L and UL phases reveal significant geographical variations. The findings as a decision support system can aid in analyzing citizen reactions toward the resources and events during an ongoing pandemic. The system can have various applications such as resource planning, crowd management, policy formulation, vaccination, prompt response, etc.

A message of the majority with scientific evidence encourages young people to show their prosocial nature in COVID-19 vaccination.

The most promising way to prevent the explosive spread of COVID-19 infection is to achieve herd immunity through vaccination. It is therefore important to motivate those who are less willing to be vaccinated. To address this issue, we conducted an online survey of 6232 Japanese people to investigate age- and gender-dependent differences in attitudes towards COVID-19 vaccination and the underlying psychological processes. We asked participants to read one of nine different messages about COVID-19 vaccination and rate their willingness to be vaccinated. We also collected their 17 social personality trait scores and demographic information. We found that males 10-20 years old were least willing to be vaccinated. We also found that prosocial traits are the driving force for young people, but the motivation in older people also depends on risk aversion and self-interest. Furthermore, an analysis of 9 different messages demonstrated that for young people (particularly males), the message emphasizing the majority's intention to vaccinate and scientific evidence for the safety of the vaccination had the strongest positive effect on the willingness to be vaccinated, suggesting that the "majority + scientific evidence" message nudges young people to show their prosocial nature in action.

Development of a patient decision aid for COVID-19 vaccination with the Comirnaty vaccine.

BACKGROUND: SARS-CoV-2 has been responsible for a pandemic since the beginning of 2020. Vaccine arrival brings a concrete solution to fight the virus. However, vaccine hesitancy is high. In France, the first available vaccine was Comirnaty from Pfizer-BioNTech. Shared decision-making, based on tools such as patient decision aids (PtDAs), can help patients make an informed choice about vaccination with Comirnaty. OBJECTIVE: The French College of Teachers in General Practice (CNGE) aimed to create a PtDA for people who have to decide whether they will receive the Comirnaty vaccine. METHODS: Development of the PtDA was performed according to the International Patient Decision Aids Standards (IPDAS). The initial design was based on a literature review and semistructured interviews with 17 patients to explore and clarify patients' expectations. A first draft of the PtDA was then alpha tested by a patient expert group and a physician expert group. The PtDA was finally beta tested in 14 prevaccine consultations. A steering group was consulted throughout the work. Patient support, community groups and the French National Authority for Health (HAS) were involved in the development process. RESULTS: A literature review identified one randomized trial on Comirnaty efficacy and safety. The first part of the PtDA allows patients to identify their own risk factors. The second part of the PtDA provides information on vaccination: benefits and risks, unknown data, and technical explanations about the mRNA vaccine. CONCLUSIONS: We developed a PtDA to be used in primary care settings for shared decision-making regarding vaccination with Comirnaty.

Effect of Individualized Preventive Care Recommendations vs Usual Care on Patient Interest and Use of Recommendations: A Pilot Randomized Clinical Trial.

Importance: This randomized clinical trial examines the feasibility and acceptability of a decision-making tool for increasing patient interest in individualized recommendations for preventive care services. Objective: To pilot a tool to help patients compare life expectancy gains from evidence-based preventive services. Design, Setting, and Participants: This randomized clinical trial examined patient and physician responses to a pilot decision tool incorporating personalized risk factors at 3 US primary care clinics between 2017 and 2020. Eligible patients were between ages 45 to 70 years with 2 or more high-risk factors. Patients were followed-up after 1 year. Interventions: The gain in life expectancy associated with guideline adherence to each recommended preventive service was estimated. Personalized estimates incorporating risk factors in electronic health records were displayed in a physician-distributed visual aid. During development, physicians discussed individualized results with patients using shared decision-making (SDM). During the trial, patients were randomized to receive individualized recommendations or usual care (nonmasked, parallel, 1:1 ratio). Main Outcomes and Measures: Primary outcome was patient interest in individualized recommendations, assessed by survey. Secondary outcomes were use of SDM, decisional comfort, readiness to change, and preventive services received within 1 year. Results: The study enrolled 104 patients (31 development, 39 intervention, 34 control), of whom 101 were included in analysis (mean [SD] age, 56.5 [5.3] years; 73 [72.3%] women; 80 [79.2%] Black patients) and 20 physicians. Intervention patients found the tool helpful and wanted to use it again, rating it a median 9 of 10 (IQR, 8-10) and 10 of 10 (8-10), respectively. Compared with the control group, intervention patients more often correctly identified the service least likely (18 [46%] vs 0; P = .03) to improve their life expectancy. A greater number of patients also identified the service most likely to improve their life expectancy (26 [69%] vs 10 [30%]; P = .07), although this result was not statistically significant. Intervention patients reported greater mean [SD] improvement in SDM (4.7 [6.9] points) and near-term readiness to change (13.8 points for top-3-ranked recommendations). Point estimates indicated that patients in the intervention group experienced greater, although non-statistically significant, reductions in percentage of body weight (-2.96%; 95% CI, -8.18% to 2.28%), systolic blood pressure (-6.42 mm Hg; 95% CI, -16.12 to 3.27 mm Hg), hemoglobin A1c (-0.68%; 95% CI, -1.82% to 0.45%), 10-year atherosclerotic cardiovascular disease risk score (-1.20%; 95% CI, -3.65% to 1.26%), and low-density lipoprotein cholesterol (-8.46 mg/dL; 95% CI, -26.63 to 9.70 mg/dL) than the control group. Nineteen of 20 physicians wanted to continue using the decision tool in the future. Conclusions and Relevance: In this clinical trial, an individualized preventive care decision support tool improved patient understanding of primary prevention and demonstrated promise for improved shared decision-making and preventive care utilization. Trial Registration: ClinicalTrials.gov Identifier: NCT03023813.

A Novel Predictive Model for Anastomotic Leakage in Colorectal Cancer Using Auto-artificial Intelligence.

AIM: Anastomotic leakage (AL) in left-sided colorectal cancer is a serious complication, with an incidence rate of 6-18%. We developed a novel predictive model for AL in colorectal surgery with double-stapling technique (DST) anastomosis using auto-artificial intelligence (AI). PATIENTS AND METHODS: A total of 256 patients who underwent curative surgery for left-sided colorectal cancer between 2017 and 2021 were included. In addition to conventional clinicopathological factors, we included the type of circular stapler using DST, conventional double-row circular stapler (DCS) or EEA™ circular stapler with Tri-Staple™ technology, 28 mm Medium/Thick (Covidien, New Haven, CT, USA) which had triple-row circular stapler (TCS) as a covariate. Auto-AI software Prediction One (Sony Network Communications Inc.) was used to predict AL with 5-fold cross validation. Predictive accuracy was assessed using the area under the receiver operating characteristic curve. Prediction One also evaluated the 'importance of variables' (IOV) using a method based on permutation feature importance. RESULTS: The area under the curve of the AI model was 0.766. The type of circular stapler used was the most influential factor contributing to AL (IOV=0.551). CONCLUSION: This auto-AI predictive model demonstrated an improvement in accuracy compared to the conventional model. It was suggested that use of a TCS may contribute to a reduction in the AL rate.

Assessing COVID-19 Prevention Strategies to Permit the Safe Opening of Residential Colleges in Fall 2021.

BACKGROUND: Effective vaccines, improved testing technologies, and decreases in COVID-19 incidence prompt an examination of the choices available to residential college administrators seeking to safely resume in-person campus activities in fall 2021. OBJECTIVE: To help college administrators design and evaluate customized COVID-19 safety plans. DESIGN: Decision analysis using a compartmental epidemic model to optimize vaccination, testing, and other nonpharmaceutical interventions depending on decision makers' preferences, choices, and assumptions about epidemic severity and vaccine effectiveness against infection, transmission, and disease progression. SETTING: U.S. residential colleges. PARTICIPANTS: Hypothetical cohort of 5000 persons (students, faculty, and staff) living and working in close proximity on campus. MEASUREMENTS: Cumulative infections over a 120-day semester. RESULTS: Under base-case assumptions, if 90% coverage can be attained with a vaccine that is 85% protective against infection and 25% protective against asymptomatic transmission, the model finds that campus activities can be resumed while holding cumulative cases below 5% of the population without the need for routine, asymptomatic testing. With 50% population coverage using such a vaccine, a similar cap on cumulative cases would require either daily asymptomatic testing of unvaccinated persons or a combination of less frequent testing and resumption of aggressive distancing and other nonpharmaceutical prevention policies. Colleges returning to pre-COVID-19 campus activities without either broad vaccination coverage or high-frequency testing put their campus population at risk for widespread viral transmission. LIMITATION: Uncertainty in data, particularly vaccine effectiveness (preventive and transmission); no distinguishing between students and employees; and assumes limited community intermixing. CONCLUSION: Vaccination coverage is the most powerful tool available to residential college administrators seeking to achieve a safe return to prepandemic operations this fall. Given the breadth of potential outcomes in the face of uncontrollable and uncertain factors, even colleges with high vaccination rates should be prepared to reinstitute or expand testing and distancing policies on short notice. PRIMARY FUNDING SOURCE: National Institute on Drug Abuse.

Proceedings Report of the Fifth Symposium on Hidradenitis Suppurativa Advances (SHSA) 2020.

The Symposium on Hidradenitis Suppurativa Advances (SHSA) is a joint meeting of the United States Hidradenitis Suppurativa Foundation (HSF) and the Canadian Hidradenitis Suppurativa Foundation (CHSF). This annual cross-disciplinary meeting brings together experts from around the world in an opportunity to discuss the most recent advances in the study of hidradenitis suppurativa (HS). The fifth annual meeting was held virtually on 9-11 October 2020. A record 347 attendees, including 79 people with HS, from 20 different countries attended. Key take-home points included: Clinicians can optimize each visit by listening, provide education, and discuss treatments; a patient decision aid for HS (HS-PDA) is a freely available tool (www.informed-decisions.org); COVID-19 severity in HS patients was not different for patients treated with/without a biologic; comorbidity screening recommendations will be published soon; neutrophil extracellular traps (NETs) may play a role in HS; memory B cells, T helper 1 cytokines, and interleukin 1 signaling contributes to HS pathogenesis and are targets for new therapies; novel therapies are showing promise including a new JAK1 inhibitor (INCB054707) and brodalumab; and HS-specific outcome measures have emerged to better monitor disease severity, flare, and progression including a patient reported measure (HiSQOL) and an HS-specific investigator global assessment. J Drugs Dermatol. 2021;20(8):868-873. doi:10.36849/JDD.5836.

Real-World Use of Dalbavancin in the Era of Empowerment of Outpatient Antimicrobial Treatment: A Careful Appraisal Beyond Approved Indications Focusing on Unmet Clinical Needs.

Dalbavancin is a novel, long-acting lipoglycopeptide characterized by a long elimination half-life coupled with excellent in vitro activity against multidrug-resistant Gram-positives. Although it is currently approved only for the treatment of acute bacterial skin and skin structure infections, an ever-growing amount of evidence supports the efficacy of dalbavancin as a long-term therapy in osteomyelitis, prosthetic joint infections, endocarditis, and bloodstream infections. This article provides a critical reappraisal of real-world use of dalbavancin for off-label indications. A search strategy using specific keywords (dalbavancin, osteomyelitis, endocarditis, long-term suppressive therapy, bloodstream infection, pharmacokinetic/pharmacodynamic profile) until April 2021 was performed on the PubMed-MEDLINE database. As for other novel antibiotics, a conundrum between approved indications and potential innovative therapeutic uses has emerged for dalbavancin as well. The promising efficacy in challenging scenarios (i.e., osteomyelitis, endocarditis, prosthetic joint infections), coupled with the unique pharmacokinetic/pharmacodynamic properties, makes dalbavancin a valuable alternative to daily in-hospital intravenous or outpatient antimicrobial regimens in the treatment of long-term Gram-positive infections. This makes dalbavancin valuable in the current COVID-19 scenario, in which hospitalization and territorial medicine empowerment are unavoidable.

Obtaining Prevalence Estimates of Coronavirus Disease 2019: A Model to Inform Decision-Making.

We evaluated whether randomly sampling and testing a set number of individuals for coronavirus disease 2019 (COVID-19) while adjusting for misclassification error captures the true prevalence. We also quantified the impact of misclassification error bias on publicly reported case data in Maryland. Using a stratified random sampling approach, 50,000 individuals were selected from a simulated Maryland population to estimate the prevalence of COVID-19. We examined the situation when the true prevalence is low (0.07%-2%), medium (2%-5%), and high (6%-10%). Bayesian models informed by published validity estimates were used to account for misclassification error when estimating COVID-19 prevalence. Adjustment for misclassification error captured the true prevalence 100% of the time, irrespective of the true prevalence level. When adjustment for misclassification error was not done, the results highly varied depending on the population's underlying true prevalence and the type of diagnostic test used. Generally, the prevalence estimates without adjustment for misclassification error worsened as the true prevalence level increased. Adjustment for misclassification error for publicly reported Maryland data led to a minimal but not significant increase in the estimated average daily cases. Random sampling and testing of COVID-19 are needed with adjustment for misclassification error to improve COVID-19 prevalence estimates.

Two novel nomograms based on inflammatory cytokines or lymphocyte subsets to differentially diagnose severe or critical and Non-Severe COVID-19.

We intend to evaluate the differences of the clinical characteristics, cytokine profiles and immunological features in patients with different severity of COVID-19, and to develop novel nomograms based on inflammatory cytokines or lymphocyte subsets for the differential diagnostics for severe or critical and non-severe COVID-19 patients. We retrospectively studied 254 COVID-19 patients, 90 of whom were severe or critical patients and 164 were non-severe patients. Severe or critical patients had significantly higher levels of inflammatory cytokines than non-severe patients as well as lower levels of lymphocyte subsets. Significantly positive correlations between cytokine profiles were observed, while they were all significantly negatively correlated with lymphocyte subsets. Two effective nomograms were developed according to two multivariable logistic regression cox models based on inflammatory cytokine profiles and lymphocyte subsets separately. The areas under the receiver operating characteristics of two nomograms were 0.834 (95% CI: 0.779-0.888) and 0.841 (95% CI: 0.756-0.925). The bootstrapped-concordance indexes of two nomograms were 0.834 and 0.841 in training set, and 0.860 and 0.852 in validation set. Calibration curves and decision curve analyses demonstrated that the nomograms were well calibrated and had significantly more clinical net benefits. Our novel nomograms can accurately predict disease severity of COVID-19, which may facilitate the identification of severe or critical patients and assist physicians in making optimized treatment suggestions.