An evaluation of materials co-created to support access to primary care in the COVID 19 pandemic : Presenter(s): Lynsey Brown, University of St Andrews, United Kingdom

Background Throughout the COVID-19 pandemic there has been hesitancy and uncertainty around access to primary care. Systems and processes for getting appointments have often changed and people have sometimes felt like a burden. Public health messaging is key in alleviating these issues. The messages conveyed often do not reach those most in need and can be confusing. Therefore, co-creating these messages with those who are most likely to benefit is necessary. This study reports on the evaluation of the messages and materials developed. Methods Evaluation (Phase 3) of materials developed in a three phased co-creation process. The materials developed, including a video, booklets, social media posts and posters, each targeting specific barriers to accessing primary care were evaluated to determine acceptability. Views on style, accessibility, intentions and trustworthiness of both Health Care Professionals (HCP) and the public (with a focus on those at greater risk from COVID-19) were explored. Methods: included an online survey for both HCPs (n=18) and the public (n=13), an offline survey for HCPs (n=4) and telephone interviews with the public (n=5). Qualitative data was analysed thematically, and descriptive statistics conducted for quantitative data. Findings Members of the public reported the materials helped them feel confident about calling to make an appointment with their GP. Some mentioned learning new things, for example the different roles in the practice. HCPs generally thought the materials would be useful to distribute to patients. Differences between practices may not be portrayed well in the materials. Both HCPs and the public agreed the materials should be endorsed by the NHS to ensure trustworthiness. Discussion Communication in healthcare is key to ensuring access and support. The co-creation process resulted in clear, useful messages that were generally positively received. Future work considering communication in healthcare may benefit from using a similar collaborative approach.

Proactive Contact Tracing.

The COVID-19 pandemic has spurred an unprecedented demand for interventions that can reduce disease spread without excessively restricting daily activity, given negative impacts on mental health and economic outcomes. Digital contact tracing (DCT) apps have emerged as a component of the epidemic management toolkit. Existing DCT apps typically recommend quarantine to all digitally-recorded contacts of test-confirmed cases. Over-reliance on testing may, however, impede the effectiveness of such apps, since by the time cases are confirmed through testing, onward transmissions are likely to have occurred. Furthermore, most cases are infectious over a short period; only a subset of their contacts are likely to become infected. These apps do not fully utilize data sources to base their predictions of transmission risk during an encounter, leading to recommendations of quarantine to many uninfected people and associated slowdowns in economic activity. This phenomenon, commonly termed as "pingdemic," may additionally contribute to reduced compliance to public health measures. In this work, we propose a novel DCT framework, Proactive Contact Tracing (PCT), which uses multiple sources of information (e.g. self-reported symptoms, received messages from contacts) to estimate app users' infectiousness histories and provide behavioral recommendations. PCT methods are by design proactive, predicting spread before it occurs. We present an interpretable instance of this framework, the Rule-based PCT algorithm, designed via a multi-disciplinary collaboration among epidemiologists, computer scientists, and behavior experts. Finally, we develop an agent-based model that allows us to compare different DCT methods and evaluate their performance in negotiating the trade-off between epidemic control and restricting population mobility. Performing extensive sensitivity analysis across user behavior, public health policy, and virological parameters, we compare Rule-based PCT to i) binary contact tracing (BCT), which exclusively relies on test results and recommends a fixed-duration quarantine, and ii) household quarantine (HQ). Our results suggest that both BCT and Rule-based PCT improve upon HQ, however, Rule-based PCT is more efficient at controlling spread of disease than BCT across a range of scenarios. In terms of cost-effectiveness, we show that Rule-based PCT pareto-dominates BCT, as demonstrated by a decrease in Disability Adjusted Life Years, as well as Temporary Productivity Loss. Overall, we find that Rule-based PCT outperforms existing approaches across a varying range of parameters. By leveraging anonymized infectiousness estimates received from digitally-recorded contacts, PCT is able to notify potentially infected users earlier than BCT methods and prevent onward transmissions. Our results suggest that PCT-based applications could be a useful tool in managing future epidemics.

Patient pathway in a tertiary referral pelvic floor unit: Telephone triage assessment clinic.

BACKGROUND: To meet the increasing demands for colorectal pelvic floor services, a dedicated telephone triage assessment clinic (TTAC) was set up to establish a more efficient pathway, and reduce waiting times and patient's visits to the hospital. The primary aim of this study was to review TTAC in patients suffering from pelvic floor dysfunction and assess its feasibility. Secondary aims include measurement of waiting times for TTAC, main presenting complaints, and main treatment outcomes, including the need for review by a consultant surgeon. METHODS: Review of data collected retrospectively in a single tertiary referral center collected from an institutional database. KEY RESULTS: Between January 2016 and October 2017, 1192 patients referred to our pelvic floor unit were suitable for TTAC. Of these, 694 patients had complete records. There were 66 without follow-up after the initial TTAC, leaving 628 patients for analysis. In all, 86% were females and 14% were males, with a mean age of 52 years (range: 18-89). The median waiting time for TTAC was 31 days (range: 0-184). The main presenting complaint during the TTAC was obstructive defecation in 69.4%, fecal incontinence in 28.5%, and rectal prolapse in 2.1%. In our study, 611 patients had conservative management (97.3%), with a median of three sessions per patient (range: 1-16), while 82 patients (13.1%) needed a surgical intervention. Only 223 patients (35.5%) were reviewed by a consultant at some stage during the study period. CONCLUSIONS AND INFERENCES: To optimize resources, an adequate triage system allowed us to streamline the pathway for each individual patient with pelvic floor dysfunction according to their symptoms and/or test results with the aim of reducing waiting times and expediting treatment.

Generalisable long COVID subtypes: findings from the NIH N3C and RECOVER programmes.

BACKGROUND: Stratification of patients with post-acute sequelae of SARS-CoV-2 infection (PASC, or long COVID) would allow precision clinical management strategies. However, long COVID is incompletely understood and characterised by a wide range of manifestations that are difficult to analyse computationally. Additionally, the generalisability of machine learning classification of COVID-19 clinical outcomes has rarely been tested. METHODS: We present a method for computationally modelling PASC phenotype data based on electronic healthcare records (EHRs) and for assessing pairwise phenotypic similarity between patients using semantic similarity. Our approach defines a nonlinear similarity function that maps from a feature space of phenotypic abnormalities to a matrix of pairwise patient similarity that can be clustered using unsupervised machine learning. FINDINGS: We found six clusters of PASC patients, each with distinct profiles of phenotypic abnormalities, including clusters with distinct pulmonary, neuropsychiatric, and cardiovascular abnormalities, and a cluster associated with broad, severe manifestations and increased mortality. There was significant association of cluster membership with a range of pre-existing conditions and measures of severity during acute COVID-19. We assigned new patients from other healthcare centres to clusters by maximum semantic similarity to the original patients, and showed that the clusters were generalisable across different hospital systems. The increased mortality rate originally identified in one cluster was consistently observed in patients assigned to that cluster in other hospital systems. INTERPRETATION: Semantic phenotypic clustering provides a foundation for assigning patients to stratified subgroups for natural history or therapy studies on PASC. FUNDING: NIH (TR002306/OT2HL161847-01/OD011883/HG010860), U.S.D.O.E. (DE-AC02-05CH11231), Donald A. Roux Family Fund at Jackson Laboratory, Marsico Family at CU Anschutz.

Integrity of assessments in challenging times

A key role of universities is the credentialing of student learning by awarding degrees and diplomas. This requires universities to have confidence in the integrity of their assessment processes and in turn, external stakeholders to have the same confidence. This study investigates the following research question: 'Has COVID-19 had an impact on the assessment and invigilation of accounting courses in Australia and New Zealand and, if so, how?' This is a critical issue for accounting faculty in many countries as COVID-19 has forced a shift in the way assessments are administered - from face to face to online. The study involved a survey of accounting faculty in Australia and New Zealand and found changes occurred to how students were assessed because of COVID-19 and a variety of institutional responses to this. The paper makes recommendations for accounting educators, universities, and the professional accounting bodies.

275 years of Northampton Hospital Child Healthcare ‘surrounded by many mercies’

54 Figure 1[Figure omitted. See PDF]ReferencesWilliams AN ‘The joy to Bless and to Relieve Mankind.’ Northampton General Infirmary 1744 Arch Dis Child. 2005 Dec;90(12):1227-9.Stonhouse J Sir. 1716-1795 entry on OCLC World Cat. http://worldcat.org/identities/1ccn-n85067392 Accessed April 6th 2018.Williams AN, O’Dell F Hektoen International Journal of Medical Humanities ‘Surrounded with many Mercies’: 270 years of patient advice https://hekint.org/2019/06/20/surrounded-with-many-mercies-270-years-of-patient-advice/

Coronavirus Disease 2019 (COVID-19) Vaccine Prioritization in Low- and Middle-Income Countries May Justifiably Depart From High-Income Countries' Age Priorities.

In high-income countries that were first to roll out coronavirus disease 2019 (COVID-19) vaccines, older adults have thus far usually been prioritized for these vaccines over younger adults. Age-based priority primarily resulted from interpreting evidence available at the time, which indicated that vaccinating the elderly first would minimize COVID-19 deaths and hospitalizations. The World Health Organization counsels a similar approach for all countries. This paper argues that some low- and middle-income countries that are short of COVID-19 vaccine doses might be justified in revising this approach and instead prioritizing certain younger persons when allocating current vaccines or future variant-specific vaccines.

NSAID use and clinical outcomes in COVID-19 patients: a 38-center retrospective cohort study.

BACKGROUND: Non-steroidal anti-inflammatory drugs (NSAIDs) are commonly used to reduce pain, fever, and inflammation but have been associated with complications in community-acquired pneumonia. Observations shortly after the start of the COVID-19 pandemic in 2020 suggested that ibuprofen was associated with an increased risk of adverse events in COVID-19 patients, but subsequent observational studies failed to demonstrate increased risk and in one case showed reduced risk associated with NSAID use. METHODS: A 38-center retrospective cohort study was performed that leveraged the harmonized, high-granularity electronic health record data of the National COVID Cohort Collaborative. A propensity-matched cohort of 19,746 COVID-19 inpatients was constructed by matching cases (treated with NSAIDs at the time of admission) and 19,746 controls (not treated) from 857,061 patients with COVID-19 available for analysis. The primary outcome of interest was COVID-19 severity in hospitalized patients, which was classified as: moderate, severe, or mortality/hospice. Secondary outcomes were acute kidney injury (AKI), extracorporeal membrane oxygenation (ECMO), invasive ventilation, and all-cause mortality at any time following COVID-19 diagnosis. RESULTS: Logistic regression showed that NSAID use was not associated with increased COVID-19 severity (OR: 0.57 95% CI: 0.53-0.61). Analysis of secondary outcomes using logistic regression showed that NSAID use was not associated with increased risk of all-cause mortality (OR 0.51 95% CI: 0.47-0.56), invasive ventilation (OR: 0.59 95% CI: 0.55-0.64), AKI (OR: 0.67 95% CI: 0.63-0.72), or ECMO (OR: 0.51 95% CI: 0.36-0.7). In contrast, the odds ratios indicate reduced risk of these outcomes, but our quantitative bias analysis showed E-values of between 1.9 and 3.3 for these associations, indicating that comparatively weak or moderate confounder associations could explain away the observed associations. CONCLUSIONS: Study interpretation is limited by the observational design. Recording of NSAID use may have been incomplete. Our study demonstrates that NSAID use is not associated with increased COVID-19 severity, all-cause mortality, invasive ventilation, AKI, or ECMO in COVID-19 inpatients. A conservative interpretation in light of the quantitative bias analysis is that there is no evidence that NSAID use is associated with risk of increased severity or the other measured outcomes. Our results confirm and extend analogous findings in previous observational studies using a large cohort of patients drawn from 38 centers in a nationally representative multicenter database.

COVID-19 Patient Vaccine Program Design and Implementation: An Academic Children's Hospital's Model, Approach, and Outcomes.

To slow the spread of the 2019 novel coronavirus disease (COVID-19) and reduce the associated morbidity and mortality, the Children's National Hospital developed a multidisciplinary, collaborative vaccine program aimed at equitably and expeditiously vaccinating the pediatric population of the surrounding community. Interdepartmental collaboration, professional expertise, and community partnerships allowed for a dynamic and successful program design that began as large volume-centralized vaccine clinics and expanded to smaller volume ambulatory clinics. This strategy proved successful at meeting local vaccine demand; however, strategies to improve vaccine uptake in communities with high rates of hesitancy are still needed to maximize vaccine equity.

DLMM as a lossless one-shot algorithm for collaborative multi-site distributed linear mixed models.

Linear mixed models are commonly used in healthcare-based association analyses for analyzing multi-site data with heterogeneous site-specific random effects. Due to regulations for protecting patients' privacy, sensitive individual patient data (IPD) typically cannot be shared across sites. We propose an algorithm for fitting distributed linear mixed models (DLMMs) without sharing IPD across sites. This algorithm achieves results identical to those achieved using pooled IPD from multiple sites (i.e., the same effect size and standard error estimates), hence demonstrating the lossless property. The algorithm requires each site to contribute minimal aggregated data in only one round of communication. We demonstrate the lossless property of the proposed DLMM algorithm by investigating the associations between demographic and clinical characteristics and length of hospital stay in COVID-19 patients using administrative claims from the UnitedHealth Group Clinical Discovery Database. We extend this association study by incorporating 120,609 COVID-19 patients from 11 collaborative data sources worldwide.

Unraveling COVID-19: A Large-Scale Characterization of 4.5 Million COVID-19 Cases Using CHARYBDIS.

Purpose: Routinely collected real world data (RWD) have great utility in aiding the novel coronavirus disease (COVID-19) pandemic response. Here we present the international Observational Health Data Sciences and Informatics (OHDSI) Characterizing Health Associated Risks and Your Baseline Disease In SARS-COV-2 (CHARYBDIS) framework for standardisation and analysis of COVID-19 RWD. Patients and Methods: We conducted a descriptive retrospective database study using a federated network of data partners in the United States, Europe (the Netherlands, Spain, the UK, Germany, France and Italy) and Asia (South Korea and China). The study protocol and analytical package were released on 11th June 2020 and are iteratively updated via GitHub. We identified three non-mutually exclusive cohorts of 4,537,153 individuals with a clinical COVID-19 diagnosis or positive test, 886,193 hospitalized with COVID-19, and 113,627 hospitalized with COVID-19 requiring intensive services. Results: We aggregated over 22,000 unique characteristics describing patients with COVID-19. All comorbidities, symptoms, medications, and outcomes are described by cohort in aggregate counts and are readily available online. Globally, we observed similarities in the USA and Europe: more women diagnosed than men but more men hospitalized than women, most diagnosed cases between 25 and 60 years of age versus most hospitalized cases between 60 and 80 years of age. South Korea differed with more women than men hospitalized. Common comorbidities included type 2 diabetes, hypertension, chronic kidney disease and heart disease. Common presenting symptoms were dyspnea, cough and fever. Symptom data availability was more common in hospitalized cohorts than diagnosed. Conclusion: We constructed a global, multi-centre view to describe trends in COVID-19 progression, management and evolution over time. By characterising baseline variability in patients and geography, our work provides critical context that may otherwise be misconstrued as data quality issues. This is important as we perform studies on adverse events of special interest in COVID-19 vaccine surveillance.

Seek COVER: using a disease proxy to rapidly develop and validate a personalized risk calculator for COVID-19 outcomes in an international network.

BACKGROUND: We investigated whether we could use influenza data to develop prediction models for COVID-19 to increase the speed at which prediction models can reliably be developed and validated early in a pandemic. We developed COVID-19 Estimated Risk (COVER) scores that quantify a patient's risk of hospital admission with pneumonia (COVER-H), hospitalization with pneumonia requiring intensive services or death (COVER-I), or fatality (COVER-F) in the 30-days following COVID-19 diagnosis using historical data from patients with influenza or flu-like symptoms and tested this in COVID-19 patients. METHODS: We analyzed a federated network of electronic medical records and administrative claims data from 14 data sources and 6 countries containing data collected on or before 4/27/2020. We used a 2-step process to develop 3 scores using historical data from patients with influenza or flu-like symptoms any time prior to 2020. The first step was to create a data-driven model using LASSO regularized logistic regression, the covariates of which were used to develop aggregate covariates for the second step where the COVER scores were developed using a smaller set of features. These 3 COVER scores were then externally validated on patients with 1) influenza or flu-like symptoms and 2) confirmed or suspected COVID-19 diagnosis across 5 databases from South Korea, Spain, and the United States. Outcomes included i) hospitalization with pneumonia, ii) hospitalization with pneumonia requiring intensive services or death, and iii) death in the 30 days after index date. RESULTS: Overall, 44,507 COVID-19 patients were included for model validation. We identified 7 predictors (history of cancer, chronic obstructive pulmonary disease, diabetes, heart disease, hypertension, hyperlipidemia, kidney disease) which combined with age and sex discriminated which patients would experience any of our three outcomes. The models achieved good performance in influenza and COVID-19 cohorts. For COVID-19 the AUC ranges were, COVER-H: 0.69-0.81, COVER-I: 0.73-0.91, and COVER-F: 0.72-0.90. Calibration varied across the validations with some of the COVID-19 validations being less well calibrated than the influenza validations. CONCLUSIONS: This research demonstrated the utility of using a proxy disease to develop a prediction model. The 3 COVER models with 9-predictors that were developed using influenza data perform well for COVID-19 patients for predicting hospitalization, intensive services, and fatality. The scores showed good discriminatory performance which transferred well to the COVID-19 population. There was some miscalibration in the COVID-19 validations, which is potentially due to the difference in symptom severity between the two diseases. A possible solution for this is to recalibrate the models in each location before use.

Information-seeking behaviours and uncertainty around accessing primary care in the changing landscape of the COVID-19 pandemic: a qualitative study.

BACKGROUND: The COVID-19 pandemic has had and will continue to have a disproportionate effect on the most vulnerable. Public health messaging has been vital to mitigate the impact of the pandemic, but messages intended to slow the transmission of the virus may also cause harm. Understanding the areas where public health messaging could be improved may help reduce this harm. AIM: To explore and understand health communication issues faced by those most likely to be impacted by the COVID-19 pandemic. DESIGN & SETTING: A qualitative study using online surveys. The area of focus was Fife, a local authority in Scotland, UK. METHOD: Two consecutive surveys were conducted. Survey 1 explored the observations of support workers and Facebook group moderators, and focused on key issues faced by service users, as well as examples of good practice (n = 19). Survey 2 was aimed at community members, and focused on issues regarding access to and communication around access to primary care (n = 34). RESULTS: Survey 1 found broad issues around communication and access to primary care services. Survey 2 emphasised key issues in accessing primary care, including: (a) the lengthy process of making appointments; (b) feeling like a burden for wanting to be seen; (c) a lack of confidence in remote triaging and consultations; and (d) not knowing what to expect before getting an appointment. CONCLUSION: Clear issues regarding access to primary care were identified. The new understanding of these issues will inform a co-creation process designed to develop clear, actionable, and effective public health messages centred on improving access to primary care.

CXC Chemokine Family☆

Chemokines are a family of small, chemoattractant cytokines that play a central role in regulating the migration of cells into inflamed tissue. The CXC-chemokine sub-family is characterized by four cysteine amino acids that form two pairs of disulfide bridges, with the two cysteines proximal to the N-terminus separated by an additional amino acid (CXC). Members of the CXC-chemokine family can be further characterized based on the presence or absence of a Glu-Leu-Arg motif, known as the ‘ELR’ motif. ELR+ CXC-chemokines are angiogenic and preferentially promote the chemoattraction of neutrophils, while ELR- CXC-chemokines are angiostatic and preferentially promote lymphocyte migration. However, all CXC-chemokines stimulate the migration of several leukocytes through the activation of G-protein-coupled receptors (GPCRs, known as CXCRs). CXC-chemokines also promote cell survival, activate various leukocyte effector functions and induce gene expression. CXC-chemokines have therefore been associated with the pathogenesis of several respiratory diseases including asthma, COPD, IPF, ARDS, cancer and infectious diseases.

Thirty-Day Outcomes of Children and Adolescents With COVID-19: An International Experience.

OBJECTIVES: To characterize the demographics, comorbidities, symptoms, in-hospital treatments, and health outcomes among children and adolescents diagnosed or hospitalized with coronavirus disease 2019 (COVID-19) and to compare them in secondary analyses with patients diagnosed with previous seasonal influenza in 2017-2018. METHODS: International network cohort using real-world data from European primary care records (France, Germany, and Spain), South Korean claims and US claims, and hospital databases. We included children and adolescents diagnosed and/or hospitalized with COVID-19 at age <18 between January and June 2020. We described baseline demographics, comorbidities, symptoms, 30-day in-hospital treatments, and outcomes including hospitalization, pneumonia, acute respiratory distress syndrome, multisystem inflammatory syndrome in children, and death. RESULTS: A total of 242 158 children and adolescents diagnosed and 9769 hospitalized with COVID-19 and 2 084 180 diagnosed with influenza were studied. Comorbidities including neurodevelopmental disorders, heart disease, and cancer were more common among those hospitalized with versus diagnosed with COVID-19. Dyspnea, bronchiolitis, anosmia, and gastrointestinal symptoms were more common in COVID-19 than influenza. In-hospital prevalent treatments for COVID-19 included repurposed medications (<10%) and adjunctive therapies: systemic corticosteroids (6.8%-7.6%), famotidine (9.0%-28.1%), and antithrombotics such as aspirin (2.0%-21.4%), heparin (2.2%-18.1%), and enoxaparin (2.8%-14.8%). Hospitalization was observed in 0.3% to 1.3% of the cohort diagnosed with COVID-19, with undetectable (n < 5 per database) 30-day fatality. Thirty-day outcomes including pneumonia and hypoxemia were more frequent in COVID-19 than influenza. CONCLUSIONS: Despite negligible fatality, complications including hospitalization, hypoxemia, and pneumonia were more frequent in children and adolescents with COVID-19 than with influenza. Dyspnea, anosmia, and gastrointestinal symptoms could help differentiate diagnoses. A wide range of medications was used for the inpatient management of pediatric COVID-19.

The contributions of William Money MRCS to smallpox vaccination and control - A little-known story.

This history of vaccinology article outlines the work of William Money (1790-1843), who conducted a study related to smallpox disease, immunity, and vaccination. His hitherto unpublished study demonstrated that smallpox could be contracted more than once; notably, results from his studies showed that vaccination was not dangerous. He was also the author of a celebrated Vade Mecum in human anatomy. Here, we outline the work he conducted in England: from serving as the house surgeon at Northampton Infirmary to his post as a surgeon at the Royal Metropolitan Hospital in London.

Clinical Characterization and Prediction of Clinical Severity of SARS-CoV-2 Infection Among US Adults Using Data From the US National COVID Cohort Collaborative.

Importance: The National COVID Cohort Collaborative (N3C) is a centralized, harmonized, high-granularity electronic health record repository that is the largest, most representative COVID-19 cohort to date. This multicenter data set can support robust evidence-based development of predictive and diagnostic tools and inform clinical care and policy. Objectives: To evaluate COVID-19 severity and risk factors over time and assess the use of machine learning to predict clinical severity. Design, Setting, and Participants: In a retrospective cohort study of 1 926 526 US adults with SARS-CoV-2 infection (polymerase chain reaction >99% or antigen <1%) and adult patients without SARS-CoV-2 infection who served as controls from 34 medical centers nationwide between January 1, 2020, and December 7, 2020, patients were stratified using a World Health Organization COVID-19 severity scale and demographic characteristics. Differences between groups over time were evaluated using multivariable logistic regression. Random forest and XGBoost models were used to predict severe clinical course (death, discharge to hospice, invasive ventilatory support, or extracorporeal membrane oxygenation). Main Outcomes and Measures: Patient demographic characteristics and COVID-19 severity using the World Health Organization COVID-19 severity scale and differences between groups over time using multivariable logistic regression. Results: The cohort included 174 568 adults who tested positive for SARS-CoV-2 (mean [SD] age, 44.4 [18.6] years; 53.2% female) and 1 133 848 adult controls who tested negative for SARS-CoV-2 (mean [SD] age, 49.5 [19.2] years; 57.1% female). Of the 174 568 adults with SARS-CoV-2, 32 472 (18.6%) were hospitalized, and 6565 (20.2%) of those had a severe clinical course (invasive ventilatory support, extracorporeal membrane oxygenation, death, or discharge to hospice). Of the hospitalized patients, mortality was 11.6% overall and decreased from 16.4% in March to April 2020 to 8.6% in September to October 2020 (P = .002 for monthly trend). Using 64 inputs available on the first hospital day, this study predicted a severe clinical course using random forest and XGBoost models (area under the receiver operating curve = 0.87 for both) that were stable over time. The factor most strongly associated with clinical severity was pH; this result was consistent across machine learning methods. In a separate multivariable logistic regression model built for inference, age (odds ratio [OR], 1.03 per year; 95% CI, 1.03-1.04), male sex (OR, 1.60; 95% CI, 1.51-1.69), liver disease (OR, 1.20; 95% CI, 1.08-1.34), dementia (OR, 1.26; 95% CI, 1.13-1.41), African American (OR, 1.12; 95% CI, 1.05-1.20) and Asian (OR, 1.33; 95% CI, 1.12-1.57) race, and obesity (OR, 1.36; 95% CI, 1.27-1.46) were independently associated with higher clinical severity. Conclusions and Relevance: This cohort study found that COVID-19 mortality decreased over time during 2020 and that patient demographic characteristics and comorbidities were associated with higher clinical severity. The machine learning models accurately predicted ultimate clinical severity using commonly collected clinical data from the first 24 hours of a hospital admission.

Testing the accuracy of a novel digital peak flow meter aligned with a smartphone app compared to a lab spirometer: A pilot work.

BACKGROUND: A new digital peak flow meter, known as Smart Peak Flow (SPF), has been developed to monitor asthma patients' peak expiratory flow (PEF) at home. It is connected wirelessly to any type of smartphone and it is used by asthma patients to self-monitor progress of their clinical condition. Thus evaluation of the SPF's ability to provide accurate PEF values is essential. The aim of this pilot work was to provide preliminary in-vivo data about the measurement agreement between the SPF and a lab spirometer for a first time. METHODS: PEF measurements were obtained by 9 healthy adults as this pilot work was terminated earlier than it was expected due to COVID-19 restrictions. PEF readings (n=27) were recorded by the comparable devices at the same time during three different expiratory maneuvers performed by each participant. The Bland and Altman plot was used to assess the agreement. RESULTS: Good agreement between the SPF and the lab spirometer was found with the mean bias being estimated 0.29 L/min. The lower and upper limits of agreement (LOA) were estimated 30.03 L/min and -30.61 L/min respectively. CONCLUSION: Due to a small sample size, no firm conclusions can be drawn regarding the SPF's accuracy. However the current promising results encourage further testing in the future.

Implementation of the COVID-19 Vulnerability Index Across an International Network of Health Care Data Sets: Collaborative External Validation Study.

BACKGROUND: SARS-CoV-2 is straining health care systems globally. The burden on hospitals during the pandemic could be reduced by implementing prediction models that can discriminate patients who require hospitalization from those who do not. The COVID-19 vulnerability (C-19) index, a model that predicts which patients will be admitted to hospital for treatment of pneumonia or pneumonia proxies, has been developed and proposed as a valuable tool for decision-making during the pandemic. However, the model is at high risk of bias according to the "prediction model risk of bias assessment" criteria, and it has not been externally validated. OBJECTIVE: The aim of this study was to externally validate the C-19 index across a range of health care settings to determine how well it broadly predicts hospitalization due to pneumonia in COVID-19 cases. METHODS: We followed the Observational Health Data Sciences and Informatics (OHDSI) framework for external validation to assess the reliability of the C-19 index. We evaluated the model on two different target populations, 41,381 patients who presented with SARS-CoV-2 at an outpatient or emergency department visit and 9,429,285 patients who presented with influenza or related symptoms during an outpatient or emergency department visit, to predict their risk of hospitalization with pneumonia during the following 0-30 days. In total, we validated the model across a network of 14 databases spanning the United States, Europe, Australia, and Asia. RESULTS: The internal validation performance of the C-19 index had a C statistic of 0.73, and the calibration was not reported by the authors. When we externally validated it by transporting it to SARS-CoV-2 data, the model obtained C statistics of 0.36, 0.53 (0.473-0.584) and 0.56 (0.488-0.636) on Spanish, US, and South Korean data sets, respectively. The calibration was poor, with the model underestimating risk. When validated on 12 data sets containing influenza patients across the OHDSI network, the C statistics ranged between 0.40 and 0.68. CONCLUSIONS: Our results show that the discriminative performance of the C-19 index model is low for influenza cohorts and even worse among patients with COVID-19 in the United States, Spain, and South Korea. These results suggest that C-19 should not be used to aid decision-making during the COVID-19 pandemic. Our findings highlight the importance of performing external validation across a range of settings, especially when a prediction model is being extrapolated to a different population. In the field of prediction, extensive validation is required to create appropriate trust in a model.