Angus B. Reed

Bio: Angus B. Reed is an academic researcher. The author has contributed to research in topics: Framingham Risk Score & Biobank. The author has an hindex of 2, co-authored 10 publications receiving 72 citations.

Population risk factors for severe disease and mortality in COVID-19: A global systematic review and meta-analysis.

Abstract: AIM: COVID-19 clinical presentation is heterogeneous, ranging from asymptomatic to severe cases. While there are a number of early publications relating to risk factors for COVID-19 infection, low sample size and heterogeneity in study design impacted consolidation of early findings. There is a pressing need to identify the factors which predispose patients to severe cases of COVID-19. For rapid and widespread risk stratification, these factors should be easily obtainable, inexpensive, and avoid invasive clinical procedures. The aim of our study is to fill this knowledge gap by systematically mapping all the available evidence on the association of various clinical, demographic, and lifestyle variables with the risk of specific adverse outcomes in patients with COVID-19. METHODS: The systematic review was conducted using standardized methodology, searching two electronic databases (PubMed and SCOPUS) for relevant literature published between 1st January 2020 and 9th July 2020. Included studies reported characteristics of patients with COVID-19 while reporting outcomes relating to disease severity. In the case of sufficient comparable data, meta-analyses were conducted to estimate risk of each variable. RESULTS: Seventy-six studies were identified, with a total of 17,860,001 patients across 14 countries. The studies were highly heterogeneous in terms of the sample under study, outcomes, and risk measures reported. A large number of risk factors were presented for COVID-19. Commonly reported variables for adverse outcome from COVID-19 comprised patient characteristics, including age >75 (OR: 2.65, 95% CI: 1.81-3.90), male sex (OR: 2.05, 95% CI: 1.39-3.04) and severe obesity (OR: 2.57, 95% CI: 1.31-5.05). Active cancer (OR: 1.46, 95% CI: 1.04-2.04) was associated with increased risk of severe outcome. A number of common symptoms and vital measures (respiratory rate and SpO2) also suggested elevated risk profiles. CONCLUSIONS: Based on the findings of this study, a range of easily assessed parameters are valuable to predict elevated risk of severe illness and mortality as a result of COVID-19, including patient characteristics and detailed comorbidities, alongside the novel inclusion of real-time symptoms and vital measurements.

Population risk factors for severe disease and mortality in COVID-19: A global systematic review and meta-analysis

Abstract: Aim COVID-19 clinical presentation is heterogeneous, ranging from asymptomatic to severe cases. While there are a number of early publications relating to risk factors for COVID-19 infection, low sample size and heterogeneity in study design impacted consolidation of early findings. There is a pressing need to identify the factors which predispose patients to severe cases of COVID-19. For rapid and widespread risk stratification, these factors should be easily obtainable, inexpensive, and avoid invasive clinical procedures. The aim of our study is to fill this knowledge gap by systematically mapping all the available evidence on the association of various clinical, demographic, and lifestyle variables with the risk of specific adverse outcomes in patients with COVID-19. Methods The systematic review was conducted using standardized methodology, searching three electronic databases (PubMed, Embase, and Web of Science) for relevant literature published between 1st January 2020 and 9th July 2020. Included studies reported characteristics of patients with COVID-19 while reporting outcomes relating to disease severity. In the case of sufficient comparable data, meta-analyses were conducted to estimate risk of each variable. Results Seventy-six studies were identified, with a total of 17,860,001 patients across 14 countries. The studies were highly heterogeneous in terms of the sample under study, outcomes, and risk measures reported. A large number of risk factors were presented for COVID-19. Commonly reported variables for adverse outcome from COVID-19 comprised patient characteristics, including age >75 (OR = 2.65 (1.81–3.90)), male sex (OR = 2.05(1.39–3.04)) and severe obesity (OR = 2.57 (1.31–5.05)). Active cancer (OR = 1.46 (1.04–2.04)) was associated with increased risk of severe outcome. A number of common symptoms and vital measures (respiratory rate and SpO2) also suggested elevated risk profiles. Conclusions Based on the findings of this study, a range of easily assessed parameters are valuable to predict elevated risk of severe illness and mortality as a result of COVID-19, including patient characteristics and detailed comorbidities, alongside the novel inclusion of real-time symptoms and vital measurements.

Machine learning approach to dynamic risk modeling of mortality in COVID-19: a UK Biobank study.

Abstract: The COVID-19 pandemic has created an urgent need for robust, scalable monitoring tools supporting stratification of high-risk patients. This research aims to develop and validate prediction models, using the UK Biobank, to estimate COVID-19 mortality risk in confirmed cases. From the 11,245 participants testing positive for COVID-19, we develop a data-driven random forest classification model with excellent performance (AUC: 0.91), using baseline characteristics, pre-existing conditions, symptoms, and vital signs, such that the score could dynamically assess mortality risk with disease deterioration. We also identify several significant novel predictors of COVID-19 mortality with equivalent or greater predictive value than established high-risk comorbidities, such as detailed anthropometrics and prior acute kidney failure, urinary tract infection, and pneumonias. The model design and feature selection enables utility in outpatient settings. Possible applications include supporting individual-level risk profiling and monitoring disease progression across patients with COVID-19 at-scale, especially in hospital-at-home settings.

Development of an accessible 10-year Digital CArdioVAscular (DiCAVA) risk assessment: a UK Biobank study

Abstract: Background: Cardiovascular diseases (CVDs) are among the leading causes of death worldwide. Predictive scores providing personalised risk of developing CVD are increasingly used in clinical practice. Most scores, however, utilise a homogenous set of features and require the presence of a physician. Objective: The aim was to develop a new risk model (DiCAVA) using statistical and machine learning techniques that could be applied in a remote setting. A secondary goal was to identify new patient-centric variables that could be incorporated into CVD risk assessments. Methods: Across 466,052 participants, Cox proportional hazards (CPH) and DeepSurv models were trained using 608 variables derived from the UK Biobank to investigate the 10-year risk of developing a CVD. Data-driven feature selection reduced the number of features to 47, after which reduced models were trained. Both models were compared to the Framingham score. Results: The reduced CPH model achieved a c-index of 0.7443, whereas DeepSurv achieved a c-index of 0.7446. Both CPH and DeepSurv were superior in determining the CVD risk compared to Framingham score. Minimal difference was observed when cholesterol and blood pressure were excluded from the models (CPH: 0.741, DeepSurv: 0.739). The models show very good calibration and discrimination on the test data. Conclusion: We developed a cardiovascular risk model that has very good predictive capacity and encompasses new variables. The score could be incorporated into clinical practice and utilised in a remote setting, without the need of including cholesterol. Future studies will focus on external validation across heterogeneous samples.

Machine learning approach to dynamic risk modeling of mortality in COVID-19: a UK Biobank cohort study

Abstract: The COVID19 pandemic has resulted in over two million deaths globally There is an urgent need for robust, scalable monitoring tools supporting resource allocation and stratification of high-risk patients This research aims to develop and validate prediction models, using the UK Biobank to estimate COVID19 mortality risk in confirmed cases We developed a random forest classification model using baseline characteristics, pre-existing conditions, symptoms, and vital signs, such that the score could dynamically assess risk of mortality with disease deterioration (AUC: 092) The design and feature selection of the framework lends itself to deployment in remote settings Possible applications include supporting individual-level risk profiling and monitoring disease progression across high volumes of patients with COVID19, especially in hospital-at-home settings The COVID19 pandemic has precipitated over 100 million confirmed cases and 23 million deaths globally The impact of the pandemic has not been limited to healthcare systems: a ripple effect has resulted in wide-ranging economic and social disruption Interventions to reduce transmission, such as lockdowns, travel restrictions, and re-allocation of health resources, are critical to limiting the impact Although large-scale vaccination programmes have begun, many countries globally will not have widespread access to vaccines until 2023, meaning that non-pharmaceutical interventions are likely to remain indispensable national strategies for some time COVID19 shows highly varied clinical presentation A significant proportion (17 to 45%) of cases are asymptomatic and require no specific care Conversely, reviews of severe complications have found that up to 32% of hospitalized COVID19 patients are admitted to ICU7 Between these two extremes, typical symptoms include fever, continuous cough, anosmia, and dyspnoea, which may range from requiring only self-management at home to inpatient care Understanding which individuals are most vulnerable to severe disease, and thereby in most need of resources, is critical to limit the impact of the virus Decision-making at all levels requires an understanding of individuals risk of severe disease Various patient characteristics, comorbidities, and lifestyle factors have been linked to greater risk of death and/or severe illness following infection Furthermore, socioeconomic factors have also been linked as risk factors for COVID19 mortality Once patients are infected with SARS CoV 2, additional physiological parameters, such as symptoms and vital signs, can inform real-time prognostication13 Laboratory testing and imaging can also inform risk stratification for early, aggressive intervention, though this data is only accessible to hospital inpatients, who are likely to be already severely affected Robust, predictive models for acquisition and prognosis of COVID 1916 18 and resource management have been developed to support risk stratification and population management at scale, offering important insights for organizational decision-making However, the individual is currently overlooked, and granular, patient-specific risk-scoring could potentially unify decision-making at all levels Existing individualized risk scores, however, often conflate risk of COVID19 acquisition with risk of mortality following infection, which can limit their utility in patient management For prediction models to achieve impact at scale, assessment of risk factors should be inexpensive and accessible to the general population, ideally without the need for specialized testing or hospital visits Such risk prediction tools, enabling improved patient triage, could be used to further increase the efficiency of, and confidence in, hospital at home solutions, which have shown promise in reducing hospital burden throughout the pandemic Risk scores in these circumstances need to be dynamic and contemporaneous, ideally incorporating symptoms and vital sign data to maximise utility to clinical and research teams Therefore, the primary aim of this study is to develop and validate a population-based prediction model, using a large, rich dataset and a selective, clinically informed approach, which dynamically estimates the COVID19 mortality risk in confirmed diagnoses

Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: a retrospective cohort study

Abstract: Summary Background Since December, 2019, Wuhan, China, has experienced an outbreak of coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Epidemiological and clinical characteristics of patients with COVID-19 have been reported but risk factors for mortality and a detailed clinical course of illness, including viral shedding, have not been well described. Methods In this retrospective, multicentre cohort study, we included all adult inpatients (≥18 years old) with laboratory-confirmed COVID-19 from Jinyintan Hospital and Wuhan Pulmonary Hospital (Wuhan, China) who had been discharged or had died by Jan 31, 2020. Demographic, clinical, treatment, and laboratory data, including serial samples for viral RNA detection, were extracted from electronic medical records and compared between survivors and non-survivors. We used univariable and multivariable logistic regression methods to explore the risk factors associated with in-hospital death. Findings 191 patients (135 from Jinyintan Hospital and 56 from Wuhan Pulmonary Hospital) were included in this study, of whom 137 were discharged and 54 died in hospital. 91 (48%) patients had a comorbidity, with hypertension being the most common (58 [30%] patients), followed by diabetes (36 [19%] patients) and coronary heart disease (15 [8%] patients). Multivariable regression showed increasing odds of in-hospital death associated with older age (odds ratio 1·10, 95% CI 1·03–1·17, per year increase; p=0·0043), higher Sequential Organ Failure Assessment (SOFA) score (5·65, 2·61–12·23; p Interpretation The potential risk factors of older age, high SOFA score, and d-dimer greater than 1 μg/mL could help clinicians to identify patients with poor prognosis at an early stage. Prolonged viral shedding provides the rationale for a strategy of isolation of infected patients and optimal antiviral interventions in the future. Funding Chinese Academy of Medical Sciences Innovation Fund for Medical Sciences; National Science Grant for Distinguished Young Scholars; National Key Research and Development Program of China; The Beijing Science and Technology Project; and Major Projects of National Science and Technology on New Drug Creation and Development.

Innate immunity: the first line of defense against SARS-CoV-2

Abstract: The coronavirus disease 2019 (COVID-19) pandemic, caused by severe acute respiratory syndrome coronavirus (SARS-CoV)-2, continues to cause substantial morbidity and mortality. While most infections are mild, some patients experience severe and potentially fatal systemic inflammation, tissue damage, cytokine storm and acute respiratory distress syndrome. The innate immune system acts as the first line of defense, sensing the virus through pattern recognition receptors and activating inflammatory pathways that promote viral clearance. Here, we discuss innate immune processes involved in SARS-CoV-2 recognition and the resultant inflammation. Improved understanding of how the innate immune system detects and responds to SARS-CoV-2 will help identify targeted therapeutic modalities that mitigate severe disease and improve patient outcomes. Kanneganti and Diamond review the key role played by innate immunity in the control and immunopathology of COVID-19.

Effectiveness of a second BNT162b2 booster vaccine against hospitalization and death from COVID-19 in adults aged over 60 years

Abstract: The rapid emergence of the B.1.1.529 (Omicron) variant of SARS-CoV-2 led to a global resurgence of coronavirus disease 2019 (COVID-19). Israeli authorities approved a fourth COVID-19 vaccine dose (second booster) for individuals aged 60 years and over who had received a first booster dose 4 or more months earlier. Evidence for the effectiveness of a second booster dose in reducing hospitalizations and mortality due to COVID-19 is warranted. This retrospective cohort study included all members of Clalit Health Services who were aged 60–100 years and who were eligible for the second booster on 3 January 2022. Hospitalizations and mortality due to COVID-19 in participants who received the second booster were compared with those for participants who received one booster dose. Cox proportional hazards regression models with time-dependent covariates were used to estimate the association between the second booster and hospitalization and death due to COVID-19 while adjusting for demographic factors and coexisting illnesses. A total of 563,465 participants met the eligibility criteria. Of those, 328,597 (58%) received a second booster dose during the 40 day study period. Hospitalization due to COVID-19 occurred in 270 of the second-booster recipients and in 550 participants who received one booster dose (adjusted hazard ratio, 0.36; 95% confidence interval (CI): 0.31–0.43). Death due to COVID-19 occurred in 92 second-booster recipients and in 232 participants who received one booster dose (adjusted hazard ratio, 0.22; 95% CI: 0.17–0.28). This study demonstrates a substantial reduction in hospitalizations and deaths due to COVID-19 conferred by a second booster in Israeli adults aged 60 years and over. A retrospective analysis of data from a large healthcare insurance provider in Israel shows that a second booster shot (fourth dose) of BNT162b2 in people aged 60 years and over results in a substantial reduction in hospitalizations and deaths due to COVID-19.

Effectiveness of the BNT162b2 Vaccine after Recovery from Covid-19

Abstract: The risk of infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) decreases substantially among patients who have recovered from coronavirus disease 2019 (Covid-19). However, it is unknown how long protective immunity lasts. Current guidelines recommend vaccination of recovered patients even though data regarding vaccine effectiveness in such cases are still limited.In this retrospective cohort study, we reviewed electronic medical records from a large health care organization in Israel to assess reinfection rates in patients who had recovered from SARS-CoV-2 infection before any vaccination against Covid-19. We compared reinfection rates among patients who had subsequently received the BNT162b2 vaccine (Pfizer-BioNTech) and those who had not been vaccinated between March 1 and November 26, 2021. We used a Cox proportional-hazards regression model with time-dependent covariates to estimate the association between vaccination and reinfection after adjustment for demographic factors and coexisting illnesses. Vaccine effectiveness was estimated as 1 minus the hazard ratio. In a secondary analysis, we evaluated the vaccine effectiveness of one dose as compared with two doses.A total of 149,032 patients who had recovered from SARS-CoV-2 infection met the eligibility criteria. Of these patients, 83,356 (56%) received subsequent vaccination during the 270-day study period. Reinfection occurred in 354 of the vaccinated patients (2.46 cases per 100,000 persons per day) and in 2168 of 65,676 unvaccinated patients (10.21 cases per 100,000 persons per day). Vaccine effectiveness was estimated at 82% (95% confidence interval [CI], 80 to 84) among patients who were 16 to 64 years of age and 60% (95% CI, 36 to 76) among those 65 years of age or older. No significant difference in vaccine effectiveness was found for one dose as compared with two doses.Among patients who had recovered from Covid-19, the receipt of at least one dose of the BNT162b2 vaccine was associated with a significantly lower risk of recurrent infection.

Effectiveness of the BNT162b2 Vaccine after Recovery from Covid-19

Abstract: Abstract Background The risk of infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) decreases substantially among patients who have recovered from coronavirus disease 2019 (Covid-19). However, it is unknown how long protective immunity lasts. Current guidelines recommend vaccination of recovered patients even though data regarding vaccine effectiveness in such cases are still limited. Methods In this retrospective cohort study, we reviewed electronic medical records from a large health care organization in Israel to assess reinfection rates in patients who had recovered from SARS-CoV-2 infection before any vaccination against Covid-19. We compared reinfection rates among patients who had subsequently received the BNT162b2 vaccine (Pfizer–BioNTech) and those who had not been vaccinated between March 1 and November 26, 2021. We used a Cox proportional-hazards regression model with time-dependent covariates to estimate the association between vaccination and reinfection after adjustment for demographic factors and coexisting illnesses. Vaccine effectiveness was estimated as 1 minus the hazard ratio. In a secondary analysis, we evaluated the vaccine effectiveness of one dose as compared with two doses. Results A total of 149,032 patients who had recovered from SARS-CoV-2 infection met the eligibility criteria. Of these patients, 83,356 (56%) received subsequent vaccination during the 270-day study period. Reinfection occurred in 354 of the vaccinated patients (2.46 cases per 100,000 persons per day) and in 2168 of 65,676 unvaccinated patients (10.21 cases per 100,000 persons per day). Vaccine effectiveness was estimated at 82% (95% confidence interval [CI], 80 to 84) among patients who were 16 to 64 years of age and 60% (95% CI, 36 to 76) among those 65 years of age or older. No significant difference in vaccine effectiveness was found for one dose as compared with two doses. Conclusions Among patients who had recovered from Covid-19, the receipt of at least one dose of the BNT162b2 vaccine was associated with a significantly lower risk of recurrent infection.