bims-covirf 2021-09-19 papers

Issue of 2021–09–19
five papers selected by
Catherine Rycroft, BresMed

BMJ. 2021 Sep 17. 374 n2244

Risk prediction of covid-19 related death and hospital admission in adults after covid-19 vaccination: national prospective cohort study.

Julia Hippisley-Cox, Carol Ac Coupland, Nisha Mehta, Ruth H Keogh, Karla Diaz-Ordaz, Kamlesh Khunti, Ronan A Lyons, Frank Kee, Aziz Sheikh, Shamim Rahman, Jonathan Valabhji, Ewen M Harrison, Peter Sellen, Nazmus Haq, Malcolm G Semple, Peter W M Johnson, Andrew Hayward, Jonathan S Nguyen-Van-Tam.

OBJECTIVES: To derive and validate risk prediction algorithms to estimate the risk of covid-19 related mortality and hospital admission in UK adults after one or two doses of covid-19 vaccination.
DESIGN: Prospective, population based cohort study using the QResearch database linked to data on covid-19 vaccination, SARS-CoV-2 results, hospital admissions, systemic anticancer treatment, radiotherapy, and the national death and cancer registries.
SETTINGS: Adults aged 19-100 years with one or two doses of covid-19 vaccination between 8 December 2020 and 15 June 2021.
MAIN OUTCOME MEASURES: Primary outcome was covid-19 related death. Secondary outcome was covid-19 related hospital admission. Outcomes were assessed from 14 days after each vaccination dose. Models were fitted in the derivation cohort to derive risk equations using a range of predictor variables. Performance was evaluated in a separate validation cohort of general practices.
RESULTS: Of 6 952 440 vaccinated patients in the derivation cohort, 5 150 310 (74.1%) had two vaccine doses. Of 2031 covid-19 deaths and 1929 covid-19 hospital admissions, 81 deaths (4.0%) and 71 admissions (3.7%) occurred 14 days or more after the second vaccine dose. The risk algorithms included age, sex, ethnic origin, deprivation, body mass index, a range of comorbidities, and SARS-CoV-2 infection rate. Incidence of covid-19 mortality increased with age and deprivation, male sex, and Indian and Pakistani ethnic origin. Cause specific hazard ratios were highest for patients with Down's syndrome (12.7-fold increase), kidney transplantation (8.1-fold), sickle cell disease (7.7-fold), care home residency (4.1-fold), chemotherapy (4.3-fold), HIV/AIDS (3.3-fold), liver cirrhosis (3.0-fold), neurological conditions (2.6-fold), recent bone marrow transplantation or a solid organ transplantation ever (2.5-fold), dementia (2.2-fold), and Parkinson's disease (2.2-fold). Other conditions with increased risk (ranging from 1.2-fold to 2.0-fold increases) included chronic kidney disease, blood cancer, epilepsy, chronic obstructive pulmonary disease, coronary heart disease, stroke, atrial fibrillation, heart failure, thromboembolism, peripheral vascular disease, and type 2 diabetes. A similar pattern of associations was seen for covid-19 related hospital admissions. No evidence indicated that associations differed after the second dose, although absolute risks were reduced. The risk algorithm explained 74.1% (95% confidence interval 71.1% to 77.0%) of the variation in time to covid-19 death in the validation cohort. Discrimination was high, with a D statistic of 3.46 (95% confidence interval 3.19 to 3.73) and C statistic of 92.5. Performance was similar after each vaccine dose. In the top 5% of patients with the highest predicted covid-19 mortality risk, sensitivity for identifying covid-19 deaths within 70 days was 78.7%.
CONCLUSION: This population based risk algorithm performed well showing high levels of discrimination for identifying those patients at highest risk of covid-19 related death and hospital admission after vaccination.

DOI: https://doi.org/10.1136/bmj.n2244

EClinicalMedicine. 2021 Oct;40 101111

Mental and neurological disorders and risk of COVID-19 susceptibility, illness severity and mortality: A systematic review, meta-analysis and call for action.

Background: Coronavirus disease 2019 (COVID-19) has evolved into a worldwide pandemic, and has been found to be closely associated with mental and neurological disorders. We aimed to comprehensively quantify the association between mental and neurological disorders, both pre-existing and subsequent, and the risk of susceptibility, severity and mortality of COVID-19.
Methods: In this systematic review and meta-analysis, we searched PubMed, Web of Science, Embase, PsycINFO, and Cochrane library databases for studies published from the inception up to January 16, 2021 and updated at July 7, 2021. Observational studies including cohort and case-control, cross-sectional studies and case series that reported risk estimates of the association between mental or neurological disorders and COVID-19 susceptibility, illness severity and mortality were included. Two researchers independently extracted data and conducted the quality assessment. Based on I2 heterogeneity, we used a random effects model to calculate pooled odds ratios (OR) and 95% confidence intervals (95% CI). Subgroup analyses and meta-regression analysis were also performed. This study was registered on PROSPERO (registration number: CRD 42021230832).
Finding: A total of 149 studies (227,351,954 participants, 89,235,737 COVID-19 patients) were included in this analysis, in which 27 reported morbidity (132,727,798), 56 reported illness severity (83,097,968) and 115 reported mortality (88,878,662). Overall, mental and neurological disorders were associated with a significant high risk of infection (pre-existing mental: OR 1·67, 95% CI 1·12-2·49; and pre-existing neurological: 2·05, 1·58-2·67), illness severity (mental: pre-existing, 1·40, 1·25-1·57; sequelae, 4·85, 2·53-9·32; neurological: pre-existing, 1·43, 1·09-1·88; sequelae, 2·17, 1·45-3·24), and mortality (mental: pre-existing, 1·47, 1·26-1·72; neurological: pre-existing, 2·08, 1·61-2·69; sequelae, 2·03, 1·66-2·49) from COVID-19. Subgroup analysis revealed that association with illness severity was stronger among younger COVID-19 patients, and those with subsequent mental disorders, living in low- and middle-income regions. Younger patients with mental and neurological disorders were associated with higher mortality than elders. For type-specific mental disorders, susceptibility to contracting COVID-19 was associated with pre-existing mood disorders, anxiety, and attention-deficit hyperactivity disorder (ADHD); illness severity was associated with both pre-existing and subsequent mood disorders as well as sleep disturbance; and mortality was associated with pre-existing schizophrenia. For neurological disorders, susceptibility was associated with pre-existing dementia; both severity and mortality were associated with subsequent delirium and altered mental status; besides, mortality was associated with pre-existing and subsequent dementia and multiple specific neurological diseases. Heterogeneities were substantial across studies in most analysis.
Interpretation: The findings show an important role of mental and neurological disorders in the context of COVID-19 and provide clues and directions for identifying and protecting vulnerable populations in the pandemic. Early detection and intervention for neurological and mental disorders are urgently needed to control morbidity and mortality induced by the COVID-19 pandemic. However, there was substantial heterogeneity among the included studies, and the results should be interpreted with caution. More studies are needed to explore long-term mental and neurological sequela, as well as the underlying brain mechanisms for the sake of elucidating the causal pathways for these associations.
Funding: This study is supported by grants from the National Key Research and Development Program of China, the National Natural Science Foundation of China, Special Research Fund of PKUHSC for Prevention and Control of COVID-19, and the Fundamental Research Funds for the Central Universities.

Keywords: COVID-19; illness severity; mental health; mortality; neurological disorders; susceptibility

DOI: https://doi.org/10.1016/j.eclinm.2021.101111

Wellcome Open Res. 2021 ;6 32

Ethnicity, pre-existing comorbidities, and outcomes of hospitalised patients with COVID-19.

Gillian Santorelli, Michael McCooe, Trevor A Sheldon, John Wright, Tom Lawton.

Background: The coronavirus disease 2019 (COVID-19) pandemic has resulted in thousands of deaths in the UK. Those with existing comorbidities and minority ethnic groups have been found to be at increased risk of mortality. We wished to determine if there were any differences in intensive care unit (ICU) admission and 30-day hospital mortality in a city with high levels of deprivation and a large community of people of South Asian heritage. Methods: Detailed information on 622 COVID-19-positive inpatients in Bradford and Calderdale between February-August 2020 were extracted from Electronic Health Records. Logistic regression and Cox proportional hazards models were used to explore the relationship between ethnicity with admission to ICU and 30-day mortality, respectively accounting for the effect of demographic and clinical confounders. Results: The sample consisted of 408 (70%) White, 142 (24%) South Asian and 32 (6%) other minority ethnic patients. Ethnic minority patients were younger, more likely to live in deprived areas, and be overweight/obese, have type 2 diabetes, hypertension and asthma compared to white patients, but were less likely to have cancer (South Asian patients only) and COPD. Male and obese patients were more likely to be admitted to ICU, and patients of South Asian ethnicity, older age, and those with cancer were less likely. Being male, older age, deprivation, obesity, and cancer were associated with 30-day mortality. The risk of death in South Asian patients was the same as in white patients HR 1.03 (0.58, 1.82). Conclusions: Despite South Asian patients being less likely to be admitted to ICU and having a higher prevalence of diabetes and obesity, there was no difference in the risk of death compared to white patients. This contrasts with other findings and highlights the value of studies of communities which may have different ethnic, deprivation and clinical risk profiles.

Keywords: COVID-19; comorbidity; ethnicity; mortality

DOI: https://doi.org/10.12688/wellcomeopenres.16580.1

Cerebrovasc Dis. 2021 Aug 06. 1-9

A History of Cerebrovascular Disease Is Independently Associated with Increased Morbidity and Mortality in Patients with COVID-19: A Cohort Study of 369,563 COVID-19 Cases in the USA.

Anna M Nia, Visish M Srinivasan, Miranda K Hayworth, Rishi R Lall, Peter Kan.

OBJECTIVES: We set out to evaluate the risk for severe coronavirus disease 2019 (COVID-19) infection and subsequent cerebrovascular disease (CVD) in the population with a prior diagnosis of CVD within the past 10 years.
METHODS: We utilized the TriNetX Analytics Network to query 369,563 CO-VID-19 cases up to December 30, 2020. We created 8 cohorts of patients with COVID-19 diagnosis based on a previous diagnosis of CVD. We measured the odds ratios, relative risks, risk differences for hospitalizations, ICU/critical care services, intubation, mortality, and CVD recurrence within 90 days of COVID-19 diagnosis, compared to a propensity-matched cohort with no prior history of CVD within 90 days of COVID-19 diagnosis.
RESULTS: 369,563 patients had a confirmed diagnosis of COVID-19 with a subset of 22,497 (6.09%) patients with a prior diagnosis of CVD within 10 years. All cohorts with a CVD diagnosis had an increased risk of hospitalization, critical care services, and mortality within 90 days of COVID-19 diagnosis. Additionally, the data demonstrate that any history of CVD is associated with significantly increased odds of subsequent CVD post-COVID-19 compared to a matched control.
CONCLUSIONS: CVD, a known complication of CO-VID-19, is more frequent in patients with a prior history of CVD. Patients with any previous diagnosis of CVD are at higher risks of morbidity and mortality from COVID-19 infection. In patients admitted to the ED due to COVID-19 symptoms, these risk factors should be promptly identified as delayed or missed risk stratification and could lead to an ineffective and untimely diagnosis of subsequent CVD, which would lead to protracted hospitalization and poor prognosis.

Keywords: COVID-19; Cerebrovascular complication; Cerebrovascular disease; Intracerebral hemorrhage; Subarachnoid hemorrhage

DOI: https://doi.org/10.1159/000517499

Sci Rep. 2021 Sep 16. 11(1): 18474

Modelling COVID-19 severity in the Republic of Ireland using patient co-morbidities, socioeconomic profile and geographic location, February to November 2020.

M Boudou, C ÓhAiseadha, P Garvey, J O'Dwyer, P Hynds.

Understanding patient progression from symptomatic COVID-19 infection to a severe outcome represents an important tool for improved diagnoses, surveillance, and triage. A series of models have been developed and validated to elucidate hospitalization, admission to an intensive care unit (ICU) and mortality in patients from the Republic of Ireland. This retrospective cohort study of patients with laboratory-confirmed symptomatic COVID-19 infection included data extracted from national COVID-19 surveillance forms (i.e., age, gender, underlying health conditions, occupation) and geographically-referenced potential predictors (i.e., urban/rural classification, socio-economic profile). Generalised linear models and recursive partitioning and regression trees were used to elucidate COVID-19 progression. The incidence of symptomatic infection over the study-period was 0.96% (n = 47,265), of whom 3781 (8%) required hospitalisation, 615 (1.3%) were admitted to ICU and 1326 (2.8%) died. Models demonstrated an increasingly efficacious fit for predicting hospitalization [AUC 0.816 (95% CI 0.809, 0.822)], admission to ICU [AUC 0.885 (95% CI 0.88 0.89)] and death [AUC of 0.955 (95% CI 0.951 0.959)]. Severe obesity (BMI ≥ 40) was identified as a risk factor across all prognostic models; severely obese patients were substantially more likely to receive ICU treatment [OR 19.630] or die [OR 10.802]. Rural living was associated with an increased risk of hospitalization (OR 1.200 (95% CI 1.143-1.261)]. Urban living was associated with ICU admission [OR 1.533 (95% CI 1.606-1.682)]. Models provide approaches for predicting COVID-19 prognoses, allowing for evidence-based decision-making pertaining to targeted non-pharmaceutical interventions, risk-based vaccination priorities and improved patient triage.

DOI: https://doi.org/10.1038/s41598-021-98008-6