COVID-19 Mortality Risk in Down Syndrome: Results From a Cohort Study Of 8 Million Adults

Background: At the start of the coronavirus disease 2019 (COVID-19) pandemic, many national health organizations emphasized nonpharmacologic interventions, such as quarantining or physical distancing. In the United Kingdom, strict self-isolation (“shielding”) was advised for those deemed to be clinically extremely vulnerable on the basis of the presence of selected medical conditions or at the discretion of their general practitioners. Down syndrome features on neither the U.K. shielding list nor the U.S. Centers for Disease Control and Prevention list of groups at “increased risk.” However, it is associated with immune dysfunction, congenital heart disease, and pulmonary pathology and, given its prevalence, may be a relevant albeit unconfirmed risk factor for severe COVID-1

Transplantation for metastatic liver disease

The liver is a common site of metastases from many cancers, particularly those originating in the gastrointestinal tract. Liver transplantation is an uncommonly used but promising and at times controversial treatment option for neuroendocrine and colorectal liver metastases. Transplantation with meticulous patient selection has been associated with excellent long-term outcomes in individuals with neuroendocrine liver metastases, but questions remain regarding the role of transplantation in those who could also be eligible for hepatectomy, the role of neoadjuvant/adjuvant treatments in minimising recurrence, and the optimal timing of the procedure. A prospective pilot study of liver transplantation for unresectable colorectal liver metastases that reported a 5-year overall survival rate of 60% reinvigorated interest in this area following initially dismal outcomes. This has been followed by larger studies, and prospective trials are ongoing to quantify the potential benefits of liver transplantation over palliative chemotherapy. This review provides a critical summary of currently available knowledge on liver transplantation for neuroendocrine and colorectal liver metastases, and highlights avenues for further study to address gaps in the evidence base

Predicting 10-year breast cancer mortality risk in the general female population in England: a model development and validation study

Background Identifying female individuals at highest risk of developing life-threatening breast cancers could inform novel stratified early detection and prevention strategies to reduce breast cancer mortality, rather than only considering cancer incidence. We aimed to develop a prognostic model that accurately predicts the 10-year risk of breast cancer mortality in female individuals without breast cancer at baseline. Methods In this model development and validation study, we used an open cohort study from the QResearch primary care database, which was linked to secondary care and national cancer and mortality registers in England, UK. The data extracted were from female individuals aged 20–90 years without previous breast cancer or ductal carcinoma in situ who entered the cohort between Jan 1, 2000, and Dec 31, 2020. The primary outcome was breast cancer-related death, which was assessed in the full dataset. Cox proportional hazards, competing risks regression, XGBoost, and neural network modelling approaches were used to predict the risk of breast cancer death within 10 years using routinely collected health-care data. Death due to causes other than breast cancer was the competing risk. Internal–external validation was used to evaluate prognostic model performance (using Harrell's C, calibration slope, and calibration in the large), performance heterogeneity, and transportability. Internal–external validation involved dataset partitioning by time period and geographical region. Decision curve analysis was used to assess clinical utility. Findings We identified data for 11 626 969 female individuals, with 70 095 574 person-years of follow-up. There were 142 712 (1·2%) diagnoses of breast cancer, 24 043 (0·2%) breast cancer-related deaths, and 696 106 (6·0%) deaths from other causes. Meta-analysis pooled estimates of Harrell's C were highest for the competing risks model (0·932, 95% CI 0·917–0·946). The competing risks model was well calibrated overall (slope 1·011, 95% CI 0·978–1·044), and across different ethnic groups. Decision curve analysis suggested favourable clinical utility across all age groups. The XGBoost and neural network models had variable performance across age and ethnic groups. Interpretation A model that predicts the combined risk of developing and then dying from breast cancer at the population level could inform stratified screening or chemoprevention strategies. Further evaluation of the competing risks model should comprise effect and health economic assessment of model-informed strategies. Funding Cancer Research UK

Identification of symptoms associated with the diagnosis of pancreatic exocrine and neuroendocrine neoplasms: a nested case-control study of the UK population

Background: Pancreatic cancer has the worst survival rate among all cancers. Almost 70% of patients were diagnosed at Stage IV. Aim: This study aimed to investigate the symptoms associated with the diagnoses of pancreatic ductal adenocarcinoma (PDAC) and neuroendocrine neoplasms (PNEN), comparatively characterise the symptomatology between the two tumour types to inform earlier diagnosis. Design and Setting: A nested case-control study was conducted using data from the QResearch database. Patients aged ≥25 years and diagnosed with PDAC or PNEN during 2000-2019 were the cases. Up to 10 controls from the same general practice were matched with each case by age, sex, and calendar year using incidence density sampling. Methods: Conditional logistic regression was used to investigate the association between the forty-two shortlisted symptoms and the diagnoses of PDAC/PNEN in different timeframes relative to the index date, adjusting for patients’ sociodemographic characteristics, lifestyle, and relevant comorbidities. Results: There were 23,640 patients diagnosed with PDAC and 596 with PNEN. Twenty-three symptoms were significantly associated with PDAC, and nine symptoms with PNEN. Jaundice and gastrointestinal bleeding were the two alarm symptoms for both tumours. Thirst and dark urine were the two new identified symptoms for PDAC. The risk of unintentional weight loss may be longer than two years before the diagnosis of PNEN. Conclusion: PDAC and PNEN have overlapping symptom profiles. The QCancer (Pancreas) risk prediction model could be updated by including the newly identified symptoms and comorbidities, which could help GP identify high-risk patients for timely investigation in primary care

Quantifying the association between ethnicity and COVID-19 mortality: a national cohort study protocol.

INTRODUCTION: Recent evidence suggests that ethnic minority groups are disproportionately at increased risk of hospitalisation and death from SARS-CoV-2 infection. Population-based evidence on potential explanatory factors across minority groups and within subgroups is lacking. This study aims to quantify the association between ethnicity and the risk of hospitalisation and mortality due to COVID-19. METHODS AND ANALYSIS: This is a retrospective cohort study of adults registered across a representative and anonymised national primary care database (QResearch) that includes data on 10 million people in England. Sociodemographic, deprivation, clinical and domicile characteristics will be summarised and compared across ethnic subgroups (categorised as per 2011 census). Cox models will be used to calculate HR for hospitalisation and COVID-19 mortality associated with ethnic group. Potential confounding and explanatory factors (such as demographic, socioeconomic and clinical) will be adjusted for within regression models. The percentage contribution of distinct risk factor classes to the excess risks seen in ethnic groups/subgroups will be calculated. ETHICS AND DISSEMINATION: The study has undergone ethics review in accordance with the QResearch agreement (reference OX102). Findings will be disseminated through peer-reviewed manuscripts, presentations at scientific meetings and conferences with national and international stakeholders

Quantifying the association between ethnicity and COVID-19 mortality: a national cohort study protocol

Introduction Recent evidence suggests that ethnic minority groups are disproportionately at increased risk of hospitalisation and death from SARS-CoV-2 infection. Population-based evidence on potential explanatory factors across minority groups and within subgroups is lacking. This study aims to quantify the association between ethnicity and the risk of hospitalisation and mortality due to COVID-19.Methods and analysis This is a retrospective cohort study of adults registered across a representative and anonymised national primary care database (QResearch) that includes data on 10 million people in England. Sociodemographic, deprivation, clinical and domicile characteristics will be summarised and compared across ethnic subgroups (categorised as per 2011 census). Cox models will be used to calculate HR for hospitalisation and COVID-19 mortality associated with ethnic group. Potential confounding and explanatory factors (such as demographic, socioeconomic and clinical) will be adjusted for within regression models. The percentage contribution of distinct risk factor classes to the excess risks seen in ethnic groups/subgroups will be calculated.Ethics and dissemination The study has undergone ethics review in accordance with the QResearch agreement (reference OX102). Findings will be disseminated through peer-reviewed manuscripts, presentations at scientific meetings and conferences with national and international stakeholders

Sickle Cell Disorders and Severe COVID-19 Outcomes: A Cohort Study

Acknowledgment: The authors thank the EMIS (Egton Medical Information Systems) practices that contribute to the database as well as the University of Nottingham and University of Oxford for expertise in establishing, developing, and supporting the QResearch database. QResearch acknowledges funding from the Nottingham Biomedical Research Centre funded by the National Institute for Health Research. The data on SARS-CoV-2 reverse transcriptase polymerase chain reaction tests were used with permission from Public Health England. The Hospital Episode Statistics data and civil registration data used in this analysis are reused by permission from NHS Digital, which retains the copyright. This study was undertaken as part of a larger project, which is detailed at www.qresearch.org/research/approved-research-programs-and-projects/quantifying-the-association-between-covid-19-ethnicity-and-mortality-a-cohort-study-across-three-uk-national-databases.Financial Support: By grant MR/V027778/1 from the UK Medical Research Council. Dr. Clift is supported by a Clinical Research Training Fellowship from Cancer Research UK (DCS-CRUK-CRTF20-AC, C2195/A31310).Disclosures: Disclosures can be viewed at www.acponline.org/authors/icmje/ConflictOfInterestForms.do?msNum=M21-1375.Reproducible Research Statement: Study protocol: Available at www.qresearch.org/research/approved-research-programs-and-projects/quantifying-the-association-between-covid-19-ethnicity-and-mortality-a-cohort-study-across-three-uk-national-databases. Statistical code: Available on request to Dr. Clift (e-mail, [email protected]). Code groups used by the researchers are available at www.qresearch.org/qcode-group-library. Data set: Access to the anonymized health care data in the QResearch database is on application to the QResearch Scientific Committee by bona fide researchers employed at U.K. academic institutions according to information on www.qresearch.org

Development and validation of clinical prediction models for breast cancer incidence and mortality: a protocol for a dual cohort study

Introduction Breast cancer is the most common cancer and the leading cause of cancer-related death in women worldwide. Risk prediction models may be useful to guide risk-reducing interventions (such as pharmacological agents) in women at increased risk or inform screening strategies for early detection methods such as screening. Methods and analysis The study will use data for women aged 20–90 years between 2000 and 2020 from QResearch linked at the individual level to hospital episodes, cancer registry and death registry data. It will evaluate a set of modelling approaches to predict the risk of developing breast cancer within the next 10 years, the ‘combined’ risk of developing a breast cancer and then dying from it within 10 years, and the risk of breast cancer mortality within 10 years of diagnosis. Cox proportional hazards, competing risks, random survival forest, deep learning and XGBoost models will be explored. Models will be developed on the entire dataset, with ‘apparent’ performance reported, and internal-external cross-validation used to assess performance and geographical and temporal transportability (two 10-year time periods). Random effects meta-analysis will pool discrimination and calibration metric estimates from individual geographical units obtained from internal-external cross-validation. We will then externally validate the models in an independent dataset. Evaluation of performance heterogeneity will be conducted throughout, such as exploring performance across ethnic groups. Ethics and dissemination Ethics approval was granted by the QResearch scientific committee (reference number REC 18/EM/0400: OX129). The results will be written up for submission to peer-reviewed journals

Supplementary data for "The diagnostic odyssey in children and adolescents with X-linked hypophosphataemia: population-based, case-control study"

This study explored the recording of clinical features and the diagnostic odyssey of children and adolescents with X-linked hypophosphataemia in primary care electronic healthcare records in the United Kingdom.</p

Sickle Cell Disorders and Severe COVID-19 Outcomes: A Cohort Study.

Background: Sickle cell disease is a collection of compound heterozygote hemoglobinopathies, including sickle cell anemia (1). The heterozygote hemoglobinopathies are characterized by erythrocyte deformation with hemolysis; immune and coagulation dysfunction; and chronic complications, including pulmonary hypertension and cardiac failure (1, 2). Sickle cell trait is a carrier status for sickle cell disease. Given the established susceptibility to other viral infections and the ethnic “patterning” of sickle cell disorders, affected persons may have increased risks for severe COVID-19. Evidence about COVID-19 risks in sickle cell disorders mostly derives from studies of hospitalized persons or selective registries (3–5). Robust quantification of risks in sickle cell disorders at a population level may be informative for public health strategies.Objective: To evaluate the risks for COVID-19–related hospitalization and death in children and adults with sickle cell disorders (disease and trait, separately) using a population-level database of linked electronic health care records.Methods and Findings: A cohort study of 12.28 million persons aged 0 to 100 years was done using QResearch, a primary care database covering approximately 18% of the English population. The cohort comprised 1317 general practices with individual-level linkage to SARS-CoV-2 test results from Public Health England, hospital admissions data, and the Office for National Statistics death register. Follow-up was from 24 January 2020 to 30 September 2020 (hospitalization) and 18 January 2021 (death). Cause-specific Cox regression models stratified by individual general practice were used to estimate hazard ratios (HRs) with 95% CIs for COVID-19–related hospitalization and COVID-19–related death associated with sickle cell disease (genotypes SC, SD, or SE; sickle cell anemia; thalassemia with hemoglobin S; sickle thalassemia; or not otherwise specified) and sickle cell trait. Models were adjusted for age, sex, and ethnicity. Hospitalization related to COVID-19 was defined as confirmed or suspected COVID-19 as reason for admission (International Classification of Diseases, 10th Revision, code U07.1 or U07.2) or admission within 14 days of a positive SARS-CoV-2 test result. Death related to COVID-19 was defined as confirmed or suspected COVID-19 on the death certificate (International Classification of Diseases, 10th Revision, code U07.1 or U07.2) or death of any cause within 28 days of confirmed SARS-CoV-2 infection. Missing ethnicity data were handled using multiple imputation (10 imputed data sets); the imputation model included end points and all variables in the Table. Analyses used Stata, version 16 (StataCorp)