Characteristics and outcomes of an international cohort of 600 000 hospitalized patients with COVID-19

BackgroundWe describe demographic features, treatments and clinical outcomes in the International Severe Acute Respiratory and emerging Infection Consortium (ISARIC) COVID-19 cohort, one of the world's largest international, standardized data sets concerning hospitalized patients.MethodsThe data set analysed includes COVID-19 patients hospitalized between January 2020 and January 2022 in 52 countries. We investigated how symptoms on admission, co-morbidities, risk factors and treatments varied by age, sex and other characteristics. We used Cox regression models to investigate associations between demographics, symptoms, co-morbidities and other factors with risk of death, admission to an intensive care unit (ICU) and invasive mechanical ventilation (IMV).ResultsData were available for 689 572 patients with laboratory-confirmed (91.1%) or clinically diagnosed (8.9%) SARS-CoV-2 infection from 52 countries. Age [adjusted hazard ratio per 10 years 1.49 (95% CI 1.48, 1.49)] and male sex [1.23 (1.21, 1.24)] were associated with a higher risk of death. Rates of admission to an ICU and use of IMV increased with age up to age 60 years then dropped. Symptoms, co-morbidities and treatments varied by age and had varied associations with clinical outcomes. The case-fatality ratio varied by country partly due to differences in the clinical characteristics of recruited patients and was on average 21.5%.ConclusionsAge was the strongest determinant of risk of death, with a ∼30-fold difference between the oldest and youngest groups; each of the co-morbidities included was associated with up to an almost 2-fold increase in risk. Smoking and obesity were also associated with a higher risk of death. The size of our international database and the standardized data collection method make this study a comprehensive international description of COVID-19 clinical features. Our findings may inform strategies that involve prioritization of patients hospitalized with COVID-19 who have a higher risk of death

Big data: Some statistical issues.

A broad review is given of the impact of big data on various aspects of investigation. There is some but not total emphasis on issues in epidemiological research

Psychometric evaluation of the 4C tinnitus management questionnaire for patients with tinnitus alone or tinnitus combined with hyperacusis

Objective To assess the psychometric properties of a new questionnaire evaluating patients’ confidence in managing their tinnitus, the 4C tinnitus management questionnaire (4C), which was designed to be used in the process of cognitive behavioural therapy. Design Retrospective cross-sectional based on patient records. Study samples 99 consecutive patients who sought help for tinnitus (with or without hyperacusis) from an audiology clinic in the UK. Pure tone average (PTA) hearing thresholds, Uncomfortable Loudness Levels (ULLs), and responses to the 4C questionnaire, Tinnitus Handicap Inventory (THI), Hyperacusis Questionnaire (HQ), and Screening for Anxiety and Depression in Tinnitus (SAD-T) questionnaire were gathered from the records of patients held at the audiology department. Results Cronbach’s alpha for the 4C was 0.91, indicating high internal consistency. Exploratory factor analysis suggested a one-factor solution. Discriminant validity was supported by weak correlations between 4C scores and PTA across ears and ULLmin (the across-frequency average ULL for the ear with lower average ULL). Convergent validity was supported by moderate correlations between 4C scores and scores for the THI, HQ, and SAD-T. Conclusions The 4C is an internally consistent questionnaire with high convergent and discriminant validity, which can be used to assess patients’ confidence in managing their tinnitus

Evaluation of a five-year predicted survival model for cystic fibrosis in later time periods.

We evaluated a multivariable logistic regression model predicting 5-year survival derived from a 1993-1997 cohort from the United States Cystic Fibrosis (CF) Foundation Patient Registry to assess whether therapies introduced since 1993 have altered applicability in cohorts, non-overlapping in time, from 1993-1998, 1999-2004, 2005-2010 and 2011-2016. We applied Kaplan-Meier statistics to assess unadjusted survival. We tested logistic regression model discrimination using the C-index and calibration using Hosmer-Lemeshow tests to examine original model performance and guide updating as needed. Kaplan-Meier age-adjusted 5-year probability of death in the CF population decreased substantially during 1993-2016. Patients in successive cohorts were generally healthier at entry, with higher average age, weight and lung function and fewer pulmonary exacerbations annually. CF-related diabetes prevalence, however, steadily increased. Newly derived multivariable logistic regression models for 5-year survival in new cohorts had similar estimated coefficients to the originals. The original model exhibited excellent calibration and discrimination when applied to later cohorts despite improved survival and remains useful for predicting 5-year survival. All models may be used to stratify patients for new studies, and the original coefficients may be useful as a baseline to search for additional but rare events that affect survival in CF

Estimating the effect of COVID-19 on trial design characteristics: a registered report

There have been reports of poor-quality research during the COVID-19 pandemic. This registered report assessed design characteristics of registered clinical trials for COVID-19 compared to non-COVID-19 trials to empirically explore the design of clinical research during a pandemic and how it compares to research conducted in non-pandemic times. We did a retrospective cohort study with a 1 : 1 ratio of interventional COVID-19 registrations to non-COVID-19 registrations, with four trial design outcomes: use of control arm, randomization, blinding and prospective registration. Logistic regression was used to estimate the odds ratio of investigating COVID-19 versus not COVID-19 and estimate direct and total effects of investigating COVID-19 for each outcome. The primary analysis showed a positive direct and total effect of COVID-19 on the use of control arms and randomization. It showed a negative direct effect of COVID-19 on blinding but no evidence of a total effect. There was no evidence of an effect on prospective registration. Taken together with secondary and sensitivity analyses, our findings are inconclusive but point towards a higher prevalence of key design characteristics in COVID-19 trials versus controls. The findings do not support much existing COVID-19 research quality literature, which generally suggests that COVID-19 led to a reduction in quality. Limitations included some data quality issues, minor deviations from the pre-registered plan and the fact that trial registrations were analysed which may not accurately reflect study design and conduct. Following in-principle acceptance, the approved stage 1 version of this manuscript was pre-registered on the Open Science Framework at https://doi.org/10.17605/OSF.IO/5YAEB. This pre-registration was performed prior to data analysis

Absorbed dose evaluation of Auger electron-emitting radionuclides: impact of input decay spectra on dose point kernels and S-values

The aim of this study was to investigate the impact of decay data provided by the newly developed stochastic atomic relaxation model BrIccEmis on dose point kernels (DPKs - radial dose distribution around a unit point source) and S-values (absorbed dose per unit cumulated activity) of 14 Auger electron (AE) emitting radionuclides, namely 67Ga, 80mBr, 89Zr, 90Nb, 99mTc, 111In, 117mSn, 119Sb, 123I, 124I, 125I, 135La, 195mPt and 201Tl. Radiation spectra were based on the nuclear decay data from the medical internal radiation dose (MIRD) RADTABS program and the BrIccEmis code, assuming both an isolated-atom and condensed-phase approach. DPKs were simulated with the PENELOPE Monte Carlo (MC) code using event-by-event electron and photon transport. S-values for concentric spherical cells of various sizes were derived from these DPKS using appropriate geometric reduction factors. The number of Auger and Coster-Kronig (CK) electrons and x-ray photons released per nuclear decay (yield) from MIRD-RADTABS were consistently higher than those calculated using BrIccEmis. DPKs for the electron spectra from BrIccEmis were considerably different from MIRD-RADTABS in the first few hundred nanometres from a point source where most of the Auger electrons are stopped. S-values were, however, not significantly impacted as the differences in DPKS in the sub-micrometre dimension were quickly diminished in larger dimensions. Overestimation in the total AE energy output by MIRD-RADTABS leads to higher predicted energy deposition by AE emitting radionuclides, especially in the immediate vicinity of the decaying radionuclides. This should be taken into account when MIRD-RADTABS data are used to simulate biological damage at nanoscale dimensions.Comment: 27 pages, 4 figures, 3 table

The role of dietary supplements, including biotics, glutamine, polyunsaturated fatty acids and polyphenols, in reducing gastrointestinal side effects in patients undergoing pelvic radiotherapy : A systematic review and meta-analysis

Funding Information: This work was supported by Cancer Research UK Programme grant [C5255/A23755]. Chee Kin Then’s DPhil is funded by the Clarendon Fund, Balliol College and CRUK. The funding body had no role in the study design, collection, analysis, interpretation of data or in writing the manuscript.Peer reviewedPublisher PD

Models for predicting risk of endometrial cancer: a systematic review

Background: Endometrial cancer (EC) is the most prevalent gynaecological cancer in the UK with a rising incidence. Various models exist to predict the risk of developing EC, for different settings and prediction timeframes. This systematic review aims to provide a summary of models and assess their characteristics and performance. Methods: A systematic search of the MEDLINE and Embase (OVID) databases was used to identify risk prediction models related to EC and studies validating these models. Papers relating to predicting the risk of a future diagnosis of EC were selected for inclusion. Study characteristics, variables included in the model, methods used, and model performance, were extracted. The Prediction model Risk-of-Bias Assessment Tool was used to assess model quality. Results: Twenty studies describing 19 models were included. Ten were designed for the general population and nine for high-risk populations. Three models were developed for premenopausal women and two for postmenopausal women. Logistic regression was the most used development method. Three models, all in the general population, had a low risk of bias and all models had high applicability. Most models had moderate (area under the receiver operating characteristic curve (AUC) 0.60–0.80) or high predictive ability (AUC > 0.80) with AUCs ranging from 0.56 to 0.92. Calibration was assessed for five models. Two of these, the Hippisley-Cox and Coupland QCancer models, had high predictive ability and were well calibrated; these models also received a low risk of bias rating. Conclusions: Several models of moderate-high predictive ability exist for predicting the risk of EC, but study quality varies, with most models at high risk of bias. External validation of well-performing models in large, diverse cohorts is needed to assess their utility. Registration: The protocol for this review is available on PROSPERO (CRD42022303085)

Greater utility of molecular subtype rather than epithelial-to-mesenchymal transition (EMT) markers for prognosis in high-risk non-muscle-invasive (HGT1) bladder cancer

Funding Information: ECO and AEK were funded by CRUK programme grant C5255/A23755. We would like to thank Marcus Green for cutting the sections and giving advice on optimisation of antibodies and to Dr Jong‐Wei Hsu for advice on antibody selection. LB was supported by the National Institute for Health Research (NIHR) Oxford Biomedical Research Centre (BRC). The views expressed are those of the authors and not necessarily those of the National Health Service (NHS), the NIHR or the Department of Health. LB is part of the PathLAKE digital pathology consortium. These new Centres are supported by a £50m investment from the Data to Early Diagnosis and Precision Medicine strand of the UK government's Industrial Strategy Challenge Fund, managed and delivered by UK Research and Innovation (UKRI).Peer reviewedPublisher PD

Associations between circulating proteins and cardiometabolic diseases: a systematic review and meta-analysis of observational and Mendelian randomisation studies

Background: Integration of large proteomics and genetic data in population-based studies can provide insights into discovery of novel biomarkers and potential therapeutic targets for cardiometabolic diseases (CMD). We aimed to synthesise existing evidence on the observational and genetic associations between circulating proteins and CMD. Methods: PubMed, Embase and Web of Science were searched until July 2023 for potentially relevant prospective observational and Mendelian randomisation (MR) studies investigating associations between circulating proteins and CMD, including coronary heart disease, stroke, type 2 diabetes, heart failure, atrial fibrillation and atherosclerosis. Two investigators independently extracted study characteristics using a standard form and pooled data using random effects models. Results: 50 observational, 25 MR and 10 studies performing both analyses were included, involving 26 414 160 non-overlapping participants. Meta-analysis of observational studies revealed 560 proteins associated with CMD, of which 133 proteins were associated with ≥2 CMDs (ie, pleiotropic). There were 245 potentially causal protein biomarkers identified in MR pooled results, involving 23 pleiotropic proteins. IL6RA and MMP12 were each causally associated with seven diseases. 22 protein-disease pairs showed directionally concordant associations in observational and MR pooled estimates. Addition of protein biomarkers to traditional clinical models modestly improved the accuracy of predicting incident CMD, with the highest improvement for heart failure (ΔC-index ~0.2). Of the 245 potentially causal proteins (291 protein-disease pairs), 3 pairs were validated by evidence of drug development from existing drug databases, 288 pairs lacked evidence of drug development and 66 proteins were drug targets approved for other indications. Conclusions: Combined analyses of observational and genetic studies revealed the potential causal role of several proteins in the aetiology of CMD. Novel protein biomarkers are promising targets for drug development and risk stratification. PROSPERO registration number: CRD42022350327