Plasma Biomarker Concentrations Associated With Return to Sport Following Sport-Related Concussion in Collegiate Athletes—A Concussion Assessment, Research, and Education (CARE) Consortium Study

Importance: Identifying plasma biomarkers associated with the amount of time an athlete may need before they return to sport (RTS) following a sport-related concussion (SRC) is important because it may help to improve the health and safety of athletes. Objective: To examine whether plasma biomarkers can differentiate collegiate athletes who RTS in less than 14 days or 14 days or more following SRC. Design, Setting, and Participants: This multicenter prospective diagnostic study, conducted by the National Collegiate Athletics Association–Department of Defense Concussion Assessment, Research, and Education Consortium, included 127 male and female athletes who had sustained an SRC while enrolled at 6 Concussion Assessment, Research, and Education Consortium Advanced Research Core sites as well as 2 partial–Advanced Research Core military service academies. Data were collected between February 2015 and May 2018. Athletes with SRC completed clinical testing and blood collection at preseason (baseline), postinjury (0-21 hours), 24 to 48 hours postinjury, time of symptom resolution, and 7 days after unrestricted RTS. Main Outcomes and Measures: A total of 3 plasma biomarkers (ie, total tau protein, glial fibrillary acidic protein [GFAP], and neurofilament light chain protein [Nf-L]) were measured using an ultrasensitive single molecule array technology and were included in the final analysis. RTS was examined between athletes who took less than 14 days vs those who took 14 days or more to RTS following SRC. Linear mixed models were used to identify significant interactions between period by RTS group. Area under the receiver operating characteristic curve analyses were conducted to examine whether these plasma biomarkers could discriminate between RTS groups. Results: The 127 participants had a mean (SD) age of 18.9 (1.3) years, and 97 (76.4%) were men; 65 (51.2%) took less than 14 days to RTS, and 62 (48.8%) took 14 days or more to RTS. Linear mixed models identified significant associations for both mean (SE) plasma total tau (24-48 hours postinjury, <14 days RTS vs ≥14 days RTS: −0.65 [0.12] pg/mL vs −0.14 [0.14] pg/mL; P = .008) and GFAP (postinjury, 14 days RTS vs ≥14 days RTS: 4.72 [0.12] pg/mL vs 4.39 [0.11] pg/mL; P = .04). Total tau at the time of symptom resolution had acceptable discrimination power (area under the receiver operating characteristic curve, 0.75; 95% CI, 0.63-0.86; P < .001). We also examined a combined plasma biomarker panel that incorporated Nf-L, GFAP, and total tau at each period to discriminate RTS groups. Although the analyses did reach significance at each time period when combined, results indicated that they were poor at distinguishing the groups (area under the receiver operating characteristic curve, <0.7). Conclusions and Relevance: The findings of this study suggest that measures of total tau and GFAP may identify athletes who will require more time to RTS. However, further research is needed to improve our ability to determine recovery following an SRC.This publication was made possible with support from the Grand Alliance Concussion Assessment, Research, and Education (CARE) Consortium, funded, in part by the NCAA and the Department of Defense. The US Army Medical Research Acquisition Activity, 820 Chandler St, Ft Detrick, MD 21702, is the awarding and administering acquisition office. This work was supported by the Office of the Assistant Secretary of Defense for Health Affairs through the Psychological Health and Traumatic Brain Injury Program under award No. W81XWH-14-2-0151

Identification of patients with stable chest pain deriving minimal value from coronary computed tomography angiography:An external validation of the PROMISE minimal-risk tool

Background: The PROspective Multicenter Imaging Study for Evaluation of chest pain (PROMISE) minimal-risk tool was recently developed to identify patients with suspected stable angina at very low risk of coronary artery disease (CAD) and clinical events. We assessed the external validity of this tool within the context of the Scottish Computed Tomography of the HEART (SCOT-HEART) multicenter randomised controlled trial of patients with suspected stable angina due to coronary disease. Methods: The minimal-risk tool was applied to 1764 patients with complete imaging and follow-up data. External validity was compared with the guideline-endorsed CAD Consortium (CADC) risk score and determined through tests of model discrimination and calibration. Results: A total of 531 (30.1%, mean age 52.4 years, female 62.0%) patients were classified as minimal-risk. Compared to the remainder of the validation cohort, this group had lower estimated pre-test probability of coronary disease according to the CADC model (30.0% vs 47.0%, p &#60; 0.001). The PROMISE minimal-risk tool improved discrimination compared with the CADC model (c-statistic 0.785 vs 0.730, p &#60; 0.001) and was improved further following re-estimation of covariate coefficients (c-statistic 0.805, p &#60; 0.001). Model calibration was initially poor (χ2 197.6, Hosmer-Lemeshow [HL] p &#60; 0.001), with significant overestimation of probability of minimal risk, but improved significantly following revision of the PROMISE minimal-risk intercept and covariate coefficients (χ2 5.6, HL p = 0.692). Conclusion and relevance: Despite overestimating the probability of minimal-risk, the PROMISE minimal-risk tool outperforms the CADC model with regards to prognostic discrimination in patients with suspected stable angina, and may assist clinicians in decisions regarding non-invasive testing

Bifactor Model of the Sport Concussion Assessment Tool Symptom Checklist: Replication and Invariance Across Time in the CARE Consortium Sample

Background: Identifying separate dimensions of concussion symptoms may inform a precision medicine approach to treatment. It was previously reported that a bifactor model identified distinct acute postconcussion symptom dimensions. Purpose: To replicate previous findings of a bifactor structure of concussion symptoms in the Concussion Assessment Research and Education (CARE) Consortium sample, examine measurement invariance from pre- to postinjury, and evaluate whether factors are associated with other clinical and biomarker measures. Study design: Cohort study (Diagnosis); Level of evidence, 2. Methods: Collegiate athletes were prospectively evaluated using the Sport Concussion Assessment Tool-3 (SCAT-3) during preseason (N = 31,557); 2789 were followed at <6 hours and 24 to 48 hours after concussion. Item-level SCAT-3 ratings were analyzed using exploratory and confirmatory factor analyses. Bifactor and higher-order models were compared for their fit and interpretability. Measurement invariance tested the stability of the identified factor structure across time. The association between factors and criterion measures (clinical and blood-based markers of concussion severity, symptom duration) was evaluated. Results: The optimal structure for each time point was a 7-factor bifactor model: a General factor, on which all items loaded, and 6 specific factors-Vestibulo-ocular, Headache, Sensory, Fatigue, Cognitive, and Emotional. The model manifested strict invariance across the 2 postinjury time points but only configural invariance from baseline to postinjury. From <6 to 24-48 hours, some dimensions increased in severity (Sensory, Fatigue, Emotional), while others decreased (General, Headache, Vestibulo-ocular). The factors correlated with differing clinical and biomarker criterion measures and showed differing patterns of association with symptom duration at different time points. Conclusion: Bifactor modeling supported the predominant unidimensionality of concussion symptoms while revealing multidimensional properties, including a large dominant General factor and 6 independent factors: Headache, Vestibulo-ocular, Sensory, Cognitive, Fatigue, and Emotional. Unlike the widely used SCAT-3 symptom severity score, which declines gradually after injury, the bifactor model revealed separable symptom dimensions that have distinct trajectories in the acute postinjury period and different patterns of association with other markers of injury severity and outcome. Clinical relevance: The SCAT-3 total score remains a valuable, robust index of overall concussion symptom severity, and the specific factors identified may inform management strategies. Because some symptom dimensions continue to worsen in the first 24 to 48 hours after injury (ie, Sensory, Fatigue, Emotional), routine follow-up in this time frame may be valuable to ensure that symptoms are managed effectively

The risk of COVID-19 death is much greater and age dependent with type I IFN autoantibodies

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection fatality rate (IFR) doubles with every 5 y of age from childhood onward. Circulating autoantibodies neutralizing IFN-α, IFN-ω, and/or IFN-β are found in ∼20% of deceased patients across age groups, and in ∼1% of individuals aged 4% of those >70 y old in the general population. With a sample of 1,261 unvaccinated deceased patients and 34,159 individuals of the general population sampled before the pandemic, we estimated both IFR and relative risk of death (RRD) across age groups for individuals carrying autoantibodies neutralizing type I IFNs, relative to noncarriers. The RRD associated with any combination of autoantibodies was higher in subjects under 70 y old. For autoantibodies neutralizing IFN-α2 or IFN-ω, the RRDs were 17.0 (95% CI: 11.7 to 24.7) and 5.8 (4.5 to 7.4) for individuals <70 y and ≥70 y old, respectively, whereas, for autoantibodies neutralizing both molecules, the RRDs were 188.3 (44.8 to 774.4) and 7.2 (5.0 to 10.3), respectively. In contrast, IFRs increased with age, ranging from 0.17% (0.12 to 0.31) for individuals <40 y old to 26.7% (20.3 to 35.2) for those ≥80 y old for autoantibodies neutralizing IFN-α2 or IFN-ω, and from 0.84% (0.31 to 8.28) to 40.5% (27.82 to 61.20) for autoantibodies neutralizing both. Autoantibodies against type I IFNs increase IFRs, and are associated with high RRDs, especially when neutralizing both IFN-α2 and IFN-ω. Remarkably, IFRs increase with age, whereas RRDs decrease with age. Autoimmunity to type I IFNs is a strong and common predictor of COVID-19 death.The Laboratory of Human Genetics of Infectious Diseases is supported by the Howard Hughes Medical Institute; The Rockefeller University; the St. Giles Foundation; the NIH (Grants R01AI088364 and R01AI163029); the National Center for Advancing Translational Sciences; NIH Clinical and Translational Science Awards program (Grant UL1 TR001866); a Fast Grant from Emergent Ventures; Mercatus Center at George Mason University; the Yale Center for Mendelian Genomics and the Genome Sequencing Program Coordinating Center funded by the National Human Genome Research Institute (Grants UM1HG006504 and U24HG008956); the Yale High Performance Computing Center (Grant S10OD018521); the Fisher Center for Alzheimer’s Research Foundation; the Meyer Foundation; the JPB Foundation; the French National Research Agency (ANR) under the “Investments for the Future” program (Grant ANR-10-IAHU-01); the Integrative Biology of Emerging Infectious Diseases Laboratory of Excellence (Grant ANR-10-LABX-62-IBEID); the French Foundation for Medical Research (FRM) (Grant EQU201903007798); the French Agency for Research on AIDS and Viral hepatitis (ANRS) Nord-Sud (Grant ANRS-COV05); the ANR GENVIR (Grant ANR-20-CE93-003), AABIFNCOV (Grant ANR-20-CO11-0001), CNSVIRGEN (Grant ANR-19-CE15-0009-01), and GenMIS-C (Grant ANR-21-COVR-0039) projects; the Square Foundation; Grandir–Fonds de solidarité pour l’Enfance; the Fondation du Souffle; the SCOR Corporate Foundation for Science; The French Ministry of Higher Education, Research, and Innovation (Grant MESRI-COVID-19); Institut National de la Santé et de la Recherche Médicale (INSERM), REACTing-INSERM; and the University Paris Cité. P. Bastard was supported by the FRM (Award EA20170638020). P. Bastard., J.R., and T.L.V. were supported by the MD-PhD program of the Imagine Institute (with the support of Fondation Bettencourt Schueller). Work at the Neurometabolic Disease lab received funding from Centre for Biomedical Research on Rare Diseases (CIBERER) (Grant ACCI20-767) and the European Union's Horizon 2020 research and innovation program under grant agreement 824110 (EASI Genomics). Work in the Laboratory of Virology and Infectious Disease was supported by the NIH (Grants P01AI138398-S1, 2U19AI111825, and R01AI091707-10S1), a George Mason University Fast Grant, and the G. Harold and Leila Y. Mathers Charitable Foundation. The Infanta Leonor University Hospital supported the research of the Department of Internal Medicine and Allergology. The French COVID Cohort study group was sponsored by INSERM and supported by the REACTing consortium and by a grant from the French Ministry of Health (Grant PHRC 20-0424). The Cov-Contact Cohort was supported by the REACTing consortium, the French Ministry of Health, and the European Commission (Grant RECOVER WP 6). This work was also partly supported by the Intramural Research Program of the National Institute of Allergy and Infectious Diseases and the National Institute of Dental and Craniofacial Research, NIH (Grants ZIA AI001270 to L.D.N. and 1ZIAAI001265 to H.C.S.). This program is supported by the Agence Nationale de la Recherche (Grant ANR-10-LABX-69-01). K.K.’s group was supported by the Estonian Research Council, through Grants PRG117 and PRG377. R.H. was supported by an Al Jalila Foundation Seed Grant (Grant AJF202019), Dubai, United Arab Emirates, and a COVID-19 research grant (Grant CoV19-0307) from the University of Sharjah, United Arab Emirates. S.G.T. is supported by Investigator and Program Grants awarded by the National Health and Medical Research Council of Australia and a University of New South Wales COVID Rapid Response Initiative Grant. L.I. reports funding from Regione Lombardia, Italy (project “Risposta immune in pazienti con COVID-19 e co-morbidità”). This research was partially supported by the Instituto de Salud Carlos III (Grant COV20/0968). J.R.H. reports funding from Biomedical Advanced Research and Development Authority (Grant HHSO10201600031C). S.O. reports funding from Research Program on Emerging and Re-emerging Infectious Diseases from Japan Agency for Medical Research and Development (Grant JP20fk0108531). G.G. was supported by the ANR Flash COVID-19 program and SARS-CoV-2 Program of the Faculty of Medicine from Sorbonne University iCOVID programs. The 3C Study was conducted under a partnership agreement between INSERM, Victor Segalen Bordeaux 2 University, and Sanofi-Aventis. The Fondation pour la Recherche Médicale funded the preparation and initiation of the study. The 3C Study was also supported by the Caisse Nationale d’Assurance Maladie des Travailleurs Salariés, Direction générale de la Santé, Mutuelle Générale de l’Education Nationale, Institut de la Longévité, Conseils Régionaux of Aquitaine and Bourgogne, Fondation de France, and Ministry of Research–INSERM Program “Cohortes et collections de données biologiques.” S. Debette was supported by the University of Bordeaux Initiative of Excellence. P.K.G. reports funding from the National Cancer Institute, NIH, under Contract 75N91019D00024, Task Order 75N91021F00001. J.W. is supported by a Research Foundation - Flanders (FWO) Fundamental Clinical Mandate (Grant 1833317N). Sample processing at IrsiCaixa was possible thanks to the crowdfunding initiative YoMeCorono. Work at Vall d’Hebron was also partly supported by research funding from Instituto de Salud Carlos III Grant PI17/00660 cofinanced by the European Regional Development Fund (ERDF/FEDER). C.R.-G. and colleagues from the Canarian Health System Sequencing Hub were supported by the Instituto de Salud Carlos III (Grants COV20_01333 and COV20_01334), the Spanish Ministry for Science and Innovation (RTC-2017-6471-1; AEI/FEDER, European Union), Fundación DISA (Grants OA18/017 and OA20/024), and Cabildo Insular de Tenerife (Grants CGIEU0000219140 and “Apuestas científicas del ITER para colaborar en la lucha contra la COVID-19”). T.H.M. was supported by grants from the Novo Nordisk Foundation (Grants NNF20OC0064890 and NNF21OC0067157). C.M.B. is supported by a Michael Smith Foundation for Health Research Health Professional-Investigator Award. P.Q.H. and L. Hammarström were funded by the European Union’s Horizon 2020 research and innovation program (Antibody Therapy Against Coronavirus consortium, Grant 101003650). Work at Y.-L.L.’s laboratory in the University of Hong Kong (HKU) was supported by the Society for the Relief of Disabled Children. MBBS/PhD study of D.L. in HKU was supported by the Croucher Foundation. J.L.F. was supported in part by the Evaluation-Orientation de la Coopération Scientifique (ECOS) Nord - Coopération Scientifique France-Colombie (ECOS-Nord/Columbian Administrative department of Science, Technology and Innovation [COLCIENCIAS]/Colombian Ministry of National Education [MEN]/Colombian Institute of Educational Credit and Technical Studies Abroad [ICETEX, Grant 806-2018] and Colciencias Contract 713-2016 [Code 111574455633]). A. Klocperk was, in part, supported by Grants NU20-05-00282 and NV18-05-00162 issued by the Czech Health Research Council and Ministry of Health, Czech Republic. L.P. was funded by Program Project COVID-19 OSR-UniSR and Ministero della Salute (Grant COVID-2020-12371617). I.M. is a Senior Clinical Investigator at the Research Foundation–Flanders and is supported by the CSL Behring Chair of Primary Immunodeficiencies (PID); by the Katholieke Universiteit Leuven C1 Grant C16/18/007; by a Flanders Institute for Biotechnology-Grand Challenges - PID grant; by the FWO Grants G0C8517N, G0B5120N, and G0E8420N; and by the Jeffrey Modell Foundation. I.M. has received funding under the European Union’s Horizon 2020 research and innovation program (Grant Agreement 948959). E.A. received funding from the Hellenic Foundation for Research and Innovation (Grant INTERFLU 1574). M. Vidigal received funding from the São Paulo Research Foundation (Grant 2020/09702-1) and JBS SA (Grant 69004). The NH-COVAIR study group consortium was supported by a grant from the Meath Foundation.Peer reviewe

The Relationship between Sport-Related Concussion and Sensation-Seeking

Sensation-seeking, or the need for novel and exciting experiences, is thought to play a role in sport-related concussion (SRC), yet much remains unknown regarding these relationships and, more importantly, how sensation-seeking influences SRC risk. The current study assessed sensation-seeking, sport contact level, and SRC history and incidence in a large sample of NCAA collegiate athletes. Data included a full study sample of 22,374 baseline evaluations and a sub-sample of 2037 incident SRC. Independent samples t-test, analysis of covariance, and hierarchical logistic regression were constructed to address study hypotheses. Results showed that (1) among participants without SRC, sensation-seeking scores were higher in athletes playing contact sports compared to those playing limited- or non-contact sports (p &lt; 0.001, R2 = 0.007, η2p = 0.003); (2) in the full study sample, a one-point increase in sensation-seeking scores resulted in a 21% greater risk of prior SRC (OR = 1.212; 95% CI: 1.154–1.272), and in the incident SRC sub-sample, a 28% greater risk of prior SRC (OR = 1.278; 95% CI: 1.104–1.480); (3) a one-point increase in sensation-seeking scores resulted in a 12% greater risk of incident SRC among the full study sample; and (4) sensation-seeking did not vary as a function of incident SRC (p = 0.281, η2p = 0.000). Our findings demonstrate the potential usefulness of considering sensation-seeking in SRC management

Better guidelines for better care:accounting for multimorbidity in clinical guidelines – structured examination of exemplar guidelines and health economic modelling

Background: Multimorbidity is common but most clinical guidelines focus on single diseases. Aim: To test the feasibility of new approaches to developing single-disease guidelines to better account for multimorbidity. Design: Literature-based and economic modelling project focused on areas where multimorbidity makes guideline application problematic. Methods: (1) Examination of accounting for multimorbidity in three exemplar National Institute for Health and Care Excellence guidelines (type 2 diabetes, depression, heart failure); (2) examination of the applicability of evidence in multimorbidity for the exemplar conditions; (3) exploration of methods for comparing absolute benefit of treatment; (4) incorporation of treatment pay-off time and competing risk of death in an exemplar economic model for long-term preventative treatments with slowly accruing benefit; and (5) development of a discrete event simulation model-based cost-effectiveness analysis for people with both depression and coronary heart disease. Results: (1) Comorbidity was rarely accounted for in the clinical research questions that framed the development of the exemplar guidelines, and was rarely accounted for in treatment recommendations. Drug–disease interactions were common only for comorbid chronic kidney disease, but potentially serious drug–drug interactions between recommended drugs were common and rarely accounted for in guidelines. (2) For all three conditions, the trials underpinning treatment recommendations largely excluded older, more comorbid and more coprescribed patients. The implications of low applicability varied by condition, with type 2 diabetes having large differences in comorbidity, whereas potentially serious drug–drug interactions were more important for depression. (3) Comparing absolute benefit of treatments for different conditions was shown to be technically feasible, but only if guideline developers are willing to make a number of significant assumptions. (4) The lifetime absolute benefit of statins for primary prevention is highly sensitive to the presence of both the direct treatment disutility of taking a daily tablet and competing risk of death. (5) It was feasible to use a discrete event simulation-based model to represent the relevant care pathways to estimate the relative cost-effectiveness of pharmacological treatments of major depressive disorder in primary care for patients who are also likely to go on and receive treatment for coronary heart disease but the analysis was reliant on eliciting some parameter values from experts, which increases the inherent uncertainty in the results. The key limitation was that real-life use in guideline development was not examined. Conclusions: Guideline developers could feasibly (1) use epidemiological data characterising the guideline population to inform consideration of applicability and interactions; (2) systematically compare the absolute benefit of long-term preventative treatments to inform decision-making in people with multimorbidity and high treatment burden; and (3) modify the output from economic models used in guideline development to examine time to benefit in terms of the pay-off time and varying competing risk of death from other conditions. Future work: Further research is needed to optimise presentation of comparative absolute benefit information to clinicians and patients, to evaluate the use of epidemiological and time-to-benefit data in guideline development, to better quantify direct treatment disutility and to better quantify benefit and harm in people with multimorbidity. Funding: The National Institute for Health Research Health Services and Delivery Research programme

Improvement in risk prediction, early detection and prevention of breast cancer in the NHS Breast Screening Programme and family history clinics: a dual cohort study

Background: In the UK, women are invited for 3-yearly mammography screening, through the NHS Breast Screening Programme (NHSBSP), from the ages of 47–50 years to the ages of 69–73 years. Women with family histories of breast cancer can, from the age of 40 years, obtain enhanced surveillance and, in exceptionally high-risk cases, magnetic resonance imaging. However, no NHSBSP risk assessment is undertaken. Risk prediction models are able to categorise women by risk using known risk factors, although accurate individual risk prediction remains elusive. The identification of mammographic breast density (MD) and common genetic risk variants [single nucleotide polymorphisms (SNPs)] has presaged the improved precision of risk models. Objectives: To (1) identify the best performing model to assess breast cancer risk in family history clinic (FHC) and population settings; (2) use information from MD/SNPs to improve risk prediction; (3) assess the acceptability and feasibility of offering risk assessment in the NHSBSP; and (4) identify the incremental costs and benefits of risk stratified screening in a preliminary cost-effectiveness analysis. Design: Two cohort studies assessing breast cancer incidence. Setting: High-risk FHC and the NHSBSP Greater Manchester, UK. Participants: A total of 10,000 women aged 20–79 years [Family History Risk Study (FH-Risk); UK Clinical Research Network identification number (UKCRN-ID) 8611] and 53,000 women from the NHSBSP [aged 46–73 years; Predicting the Risk of Cancer At Screening (PROCAS) study; UKCRN-ID 8080]. Interventions: Questionnaires collected standard risk information, and mammograms were assessed for breast density by a number of techniques. All FH-Risk and 10,000 PROCAS participants participated in deoxyribonucleic acid (DNA) studies. The risk prediction models Manual method, Tyrer–Cuzick (TC), BOADICEA (Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm) and Gail were used to assess risk, with modelling based on MD and SNPs. A preliminary model-based cost-effectiveness analysis of risk stratified screening was conducted. Main outcome measures: Breast cancer incidence. Data sources: The NHSBSP; cancer registration. Results: A total of 446 women developed incident breast cancers in FH-Risk in 97,958 years of follow-up. All risk models accurately stratified women into risk categories. TC had better risk precision than Gail, and BOADICEA accurately predicted risk in the 6268 single probands. The Manual model was also accurate in the whole cohort. In PROCAS, TC had better risk precision than Gail [area under the curve (AUC) 0.58 vs. 0.54], identifying 547 prospective breast cancers. The addition of SNPs in the FH-Risk case–control study improved risk precision but was not useful in BRCA1 (breast cancer 1 gene) families. Risk modelling of SNPs in PROCAS showed an incremental improvement from using SNP18 used in PROCAS to SNP67. MD measured by visual assessment score provided better risk stratification than automatic measures, despite wide intra- and inter-reader variability. Using a MD-adjusted TC model in PROCAS improved risk stratification (AUC = 0.6) and identified significantly higher rates (4.7 per 10,000 vs. 1.3 per 10,000; p < 0.001) of high-stage cancers in women with above-average breast cancer risks. It is not possible to provide estimates of the incremental costs and benefits of risk stratified screening because of lack of data inputs for key parameters in the model-based cost-effectiveness analysis. Conclusions: Risk precision can be improved by using DNA and MD, and can potentially be used to stratify NHSBSP screening. It may also identify those at greater risk of high-stage cancers for enhanced screening. The cost-effectiveness of risk stratified screening is currently associated with extensive uncertainty. Additional research is needed to identify data needed for key inputs into model-based cost-effectiveness analyses to identify the impact on health-care resource use and patient benefits. Future work: A pilot of real-time NHSBSP risk prediction to identify women for chemoprevention and enhanced screening is required. Funding: The National Institute for Health Research Programme Grants for Applied Research programme. The DNA saliva collection for SNP analysis for PROCAS was funded by the Genesis Breast Cancer Prevention Appeal

The risk of COVID-19 death is much greater and age dependent with type I IFN autoantibodies

International audienceSignificanceThere is growing evidence that preexisting autoantibodies neutralizing type I interferons (IFNs) are strong determinants of life-threatening COVID-19 pneumonia. It is important to estimate their quantitative impact on COVID-19 mortality upon SARS-CoV-2 infection, by age and sex, as both the prevalence of these autoantibodies and the risk of COVID-19 death increase with age and are higher in men. Using an unvaccinated sample of 1,261 deceased patients and 34,159 individuals from the general population, we found that autoantibodies against type I IFNs strongly increased the SARS-CoV-2 infection fatality rate at all ages, in both men and women. Autoantibodies against type I IFNs are strong and common predictors of life-threatening COVID-19. Testing for these autoantibodies should be considered in the general population