AIMS:This study examined the concordance of associations of single nucleotide polymorphisms (SNPs) for acute coronary artery disease (CAD) discovered in Europeans with acute CAD, chronic ischaemic heart disease (IHD) or stroke in Chinese adults. METHODS:In a nested case-control study of acute CAD (4,748 cases/66,227 controls) in China Kadoorie Biobank (CKB), we compared associations of 224 SNPs for acute CAD discovered in Europeans in CARDIOGRAMplusC4D (CC4D) with acute CAD in Chinese. We compared associations of a genetic score (GS-CAD) for 224 SNPs for acute CAD with chronic IHD (n = 9,000), heart failure (n = 1,229), stroke (n = 10,208), CVD risk factors (n = 70,364), and plasma proteomics (n = 3,904) in CKB. RESULTS:The strength of associations of 224 CAD SNPs discovered in Europeans was moderately correlated with those for acute CAD in Chinese (r = 0.53), but significant associations were only replicated (P < 0.05) for 25 SNPs. The strength of associations with acute CAD for a 1 SD higher GS for acute CAD in Chinese was 3-fold greater than for chronic IHD or heart failure (1.22 [1.18-1.26] vs. 1.07 [1.05-1.10] vs. 1.06 [1.00-1.12], respectively) and 7-fold greater than for stroke (1.03; 1.01-1.05). Higher levels of GS-CAD were strongly associated with low-density lipoprotein cholesterol, moderately with systolic blood pressure and weakly with body mass index, but not with proteomics. CONCLUSIONS:The findings demonstrate a moderate concordance of genetic determinants for acute CAD between Europeans and Chinese that were specific for CAD and only weakly associated with chronic IHD, heart failure, or ischaemic stroke.

Despite a proliferation of statistical methodologies and developments within randomised controlled trials (RCTs) in recent decades, it is unclear which approaches are being implemented in practice. Oxford Clinical Trials Research Unit (OCTRU) is a UK Clinical Research Collaboration (UKCRC) registered Clinical Trials Unit (CTU) that has been operational since 2013 based in the Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences at the University of Oxford. We performed a review of all published RCTs conducted within OCTRU, with particular emphasis on trial methodology, statistical study design and statistical analysis. Studies were considered eligible if they were: RCTs conducted by OCTRU, have been completed and disseminated their primary results. Studies were ineligible if they were: a pilot or feasibility trial, a simulation study, a secondary analysis of an existing RCT, or a phase I trial. Phase II trials were considered if they were randomised. We performed double data extraction of all fields for all eligible trials. General trial information, such as primary disease area, main funding source, sample size, trial design and analysis information (e.g. number of study outcomes and analyses performed), were extracted and summarised. An analysis was defined as any time a statistical model was fit or a corresponding statistical test (e.g. χ2 test) and/or estimation of a parameter was performed. Of the 142 OCTRU studies registered funded (as of June 2023), 70 were completed and written up and 27 were eligible at the time of this review. The rest were ongoing or found to be ineligible. Included studies were published between 2014 and 2023, the majority in the last 5 years (20/27, 74

The treat-to-target (T2T) strategy in psoriatic arthritis (PsA) is not commonly implemented in routine care primarily due to feasibility and cost concerns. The aim of the Multicentre Observational Initiative in Treat-to-Target Outcomes in Psoriatic Arthritis (MONITOR-PsA) cohort was to assess clinical and patient-reported outcomes with a pragmatic routine implementation of a T2T approach. Patients from rheumatology departments of secondary care hospitals in the UK were included if they had a clinical diagnosis of PsA confirmed by the CASPAR criteria; active PsA with ≥ 1 tender or swollen joints or enthesis; not previously had treated with DMARDs for articular disease. Patients were assessed every 12 weeks during the first year of therapy and treated according to the T2T strategy. The primary outcome was the proportion of patients achieving the PsA Disease Activity Score (PASDAS) ‘good’ response at 48 weeks. Secondary outcomes included the proportion of achieving PASDAS moderate responses and DAPSA remission status at 48 weeks. We recruited 300 patients; 197 with polyarticular PsA and 103 with oligoarticular (<4 active joints) PsA from April 2018 to October 2022. 66 patients were in an embedded trial of intensive medication, so 234 patients were analysed receiving step up T2T care. Baseline characteristics are listed in Table 1. Median duration of symptoms prior to diagnosis of PsA was 10.91 [IQR 4.2, 28.7] months. PsA was moderately active, with a median PASDAS of 5.2 [4.4, 6.0] and a median DAPSA of 24.8 [17.6, 35.3]. 31.4% of patients had structural damage. The proportion of patients achieving the PASDAS ‘moderate’ and ‘good’ responses at 48 weeks were 35/103 (34.0%) and 39/103 (37.9%), respectively. The proportion of patients achieving MDA response and DAPSA remission status at 48 weeks was 75/156 (48.1%) and 38/130 (29.2%), respectively. 116/167 (69.5%) patients achieved PSAID patient acceptable symptom state at 48 weeks. In this pragmatic routine implementation of a T2T approach, we report results close to the T2T outcomes in the TICOPA trial (PASDAS good response). This suggests that a pragmatic T2T approach can be implemented with good outcomes in routine practice. J. Letarouilly: Consultancies; AbbVie, Celltrion, Janssen, MSD. Honoraria; Abbvie, Amgen, Biogen, BMS, Galapagos, Janssen, Lilly, Novartis, Pfizer. Grants/research support; Pfizer. E. Saeedi: None. L. James: None. N. Gullick: Consultancies; Abbvie, Eli Lilly, Novartis, Janssen, UCB. Grants/research support; Abbvie, Alfasigma, Astra Zeneca, Eli Lilly, Janssen, Novartis, UCB. A. Francis: None. D.R. Jadon: None. W. Tillett: Grants/research support; Abbvie, Amgen, BMS, Eli Lilly, GSK, Janssen, MSD, Novartis, Pfizer, UCB. Y. Sinomati: None. L.J. Tucker: None. N. Mian: None. I. Rombach: None. I. Marian: None. S. Massa: None. L. Coates: Consultancies; Abbvie, Amgen, BMS, Celgene, Eli Lilly, Gilead, Galapagos, Janssen, Moonlake, Novartis, Pfizer, UCB. Honoraria; Abbvie, Amgen, Biogen, Celgene, Eli Lilly, Galapagos, Gilead, GSK, Janssen, Medac, Novartis, Pfizer, UCB. Grants/research support; Abbvie, Amgen, Celgene, Eli Lilly, Janseen, Novartis, Pfizer, UCB.

Aims The aims of this study were to report the outcomes of patients with a complex fracture of the lower limb in the five years after they took part in the Wound Healing in Surgery for Trauma (WHIST) trial. Methods The WHIST trial compared negative pressure wound therapy (NPWT) dressings with standard dressings applied at the end of the first operation for patients undergoing internal fixation of a complex fracture of the lower limb. Complex fractures included periarticular fractures and open fractures when the wound could be closed primarily at the end of the first debridement. A total of 1,548 patients aged ≥ 16 years completed the initial follow-up, six months after injury. In this study we report the pre-planned analysis of outcome data up to five years. Patients reported their Disability Rating Index (DRI) (0 to 100, in which 100 = total disability), and health-related quality of life, chronic pain scores and neuropathic pain scores annually, using a self-reported questionnaire. Complications, including further surgery related to the fracture, were also recorded. Results A total of 1,015 of the original patients (66%) provided at least one set of outcome data during the five years of follow-up. There was no evidence of a difference in patient-reported disability between the two groups at five years (NPWT group mean DRI 30.0 (SD 26.5), standard dressing group mean DRI 31.5 (SD 28.8), adjusted difference -0.86 (95% CI -4.14 to 2.40; p = 0.609). There was also no evidence of a difference in the complication rates at this time. Conclusion We found no evidence of a difference in disability ratings between NPWT compared with standard wound dressings in the five years following the surgical treatment of a complex fracture of the lower limb. Patients in both groups reported high levels of persistent disability and reduced quality of life, with little evidence of improvement during this time. Cite this article: Bone Joint J 2024;106-B(8):858–864.

Objectives: The aim of the Severe Psoriatic arthritis – Early intervEntion to control Disease trial is to compare outcomes in psoriatic arthritis (PsA) patients with poor prognostic factors treated with standard step-up conventional synthetic disease-modifying anti-rheumatic drugs (csDMARDs), combination csDMARDs or a course of early biologics. Design: This multicentre UK trial was embedded within the MONITOR-PsA cohort, which uses a trial within cohort design. Methods and analysis: Patients with newly diagnosed PsA and at least one poor prognostic factor (polyarthritis, C-reactive protein >5 mg/dL, health assessment questionnaire >1, radiographic erosions) were randomized equally and open-label to either standard care with ‘step-up’ csDMARD therapy, initial therapy with combination csDMARDs (methotrexate with either sulfasalazine or leflunomide) or to early biologics induction therapy (adalimumab plus methotrexate). The primary outcome is the PsA disease activity score at week 24. Ethics: Ethical approval for the study was granted by the South Central Research Ethics Committee (ref 18/SC/0107). Discussion: Treatment recommendations for PsA suggest more intensive therapy for those with poor prognostic factors but there are no studies that have previously used prognostic factors to guide therapy. Applying initial intensive therapy has shown improved outcomes in other inflammatory arthritides but has never been tried in PsA. Combination csDMARDs have shown some superiority over single therapies but there are limited data and concerns about side effects. Early use of biologics has also been shown to be superior to methotrexate but these drugs are costly and not usually funded first line. However, if a short course of biologics can rapidly suppress inflammation allowing treatment to be withdrawn and response maintained on methotrexate, this may be a cost-effective model for early use. Trial registration: ClinicalTrials.gov (NCT03739853) and EudraCT (2017-004542-24).

Objective The Anti-Freaze-F (AFF) trial assessed the feasibility of conducting a definitive trial to determine whether intra-articular injection of adalimumab can reduce pain and improve function in people with pain-predominant early-stage frozen shoulder.Design Multicentre, randomised feasibility trial, with embedded qualitative study.Setting Four UK National Health Service (NHS) musculoskeletal and related physiotherapy services.Participants Adults ≥18 years with new episode of shoulder pain attributable to early-stage frozen shoulder.Interventions Participants were randomised (centralised computer generated 1:1 allocation) to either ultrasound-guided intra-articular injection of: (1) adalimumab (160 mg) or (2) placebo (saline (0.9% sodium chloride)). Participants and outcome assessors were blinded to treatment allocation. Second injection of allocated treatment (adalimumab 80 mg) or equivalent placebo was administered 2–3 weeks later.Primary feasibility objectives (1) Ability to screen and identify participants; (2) willingness of eligible participants to consent and be randomised; (3) practicalities of delivering the intervention; (4) SD of the Shoulder Pain and Disability Index (SPADI) score and attrition rate at 3 months.Results Between 31 May 2022 and 7 February 2023, 156 patients were screened of whom 39 (25%) were eligible. The main reasons for ineligibility were other shoulder disorder (38.5%; n=45/117) or no longer in pain-predominant frozen shoulder (33.3%; n=39/117). Of the 39 eligible patients, nine (23.1%) consented to be randomised (adalimumab n=4; placebo n=5). The main reason patients declined was because they preferred receiving steroid injection (n=13). All participants received treatment as allocated. The mean time from randomisation to first injection was 12.3 (adalimumab) and 7.2 days (placebo). Completion rates for patient-reported and clinician-assessed outcomes were 100%.Conclusion This study demonstrated that current NHS musculoskeletal physiotherapy settings yielded only small numbers of participants, too few to make a trial viable. This was because many patients had passed the early stage of frozen shoulder or had already formulated a preference for treatment.Trial registration number ISRCTN 27075727, EudraCT 2021-03509-23, ClinicalTrials.gov NCT05299242 (REC 21/NE/0214).

Abstract Background The relevance of tobacco smoking for infectious respiratory diseases (IRD) is uncertain. We investigated the associations of cigarette smoking with severe IRD resulting in hospitalization or death in UK adults. Methods We conducted a prospective study of cigarette smoking and risk of severe IRD in UK Biobank. The outcomes included pneumonia, other acute lower respiratory tract infections (OA-LRTI) and influenza. Multivariable Cox regression analyses were used to estimate hazard ratios (HRs) of severe IRD associated with smoking habits after adjusting for confounding factors. Results Among 341 352 participants with no prior history of major chronic diseases, there were 12 384 incident cases with pneumonia, 7054 with OA-LRTI and 795 with influenza during a 12-year follow-up. Compared with non-smokers, current smoking was associated with ⁓2-fold higher rates of severe IRD (HR 2.40 [2.27–2.53] for pneumonia, 1.99 [1.84–2.14] for OA-LRTI and 1.82 [95% confidence interval: 1.47–2.24] for influenza). Incidence of all severe IRDs were positively associated with amount of cigarettes smoked. The HRs for each IRD (except influenza) also declined with increasing duration since quitting. Conclusions Current cigarette smoking was positively associated with higher rates of IRD and the findings extend indications for tobacco control measures and vaccination of current smokers for prevention of severe IRD.

Journal Article Corrected proof Scientific Business Abstracts Get access Keith Siew, Keith Siew University College London, London, United Kingdom Search for other works by this author on: Oxford Academic PubMed Google Scholar Vaksha Patel, Vaksha Patel University College London, London, United Kingdom Search for other works by this author on: Oxford Academic PubMed Google Scholar Jasminka Zimmermann, Jasminka Zimmermann University College London, London, United Kingdom Search for other works by this author on: Oxford Academic PubMed Google Scholar Michael Vaughan, Michael Vaughan University of Cork, Eire Search for other works by this author on: Oxford Academic PubMed Google Scholar Christopher Cheshire, Christopher Cheshire Crick Institute, London, United Kingdom Search for other works by this author on: Oxford Academic PubMed Google Scholar Angela Kubik, Angela Kubik NASA Ames Research Centre, USA Search for other works by this author on: Oxford Academic PubMed Google Scholar Rebecca Finch, Rebecca Finch University of Staffordshire, United Kingdom Search for other works by this author on: Oxford Academic PubMed Google Scholar Zhongwang Li, Zhongwang Li University College London, London, United Kingdom Search for other works by this author on: Oxford Academic PubMed Google Scholar Selin Altinok, Selin Altinok University North Carolina, Chapel Hill, USA Search for other works by this author on: Oxford Academic PubMed Google Scholar Willian De Silvera, Willian De Silvera University of Staffordshire, United Kingdom Search for other works by this author on: Oxford Academic PubMed Google Scholar ... 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University Medical School, Swansea, United Kingdom Search for other works by this author on: Oxford Academic PubMed Google Scholar Hywel T Evans, Hywel T Evans Swansea University Medical School, Swansea, United Kingdom Search for other works by this author on: Oxford Academic PubMed Google Scholar Ian Farr, Ian Farr Swansea University Medical School, Swansea, United Kingdom Search for other works by this author on: Oxford Academic PubMed Google Scholar Chris Orton, Chris Orton Swansea University Medical School, Swansea, United Kingdom Search for other works by this author on: Oxford Academic PubMed Google Scholar David V Ford, David V Ford Swansea University Medical School, Swansea, United Kingdom Search for other works by this author on: Oxford Academic PubMed Google Scholar Aziz Sheikh, Aziz Sheikh Usher Institute, University of Edinburgh, Edinburgh, United Kingdom Search for other works by this author on: Oxford Academic PubMed Google Scholar Jennifer K Quint, Jennifer K Quint School of Public Health & National Heart and Lung InstituteImperial College London, London, United Kingdom Search for other works by this author on: Oxford Academic PubMed Google Scholar David Ferenbach, David Ferenbach University of Edinburgh, Edinburgh, United Kingdom Search for other works by this author on: Oxford Academic PubMed Google Scholar Lowri Allen, Lowri Allen Diabetes Research Group, Cardiff University, Cardiff, United Kingdom Search for other works by this author on: Oxford Academic PubMed Google Scholar Georgina Mortimer, Georgina Mortimer Diabetes and Metabolism, Bristol Medical School, University of Bristol, Bristol, United Kingdom Search for other works by this author on: Oxford Academic PubMed Google Scholar Rana Fareed, Rana Fareed Diabetes and Metabolism, Bristol Medical School, University of Bristol, Bristol, United Kingdom Search for other works by this author on: Oxford Academic PubMed Google Scholar Claire Williams, Claire Williams Diabetes and Metabolism, 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London, United Kingdom Search for other works by this author on: Oxford Academic PubMed Google Scholar Detlef Böckenhauer, Detlef Böckenhauer University College London, London, United Kingdom Search for other works by this author on: Oxford Academic PubMed Google Scholar Rachel Jennings, Rachel Jennings Faculty of Biology, Medicine and Health, University of Manchester, Manchester, United KingdomEndocrinology department, Manchester University NHS Foundation Trust, Manchester, United Kingdom Search for other works by this author on: Oxford Academic PubMed Google Scholar Syed Murtuza Baker, Syed Murtuza Baker Faculty of Biology, Medicine and Health, University of Manchester, Manchester, United Kingdom Search for other works by this author on: Oxford Academic PubMed Google Scholar Peyman Zarrineh, Peyman Zarrineh Faculty of Biology, Medicine and Health, University of Manchester, Manchester, United Kingdom Search for other works by this author on: Oxford Academic PubMed Google Scholar Ali 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United Kingdom Search for other works by this author on: Oxford Academic PubMed Google Scholar Sophia Apostolidou, Sophia Apostolidou MRC Clinical Trials Unit at UCL, Institute of Clinical Trials and Methodology, London, United Kingdom Search for other works by this author on: Oxford Academic PubMed Google Scholar Usha Menon, Usha Menon MRC Clinical Trials Unit at UCL, Institute of Clinical Trials and Methodology, London, United Kingdom Search for other works by this author on: Oxford Academic PubMed Google Scholar Jamie Blundell, Jamie Blundell University of Cambridge, Cambridge, United Kingdom Search for other works by this author on: Oxford Academic PubMed Google Scholar Claire Shovlin, Claire Shovlin National Heart and Lung Institute, Imperial College London, London, United KingdomNIHR Imperial Biomedical Research Centre, London, UKImperial College Healthcare NHS Trust, London, UK Search for other works by this author on: Oxford Academic PubMed Google Scholar Maria 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London Search for other works by this author on: Oxford Academic PubMed Google Scholar Mehrdad Mizani, Mehrdad Mizani University College London Search for other works by this author on: Oxford Academic PubMed Google Scholar Laura Pasea, Laura Pasea University College London Search for other works by this author on: Oxford Academic PubMed Google Scholar Spiros Denaxas, Spiros Denaxas University College London Search for other works by this author on: Oxford Academic PubMed Google Scholar Richard Corbett, Richard Corbett Imperial College Healthcare NHS Trust Search for other works by this author on: Oxford Academic PubMed Google Scholar Jilbilly Mamza, Jilbilly Mamza Astra Zeneca Search for other works by this author on: Oxford Academic PubMed Google Scholar He Gao, He Gao Astra Zeneca Search for other works by this author on: Oxford Academic PubMed Google Scholar Tamsin Morris, Tamsin Morris Astra Zeneca Search for other works by this author on: Oxford Academic PubMed Google Scholar 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and Epidemiological Studies Unit, Nuffield Department of Population Health, Big Data Institute, University of Oxford, Oxford, OX3 7LF Search for other works by this author on: Oxford Academic PubMed Google Scholar Sarah Lewington, Sarah Lewington Clinical Trial Service Unit and Epidemiological Studies Unit, Nuffield Department of Population Health, Big Data Institute, University of Oxford, Oxford, OX3 7LFMedical Research Council Population Health Research, Nuffield Department of Population Health, University of Oxford, Oxford, OX3 7LF Search for other works by this author on: Oxford Academic PubMed Google Scholar Sofia Massa, Sofia Massa Oxford Clinical Trials Research Unit, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, OX3 7LD Search for other works by this author on: Oxford Academic PubMed Google Scholar Philip Bath, Philip Bath University of Nottingham, Nottingham, United Kingdom Search for other works by this author on: Oxford Academic PubMed Google Scholar Joanna Wardlaw, Joanna Wardlaw University of Edinburgh, Edinburgh, United Kingdom Search for other works by this author on: Oxford Academic PubMed Google Scholar Gashirai Mbizvo, Gashirai Mbizvo From the The University of Liverpool, Liverpool, United Kingdom Search for other works by this author on: Oxford Academic PubMed Google Scholar Christian Schnier, Christian Schnier The University of Edinburgh, Edinburgh, United Kingdom Search for other works by this author on: Oxford Academic PubMed Google Scholar Colin Simpson, Colin Simpson Victoria University of Wellington Search for other works by this author on: Oxford Academic PubMed Google Scholar Richard Chin, Richard Chin The University of Edinburgh, Edinburgh, United Kingdom Search for other works by this author on: Oxford Academic PubMed Google Scholar Susan Duncan, Susan Duncan The University of Edinburgh, Edinburgh, United Kingdom Search for other works by this author on: Oxford Academic 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This study assesses the associations between body mass index and risk of hospitalization for or death due to COVID-19, lower respiratory tract infections, and upper respiratory tract infections.

Background Current guidelines for healthcare of community-dwelling older people advocate screening for frailty to predict adverse health outcomes, but there is no consensus on the optimum instrument to use in such settings. The objective of this systematic review of population studies was to compare the ability of the frailty index (FI) and frailty phenotype (FP) instruments to predict all-cause mortality in older people. Methods Studies published before 27 July 2022 were identified using Ovid MEDLINE, Embase, Scopus, Web of Science and CINAHL databases. The eligibility criteria were population-based prospective studies of community-dwelling older adults (aged 65 years or older) and evaluation of both the FI and FP for prediction of all-cause mortality. The Scottish Intercollegiate Guidelines Network’s Methodology checklist was used to assess study quality. The areas under the receiver operator characteristic curves (AUC) were compared, and the proportions of included studies that achieved acceptable discriminatory power (AUC > 0.7) were calculated for each frailty instrument. The results were stratified by the use of continuous or categorical formats of each instrument. The review was reported in accordance with the PRISMA and SWiM guidelines. Results Among 8 studies (range: 909 to 7713 participants), both FI and FP had comparable predictive power for all-cause mortality. The AUC values ranged from 0.66 to 0.84 for FI continuous, 0.60 to 0.80 for FI categorical, 0.63 to 0.80 for FP continuous and 0.57 to 0.79 for FP categorical. The proportion of studies achieving acceptable discriminatory power were 75%, 50%, 63%, and 50%, respectively. The predictive ability of each frailty instrument was unaltered by the number of included items. Conclusions Despite differences in their content, both the FI and FP instruments had modest but comparable ability to predict all-cause mortality. The use of continuous rather than categorical formats in either instrument enhanced their ability to predict all-cause mortality.

BACKGROUND:Atrial fibrillation (AF) has a higher prevalence in men than in women and is associated with measures of adiposity and lean mass (LM). However, it remains uncertain whether the risks of AF associated with these measures vary by sex. METHODS:Among 477 904 UK Biobank participants aged 40-69 without prior AF, 23 134 incident AF cases were identified (14 400 men, 8734 women; median follow-up 11.1 years). Cox proportional hazards models were used to estimate the covariate adjusted hazard ratios (HRs) describing the association of AF with weight, measures of adiposity [fat mass (FM), waist circumference (WC)] and LM, and their independent relevance, by sex. RESULTS:Weight and WC were independently associated with risk of AF [HR: 1.25 (1.23-1.27) per 10 kg, HR: 1.11 (1.09-1.14) per 10 cm, respectively], with comparable effects in both sexes. The association with weight was principally driven by LM, which, per 5 kg, conferred double the risk of AF compared with FM when mutually adjusted [HR: 1.20 (1.19-1.21), HR: 1.10 (1.09-1.11), respectively]; however, the effect of LM was weaker in men than in women (p-interaction = 4.3 x 10-9). Comparing the relative effects of LM, FM and WC identified different patterns within each sex; LM was the strongest predictor for both, whereas WC was stronger than FM in men but not in women. CONCLUSIONS:LM and FM (as constituents of weight) and WC are risk factors for AF. However, the independent relevance of general adiposity for AF was more limited in men than in women. The relevance of both WC and LM suggests a potentially important role for visceral adiposity and muscle mass in AF development.

Background and Objectives Contemporary cardiovascular disease (CVD) risk prediction models are rarely applied in routine clinical practice in China due to substantial regional differences in absolute risks of major CVD types within China. Moreover, the inclusion of blood lipids in most risk prediction models also limits their use in the Chinese population. We developed 10-year CVD risk prediction models excluding blood lipids that may be applicable to diverse regions of China. Methods We derived sex-specific models separately for ischemic heart disease (IHD), ischemic stroke (IS), and hemorrhagic stroke (HS) in addition to total CVD in the China Kadoorie Biobank. Participants were age 30-79 years without CVD at baseline. Predictors included age, systolic and diastolic blood pressure, use of blood pressure-lowering treatment, current daily smoking, diabetes, and waist circumference. Total CVD risks were combined in terms of conditional probability using the predicted risks of 3 submodels. Risk models were recalibrated in each region by 2 methods (practical and ideal) and risk prediction was estimated before and after recalibration. Results Model derivation involved 489,596 individuals, including 45,947 IHD, 43,647 IS, and 11,168 HS cases during 11 years of follow-up. In women, the Harrell C was 0.732 (95% CI 0.706-0.758), 0.759 (0.738-0.779), and 0.803 (0.778-0.827) for IHD, IS, and HS, respectively. The Harrell C for total CVD was 0.734 (0.732-0.736), 0.754 (0.752-0.756), and 0.774 (0.772-0.776) for models before recalibration, after practical recalibration, and after ideal recalibration. The calibration performances improved after recalibration, with models after ideal recalibration showing the best model performances. The results for men were comparable to those for women. Discussion Our CVD risk prediction models yielded good discrimination of IHD and stroke subtypes in addition to total CVD without including blood lipids. Flexible recalibration of our models for different regions could enable more widespread use using resident health records covering the overall Chinese population. Classification of Evidence This study provides Class I evidence that a prediction model incorporating accessible clinical variables predicts 10-year risk of IHD, IS, and HS in the Chinese population age 30-79 years.

A recent paper by Etard et al. highlights how clustering of Ebola exposures around a few cases, and overdispersion of contacts per case, leads to super-spreader events and epidemic propagation. 1 Etard J.F. Touré A. Sow M.S. et al. Heterogeneity of contact patterns with Ebola virus disease cases. J Infect. 2021; Abstract Full Text Full Text PDF PubMed Scopus (1) Google Scholar Based on this evidence and that seen with SARS-Cov-2, 2 Majra D. Benson J. Pitts J. et al. SARS-CoV-2 (COVID-19) superspreader events. J Infect. 2021; 82: 36-40 Abstract Full Text Full Text PDF PubMed Scopus (17) Google Scholar they suggest that targeting epidemic screening and communication control strategies in low resource settings may be beneficial.

Objective: Asymptomatic carotid stenosis (ACS) is associated with an increased risk of ischaemic stroke and myocardial infarction. Risk scores have been developed to detect individuals at high risk of ACS, thereby enabling targeted screening, but previous external validation showed scope for refinement of prediction by adding additional predictors. The aim of this study was to develop a novel risk score in a large contemporary screened population. Methods: A prediction model was developed for moderate (>= 50%) and severe (>= 70%) ACS using data from 596 469 individuals who attended screening clinics. Variables that predicted the presence of >= 50% and >= 70% ACS independently were determined using multivariable logistic regression. Internal validation was performed using bootstrapping techniques. Discrimination was assessed using area under the receiver operating characteristic curves (AUR005) and agreement between predicted and observed cases using calibration plots. Results: Predictors of >= 50% and >= 70% ACS were age, sex, current smoking, diabetes mellitus, prior stroke/ transient ischaemic attack, coronary artery disease, peripheral arterial disease, blood pressure, and blood lipids. Models discriminated between participants with and without ACS reliably, with an AUROC of 0.78 (95% confidence interval [CI] 0.77-0.78) for >= 50% ACS and 0.82 (95% CI 0.81-0.82) for >= 70% ACS. The number needed to screen in the highest decile of predicted risk to detect one case with >= 50% ACS was 13 and that of >= 70% ACS was 58. Targeted screening of the highest decile identified 41% of cases with >= 50% ACS and 51% with >= 70% ACS. Conclusion: The novel risk model predicted the prevalence of ACS reliably and performed better than previous models. Targeted screening among the highest decile of predicted risk identified around 40% of all cases with >= 50% ACS. Initiation or intensification of cardiovascular risk management in detected cases might help to reduce both carotid related ischaemic strokes and myocardial infarctions. Conclusion: The novel risk model predicted the prevalence of ACS reliably and performed better than previous models. Targeted screening among the highest decile of predicted risk identified around 40% of all cases with >= 50% ACS. Initiation or intensification of cardiovascular risk management in detected cases might help to reduce both carotid related ischaemic strokes and myocardial infarctions.

Background: The associations of cause-specific mortality with alcohol consumption have been studied mainly in higher-income countries. We relate alcohol consumption to mortality in Cuba. Methods: In 1996-2002, 146 556 adults were recruited into a prospective study from the general population in five areas of Cuba. Participants were interviewed, measured and followed up by electronic linkage to national death registries until January 1, 2017. After excluding all with missing data or chronic disease at recruitment, Cox regression (adjusted for age, sex, province, education, and smoking) was used to relate mortality rate ratios (RRs) at ages 35–79 years to alcohol consumption. RRs were corrected for long-term variability in alcohol consumption using repeat measures among 20 593 participants resurveyed in 2006-08. Findings: After exclusions, there were 120 623 participants aged 35-79 years (mean age 52 [SD 12]; 67 694 [56%] women). At recruitment, 22 670 (43%) men and 9490 (14%) women were current alcohol drinkers, with 15 433 (29%) men and 3054 (5%) women drinking at least weekly; most alcohol consumption was from rum. All-cause mortality was positively and continuously associated with weekly alcohol consumption: each additional 35cl bottle of rum per week (110g of pure alcohol) was associated with ∼10% higher risk of all-cause mortality (RR 1.08 [95%CI 1.05-1.11]). The major causes of excess mortality in weekly drinkers were cancer, vascular disease, and external causes. Non-drinkers had ∼10% higher risk (RR 1.11 [1.09-1.14]) of all-cause mortality than those in the lowest category of weekly alcohol consumption (<1 bottle/week), but this association was almost completely attenuated on exclusion of early follow-up. Interpretation: In this large prospective study in Cuba, weekly alcohol consumption was continuously related to premature mortality. Reverse causality is likely to account for much of the apparent excess risk among non-drinkers. The findings support limits to alcohol consumption that are lower than present recommendations in Cuba. Funding: Medical Research Council, British Heart Foundation, Cancer Research UK, CDC Foundation (with support from Amgen)

Background Cardiovascular disease accounts for about one-third of all premature deaths (ie, age < 70) in Cuba. Yet, the relevance of major risk factors, including systolic blood pressure (SBP), diabetes, and body-mass index (BMI), to cardiovascular mortality in this population remains unclear. Methods In 1996–2002, 146,556 adults were recruited from the general population in five areas of Cuba. Participants were interviewed, measured (height, weight and blood pressure) and followed up by electronic linkage to national death registries until Jan 1, 2017; in 2006–08, 24,345 participants were resurveyed. After excluding all with missing data, cardiovascular disease at recruitment, and those who died in the first 5 years, Cox regression (adjusted for age, sex, education, smoking, alcohol and, where appropriate, BMI) was used to relate cardiovascular mortality rate ratios (RRs) at ages 35–79 years to SBP, diabetes and BMI; RR were corrected for regression dilution to give associations with long-term average (ie, ‘usual’) levels of SBP and BMI. Results After exclusions, there were 125,939 participants (mean age 53 [SD12]; 55% women). Mean SBP was 124 mmHg (SD15), 5% had diabetes, and mean BMI was 24.2 kg/m 2 (SD3.6); mean SBP and diabetes prevalence at recruitment were both strongly related to BMI. During follow-up, there were 4112 cardiovascular deaths (2032 ischaemic heart disease, 832 stroke, and 1248 other). Cardiovascular mortality was positively associated with SBP (>=120 mmHg), diabetes, and BMI (>=22.5 kg/m 2 ): 20 mmHg higher usual SBP about doubled cardiovascular mortality (RR 2.02, 95%CI 1.88–2.18]), as did diabetes (2.15, 1.95–2.37), and 10 kg/m 2 higher usual BMI (1.92, 1.64–2.25). RR were similar in men and in women. The association with BMI and cardiovascular mortality was almost completely attenuated following adjustment for the mediating effect of SBP. Elevated SBP (>=120 mmHg), diabetes and raised BMI (>=22.5 kg/m 2 ) accounted for 27%, 14%, and 16% of cardiovascular deaths, respectively. Conclusions This large prospective study provides direct evidence for the effects of these major risk factors on cardiovascular mortality in Cuba. Despite comparatively low levels of these risk factors by international standards, the strength of their association with cardiovascular death means they nevertheless exert a substantial impact on premature mortality in Cuba.

Background Significant asymptomatic carotid stenosis ( ACS ) is associated with higher risk of strokes. While the prevalence of moderate and severe ACS is low in the general population, prediction models may allow identification of individuals at increased risk, thereby enabling targeted screening. We identified established prediction models for ACS and externally validated them in a large screening population. Methods and Results Prediction models for prevalent cases with ≥50% ACS were identified in a systematic review (975 studies reviewed and 6 prediction models identified [3 for moderate and 3 for severe ACS ]) and then validated using data from 596 469 individuals who attended commercial vascular screening clinics in the United States and United Kingdom. We assessed discrimination and calibration. In the validation cohort, 11 178 (1.87%) participants had ≥50% ACS and 2033 (0.34%) had ≥70% ACS . The best model included age, sex, smoking, hypertension, hypercholesterolemia, diabetes mellitus, vascular and cerebrovascular disease, measured blood pressure, and blood lipids. The area under the receiver operating characteristic curve for this model was 0.75 (95% CI, 0.74–0.75) for ≥50% ACS and 0.78 (95% CI, 0.77–0.79) for ≥70% ACS . The prevalence of ≥50% ACS in the highest decile of risk was 6.51%, and 1.42% for ≥70% ACS . Targeted screening of the 10% highest risk identified 35% of cases with ≥50% ACS and 42% of cases with ≥70% ACS . Conclusions Individuals at high risk of significant ACS can be selected reliably using a prediction model. The best‐performing prediction models identified over one third of all cases by targeted screening of individuals in the highest decile of risk only.

Introduction: Around 15-20% of strokes are due to carotid stenosis, but these strokes (which are commonly fatal or disabling) are potentially preventable with medical therapy and carotid intervention in selected cases. Population screening for ACS is not recommended, but risk prediction models might identify patients at high risk of ACS, thereby allowing targeted screening. We identified existing prediction models of the prevalence of ACS and externally validate them in a large screening population. Methods: We conducted a systematic search of PubMed and EMBASE for prediction models of the prevalence of ACS ≥50%. We externally validated identified models for both the predicted outcome moderate (≥50%) and severe (≥70%) stenosis in a dataset of 596,469 individuals attending vascular screening clinics in the US and UK. We assessed discrimination with the area under receiver operating characteristic (AUROC) curve and calibration with calibration plots. Results: After screening 975 studies, six risk prediction models were identified. Three were developed to predict moderate and three severe ACS. Included predictors were age, sex, smoking, hypertension, hypercholesterolemia, diabetes, vascular and cerebrovascular disease, height, measured blood pressure, and blood lipids. In the external validation cohort, 11,178 (1.87%) participants had ≥50% ACS and 2,033 (0.34%) had ≥70% ACS. The AUROC curve of the best model was 0.75 (95% CI 0.75-0.75) for ≥50% ACS and 0.78 (95% CI 0.77-0.79) for ≥70% ACS. Using this risk prediction model, the observed prevalence of ≥50% ACS in the highest decile of risk was 6.54%, with a number needed to screen (NNS) of 15. The observed prevalence of ≥70% ACS in the highest decile of risk was 1.42%, with an NNS of 70. Screening these high-risk patients will identify 34.9% of the patients with ≥50% ACS and 41.7% with ≥70% ACS. Conclusion: Cohorts of patients at elevated risk of ACS can be identified reliably, with the prevalence of ACS in the highest risk decile threefold higher than in the overall population. A targeted screening program restricted to the 10% of the population at highest risk will identify more than1/3 of all significant stenoses. Disclosure: Professor Halliday's research is funded by the UK Health Research (NIHR) Oxford Biomedical Research Centre (BRC). Registration: PROSPERO CRD42019108136

Objective: to examine the associations of cardiovascular disease (CVD) and cardiovascular risk factors with frailty. Design: a cross-sectional study. Setting: the Irish Longitudinal Study on Ageing (TILDA). Participants: frailty measures were obtained on 5,618 participants and a subset of 4,330 participants with no prior history of CVD. Exposures for observational study: cardiovascular risk factors were combined in three composite CVD risk scores (Systematic Coronary Risk Evaluation [SCORE], Ideal Cardiovascular Health [ICH] and Cardiovascular Health Metrics [CHM]). Main outcome measures: a frailty index (40-items) was used to screen for frailty. Methods: the associations of CVD risk factors with frailty were examined using logistic regression. Results: overall, 16.4% of participants had frailty (7.6% at 50-59 years to 42.5% at 80+ years), and the prevalence was higher in those with versus those without prior CVD (43.0% vs. 10.7%). Among those without prior CVD, mean levels of CVD risk factors were closely correlated with higher frailty index scores. Combined CVD risk factors, assessed using SCORE, were linearly and positively associated with frailty. Compared to low-to-moderate SCOREs, the odds ratio (OR) (95% confidence interval, CI) of frailty for those with very high risk was 3.18 (2.38-4.25). Conversely, ICH was linearly and inversely associated with frailty, with an OR for optimal health of 0.29 (0.21-0.40) compared with inadequate health. Conclusions: the concordant positive associations of SCORE and inverse associations of ICH and CHM with frailty highlight the potential importance of optimum levels of CVD risk factors for prevention of disability in frail older people.

Background It is becoming increasingly common to publish information about the quality and performance of healthcare organisations and individual professionals. However, we do not know how this information is used, or the extent to which such reporting leads to quality improvement by changing the behaviour of healthcare consumers, providers, and purchasers. Objectives To estimate the effects of public release of performance data, from any source, on changing the healthcare utilisation behaviour of healthcare consumers, providers (professionals and organisations), and purchasers of care. In addition, we sought to estimate the effects on healthcare provider performance, patient outcomes, and staff morale. Search methods We searched CENTRAL, MEDLINE, Embase, and two trials registers on 26 June 2017. We checked reference lists of all included studies to identify additional studies. Selection criteria We searched for randomised or non-randomised trials, interrupted time series, and controlled before-after studies of the effects of publicly releasing data regarding any aspect of the performance of healthcare organisations or professionals. Each study had to report at least one main outcome related to selecting or changing care. Data collection and analysis Two review authors independently screened studies for eligibility and extracted data. For each study, we extracted data about the target groups (healthcare consumers, healthcare providers, and healthcare purchasers), performance data, main outcomes (choice of healthcare provider, and improvement by means of changes in care), and other outcomes (awareness, attitude, knowledge of performance data, and costs). Given the substantial degree of clinical and methodological heterogeneity between the studies, we presented the findings for each policy in a structured format, but did not undertake a meta-analysis. Main results We included 12 studies that analysed data from more than 7570 providers (e.g. professionals and organisations), and a further 3,333,386 clinical encounters (e.g. patient referrals, prescriptions). We included four cluster-randomised trials, one cluster-non-randomised trial, six interrupted time series studies, and one controlled before-after study. Eight studies were undertaken in the USA, and one each in Canada, Korea, China, and The Netherlands. Four studies examined the effect of public release of performance data on consumer healthcare choices, and four on improving quality. There was low-certainty evidence that public release of performance data may make little or no difference to long-term healthcare utilisation by healthcare consumers (3 studies; 18,294 insurance plan beneficiaries), or providers (4 studies; 3,000,000 births, and 67 healthcare providers), or to provider performance (1 study; 82 providers). However, there was also low-certainty evidence to suggest that public release of performance data may slightly improve some patient outcomes (5 studies, 315,092 hospitalisations, and 7502 providers). There was low-certainty evidence from a single study to suggest that public release of performance data may have differential effects on disadvantaged populations. There was no evidence about effects on healthcare utilisation decisions by purchasers, or adverse effects. Authors' conclusions The existing evidence base is inadequate to directly inform policy and practice. Further studies should consider whether public release of performance data can improve patient outcomes, as well as healthcare processes.