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Background Twice weekly lateral flow tests (LFTs) for secondary school children was UK Government policy from 8 March 2021. We evaluate use of LFTs (both supervised at test centres, and home test kits) in school-aged children in Cheshire and Merseyside. Methods We report (i) number of LFT positives (ii) proportion of LFT positive with confirmatory reverse transcription polymerase chain reaction (PCR) test within 2 days, and (iii) agreement between LFT-positive and confirmatory PCR, and dependence of (i–iii) on COVID-19 prevalence. Findings 1 248 468 LFTs were taken by 211 255 12–18 years old, and 163 914 by 52 116 5–11 years old between 6 November 2020 and 31 July 2021. Five thousand three hundred and fourteen (2.5%) 12–18 years old and 1996 (3.8%) 5–11 years old returned LFT positives, with 3829 (72.1%) and 1535 (76.9%) confirmatory PCRs, and 3357 (87.7%) and 1383 (90.1%) confirmatory PCR-positives, respectively. Monthly proportions of LFT positive with PCR negative varied between 4.7% and 35.3% in 12–18 years old (corresponding proportion of all tests positive: 9.7% and 0.3%). Deprivation and non-White ethnicity were associated with reduced uptake of confirmatory PCR. Interpretation Substantial inequalities in confirmatory testing need more attention to avoid further disadvantage through education loss. When prevalence is low additional measures, including confirmatory testing, are needed. Local Directors of Public Health taking more control over schools testing may be needed. Funding DHSC, MRC, NIHR, EPSRC. [ABSTRACT FROM AUTHOR]

Introduction: Recurrent pulmonary exacerbations lead to progressive lung damage in cystic fibrosis (CF). Inhaled medications (mucoactive agents and antibiotics) help prevent exacerbations, but objectively measured adherence is low. We investigated whether a multi-component (complex) self-management intervention to support adherence would reduce exacerbation rates over 12 months.Methods: Between October 2017 and May 2018, adults with CF (aged ≥16 years; 19 UK centres) were randomised to the intervention (data-logging nebulisers, a digital platform and behavioural change sessions with trained clinical interventionists) or usual care (data-logging nebulisers). Outcomes included pulmonary exacerbations (primary outcome), objectively measured adherence, body mass index (BMI), lung function (FEV1) and Cystic Fibrosis Questionnaire-Revised (CFQ-R). Analyses were by intent to treat over 12 months.Results: Among intervention (n=304) and usual care (n=303) participants (51% female, median age 31 years), 88% completed 12-month follow-up. Mean exacerbation rate was 1.63/year with intervention and 1.77/year with usual care (adjusted ratio 0.96; 95% CI 0.83 to 1.12; p=0.64). Adjusted mean differences (95% CI) were in favour of the intervention versus usual care for objectively measured adherence (9.5% (8.6% to 10.4%)) and BMI (0.3 (0.1 to 0.6) kg/m2), with no difference for %FEV1 (1.4 (-0.2 to 3.0)). Seven CFQ-R subscales showed no between-group difference, but treatment burden reduced for the intervention (3.9 (1.2 to 6.7) points). No intervention-related serious adverse events occurred.Conclusions: While pulmonary exacerbations and FEV1 did not show statistically significant differences, the intervention achieved higher objectively measured adherence versus usual care. The adherence difference might be inadequate to influence exacerbations, though higher BMI and lower perceived CF treatment burden were observed. [ABSTRACT FROM AUTHOR]

Objective: To assess associations between current use of sodium-glucose cotransporter 2 inhibitors (SGLT2is), glucagon-like peptide 1 receptor agonists (GLP-1RAs), and their combination and risk for major adverse cardiac and cerebrovascular events (MACCE) and heart failure (HF) in people with type 2 diabetes.Research Design and Methods: In three nested case-control studies involving patients with type 2 diabetes in England and Wales (primary care data from the Clinical Practice Research Datalink and Secure Anonymised Information Linkage Databank with linkage to hospital and mortality records), we matched each patient experiencing an event with up to 20 control subjects. Adjusted odds ratios (ORs) for MACCE and HF among patients receiving SGLT2i or GLP-1RA regimens versus other combinations were estimated using conditional logistic regression and pooled using random-effects meta-analysis.Results: Among 336,334 people with type 2 diabetes and without cardiovascular disease, 18,531 (5.5%) experienced a MACCE. In a cohort of 411,206 with type 2 diabetes and without HF, 17,451 (4.2%) experienced an HF event. Compared with other combination regimens, the adjusted pooled OR and 95% CI for MACCE associated with SGLT2i regimens was 0.82 (0.73, 0.92), with GLP-1RA regimens 0.93 (0.81, 1.06), and with the SGLT2i/GLP-1RA combination 0.70 (0.50, 0.98). Corresponding data for HF were SGLT2i 0.49 (0.42, 0.58), GLP-1RA 0.82 (0.71, 0.95), and SGLT2i/GLP-1RA combination 0.43 (0.28, 0.64).Conclusions: SGLT2i and SGLT2i/GLP-1RA combination regimens may be beneficial in primary prevention of MACCE and HF and GLP-1RA for HF. These data call for primary prevention trials using these agents and their combination. [ABSTRACT FROM AUTHOR]

Clinical prediction models (CPMs) can predict clinically relevant outcomes or events. Typically, prognostic CPMs are derived to predict the risk of a single future outcome. However, there are many medical applications where two or more outcomes are of interest, meaning this should be more widely reflected in CPMs so they can accurately estimate the joint risk of multiple outcomes simultaneously. A potentially naïve approach to multi-outcome risk prediction is to derive a CPM for each outcome separately, then multiply the predicted risks. This approach is only valid if the outcomes are conditionally independent given the covariates, and it fails to exploit the potential relationships between the outcomes. This paper outlines several approaches that could be used to develop CPMs for multiple binary outcomes. We consider four methods, ranging in complexity and conditional independence assumptions: namely, probabilistic classifier chain, multinomial logistic regression, multivariate logistic regression, and a Bayesian probit model. These are compared with methods that rely on conditional independence: separate univariate CPMs and stacked regression. Employing a simulation study and real-world example, we illustrate that CPMs for joint risk prediction of multiple outcomes should only be derived using methods that model the residual correlation between outcomes. In such a situation, our results suggest that probabilistic classification chains, multinomial logistic regression or the Bayesian probit model are all appropriate choices. We call into question the development of CPMs for each outcome in isolation when multiple correlated or structurally related outcomes are of interest and recommend more multivariate approaches to risk prediction. [ABSTRACT FROM AUTHOR]

A clinical prediction model is a tool for predicting healthcare outcomes, usually within a specific population and context. A common approach is to develop a new clinical prediction model for each population and context; however, this wastes potentially useful historical information. A better approach is to update or incorporate the existing clinical prediction models already developed for use in similar contexts or populations. In addition, clinical prediction models commonly become miscalibrated over time, and need replacing or updating. In this article, we review a range of approaches for re-using and updating clinical prediction models; these fall in into three main categories: simple coefficient updating, combining multiple previous clinical prediction models in a meta-model and dynamic updating of models. We evaluated the performance (discrimination and calibration) of the different strategies using data on mortality following cardiac surgery in the United Kingdom: We found that no single strategy performed sufficiently well to be used to the exclusion of the others. In conclusion, useful tools exist for updating existing clinical prediction models to a new population or context, and these should be implemented rather than developing a new clinical prediction model from scratch, using a breadth of complementary statistical methods. [ABSTRACT FROM AUTHOR]

Objectives: This study 1) investigated life expectancy and cause-specific mortality rates associated with type 2 diabetes and 2) quantified these relationships in ethnic subgroups.Research Design and Methods: This was a cohort study using Clinical Practice Research Datalink data from 383 general practices in England with linked hospitalization and mortality records. A total of 187,968 patients with incident type 2 diabetes from 1998 to 2015 were matched to 908,016 control subjects. Abridged life tables estimated years of life lost, and a competing risk survival model quantified cause-specific hazard ratios (HRs).Results: A total of 40,286 deaths occurred in patients with type 2 diabetes. At age 40, white men with diabetes lost 5 years of life and white women lost 6 years compared with those without diabetes. A loss of between 1 and 2 years was observed for South Asians and blacks with diabetes. At age older than 65 years, South Asians with diabetes had up to 1.1 years' longer life expectancy than South Asians without diabetes. Compared with whites with diabetes, South Asians with diabetes had lower adjusted risks for mortality from cardiovascular (HR 0.82; 95% CI 0.75, 0.89), cancer (HR 0.43; 95% CI 0.36, 0.51), and respiratory diseases (HR 0.60; 95% CI 0.48, 0.76). A similar pattern was observed in blacks with diabetes compared with whites with diabetes.Conclusions: Type 2 diabetes was associated with more years of life lost among whites than among South Asians or blacks, with older South Asians experiencing longer life expectancy compared with South Asians without diabetes. The findings support optimized cardiovascular disease risk factor management, especially in whites with type 2 diabetes. [ABSTRACT FROM AUTHOR]

Background: Clinical prediction models (CPMs) are increasingly deployed to support healthcare decisions but they are derived inconsistently, in part due to limited data. An emerging alternative is to aggregate existing CPMs developed for similar settings and outcomes. This simulation study aimed to investigate the impact of between-population-heterogeneity and sample size on aggregating existing CPMs in a defined population, compared with developing a model de novo.Methods: Simulations were designed to mimic a scenario in which multiple CPMs for a binary outcome had been derived in distinct, heterogeneous populations, with potentially different predictors available in each. We then generated a new 'local' population and compared the performance of CPMs developed for this population by aggregation, using stacked regression, principal component analysis or partial least squares, with redevelopment from scratch using backwards selection and penalised regression.Results: While redevelopment approaches resulted in models that were miscalibrated for local datasets of less than 500 observations, model aggregation methods were well calibrated across all simulation scenarios. When the size of local data was less than 1000 observations and between-population-heterogeneity was small, aggregating existing CPMs gave better discrimination and had the lowest mean square error in the predicted risks compared with deriving a new model. Conversely, given greater than 1000 observations and significant between-population-heterogeneity, then redevelopment outperformed the aggregation approaches. In all other scenarios, both aggregation and de novo derivation resulted in similar predictive performance.Conclusion: This study demonstrates a pragmatic approach to contextualising CPMs to defined populations. When aiming to develop models in defined populations, modellers should consider existing CPMs, with aggregation approaches being a suitable modelling strategy particularly with sparse data on the local population. [ABSTRACT FROM AUTHOR]

Background: Well-designed clinical prediction models (CPMs) often out-perform clinicians at estimating probabilities of clinical outcomes, though their adoption by family physicians is variable. How family physicians interact with CPMs is poorly understood, therefore a better understanding and framing within a context-sensitive theoretical framework may improve CPM development and implementation. The aim of this study was to investigate why family physicians do or do not use CPMs, interpreting these findings within a theoretical framework to provide recommendations for the development and implementation of future CPMs.Methods: Mixed methods study in North West England that comprised an online survey and focus groups.Results: One hundred thirty eight respondents completed the survey, which found the main perceived advantages to using CPMs were that they guided appropriate treatment (weighted rank [r] = 299; maximum r = 414 throughout), justified treatment decisions (r = 217), and incorporated a large body of evidence (r = 156). The most commonly reported barriers to using CPMs were lack of time (r = 163), irrelevance to some patients (r = 161), and poor integration with electronic health records (r = 147). Eighteen clinicians participated in two focus groups (i.e. nine in each), which revealed 13 interdependent themes affecting CPM use under three overarching domains: clinician factors, CPM factors and contextual factors. Themes were interdependent, indicating the tensions family physicians experience in providing evidence-based care for individual patients.Conclusions: The survey and focus groups showed that CPMs were valued when they supported clinical decision making and were robust. Barriers to their use related to their being time-consuming, difficult to use and not always adding value. Therefore, to be successful, CPMs should offer a relative advantage to current working, be easy to implement, be supported by training, policy and guidelines, and fit within the organisational culture. [ABSTRACT FROM AUTHOR]

Background: Chronic kidney disease (CKD) is a major and increasing constituent of disease burdens worldwide. Early identification of patients at increased risk of developing CKD can guide interventions to slow disease progression, initiate timely referral to appropriate kidney care services, and support targeting of care resources. Risk prediction models can extend laboratory-based CKD screening to earlier stages of disease; however, to date, only a few of them have been externally validated or directly compared outside development populations. Our objective was to validate published CKD prediction models applicable in primary care.Methods: We synthesised two recent systematic reviews of CKD risk prediction models and externally validated selected models for a 5-year horizon of disease onset. We used linked, anonymised, structured (coded) primary and secondary care data from patients resident in Salford (population ~234 k), UK. All adult patients with at least one record in 2009 were followed-up until the end of 2014, death, or CKD onset (n = 178,399). CKD onset was defined as repeated impaired eGFR measures over a period of at least 3 months, or physician diagnosis of CKD Stage 3-5. For each model, we assessed discrimination, calibration, and decision curve analysis.Results: Seven relevant CKD risk prediction models were identified. Five models also had an associated simplified scoring system. All models discriminated well between patients developing CKD or not, with c-statistics around 0.90. Most of the models were poorly calibrated to our population, substantially over-predicting risk. The two models that did not require recalibration were also the ones that had the best performance in the decision curve analysis.Conclusions: Included CKD prediction models showed good discriminative ability but over-predicted the actual 5-year CKD risk in English primary care patients. QKidney, the only UK-developed model, outperformed the others. Clinical prediction models should be (re)calibrated for their intended uses. [ABSTRACT FROM AUTHOR]

OBJECTIVES To estimate the potential impact of universal screening for primary prevention of cardiovascular disease (National Health Service Health Checks) on disease burden and socioeconomic inequalities in health in England, and to compare universal screening with alternative feasible strategies. DESIGN Microsimulation study of a close-to-reality synthetic population. Five scenarios were considered: baseline scenario, assuming that current trends in risk factors will continue in the future; universal screening; screening concentrated only in the most deprived areas; structural population-wide intervention; and combination of population-wide intervention and concentrated screening. SETTING Synthetic population with similar characteristics to the community dwelling population of England. PARTICIPANTS Synthetic people with traits informed by the health survey for England. MAIN OUTCOME MEASURE Cardiovascular disease cases and deaths prevented or postponed by 2030, stratified by fifths of socioeconomic status using the index of multiple deprivation. RESULTS Compared with the baseline scenario, universal screening may prevent or postpone approximately 19 000 cases (interquartile range 11 000-28 000) and 3000 deaths (-1000-6000); concentrated screening 17 000 cases (9000-26 000) and 2000 deaths (-1000-5000); population-wide intervention 67 000 cases (57 000-77 000) and 8000 deaths (4000-11 000); and the combination of the population-wide intervention and concentrated screening 82 000 cases (73 000-93 000) and 9000 deaths (6000-13 000). The most equitable strategy would be the combination of the population-wide intervention and concentrated screening, followed by concentrated screening alone and the population-wide intervention. Universal screening had the least apparent impact on socioeconomic inequalities in health. CONCLUSIONS When primary prevention strategies for reducing cardiovascular disease burden and inequalities are compared, universal screening seems less effective than alternative strategies, which incorporate population-wide approaches. Further research is needed to identify the best mix of population-wide and risk targeted CVD strategies to maximise cost effectiveness and minimise inequalities. [ABSTRACT FROM AUTHOR]

Introduction: There are widespread moves to develop risk-stratified approaches to population-based breast screening. The public needs to favour receiving breast cancer risk information, which ideally should produce no detrimental effects. This study investigates risk perception, the proportion wishing to know their 10-year risk and whether subsequent screening attendance is affected.Methods: Fifty thousand women attending the NHS Breast Screening Programme completed a risk assessment questionnaire. Ten-year breast cancer risks were estimated using a validated algorithm (Tyrer-Cuzick) adjusted for visually assessed mammographic density. Women at high risk (⩾8%) and low risk (<1%) were invited for face-to-face or telephone risk feedback and counselling.Results: Of those invited to receive risk feedback, more high-risk women, 500 out of 673 (74.3%), opted to receive a consultation than low-risk women, 106 out of 193 (54.9%) (P<0.001). Women at high risk were significantly more likely to perceive their risk as high (P<0.001) and to attend their subsequent mammogram (94.4%) compared with low-risk women (84.2%; P=0.04) and all attendees (84.3%; ⩽0.0001).Conclusions: Population-based assessment of breast cancer risk is feasible. The majority of women wished to receive risk information. Perception of general population breast cancer risk is poor. There were no apparent adverse effects on screening attendance for high-risk women whose subsequent screening attendance was increased. [ABSTRACT FROM AUTHOR]

Objective: Glycemic variability is emerging as a measure of glycemic control, which may be a reliable predictor of complications. This systematic review and meta-analysis evaluates the association between HbA1c variability and micro- and macrovascular complications and mortality in type 1 and type 2 diabetes.Research Design and Methods: Medline and Embase were searched (2004-2015) for studies describing associations between HbA1c variability and adverse outcomes in patients with type 1 and type 2 diabetes. Data extraction was performed independently by two reviewers. Random-effects meta-analysis was performed with stratification according to the measure of HbA1c variability, method of analysis, and diabetes type.Results: Seven studies evaluated HbA1c variability among patients with type 1 diabetes and showed an association of HbA1c variability with renal disease (risk ratio 1.56 [95% CI 1.08-2.25], two studies), cardiovascular events (1.98 [1.39-2.82]), and retinopathy (2.11 [1.54-2.89]). Thirteen studies evaluated HbA1c variability among patients with type 2 diabetes. Higher HbA1c variability was associated with higher risk of renal disease (1.34 [1.15-1.57], two studies), macrovascular events (1.21 [1.06-1.38]), ulceration/gangrene (1.50 [1.06-2.12]), cardiovascular disease (1.27 [1.15-1.40]), and mortality (1.34 [1.18-1.53]). Most studies were retrospective with lack of adjustment for potential confounders, and inconsistency existed in the definition of HbA1c variability.Conclusions: HbA1c variability was positively associated with micro- and macrovascular complications and mortality independently of the HbA1c level and might play a future role in clinical risk assessment. [ABSTRACT FROM AUTHOR]

Integrated care pathways (ICPs) define a chronological sequence of steps, most commonly diagnostic or treatment, to be followed in providing care for patients. Care pathways help to ensure quality standards are met and to reduce variation in practice. Although research on the computerisation of ICP progresses, there is still little knowledge on what are the requirements for designing user-friendly and usable electronic care pathways, or how users (normally health care professionals) interact with interfaces that support design, analysis and visualisation of ICPs. The purpose of the study reported in this paper was to address this gap by evaluating the usability of a novel web-based tool called COCPIT (Collaborative Online Care Pathway Investigation Tool). COCPIT supports the design, analysis and visualisation of ICPs at the population level. In order to address the aim of this study, an evaluation methodology was designed based on heuristic evaluations and a mixed method usability test. The results showed that modular visualisation and direct manipulation of information related to the design and analysis of ICPs is useful for engaging and stimulating users. However, designers should pay attention to issues related to the visibility of the system status and the match between the system and the real world, especially in relation to the display of statistical information about care pathways and the editing of clinical information within a care pathway. The paper concludes with recommendations for interface design. [ABSTRACT FROM AUTHOR]

Background: Cognitive behaviour therapy (CBT) is recommended for the treatment of psychosis; however, only a small proportion of service users have access to this intervention. Smartphone technology using software applications (apps) could increase access to psychological approaches for psychosis. This paper reports the protocol development for a clinical trial of smartphone-based CBT.Methods/design: We present a study protocol that describes a single-blind randomised controlled trial comparing a cognitive behaviour therapy-informed software application (Actissist) plus Treatment As Usual (TAU) with a symptom monitoring software application (ClinTouch) plus TAU in early psychosis. The study consists of a 12-week intervention period. We aim to recruit and randomly assign 36 participants registered with early intervention services (EIS) across the North West of England, UK in a 2:1 ratio to each arm of the trial. Our primary objective is to determine whether in people with early psychosis the Actissist app is feasible to deliver and acceptable to use. Secondary aims are to determine whether Actissist impacts on predictors of first episode psychosis (FEP) relapse and enhances user empowerment, functioning and quality of life. Assessments will take place at baseline, 12 weeks (post-treatment) and 22-weeks (10 weeks post-treatment) by assessors blind to treatment condition. The trial will report on the feasibility and acceptability of Actissist and compare outcomes between the randomised arms. The study also incorporates semi-structured interviews about the experience of participating in the Actissist trial that will be qualitatively analysed to inform future developments of the Actissist protocol and app.Discussion: To our knowledge, this is the first controlled trial to test the feasibility, acceptability, uptake, attrition and potential efficacy of a CBT-informed smartphone app for early psychosis. Mobile applications designed to deliver a psychologically-informed intervention offer new possibilities to extend the reach of traditional mental health service delivery across a range of serious mental health problems and provide choice about available care.Trial Registration: ISRCTN34966555. Date of first registration: 12 June 2014. [ABSTRACT FROM AUTHOR]

Introduction: The extent of preventable medication-related hospital admissions and medication-related issues in primary care is significant enough to justify developing decision support systems for medication safety surveillance. The prerequisite for such systems is defining a relevant set of medication safety-related indicators and understanding the influence of both patient and general practice characteristics on medication prescribing and monitoring.Objective: The aim of the study was to investigate the feasibility of linked primary and secondary care electronic health record data for surveillance of medication safety, examining not only prescribing but also monitoring, and associations with patient- and general practice-level characteristics.Methods: A cross-sectional study was conducted using linked records of patients served by one hospital and over 50 general practices in Salford, UK. Statistical analysis consisted of mixed-effects logistic models, relating prescribing safety indicators to potential determinants.Results: The overall prevalence (proportion of patients with at least one medication safety hazard) was 5.45 % for prescribing indicators and 7.65 % for monitoring indicators. Older patients and those on multiple medications were at higher risk of prescribing hazards, but at lower risk of missed monitoring. The odds of missed monitoring among all patients were 25 % less for males, 50 % less for patients in practices that provide general practitioner training, and threefold higher in practices serving the most deprived compared with the least deprived areas. Practices with more prescribing hazards did not tend to show more monitoring issues.Conclusions: Systematic collection, collation, and analysis of linked primary and secondary care records produce plausible and useful information about medication safety for a health system. Medication safety surveillance systems should pay close attention to patient age and polypharmacy with respect to both prescribing and monitoring failures; treat prescribing and monitoring as different statistical processes, rather than a combined measure of prescribing safety; and audit the socio-economic equity of missed monitoring. [ABSTRACT FROM AUTHOR]

Background: Digital self-monitoring, particularly of weight, is increasingly prevalent. The associated data could be reused for clinical and research purposes.Objective: The aim was to compare participants who use connected smart scale technologies with the general population and explore how use of smart scale technology affects, or is affected by, weight change.Methods: This was a retrospective study comparing 2 databases: (1) the longitudinal height and weight measurement database of smart scale users and (2) the Health Survey for England, a cross-sectional survey of the general population in England. Baseline comparison was of body mass index (BMI) in the 2 databases via a regression model. For exploring engagement with the technology, two analyses were performed: (1) a regression model of BMI change predicted by measures of engagement and (2) a recurrent event survival analysis with instantaneous probability of a subsequent self-weighing predicted by previous BMI change.Results: Among women, users of self-weighing technology had a mean BMI of 1.62 kg/m(2) (95% CI 1.03-2.22) lower than the general population (of the same age and height) (P<.001). Among men, users had a mean BMI of 1.26 kg/m(2) (95% CI 0.84-1.69) greater than the general population (of the same age and height) (P<.001). Reduction in BMI was independently associated with greater engagement with self-weighing. Self-weighing events were more likely when users had recently reduced their BMI.Conclusions: Users of self-weighing technology are a selected sample of the general population and this must be accounted for in studies that employ these data. Engagement with self-weighing is associated with recent weight change; more research is needed to understand the extent to which weight change encourages closer monitoring versus closer monitoring driving the weight change. The concept of isolated measures needs to give way to one of connected health metrics. [ABSTRACT FROM AUTHOR]

Background: Several single nucleotide polymorphisms (SNPs) at different loci have been associated with breast cancer susceptibility, accounting for around 10% of the familial component. Recent studies have found direct associations between specific SNPs and breast cancer in BRCA1/2 mutation carriers. Our aim was to determine whether validated susceptibility SNP scores improve the predictive ability of risk models in comparison/conjunction to other clinical/demographic information.Methods: Female BRCA1/2 carriers were identified from the Manchester genetic database, and included in the study regardless of breast cancer status or age. DNA was extracted from blood samples provided by these women and used for gene and SNP profiling. Estimates of survival were examined with Kaplan-Meier curves. Multivariable Cox proportional hazards models were fit in the separate BRCA datasets and in menopausal stages screening different combinations of clinical/demographic/genetic variables. Nonlinear random survival forests were also fit to identify relevant interactions. Models were compared using Harrell's concordance index (1 - c-index).Results: 548 female BRCA1 mutation carriers and 523 BRCA2 carriers were identified from the database. Median Kaplan-Meier estimate of survival was 46.0 years (44.9-48.1) for BRCA1 carriers and 48.9 (47.3-50.4) for BRCA2. By fitting Cox models and random survival forests, including both a genetic SNP score and clinical/demographic variables, average 1 - c-index values were 0.221 (st.dev. 0.019) for BRCA1 carriers and 0.215 (st.dev. 0.018) for BRCA2 carriers.Conclusions: Random survival forests did not yield higher performance compared to Cox proportional hazards. We found improvement in prediction performance when coupling the genetic SNP score with clinical/demographic markers, which warrants further investigation. [ABSTRACT FROM AUTHOR]

Background: To examine the association between the degree of risk factor control and cardiovascular disease (CVD) risk in type 2 diabetes and to assess if the presence of cardio-renal disease modifies these relationships.Methods: A retrospective cohort study using data from English practices from CPRD GOLD (Clinical Practice Research Datalink) and the SCI-Diabetes dataset (Scottish Care Information-Diabetes), with linkage to hospital and mortality data. We identified 101 749 with type 2 diabetes (T2D) in CPRD matched with 378 938 controls without diabetes and 330 892 with type 2 diabetes in SCI-Diabetes between 2006 and 2015. The main exposure was number of optimized risk factors: nonsmoker, total cholesterol ≤4 mmol/L, triglycerides ≤1.7 mmol/L, glycated haemoglobin (HbA1c) ≤53 mmol/mol (≤7.0%), systolic blood pressure <140mm Hg, or <130 mm Hg if high risk. Cox models were used to assess cardiovascular risk associated with levels of risk factor control.Results: In CPRD, the mean baseline age in T2D was 63 years and 28% had cardio-renal disease (SCI-Diabetes: 62 years; 35% cardio-renal disease). Over 3 years follow-up (SCI-Diabetes: 6 years), CVD events occurred among 27 900 (27%) CPRD-T2D, 101 362 (31%) SCI-Diabetes-T2D, and 75 520 (19%) CPRD-controls. In CPRD, compared with controls, T2D participants with optimal risk factor control (all risk factors controlled) had a higher risk of CVD events (adjusted hazard ratio, 1.21; 95% confidence interval, 1.12-1.29). In T2D participants from CPRD and SCI-Diabetes, pooled hazard ratios for CVD associated with 5 risk factors being elevated versus optimal risk factor control were 1.09 (95% confidence interval, 1.01-1.17) in people with cardio-renal disease but 1.96 (95% confidence interval, 1.82-2.12) in people without cardio-renal disease. People without cardio-renal disease were younger and more likely to have likely to have suboptimal risk factor control but had fewer prescriptions for risk factor modifying medications than those with cardio-renal disease.Conclusions: Optimally managed people with T2D have a 21% higher CVD risk when compared with controls. People with T2D without cardio-renal disease would be predicted to benefit greatly from CVD risk factor intervention. [ABSTRACT FROM AUTHOR]

Background: Audit and Feedback (A&F) is a widely used quality improvement technique that measures clinicians' clinical performance and reports it back to them. Computerised A&F (e-A&F) system interfaces may consist of four key components: (1) Summaries of clinical performance; (2) Patient lists; (3) Patient-level data; (4) Recommended actions. There is a lack of evidence regarding how to best design e-A&F interfaces; establishing such evidence is key to maximising usability, and in turn improving patient safety.Aim: To evaluate the usability of a novel theoretically-informed and research-led e-A&F system for primary care (the Performance Improvement plaN GeneratoR: PINGR).Objectives: (1) Describe PINGR's design, rationale and theoretical basis; (2) Identify usability issues with PINGR; (3) Understand how these issues may interfere with the cognitive goals of end-users; (4) Translate the issues into recommendations for the user-centred design of e-A&F systems.Methods: Eight experienced health system evaluators performed a usability inspection using an innovative hybrid approach consisting of five stages: (1) Development of representative user tasks, Goals, and Actions; (2) Combining Heuristic Evaluation and Cognitive Walkthrough methods into a single protocol to identify usability issues; (3) Consolidation of issues; (4) Severity rating of consolidated issues; (5) Analysis of issues according to usability heuristics, interface components, and Goal-Action structure.Results: A final list of 47 issues were categorised into 8 heuristic themes. The most error-prone heuristics were 'Consistency and standards' (13 usability issues; 28% of the total) and 'Match between system and real world' (n=10, 21%). The recommended actions component of the PINGR interface had the most usability issues (n=21, 45%), followed by patient-level data (n=5, 11%), patient lists (n=4, 9%), and summaries of clinical performance (n=4, 9%). The most error-prone Actions across all user Goals were: (1) Patient selection from a list; (2) Data identification from a figure (both population-level and patient-level); (3) Disagreement with a system recommendation.Conclusions: By contextualising our findings within the wider literature on health information system usability, we provide recommendations for the design of e-A&F system interfaces relating to their four key components, in addition to how they may be integrated within a system. [ABSTRACT FROM AUTHOR]

Background: The transradial access (TRA) site has become the default access site for percutaneous coronary intervention in the United Kingdom, with randomized trials and national registry data showing reductions in mortality associated with TRA use. This study evaluates regional changes in access site practice in England and Wales over time, examines whether changes in access site practice have been uniform nationally and across different patient subgroups, and provides national estimates for the potential number of lives saved or lost associated with regional differences in access site practice.Methods and Results: Using the British Cardiovascular Intervention Society database, we investigated outcomes for growth of TRA in different regions in England and Wales in 448 853 patients who underwent percutaneous coronary intervention from 2005 to 2012. Multiple logistic regression was used to quantify the effect of TRA on 30-day mortality and quantify lives saved and lost by differences in TRA adoption. TRA use increased from 14.0% to 58.6% in 417 038 PCI patients with large variations in different parts of the country. TRA was independently associated with a decreased risk of 30-day mortality (odds ratio=0.70; 95% confidence interval=0.66-0.74), with significant but small differences observed across different regions. The number of estimated lives saved was 450 (95% confidence interval=275-650), and we estimate that an additional 264 (95% confidence interval=153-399) lives would have been saved if TRA adoption were uniform nationally.Conclusions: TRA has become the dominant percutaneous coronary intervention approach in the United Kingdom, with a wide variation in different parts of the country. Changes in practice have contributed to mortality reductions, and inequalities have resulted in missed opportunities for further improvements. [ABSTRACT FROM AUTHOR]