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Supporting UK Composites

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What is a Composite?

A composite material is composed of at least two materials, which combine to give properties superior to those of the individual constituents.

For our website we refer to fibre reinforced polymer (FRP) composites, usually with carbon, glass, aramid, polymer or natural fibres embedded in a polymer matrix. Other matrix materials can be used and composites may also contain fillers or nano-materials such as graphene.

The many component materials and different processes that can be used make composites extremely versatile and efficient. They typically result in lighter, stronger, more durable solutions compared to traditional materials.

Why Use Composites?

The primary reason composite materials are chosen for components is because of weight saving for its relative stiffness and strength. For example, carbon-fibre reinforced composite can be five times stronger than 1020 grade steel while having only one fifth of the weight. Aluminium (6061 grade) is much nearer in weight to carbon-fibre composite, though still somewhat heavier, but the composite can have twice the modulus and up to seven times the strength.

Growth in the Composites Industry

The composites industry is an exciting industry to work in because new materials, processes and applications are being developed all the time – like using hybrid virgin and recycled fibres, faster and more automated manufacturing. The global composites materials market is growing at about 5% per year, with carbon fibre demand growing at 12% per year.

With around 1500 British companies involved, the UK composites product market was estimated at £2.3bn in 2015, and could grow to £12bn by 2030 ( The 2016 UK Composites Strategy )

When Should you use Composites?

As with all engineering materials, composites have particular strengths and weaknesses, which should be considered at the specifying stage. Composites are by no means the right material for every job.

However, a major driving force behind the development of composites has been that the combination of the reinforcement and the matrix can be changed to meet the required final properties of a component. For example, if the final component needs to be fire-resistant, a fire-retardant matrix can be used in the development stage so that it has this property.

Weight reduction

Durability and maintenance

Added functionality

Design freedom

Reference:
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Original Article
Open Access
Husam Abdel-Qadir , Jiming Fang , Douglas S. Lee , Jack V. Tu , Eitan Amir , Peter C. Austin , Geoffrey M. Anderson
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Circulation: Cardiovascular Quality and Outcomes. 2018; 11: e004580
Husam Abdel-Qadir
Department of Medicine, Women’s College Hospital, Toronto, ON, Canada (H.A.-Q.). Division of Cardiology, Peter Munk Cardiac Centre and the Ted Rogers Centre for Heart Research, University Health Network, Toronto, ON, Canada (H.A.-Q., D.S.L.). Institute for Clinical Evaluative Sciences, Toronto, ON, Canada (H.A.-Q., J.F., D.S.L., J.V.T., E.A., P.C.A., G.M.A.). Institute of Health Policy, Management, and Evaluation, University of Toronto, ON, Canada (H.A.-Q., D.S.L., J.V.T., E.A., P.C.A., G.M.A.).
Jiming Fang
Institute for Clinical Evaluative Sciences, Toronto, ON, Canada (H.A.-Q., J.F., D.S.L., J.V.T., E.A., P.C.A., G.M.A.).
Douglas S. Lee
Division of Cardiology, Peter Munk Cardiac Centre and the Ted Rogers Centre for Heart Research, University Health Network, Toronto, ON, Canada (H.A.-Q., D.S.L.). Institute for Clinical Evaluative Sciences, Toronto, ON, Canada (H.A.-Q., J.F., D.S.L., J.V.T., E.A., P.C.A., G.M.A.). Institute of Health Policy, Management, and Evaluation, University of Toronto, ON, Canada (H.A.-Q., D.S.L., J.V.T., E.A., P.C.A., G.M.A.).
Jack V. Tu
Institute for Clinical Evaluative Sciences, Toronto, ON, Canada (H.A.-Q., J.F., D.S.L., J.V.T., E.A., P.C.A., G.M.A.). Institute of Health Policy, Management, and Evaluation, University of Toronto, ON, Canada (H.A.-Q., D.S.L., J.V.T., E.A., P.C.A., G.M.A.). Department of Medicine, Sunnybrook Health Sciences Centre, Toronto, ON, Canada (J.V.T.).
Eitan Amir
Institute for Clinical Evaluative Sciences, Toronto, ON, Canada (H.A.-Q., J.F., D.S.L., J.V.T., E.A., P.C.A., G.M.A.). Institute of Health Policy, Management, and Evaluation, University of Toronto, ON, Canada (H.A.-Q., D.S.L., J.V.T., E.A., P.C.A., G.M.A.). Department of Medical Oncology, Princess Margaret Cancer Centre, Toronto, ON, Canada (E.A.).
Peter C. Austin
Institute for Clinical Evaluative Sciences, Toronto, ON, Canada (H.A.-Q., J.F., D.S.L., J.V.T., E.A., P.C.A., G.M.A.). Institute of Health Policy, Management, and Evaluation, University of Toronto, ON, Canada (H.A.-Q., D.S.L., J.V.T., E.A., P.C.A., G.M.A.).
Geoffrey M. Anderson
Institute for Clinical Evaluative Sciences, Toronto, ON, Canada (H.A.-Q., J.F., D.S.L., J.V.T., E.A., P.C.A., G.M.A.). Institute of Health Policy, Management, and Evaluation, University of Toronto, ON, Canada (H.A.-Q., D.S.L., J.V.T., E.A., P.C.A., G.M.A.).

Background: Ignoring competing risks in time-to-event analyses can lead to biased risk estimates, particularly for elderly patients with multimorbidity. We aimed to demonstrate the impact of considering competing risks when estimating the cumulative incidence and risk of stroke among elderly atrial fibrillation patients.

Methods and Results: Using linked administrative databases, we identified patients with atrial fibrillation aged ≥66 years discharged from hospital in ON, Canada between January 1, 2007, and March 31, 2011. We estimated the cumulative incidence of stroke hospitalization using the complement of the Kaplan–Meier function and the cumulative incidence function. This was repeated after stratifying the cohort by presence of prespecified comorbidities: chronic kidney disease, chronic obstructive pulmonary disease, cancer, or dementia. The full cohort was used to regress components of the CHADSVASc (congestive heart failure, hypertension, age, diabetes mellitus, stroke, vascular disease, sex) score on the hazard of stroke hospitalization using the Fine-Gray and Cox methods. These models were subsequently used to predict the 5-year risk of stroke hospitalization. Among 136 156 patients, the median CHADSVASc score was 4 and 84 728 patients (62.2%) had ≥1 prespecified comorbidity. The 5-year cumulative incidence of stroke was 5.4% (95% confidence interval, 5.3%–5.5%), whereas that of death without stroke was 48.8% (95% confidence interval, 48.5%–49.1%). The incidence of both events was overestimated by the Kaplan–Meier method; stroke incidence was overestimated by a relative factor of 39%. The degree of overestimation was larger among patients with non-CHADSVASc comorbidity because of higher incidence of death without stroke. The Fine-Gray model demonstrated better calibration than the Cox model, which consistently overpredicted stroke incidence.

Conclusions: The incidence of death without stroke was 9-fold higher than that of stroke, leading to biased estimates of stroke risk with traditional time-to-event methods. Statistical methods that appropriately account for competing risks should be used to mitigate this bias.

WHAT IS KNOWN

The complement of the Kaplan–Meier survival estimate is used frequently to estimate the cumulative incidence of outcomes over time.

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