Keele University

About: Keele University is a education organization based out in Newcastle-under-Lyme, United Kingdom. It is known for research contribution in the topics: Population & Stars. The organization has 11318 authors who have published 26323 publications receiving 894671 citations. The organization is also known as: Keele University.

Individual Risk factors for Hip Osteoarthritis: Obesity, Hip Injury and Physical Activity

Abstract: Hip osteoarthritis is a major cause of pain and disability. The authors explored individual risk factors for hip osteoarthritis in a population-based case-control study. The study was performed in two English health districts (Portsmouth and North Staffordshire) from 1993 to 1995. A total of 611 patients (210 men and 401 women) listed for hip replacement because of osteoarthritis over an 18-month period were compared with an equal number of controls selected from the general population and individually matched for age, sex, and family practitioner. Information about suspected risk factors was obtained by a questionnaire administered at interview and a short physical examination. Obesity (odds ratio (OR) = 1.7, 95% confidence interval (CI) 1.3-2.4; highest vs. lowest third of body mass index), previous hip injury (OR = 4.3, 95% CI 2.2-8.4), and the presence of Heberden's nodes (OR = 1.6, 95% CI 1.2-2.2) were independent risk factors for hip osteoarthritis among men and women. Hip injury was more closely related to unilateral as compared with bilateral disease. There were a negative association between cigarette smoking and osteoarthritis among men and a weak positive association with prolonged regular sporting activity. Obesity and hip injury are important independent risk factors for hip osteoarthritis, which might be amenable to primary prevention. Hip osteoarthritis may also arise as part of the polyarticular involvement found in generalized osteoarthritis.

A guide to systematic review and meta-analysis of prognostic factor studies.

Abstract: Prognostic factors are associated with the risk of future health outcomes in individuals with a particular health condition or some clinical start point (eg, a particular diagnosis). Research to identify genuine prognostic factors is important because these factors can help improve risk stratification, treatment, and lifestyle decisions, and the design of randomised trials. Although thousands of prognostic factor studies are published each year, often they are of variable quality and the findings are inconsistent. Systematic reviews and meta-analyses are therefore needed that summarise the evidence about the prognostic value of particular factors. In this article, the key steps involved in this review process are described.

External validation of clinical prediction models using big datasets from e-health records or IPD meta-analysis: opportunities and challenges

Abstract: Access to big datasets from e-health records and individual participant data (IPD) meta-analysis is signalling a new advent of external validation studies for clinical prediction models. In this article, the authors illustrate novel opportunities for external validation in big, combined datasets, while drawing attention to methodological challenges and reporting issues.