Abstract
Numerous risk tools are now available, which predict either current or future risk of a cancer diagnosis. In theory, these tools have the potential to improve patient outcomes through enhancing the consistency and quality of clinical decision-making, facilitating equitable and cost-effective distribution of finite resources such as screening tests or preventive interventions, and encouraging behaviour change. These potential uses have been recognised by the National Cancer Institute as an ‘area of extraordinary opportunity' and an increasing number of risk prediction models continue to be developed. The data on predictive utility (discrimination and calibration) of these models suggest that some have potential for clinical application; however, the focus on implementation and impact is much more recent and there remains considerable uncertainty about their clinical utility and how to implement them in order to maximise benefits and minimise harms such as over-medicalisation, anxiety and false reassurance. If the potential benefits of risk prediction models are to be realised in clinical practice, further validation of the underlying risk models and research to assess the acceptability, clinical impact and economic implications of incorporating them in practice are needed.
Highlights
A risk prediction model aims to predict the probability or risk of a condition or event among individuals, or occasionally groups, based on a combination of known or measured characteristics
Many have been developed for a range of cancers; in the United Kingdom the best known are the risk assessment tools (RATs) developed from case–control studies in primary care (Hamilton, 2009) and the QCancer series derived from cohorts from primary-care electronic health records (e.g., Hippisley-Cox and Coupland, 2015; Box 2)
For example, risk models range from those in which 50% of the population would be classified as high risk and 80% of melanomas would be detected from that high-risk group, to those in which only 20% would be identified as high risk and only 50% of cases would be detected (Usher-Smith et al, 2014)
Summary
Juliet Usher-Smith*,1, Jon Emery, Willie Hamilton, Simon J Griffin and Fiona M Walter. Numerous risk tools are available, which predict either current or future risk of a cancer diagnosis In theory, these tools have the potential to improve patient outcomes through enhancing the consistency and quality of clinical decision-making, facilitating equitable and cost-effective distribution of finite resources such as screening tests or preventive interventions, and encouraging behaviour change. In theory, these tools have the potential to improve patient outcomes through enhancing the consistency and quality of clinical decision-making, facilitating equitable and cost-effective distribution of finite resources and encouraging behaviour change. We focused on systematic reviews, meta-analyses, randomised controlled trials and observational studies in primary care
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