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Co-founded by Professor Julia Hippisley-Cox, the QResearch database uses GP data to improve cancer risk prediction.

The UK has one of the poorest survival rates for cancer compared to other high-income countries1. This is thought to be partly related to late presentation, and delays in diagnosis and treatment. To diagnose cancers earlier, there needs to be a more targeted investigation of at-risk and symptomatic patients, and an increased public awareness of symptoms.

Currently, there are three major clinical pathways that facilitate detection of cancer: population-based screening, surveillance of high-risk patients with diagnosed pre-malignant conditions, and GP-directed hospital referrals. However, these approaches miss many patients who present with late-stage disease and screening selection is based on a very limited number of input data points, such as those in a particular age range. Further, 95-99% patients being screened and monitored will never develop lethal disease. This “unnecessary” screening is hugely costly for the NHS and creates anxiety in the patient population. Better risk stratification to identify both those at very low cancer risk that could be moved onto reduced screening/surveillance and those at higher risk who would need more intense surveillance would enable resources to be deployed more effectively.

The QResearch database is one of the largest clinical research databases in Europe, covering 35 million patients from 1,500 GP practices throughout the UK. It includes longitudinal data collected over 25 years that is linked at an individual patient level to Hospital Episode Statistics (HES), mortality data and cancer registration (more details here), making it an extremely rich resource for cancer research. QCancer®, an evolving set of risk prediction models that uses QResearch data, has been implemented into over 4,300 GP computer systems across the UK. Broadly, the QCancer prediction models are designed to:

  1. Quantify the absolute risk that a patient has an existing cancer based on combinations of readily available risk factors and symptoms2,3.
  2. Estimate the future risk of major cancers over the next 10 years to improve the evidence base for screening and targeting interventions to those at highest risk4.
  3. Estimate survival among those with an existing cancer, taking into account information about their tumour (type, stage, grade) as well as information on risk factors and treatments5.

Profile photo of Julia Hippisley-CoxQResearch is supported by a CRUK Oxford Centre infrastructure award, the Oxford-Wellcome Institutional Strategic Support Fund (ISSF), and the John Fell Fund, with projects in the early detection of liver cancer (within DeLIVER), lung cancer (within DART), pancreatic cancer, colon cancer and multiple myeloma underway in Oxford. Please see the QResearch website or contact Julia Hippisley–Cox for more information. Julia is Professor of Clinical Epidemiology and General Practice based in the Nuffield Department of Primary Care Health Sciences and founder of ClinRisk Ltd (which produces open and closed source software to ensure the reliable implementation of clinical risk algorithms in clinical computer systems).

 

References

1. Arnold M, Rutherford MJ, Bardot A et al. (2019). Progress in cancer survival, mortality, and incidence in seven high-income countries 1995–2014 (ICBP SURVMARK-2): a population-based study. Lancet Oncology 20(11),1493-1505
2. Hippisley-Cox J, Coupland C (2013).Symptoms and risk factors to identify men with suspected cancer in primary care: derivation and validation of an algorithm. British Journal of General Practice 2013; e1-e10
3. Hippisley-Cox J, Coupland C (2013). Symptoms and risk factors to identify women with suspected cancer in primary care: derivation and validation of an algorithm. British Journal of General Practice 2013; e11-e21
4. Hippisley-Cox J, Coupland CA (2015).Development and validation of risk prediction algorithms to estimate future risk of common cancers in primary care: prospective cohort study. BMJ Open 2015; doi 10.1136/bmjopen-2015-007825
5. Hippisley-Cox J, Coupland C (2017). Development and validation of risk prediction equations to estimate survival in patients with colorectal cancer: cohort study. BMJ 2017;357 doi: 10.1136/bmj.j2497

 

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