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Identifying women with suspected ovarian cancer in primary care: derivation and validation of algorithm

BMJ 2012; 344 doi: (Published 04 January 2012) Cite this as: BMJ 2012;344:d8009
  1. Julia Hippisley-Cox, professor of clinical epidemiology and general practice,
  2. Carol Coupland, associate professor in medical statistics
  1. 1Division of Primary Care, University of Nottingham, Nottingham NG2 7RD, UK
  1. Correspondence to: J Hippisley-Cox julia.hippisley-cox{at}
  • Accepted 20 October 2011


Objective To derive and validate an algorithm to estimate the absolute risk of having ovarian cancer in women with and without symptoms.

Design Cohort study with data from 375 UK QResearch general practices for development and 189 for validation.

Participants Women aged 30-84 without a diagnosis of ovarian cancer at baseline and without appetite loss, weight loss, abdominal pain, abdominal distension, rectal bleeding, or postmenopausal bleeding recorded in previous 12 months.

Main outcome The primary outcome was incident diagnosis of ovarian cancer recorded in the next two years.

Methods Risk factors examined included age, family history of ovarian cancer, previous cancers other than ovarian, body mass index (BMI), smoking, alcohol, deprivation, loss of appetite, weight loss, abdominal pain, abdominal distension, rectal bleeding, postmenopausal bleeding, urinary frequency, diarrhoea, constipation, tiredness, and anaemia. Cox proportional hazards models were used to develop the risk equation. Measures of calibration and discrimination assessed performance in the validation cohort.

Results In the derivation cohort there were 976 incident cases of ovarian cancer from 2.03 million person years. Independent predictors were age, family history of ovarian cancer (9.8-fold higher risk), anaemia (2.3-fold higher), abdominal pain (sevenfold higher), abdominal distension (23-fold higher), rectal bleeding (twofold higher), postmenopausal bleeding (6.6-fold higher), appetite loss (5.2-fold higher), and weight loss (twofold higher). On validation, the algorithm explained 57.6% of the variation. The receiver operating characteristics curve (ROC) statistic was 0.84, and the D statistic was 2.38. The 10% of women with the highest predicted risks contained 63% of all ovarian cancers diagnosed over the next two years.

Conclusion The algorithm has good discrimination and calibration and, after independent validation in an external cohort, could potentially be used to identify those at highest risk of ovarian cancer to facilitate early referral and investigation. Further research is needed to assess how best to implement the algorithm, its cost effectiveness, and whether, on implementation, it has any impact on health outcomes.


  • We acknowledge the contribution of EMIS practices who contribute to QResearch and EMIS for expertise in establishing, developing, and supporting the database. A simple web calculator to implement the QCancer (ovary) algorithm is available at

  • Contributors: JH-C initiated the study, undertook the literature review, data extraction, data manipulation, and primary data analysis, and wrote the first draft of the paper. CC contributed to the design, analysis, interpretation and drafting of the paper. JH-C is guarantor.

  • Funding: This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.

  • Competing interests: Both authors have completed the ICMJE uniform disclosure form at (available on request from the corresponding author) and declare: JH-C is co-director of QResearch, a not-for-profit organisation that is a joint partnership between the University of Nottingham and EMIS (leading commercial supplier of IT for 60% of general practices in the UK); JH-C is also a paid director of ClinRisk, which produces software to ensure the reliable and updatable implementation of clinical risk algorithms within clinical computer systems to help improve patient care. CC is a paid consultant statistician for ClinRisk. This work and any views expressed within it are solely those of the co-authors and not of any affiliated bodies or organisations.

  • Ethical approval: All QResearch studies are independently reviewed in accordance with the QResearch agreement with Trent multicentre ethics committee (UK).

  • Data sharing: The algorithms presented in this paper will be released as Open Source Software under the GNU lesser GPL v3.

This is an open-access article distributed under the terms of the Creative Commons Attribution Non-commercial License, which permits use, distribution, and reproduction in any medium, provided the original work is properly cited, the use is non commercial and is otherwise in compliance with the license. See: and

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