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Ovarian cancer prediction: development of a scoring system for primary care
Author(s) -
Grewal K,
Hamilton W,
Sharp D
Publication year - 2013
Publication title -
bjog: an international journal of obstetrics and gynaecology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.157
H-Index - 164
eISSN - 1471-0528
pISSN - 1470-0328
DOI - 10.1111/1471-0528.12200
Subject(s) - receiver operating characteristic , medicine , ovarian cancer , confidence interval , logistic regression , incidence (geometry) , population , conditional logistic regression , stage (stratigraphy) , cancer , gynecology , environmental health , mathematics , biology , paleontology , geometry
Objective Recent studies have identified specific symptoms of ovarian cancer at all stages, raising the hope of reducing diagnostic delays. We aimed to devise a scoring system for symptoms of ovarian cancer in primary care. Design Secondary analysis of data from a case–control study. Setting Thirty‐nine general practices in Exeter, mid‐Devon and east Devon. Population Two hundred and twelve women with ovarian cancer and 1060 age‐, sex‐ and practice‐matched controls. Methods Conditional logistic regression was used to produce an additive scoring system and its receiver operator characteristic ( ROC ) curve. Several different cut‐offs were then tested using a simple costs model. Main outcome measures The ROC curve value. Results Each woman was assigned a score based on her symptoms in the year before diagnosis: we added a score for women aged ≥50 years, reflecting their increased incidence of ovarian cancer. The area under the ROC curve was 0.883 (95% confidence interval 0.853–0.912). The chosen cut‐off had a sensitivity of 72.6% and a specificity of 91.3%. Conclusion This scoring system could potentially direct general practitioners to appropriate investigations for ovarian cancer on the basis of symptoms and save a substantial number of unnecessary ultrasound scans being requested.