K Grewal1, W Hamilton, D Sharp. 1. University Hospital Bristol, Bristol Royal Infirmary, Bristol, UK. grewal.karen86@gmail.com
Abstract
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.
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.
Authors: Antonieta Medina-Lara; Bogdan Grigore; Ruth Lewis; Jaime Peters; Sarah Price; Paolo Landa; Sophie Robinson; Richard Neal; William Hamilton; Anne E Spencer Journal: Health Technol Assess Date: 2020-11 Impact factor: 4.014
Authors: Garth Funston; Victoria Hardy; Gary Abel; Emma J Crosbie; Jon Emery; Willie Hamilton; Fiona M Walter Journal: Cancers (Basel) Date: 2020-12-08 Impact factor: 6.639