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Could Ovarian Cancer Prediction Models Improve the Triage of Symptomatic Women in Primary Care? A Modelling Study Using Routinely Collected Data.

ABSTRACT: CA125 is widely used as an initial investigation in women presenting with symptoms of possible ovarian cancer. We sought to develop CA125-based diagnostic prediction models and to explore potential implications of implementing model-based thresholds for further investigation in primary care. This retrospective cohort study used routinely collected primary care and cancer registry data from symptomatic, CA125-tested women in England (2011-2014). A total of 29,962 women were included, of whom 279 were diagnosed with ovarian cancer. Logistic regression was used to develop two models to estimate ovarian cancer probability: Model 1 consisted of age and CA125 level; Model 2 incorporated further risk factors. Model discrimination (AUC) was evaluated using 10-fold cross-validation. The sensitivity and specificity of various model risk thresholds (≥1% to ≥3%) were compared with that of the current CA125 cut-off (≥35 U/mL). Model 1 exhibited excellent discrimination (AUC: 0.94) on cross-validation. The inclusion of additional variables (Model 2) did not improve performance. At a risk threshold of ≥1%, Model 1 exhibited greater sensitivity (86.4% vs. 78.5%) but lower specificity (89.1% vs. 94.5%) than CA125 (≥35 U/mL). Applying the ≥1% model threshold to the cohort in place of the current CA125 cut-off, 1 in every 74 additional women identified had ovarian cancer. Following external validation, Model 1 could be used as part of a 'risk-based triage' system in which women at high risk of undiagnosed ovarian cancer are selected for urgent specialist investigation, while women at 'low risk but not no risk' are offered non-urgent investigation or interval CA125 re-testing. Such an approach has the potential to expedite ovarian cancer diagnosis, but further research is needed to evaluate the clinical impact and health-economic implications.

SUBMITTER: Funston G

PROVIDER: S-EPMC8228892 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundGuidelines recommend identifying in early pregnancy women at elevated risk of pre-eclampsia. The aim of this study was to develop and validate a pre-eclampsia risk prediction model for nulliparous women attending routine antenatal care "the Western Sydney (WS) model"; and to compare its performance with the National Institute of Health and Care Excellence (NICE) risk factor-list approach for classifying women as high-risk.MethodsThis retrospective cohort study included all nulliparous women who gave birth in three public hospitals in the Western-Sydney-Local-Health-District, Australia 2011-2014. Using births from 2011 to 2012, multivariable logistic regression incorporated established maternal risk factors to develop and internally validate the WS model. The WS model was then externally validated using births from 2013 to 2014, assessing its discrimination and calibration. We fitted the final WS model for all births from 2011 to 2014, and compared its accuracy in predicting pre-eclampsia with the NICE approach.ResultsAmong 12,395 births to nulliparous women in 2011-2014, there were 293 (2.4%) pre-eclampsia events. The WS model included: maternal age, body mass index, ethnicity, multiple pregnancy, family history of pre-eclampsia, autoimmune disease, chronic hypertension and chronic renal disease. In the validation sample (6201 births), the model c-statistic was 0.70 (95% confidence interval 0.65-0.75). The observed:expected ratio for pre-eclampsia was 0.91, with a Hosmer-Lemeshow goodness-of-fit test p-value of 0.20. In the entire study sample of 12,395 births, 374 (3.0%) women had a WS model-estimated pre-eclampsia risk ≥8%, the pre-specified risk-threshold for considering aspirin prophylaxis. Of these, 54 (14.4%) developed pre-eclampsia (sensitivity 18% (14-23), specificity 97% (97-98)). Using the NICE approach, 1173 (9.5%) women were classified as high-risk, of which 107 (9.1%) developed pre-eclampsia (sensitivity 37% (31-42), specificity 91% (91-92)). The final model showed similar accuracy to the NICE approach when using lower risk-threshold of ≥4% to classify women as high-risk for pre-eclampsia.ConclusionThe WS risk model that combines readily-available maternal characteristics achieved modest performance for prediction of pre-eclampsia in nulliparous women. The model did not outperform the NICE approach, but has the advantage of providing individualised absolute risk estimates, to assist with counselling, inform decisions for further testing, and consideration of aspirin prophylaxis.

Project description:ObjectiveTo examine whether gender differences in primary care consultation rates (1) vary by age and deprivation status and (2) diminish when consultation for reproductive reasons or common underlying morbidities are accounted for.DesignCross-sectional study of a cohort of patients registered with general practice.SettingUK primary care.SubjectsPatients (1 869 149 men and 1 916 898 women) registered with 446 eligible practices in 2010.Primary outcome measuresPrimary care consultation rate.ResultsThis study analyses routinely collected primary care consultation data. The crude consultation rate was 32% lower in men than women. The magnitude of gender difference varied across the life course, and there was no 'excess' female consulting in early and later life. The greatest gender gap in primary care consultations was seen among those aged between 16 and 60 years. Gender differences in consulting were higher in people from more deprived areas than among those from more affluent areas. Accounting for reproductive-related consultations diminished but did not eradicate the gender gap. However, consultation rates in men and women who had comparable underlying morbidities (as assessed by receipt of medication) were similar; men in receipt of antidepressant medication were only 8% less likely to consult than women in receipt of antidepressant medication (relative risk (RR) 0.916, 95% CI 0.913 to 0.918), and men in receipt of medication to treat cardiovascular disease were just 5% less likely to consult (RR=0.950, 95% CI 0.948 to 0.952) than women receiving similar medication. These small gender differences diminished further, particularly for depression (RR=0.950, 95% CI 0.947 to 0.953), after also taking account of reproductive consultations.ConclusionsOverall gender differences in consulting are most marked between the ages of 16 and 60 years; these differences are only partially accounted for by consultations for reproductive reasons. Differences in consultation rates between men and women were largely eradicated when comparing men and women in receipt of medication for similar underlying morbidities.

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Could Ovarian Cancer Prediction Models Improve the Triage of Symptomatic Women in Primary Care? A Modelling Study Using Routinely Collected Data.

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