RESUMO
OBJECTIVES: The objective of this study was to collect information on human Chorionic Gonadotrophin (hCG) laboratory testing and reporting in women with Gestational Trophoblastic Disease (GTD), to assess the associated challenges, and to offer perspectives on hCG testing harmonisation. DESIGN: Information was collected from laboratories by electronic survey (Survey Monkey®) using a questionnaire designed by members of the European Organisation for the Treatment of Trophoblastic Disease (EOTTD) hCG Working Party. PARTICIPANTS: The questionnaire was distributed by the EOTTD board to member laboratories and their associated scientists who work within the GTD field. SETTING: The questionnaire was distributed and accessed via an online platform. METHODS: The questionnaire consisted of 5 main sections. These included methods used for hCG testing, quality procedures, reporting of results, laboratory operational aspects, and non-GTD testing capability. In addition to reporting these survey results, examples of case scenarios which illustrate the difficulties faced by laboratories providing hCG measurement for GTD patient management were described. The benefits and challenges of using centralised versus non-centralised hCG testing were discussed alongside the utilisation of regression curves for management of GTD patients. RESULTS: Information from the survey was collated and presented for each section and showed huge variability in responses across laboratories even for those using the same hCG testing platforms. An educational example was presented, highlighting the consequence of using inappropriate hCG assays on clinical patient management (Educational Example A), along with an example of biotin interference (Educational Example B) and an example of high-dose hook effect (Educational Example C), demonstrating the importance of knowing the limitations of hCG tests. The merits of centralised versus non-centralised hCG testing and use of hCG regression curves to aid patient management were discussed. LIMITATIONS: To ensure the survey was completed by laboratories providing hCG testing for GTD management, the questionnaire was distributed by the EOTTD board. It was assumed the EOTTD board held the correct laboratory contact, and that the questionnaire was completed by a scientist with in-depth knowledge of laboratory procedures. CONCLUSIONS: The hCG survey highlighted a lack of harmonisation of hCG testing across laboratories. Healthcare professionals involved in the management of women with GTD should be aware of this limitation. Further work is needed to ensure an appropriate quality assured laboratory service is available for hCG monitoring in women with GTD.
RESUMO
Gestational trophoblastic neoplasia (GTN) patients are treated according to the eight-variable International Federation of Gynaecology and Obstetrics (FIGO) scoring system, that aims to predict first-line single-agent chemotherapy resistance. FIGO is imperfect with one-third of low-risk patients developing disease resistance to first-line single-agent chemotherapy. We aimed to generate simplified models that improve upon FIGO. Logistic regression (LR) and multilayer perceptron (MLP) modelling (n = 4191) generated six models (M1-6). M1, all eight FIGO variables (scored data); M2, all eight FIGO variables (scored and raw data); M3, nonimaging variables (scored data); M4, nonimaging variables (scored and raw data); M5, imaging variables (scored data); and M6, pretreatment hCG (raw data) + imaging variables (scored data). Performance was compared to FIGO using true and false positive rates, positive and negative predictive values, diagnostic odds ratio, receiver operating characteristic (ROC) curves, Bland-Altman calibration plots, decision curve analysis and contingency tables. M1-6 were calibrated and outperformed FIGO on true positive rate and positive predictive value. Using LR and MLP, M1, M2 and M4 generated small improvements to the ROC curve and decision curve analysis. M3, M5 and M6 matched FIGO or performed less well. Compared to FIGO, most (excluding LR M4 and MLP M5) had significant discordance in patient classification (McNemar's test P < .05); 55-112 undertreated, 46-206 overtreated. Statistical modelling yielded only small gains over FIGO performance, arising through recategorisation of treatment-resistant patients, with a significant proportion of under/overtreatment as the available data have been used a priori to allocate primary chemotherapy. Streamlining FIGO should now be the focus.