PREDICT-GTN 1: Can we improve the FIGO scoring system in gestational trophoblastic neoplasia?

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.

Seasonal and occupational trends of five organophosphate pesticides in house dust.

Since 1998, the University of Washington's Center for Child Environmental Health Risks Research has followed a community-based participatory research strategy in the Lower Yakima Valley of Washington State to assess pesticide exposure among families of Hispanic farmworkers. As a part of this longitudinal study, house dust samples were collected from both farmworker and non-farmworker households, across three agricultural seasons (thinning, harvest and non-spray). The household dust samples were analyzed for five organophosphate pesticides: azinphos-methyl, phosmet, malathion, diazinon, and chlorpyrifos. Organophosphate pesticide levels in house dust were generally reflective of annual use rates and varied by occupational status and agricultural season. Overall, organophosphate pesticide concentrations were higher in the thinning and harvest seasons than in the non-spray season. Azinphos-methyl was found in the highest concentrations across all seasons and occupations. Farmworker house dust had between 5- and 9-fold higher concentrations of azinphos-methyl than non-farmworker house dust. Phosmet was found in 5-7-fold higher concentrations in farmworker house dust relative to non-farmworker house dust. Malathion and chlorpyriphos concentrations in farmworker house dust ranged between 1.8- and 9.8-fold higher than non-farmworker house dust. Diazinon showed a defined seasonal pattern that peaked in the harvest season and did not significantly differ between farmworker and non-farmworker house dust. The observed occupational differences in four out of five of the pesticide residues measured provides evidence supporting an occupational take home pathway, in which workers may bring pesticides home on their skin or clothing. Further, these results demonstrate the ability of dust samples to inform the episodic nature of organophosphate pesticide exposures and the need to collect multiple samples for complete characterization of exposure potential.