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.     

Patient preferences on the use of technology in cancer surveillance after curative surgery: A cross-sectional analysis

Background

Advances in communication technology have enabled new methods of delivering test results to cancer survivors. We sought to determine patient preferences regarding the use of newer technology in delivering test results during cancer surveillance.

Feasibility of investigating differential proteomic expression in depression: implications for biomarker development in mood disorders

The objective of this study was to determine whether proteomic profiling in serum samples can be utilized in identifying and differentiating mood disorders. A consecutive sample of patients with a confirmed diagnosis of unipolar (UP n=52) or bipolar depression (BP-I n=46, BP-II n=49) and controls (n=141) were recruited. A 7.5-ml blood sample was drawn for proteomic multiplex profiling of 320 proteins utilizing the Myriad RBM Discovery Multi-Analyte Profiling platform. After correcting for multiple testing and adjusting for covariates, growth differentiation factor 15 (GDF-15), hemopexin (HPX), hepsin (HPN), matrix metalloproteinase-7 (MMP-7), retinol-binding protein 4 (RBP-4) and transthyretin (TTR) all showed statistically significant differences among groups. In a series of three post hoc analyses correcting for multiple testing, MMP-7 was significantly different in mood disorder (BP-I+BP-II+UP) vs controls, MMP-7, GDF-15, HPN were significantly different in bipolar cases (BP-I+BP-II) vs controls, and GDF-15, HPX, HPN, RBP-4 and TTR proteins were all significantly different in BP-I vs controls. Good diagnostic accuracy (ROC-AUC⩾0.8) was obtained most notably for GDF-15, RBP-4 and TTR when comparing BP-I vs controls. While based on a small sample not adjusted for medication state, this discovery sample with a conservative method of correction suggests feasibility in using proteomic panels to assist in identifying and distinguishing mood disorders, in particular bipolar I disorder. Replication studies for confirmation, consideration of state vs trait serial assays to delineate proteomic expression of bipolar depression vs previous mania, and utility studies to assess proteomic expression profiling as an advanced decision making tool or companion diagnostic are encouraged.     

Current and emerging pharmacotherapy for emergency management of preeclampsia

Introduction: Preeclampsia is a disease specific to pregnancy characterised by new onset hypertension with maternal organ dysfunction and/or fetal growth restriction. It remains a major cause of maternal and perinatal morbidity and mortality. For fifty years, antihypertensives have been the mainstay of treating preeclampsia, reducing maternal morbidity and mortality. With increased knowledge of the mechanisms underlying the disease has come opportunities for novel therapies that complement antihypertensives by protecting the maternal vasculature.

Areas covered: In this review, the authors consider, in detail, the antihypertensives commonly used today in the emergency care of women with severe preeclampsia. They also review less common anti-hypertensive agents and discuss the role of magnesium sulphate in the management of preeclampsia and the prevention of eclampsia. Finally, they explore novel therapeutics for the acute management of preeclampsia.

Expert opinion: The rapid control of maternal hypertension will, and must, remain the mainstay of emergency treatment for women with severe preeclampsia. The role of magnesium sulphate as a primary prevention for eclampsia is context dependant and should not displace a focus on correcting blood pressure safely. The exploration of novel adjuvant therapies will likely allow us to prolong pregnancy longer and improve perinatal outcomes safely for the mother.