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.     

Education Outcomes of Children Born Late Preterm: A Retrospective Whole-Population Cohort Study

Maternal and Child Health Journal - Early life exposures can have an impact on a child’s developmental trajectory and children born late preterm (34–36&nbsp;weeks gestational age)...     

Differentiating poor validity from probable impairment on the medical symptom validity test: a cross-validation study

Aims: In neuropsychological evaluations, it is often difficult to ascertain whether poor performance on measures of validity is due to poor effort or malingering, or whether there is genuine cognitive impairment. Dunham and Denney created an algorithm to assess this question using the Medical Symptom Validity Test (MSVT). We assessed the ability of their algorithm to detect poor validity versus probable impairment, and concordance of failure on the MSVT with other freestanding tests of performance validity.

Methods: Two previously published datasets (n?=?153 and n?=?641, respectively) from outpatient neuropsychological evaluations were used to test Dunham and Denney’s algorithm, and to assess concordance of failure rates with the Test of Memory Malingering and the forced choice measure of the California Verbal Learning Test, two commonly used performance validity tests.

Results: In both datasets, none of the four cutoff scores for failure on the MSVT (70%, 75%, 80%, or 85%) identified a poor validity group with proportionally aligned failure rates on other freestanding measures of performance validity. Additionally, the protocols with probable impairment did not differ from those with poor validity on cognitive measures.

Conclusions: Despite what appeared to be a promising approach to evaluating failure on the easy MSVT subtests when clinical data are unavailable (as recommended in the advanced interpretation program, or advanced interpretation [AI], of the MSVT), the current findings indicate the AI remains the gold standard for doing so. Future research should build on this effort to address shortcomings in measures of effort in neuropsychological evaluations.     

Impact of overweight and obesity on US renal cell carcinoma incidence trends (1995-2018)

In the United States, renal cell carcinoma (RCC) incidence and the prevalence of obesity, an established risk factor for RCC, have been increasing for several decades. RCC is more common among older individuals. We sought to quantify the contribution of excess adiposity to the rising incidence of RCC among individuals 60 years or older. National Institutes of Health-American Association of Retired Persons Diet and Health Study data (n = 453 859 participants, enrolled in 1995-1996, age at enrollment 50-71 years) were used to estimate multivariable-adjusted hazard ratios (HRs) for RCC across body mass index categories and HRs associated with smoking. Population attributable fractions (PAFs) were calculated using estimated HRs and annual overweight/obesity prevalence from the National Health Interview Survey (1985-2008). PAF estimates were combined with RCC incidence from Surveillance, Epidemiology and End Results-13 to calculate annual percent changes in RCC incidence attributable (and unrelated) to overweight/obesity. We found that between 1995 and 2018, among individuals aged 60 years and older, PAF for overweight/obesity increased from 18% to 29% for all RCCs. In comparison, the PAF for smoking declined from 12% to 9%. RCC incidence increased 1.8% per year (95% confidence interval [CI] 1.5%-2.1%) overall, while RCC incidence attributable to overweight/obesity increased 3.8% per year (95%CI 3.5%-4.2%) and RCC incidence unrelated to overweight/obesity increased 1.2% per year (95% CI 0.9%-1.4%). In conclusion, overweight/obesity appears to have contributed importantly to the rising incidence of RCC in the United States since the mid-1990s. Public health interventions focused on reducing overweight and obesity could help substantially in curbing this trend.