Trends in radiation dose for low grade gliomas across the United States

Journal of Neuro-Oncology - Adjuvant radiation is often used in patients with low grade gliomas with high-risk characteristics with a recommended dose of 45–54 Gy. We used the...     

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.     

Effect of mortality from COVID-19 on inpatient outcomes

When hospitals first encountered coronavirus disease 2019 (COVID-19), there was a dearth of therapeutic options and nearly 1 in 3 patients died from the disease. By the summer of 2020, as deaths from the disease declined nationally, multiple single-center studies began to report declining mortality of patients with COVID-19. To evaluate the effect of COVID-19 on hospital-based mortality, we searched the Vizient Clinical Data Base for outcomes data from approximately 600 participating hospitals, including 130 academic medical centers, from January 2017 through December 2020. More than 32 million hospital admissions were included in the analysis. After an initial spike, mortality from COVID-19 declined in all regions of the country to under 10% by June 2020 and remained constant for the remainder of the year. Despite this, inpatient, all-cause mortality has increased since the beginning of the pandemic, even those without respiratory failure. Inpatient mortality has particularly increased in elderly patients and in those requiring intubation for respiratory failure. Since June 2020, COVID-19 kills one in every 10 patients admitted to the hospital with this diagnosis. The addition of this new disease has raised overall hospital mortality especially those who require intubation for respiratory failure.