Predicting COVID-19 hospitalizations: The importance of healthcare hotlines, test positivity rates and vaccination coverage.

In this study, we developed a negative binomial regression model for one-week ahead spatio-temporal predictions of the number of COVID-19 hospitalizations in Uppsala County, Sweden. Our model utilized weekly aggregated data on testing, vaccination, and calls to the national healthcare hotline. Variable importance analysis revealed that calls to the national healthcare hotline were the most important contributor to prediction performance when predicting COVID-19 hospitalizations. Our results support the importance of early testing, systematic registration of test results, and the value of healthcare hotline data in predicting hospitalizations. The proposed models may be applied to studies modeling hospitalizations of other viral respiratory infections in space and time assuming count data are overdispersed. Our suggested variable importance analysis enables the calculation of the effects on the predictive performance of each covariate. This can inform decisions about which types of data should be prioritized, thereby facilitating the allocation of healthcare resources.

Three-Dimensional Volumetric Segmentation of Pituitary Tumors: Assessment of Inter-rater Agreement and Comparison with Conventional Geometric Equations.

Background The assessment of pituitary tumor (PT) volume is important in the treatment and follow-up of patients with PT. Previously, PT volume estimation has been performed by conventional geometric equations (CGE) such as abc/2 (simplified ellipsoid volume equation) and 4πr 3 /3 (sphere), both presuming a symmetric tumor shape, which occurs uncommonly in patients with PT. In contrast, three-dimensional (3D) voxel-based software segmentation takes the irregular and asymmetric shapes that PTs often possess into account and might be a more accurate method for PT volume segmentation. The purpose of this study is twofold. (1) To compare 3D segmentation with CGE for PT volume estimation. (2) To assess inter-rater reliability in 3D segmentation of PTs. Methods Nineteen high-resolution (1mm slice thickness) T1-weighted MRI examinations of patients with PT were independently analyzed and manually segmented, using the software ITK-SNAP, by two certified neuroradiologists. Concurrently, the volumes of the PTs were estimated with abc/2 and 4πr 3 /3 by a clinician, and the results were compared with the corresponding segmented volumes. Results There was a significant decrease in PT volume attained from the segmentations compared with the calculations made with abc/2 ( p < 0.001, mean volume 18% higher than segmentation) and 4πr 3 /3 ( p < 0.001, mean volume 28% higher than segmentation). The intraclass correlation coefficient (ICC) for the two sets of segmented PTs was 0.99. Conclusion CGE ( abc/2 and 4πr 3 /3 ) significantly overestimates PT volume compared with 3D volumetric segmentation. The inter-rater agreement on manual 3D volumetric software segmentation is excellent.