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One major bottleneck in the ongoing COVID-19 pandemic is the limited number of critical care beds. Due to the dynamic development of infections and the time lag between when patients are infected and when a proportion of them enters an intensive care unit (ICU), the need for future intensive care can easily be underestimated. To infer future ICU load from reported infections, we suggest a simple statistical model that (1) accounts for time lags and (2) allows for making predictions depending on different future growth of infections. We have evaluated our model for three heavily affected regions in Europe, namely Berlin (Germany), Lombardy (Italy), and Madrid (Spain). Before extensive containment measures made an impact, we first estimate the region-specific model parameters, namely ICU rate, time lag between infection, and ICU admission as well as length of stay in ICU. Whereas for Berlin, an ICU rate of 6%, a time lag of 6 days, and a stay of 12 days in ICU provide the best fit of the data, for Lombardy and Madrid the ICU rate was higher (18% and 15%) and the time lag (0 and 3 days) and the stay in ICU (3 and 8 days) shorter. The region-specific models are then used to predict future ICU load assuming either a continued exponential phase with varying growth rates (0-15%) or linear growth. By keeping the growth rates flexible, this model allows for taking into account the potential effect of diverse containment measures. Thus, the model can help to predict a potential exceedance of ICU capacity depending on future growth. A sensitivity analysis for an extended time period shows that the proposed model is particularly useful for exponential phases of the disease.

Background & Aims: Estimation errors associated with bioelectric impedance evaluation may affect the accuracy of body composition and its prognostic value. We evaluated the prognostic value of a new body composition parameter (Nutrigram®) obtained from bioimpedance vectorial analysis-derived body cell mass and its association with nutritional and functional status., Design: Data of Italian and German cancer patients observed prospectively until death were used. Multivariable models (adjusted for age, gender, hydration status, performance status, and disease's stage) were built in both cohorts to assess the association between body composition outcome parameters (low fat-free mass [FFM], <15 [females] and <17 [males] kg/m 2 ; low standardized phase angle [SPA], <-1.65; low Nutrigram®, <510 [females] and <660 [males] mg/24 h/m) and 1-year all-cause mortality, low body mass index (BMI; <20 [<70 years] and <22 [≥70 years] kg/m 2 ), clinically significant weight loss (WL; ≥10% in 6 months) and low handgrip strength (HG; <20 [females] and <30 [males] kg)., Results: Low Nutrigram® was independently associated with mortality in both Italian (HR = 1.84 [95%CI, 1.18-2.86]; P = 0.007) and German cohorts (HR = 1.52 [95%CI, 1.17-2.07]; P = 0.008). Low FFMI and low SPA did not predict survival in the German cohort. In patients with low Nutrigram®, worse nutritional and functional status were observed in both study populations. Performance of models addressing the study endpoints showed substantial consistency with both cohorts, particularly of those including low Nutrigram®., Conclusions: We validated a new prognostic body composition parameter, which is easier to interpret than standard nutritional parameters and may be useful for identifying cancer patients at nutritional risk, requiring early nutritional support., Competing Interests: Conflict of interest All Authors certify that there are no affiliations with or involvement in any organization or entity with a direct financial interest in the subject matter or materials discussed in the manuscript., (Copyright © 2020 Elsevier Ltd and European Society for Clinical Nutrition and Metabolism. All rights reserved.)

Background: This retrospective German and Italian multicenter analysis aimed to compare the role of normofractionated stereotactic radiotherapy (nFSRT) to CyberKnife-based hypofractionated stereotactic radiotherapy (CK-hFSRT) for skull base meningiomas., Methods: Overall, 341 patients across three centers were treated with either nFSRT or CK-hFSRT for skull base meningioma. Treatment planning was based on computed tomography (CT) and magnetic resonance imaging (MRI) following institutional guidelines. Most nFSRT patients received 33 × 1.8 Gy, and most CK-hFSRT patients received 5 × 5 Gy. The median follow-up time was 36 months (range: 1-232 months)., Results: In the CK-hFSRT group, the 1-, 3-, and 10-year local control (LC) rates were 99.4, 96.8, and 80.3%, respectively. In the nFSRT group, the 1-, 3-, and 10-year LC rates were 100, 99, and 79.1%, respectively. There were no significant differences in LC rates between the nFSRT and CK-hFSRT groups (p = 0.56, hazard ratio = 0.76, 95% confidence interval, 0.3-1.9). In the CK-hFSRT group, only one case (0.49%) of severe toxicity (CTCAE 4.0 ≥ 3) was observed. In the nFSRT group, three cases (2.1%) of grade III toxicity were observed., Conclusion: This analysis of pooled data from three centers showed excellent LC and low side effect rates for patients treated with CK-hFSRT or nFSRT. The efficacy, safety, and convenience of a shortened treatment period provide a compelling case for the use of CK-hFSRT in patients with moderate size skull base meningioma and provided that OAR constraints are met.