Is an intraoperative liver function assessment possible? Application of the 13C-methacetin-breath-test during major liver resections - a pilot study.

BACKGROUND: As prevention of posthepatectomy-liver-failure is crucial, there is need of dynamic assessment of liver function, even intraoperatively. 13C-methacetin-breath-test estimates the organ's microsomal functional capacity. This is its first intraoperative evaluation in major liver surgery. METHODS: 30 patients planed for resection of ≥3 liver segments, between March-November 2019, were prospectively enrolled in this "single-center", pilot study. Using the 13C-methacetin-breath-test, liver function was assessed four times: preoperatively, intraoperatively before and after resection and postoperatively. The resulted maximum-liver-function-capacity (LiMAx)-values and delta-over-baseline (DOB)-curves were compared, further analyzed and correlated to respective liver volumes. RESULTS: The intraoperative LiMAx-values before resection were mostly lower than the preoperative ones (-11.3% ± 28%). The intraoperative measurements after resection resulted to mostly higher values than the postoperative ones (42.35% ± 46.19%). Pharmacokinetically, an interference between the two intraoperative tests was observed. There was no strong correlation between residual liver volume and function with a percentual residual-LiMAx mostly lower than the percentual residual volume (-17.7% ± 4.1%). CONCLUSIONS: Intraoperative application of the 13C-methacetin-breath-test during major liver resections seems to deliver lower values than the standard preoperative test. As multiple intraoperative tests interfere significantly to each other, a single intraoperative measurement is suggested. Multicentric standardized measurements could define the "normal" range for intraoperative measurements and control their predictive value.

DeepVaR: a framework for portfolio risk assessment leveraging probabilistic deep neural networks.

Determining and minimizing risk exposure pose one of the biggest challenges in the financial industry as an environment with multiple factors that affect (non-)identified risks and the corresponding decisions. Various estimation metrics are utilized towards robust and efficient risk management frameworks, with the most prevalent among them being the Value at Risk (VaR). VaR is a valuable risk-assessment approach, which offers traders, investors, and financial institutions information regarding risk estimations and potential investment insights. VaR has been adopted by the financial industry for decades, but the generated predictions lack efficiency in times of economic turmoil such as the 2008 global financial crisis and the COVID-19 pandemic, which in turn affects the respective decisions. To address this challenge, a variety of well-established variations of VaR models are exploited by the financial community, including data-driven and data analytics models. In this context, this paper introduces a probabilistic deep learning approach, leveraging time-series forecasting techniques with high potential of monitoring the risk of a given portfolio in a quite efficient way. The proposed approach has been evaluated and compared to the most prominent methods of VaR calculation, yielding promising results for VaR 99% for forex-based portfolios. Supplementary Information: The online version contains supplementary material available at 10.1007/s42521-022-00050-0.