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Abstract Background and aims Relief of cholestasis in hilar cholangiocarcinoma is commonly undertaken in both curative and palliative treatment plans. There are numerous open questions with regard to the ideal biliary drainage strategy – including what constitutes clinical success (CS). In the existing data, curative patients and patients from the Western world are underrepresented. Patients and methods We performed a retrospective analysis of patients with complex malignant hilar obstruction (Bismuth-Corlette II and higher) due to cholangiocarcinoma who underwent biliary drainage at a German referral center between 2010 and 2020. We aimed to define CS and complication rates and directly compare outcomes in curative and palliative patients. Results 56 curative and 72 palliative patients underwent biliary drainage. In patients with curative intent, CS was achieved significantly more often regardless of what definition of CS was applied (e.g., total serum bilirubin (TSB) 75% reduction of TSB: 57.1% vs. 29.2%, p = 0.003). This observation held true only when subgroups with the same Bismuth-Corlette stage were compared. Moreover, palliative patients experienced a significantly greater percentage of adverse events (33.3% vs. 12.5%, p = 0.01). Curative intent treatment and TSB at presentation were predictive factors of CS regardless of what definition of CS was applied. The observed CS rates are comparable to published studies involving curative patients, but inferior to reported CS rates in palliative series mostly from Asia. Conclusions Biliary drainage in complex malignant hilar obstruction due to cholangiocarcinoma is more likely to be successful and less likely to cause adverse events in curative patients compared to palliative patients.

BackgroundCachexia is a body wasting syndrome that significantly affects well-being and prognosis of cancer patients, without effective treatment. Serum metabolites take part in pathophysiological processes of cancer cachexia, but apart from altered levels of select serum metabolites, little is known on the global changes of the overall serum metabolome, which represents a functional readout of the whole-body metabolic state. Here, we aimed to comprehensively characterize serum metabolite alterations and analyze associated pathways in cachectic cancer patients to gain new insights that could help instruct strategies for novel interventions of greater clinical benefit.MethodsSerum was sampled from 120 metastatic cancer patients (stage UICC IV). Patients were grouped as cachectic or non-cachectic according to the criteria for cancer cachexia agreed upon international consensus (main criterium: weight loss adjusted to body mass index). Samples were pooled by cachexia phenotype and assayed using non-targeted gas chromatography-mass spectrometry (GC-MS). Normalized metabolite levels were compared using t-test (p < 0.05, adjusted for false discovery rate) and partial least squares discriminant analysis (PLS-DA). Machine-learning models were applied to identify metabolite signatures for separating cachexia states. Significant metabolites underwent MetaboAnalyst 5.0 pathway analysis.ResultsComparative analyses included 78 cachectic and 42 non-cachectic patients. Cachectic patients exhibited 19 annotable, significantly elevated (including glucose and fructose) or decreased (mostly amino acids) metabolites associating with aminoacyl-tRNA, glutathione and amino acid metabolism pathways. PLS-DA showed distinct clusters (accuracy: 85.6%), and machine-learning models identified metabolic signatures for separating cachectic states (accuracy: 83.2%; area under ROC: 88.0%). We newly identified altered blood levels of erythronic acid and glucuronic acid in human cancer cachexia, potentially linked to pentose-phosphate and detoxification pathways.ConclusionWe found both known and yet unknown serum metabolite and metabolic pathway alterations in cachectic cancer patients that collectively support a whole-body metabolic state with impaired detoxification capability, altered glucose and fructose metabolism, and substrate supply for increased and/or distinct metabolic needs of cachexia-associated tumors. These findings together imply vulnerabilities, dependencies and targets for novel interventions that have potential to make a significant impact on future research in an important field of cancer patient care.

Abstract Background Machine learning (ML) algorithms have been trained to early predict critical in-hospital events from COVID-19 using patient data at admission, but little is known on how their performance compares with each other and/or with statistical logistic regression (LR). This prospective multicentre cohort study compares the performance of a LR and five ML models on the contribution of influencing predictors and predictor-to-event relationships on prediction model´s performance. Methods We used 25 baseline variables of 490 COVID-19 patients admitted to 8 hospitals in Germany (March–November 2020) to develop and validate (75/25 random-split) 3 linear (L1 and L2 penalty, elastic net [EN]) and 2 non-linear (support vector machine [SVM] with radial kernel, random forest [RF]) ML approaches for predicting critical events defined by intensive care unit transfer, invasive ventilation and/or death (composite end-point: 181 patients). Models were compared for performance (area-under-the-receiver-operating characteristic-curve [AUC], Brier score) and predictor importance (performance-loss metrics, partial-dependence profiles). Results Models performed close with a small benefit for LR (utilizing restricted cubic splines for non-linearity) and RF (AUC means: 0.763–0.731 [RF–L1]); Brier scores: 0.184–0.197 [LR–L1]). Top ranked predictor variables (consistently highest importance: C-reactive protein) were largely identical across models, except creatinine, which exhibited marginal (L1, L2, EN, SVM) or high/non-linear effects (LR, RF) on events. Conclusions Although the LR and ML models analysed showed no strong differences in performance and the most influencing predictors for COVID-19-related event prediction, our results indicate a predictive benefit from taking account for non-linear predictor-to-event relationships and effects. Future efforts should focus on leveraging data-driven ML technologies from static towards dynamic modelling solutions that continuously learn and adapt to changes in data environments during the evolving pandemic. Trial registration number: NCT04659187.

IntroductionAuditory brainstem response (ABR) is the gold standard for hearing testing in dogs. ABR is commonly used in puppies to diagnose congenital sensorineural deafness. Long test times limit the use for a more comprehensive hearing screening in veterinary practice. This study aimed to establish a super-fast hearing screening protocol in dogs.MethodsHearing thresholds were routinely measured with a mobile device designed for newborn hearing screening in 90 dogs. We introduced modifications of the ABR protocol, e. g., a binaural test mode, higher stimulus rates, a broadband chirp stimulus, and an algorithm for automatic peak V detection in a stepwise fashion. Hearing thresholds were then measured with fast protocols utilizing either 30 Hz click or 90 Hz broadband chirp stimuli with 80, 60, 40, 30, 20, 10, 0 and −10 dBnHL stimulation intensities. Interrater reliability, agreement between click and chirp hearing thresholds and correlations with clinical characteristics of the dogs were assessed.ResultsUsing all innovations, the test time for hearing threshold assessment in both ears was reduced to 1.11 min (mean). The chirp stimulus accentuated both, peak V and the subsequent trough, which are essential features for judgement of the hearing threshold, but preceding peaks were less conspicuous. Interrater reliability and agreement between click and chirp hearing threshold was excellent. Dogs >10 years of age and dogs with abnormal hearing score or otitis score had significantly higher hearing thresholds than younger dogs (p ≤ 0.001) or dogs without abnormalities (p