The value of virtual reality (VR) simulators for robot-assisted surgery (RAS) for skill assessment and training of surgeons has not been established. This systematic review and meta-analysis aimed to identify evidence on transferability of surgical skills acquired on robotic VR simulators to the operating room and the predictive value of robotic VR simulator performance for intraoperative performance. MEDLINE, Cochrane Central Register of Controlled Trials, and Web of Science were searched systematically. Risk of bias was assessed using the Medical Education Research Study Quality Instrument and the Newcastle–Ottawa Scale for Education. Correlation coefficients were chosen as effect measure and pooled using the inverse-variance weighting approach. A random-effects model was applied to estimate the summary effect. A total of 14 131 potential articles were identified; there were eight studies eligible for qualitative and three for quantitative analysis. Three of four studies demonstrated transfer of surgical skills from robotic VR simulators to the operating room measured by time and technical surgical performance. Two of three studies found significant positive correlations between robotic VR simulator performance and intraoperative technical surgical performance; quantitative analysis revealed a positive combined correlation (r = 0.67, 95 per cent c.i. 0.22 to 0.88). Technical surgical skills acquired through robotic VR simulator training can be transferred to the operating room, and operating room performance seems to be predictable by robotic VR simulator performance. VR training can therefore be justified before operating on patients. This systematic review and meta-analysis presents current evidence on transferability of surgical skills acquired on robotic VR simulators to the real operating room, and on the predictability of intraoperative performance by robotic VR simulator performances. The limited data currently available support the use of robotic VR simulators for surgical skill acquisition and assessment. This systematic review and meta-analysis presents current evidence on transferability of surgical skills acquired on robotic VR simulators to the real operating room, and on the predictability of intraoperative performance by robotic VR simulator performances. The limited data currently available support the use of robotic VR simulators for surgical skill acquisition and assessment.

Objective: Current workup for the pre-operative distinction between frequent adrenocortical adenomas (ACAs) and rare but aggressive adrenocortical carcinomas (ACCs) combines imaging and biochemical testing. We here investigated the potential of plasma steroid hormone profiling by liquid chromatography tandem mass spectrometry (LC-MS/MS) for the diagnosis of malignancy in adrenocortical tumors. Design: Retrospective cohort study of prospectively collected EDTA-plasma samples in a single tertiary reference center. Methods: Steroid hormone profiling by liquid chromatography tandem mass spectrometry (LC-MS/MS) in random plasma samples and logistic regression modeling. Results: Fifteen steroid hormones were quantified in 66 ACAs (29 males; M) and 42 ACC (15 M) plasma samples. Significantly higher abundances in ACC vs ACA were observed for 11-deoxycorticosterone, progesterone, 17-hydroxyprogesterone, 11-deoxycortisol, DHEA, DHEAS and estradiol (all P < 0.05). Maximal areas under the curve (AUC) for discrimination between ACA and ACC for single analytes were only 0.76 (estradiol) and 0.77 (progesterone), respectively. Logistic regression modeling enabled the discovery of diagnostic signatures composed of six specific steroids for male and female patients with AUC of 0.95 and 0.94, respectively. Positive predictive values in males and females were 92 and 96%, negative predictive values 90 and 86%, respectively. Conclusion: This study in a large adrenal tumor patient cohort demonstrates the value of plasma steroid hormone profiling for diagnosis of ACC. Application of LC-MS/MS analysis and of our model may facilitate diagnosis of malignancy in non-expert centers. We propose to continuously evaluate and improve diagnostic accuracy of LC-MS/MS profiling by applying machine-learning algorithms to prospectively obtained steroid hormone profiles.

Background and Purpose: Failure of early neurological improvement (fENI) despite successful mechanical thrombectomy in the anterior circulation is a clinically frequent occurrence. Purpose of this analysis was to define independent clinical, radiological, laboratory, or procedural predictors for fENI. Methods: Retrospective single-center analysis of patients treated for acute ischemic stroke in the anterior circulation ensuing successful mechanical thrombectomy between January 2014 and April 2019. Patients were compared according to fENI (equal or higher National Institutes of Health Stroke Scale) and ENI (lower National Institutes of Health Stroke Scale at discharge). Thirty-eight variables were examined in multivariable analysis for association with fENI. Results: Five hundred forty-nine out of 1146 patients experienced successful recanalization (modified Treatment in Cerebral Ischemia 2c-3). fENI occurred in 115/549 (20.9%) patients. Independent predictors of fENI were premorbid modified Rankin Scale (odds ratio [OR] per point [IC], 1.21 [1.00–1.46], P =0.049), end-stage renal failure (OR [IC], 12.18 [2.01–73.63], P =0.007), admission glucose (OR [IC], 1.018 [1.004–1.013] per mg/dL, P =0.001), bridging IV lysis (OR [IC], 0.57 [0.35–0.93], P : 0.024), time from groin puncture to final recanalization (OR [IC], 1.004 [1.001–1.007] per minute, P =0.015), general anesthesia during mechanical thrombectomy (OR, 2.41 [1.43–4.08], P <0.001), symptomatic intracranial hemorrhage (OR [CI], 6.81 [1.84–25.16], P =0.004), and follow-up Alberta Stroke Program Early CT Score (OR [IC], 0.76 [0.69–0.84] per point, P <0.001). In a secondary analysis, involvement of the regions internal capsule, M4 and M5 (motor cortex) were further independent predictors for fENI. Patients with ENI were more likely to experience a good outcome (modified Rankin Scale on day 90, 0–2: n=229/435 [52.8%] versus n=13/115 [11.3%]; P <0.001). Conclusions: The extent of infarction and the involvement of motor cortex and internal capsule as well as higher premorbid modified Rankin Scale, end-stage renal failure, high glucose level on admission, absence of bridging IV lysis, general anesthesia, and a longer therapy interval are presumably independent predictors for fENI in patients with successful mechanical thrombectomy.

Background: Cardiac manifestation of COVID-19 has been reported during the COVID pandemic. The role of cardiac arrhythmias in COVID-19 is insufficiently understood. This study assesses the incidence of cardiac arrhythmias and their prognostic implications in hospitalized COVID-19-patients. Methods: A total of 166 patients from eight centers who were hospitalized for COVID-19 from 03/2020–06/2020 were included. Medical records were systematically analyzed for baseline characteristics, biomarkers, cardiac arrhythmias and clinical outcome parameters related to the index hospitalization. Predisposing risk factors for arrhythmias were identified. Furthermore, the influence of arrhythmia on the course of disease and related outcomes was assessed using univariate and multiple regression analyses. Results: Arrhythmias were detected in 20.5% of patients. Atrial fibrillation was the most common arrhythmia. Age and cardiovascular disease were predictors for new-onset arrhythmia. Arrhythmia was associated with a pronounced increase in cardiac biomarkers, prolonged hospitalization, and admission to intensive- or intermediate-care-units, mechanical ventilation and in-hospital mortality. In multiple regression analyses, incident arrhythmia was strongly associated with duration of hospitalization and mechanical ventilation. Cardiovascular disease was associated with increased mortality. Conclusions: Arrhythmia was the most common cardiac event in association with hospitalization for COVID-19. Older age and cardiovascular disease predisposed for arrhythmia during hospitalization. Whereas in-hospital mortality is affected by underlying cardiovascular conditions, arrhythmia during hospitalization for COVID-19 is independently associated with prolonged hospitalization and mechanical ventilation. Thus, incident arrhythmia may indicate a patient subgroup at risk for a severe course of disease.

Purpose: Frailty in older adults is associated with multiple adverse health outcomes, while evidence on its successful prevention has been scarce. Therefore, we analyzed the effectiveness of different interventions for the prevention of frailty onset. Methods: In this systematic review, eight databases were searched for randomized controlled trials of interventions in non-frail (i.e., robust or pre-frail) adults aged ≥ 60 years that assessed frailty incidence at follow-up. Additive component network meta-analysis (CNMA) was conducted to isolate the effect of different intervention types on the main outcome of frailty incidence, reporting relative risk (RR) with 95% confidence intervals (CI). The effect on gait speed was analyzed as an additional outcome using a classic network meta-analysis and the standardized mean difference (SMD) with 95% CI. Results: We screened 24,263 records and identified 11 eligible trials. Nine trials (842 participants, all categorized according to the physical phenotype) in pre-frail (seven RCTs) and robust/pre-frail (two RCTs) older adults were included in the CNMA. Physical exercise significantly reduced frailty incidence at follow-up (RR 0.26, 95% CI 0.08; 0.83), while this was not found for nutritional interventions (RR 1.16, 95% CI 0.33; 4.10). Interventions based on physical exercise also improved gait speed (SMD 1.55, 95% CI 1.16; 1.95). In addition, 22 eligible trial protocols without published results were identified. Conclusion: Interventions based on physical exercise appear to be effective in preventing the onset of frailty in older adults. Although the available data are still limited, results from ongoing trials may add to the body of evidence in the foreseeable future.

Identification of disease-associated autoantibodies is of high importance. Their assessment could complement current diagnostic modalities and assist the clinical management of patients. We aimed at developing and validating high-throughput protein microarrays able to screen patients’ sera to determine disease-specific autoantibody-signatures for pancreatic cancer (PDAC), chronic pancreatitis (CP), autoimmune pancreatitis and their subtypes (AIP-1 and AIP-2). In-house manufactured microarrays were used for autoantibody-profiling of IgG-enriched preoperative sera from PDAC-, CP-, AIP-1-, AIP-2-, other gastrointestinal disease (GID) patients and healthy controls. As a top-down strategy, three different fluorescence detection-based protein-microarrays were used: large with 6400, intermediate with 345, and small with 36 full-length human recombinant proteins. Large-scale analysis revealed 89 PDAC, 98 CP and 104 AIP immunogenic antigens. Narrowing the selection to 29 autoantigens using pooled sera first and individual sera afterwards allowed a discrimination of CP and AIP from PDAC. For validation, predictive models based on the identified antigens were generated which enabled discrimination between PDAC and AIP-1 or AIP-2 yielded high AUC values of 0.940 and 0.925, respectively. A new repertoire of autoantigens was identified and their assembly as a multiplex test will provide a fast and cost-effective tool for differential diagnosis of pancreatic diseases with high clinical relevance.