Effect of obstructive sleep apnoea on retinal microvascular function: a randomised controlled trial

Graefe's Archive for Clinical and Experimental Ophthalmology - Retinal microvascular endothelial dysfunction is thought to be of importance in the development of ocular vascular diseases....     

Seltene Superinfektion bei einem COVID-19-Patienten – Eine Chronologie

Die Anaesthesiologie - Bei einem 55-jährigen Mann, der sich zur elektiven Ablation bei Vorhofflimmern in der Klinik befand, wurde nach einer Reanimationssituation auf der peripheren Station...     

Das prozessierte EEG zur personalisierten Dosierung von Anästhetika während Allgemeinanästhesie

Die Anaesthesiologie - Die Elektroenzephalogramm(EEG)-gesteuerte Anästhesie ist aus modernen Operationssälen nicht mehr wegzudenken und hat sich als Standard-Monitoring etabliert. Viele...     

Machine learning to predict myocardial injury and death after non-cardiac surgery

Myocardial injury due to ischaemia within 30 days of non-cardiac surgery is prognostically relevant. We aimed to determine the discrimination, calibration, accuracy, sensitivity and specificity of single-layer and multiple-layer neural networks for myocardial injury and death within 30 postoperative days. We analysed data from 24,589 participants in the Vascular Events in Non-cardiac Surgery Patients Cohort Evaluation study. Validation was performed on a randomly selected subset of the study population. Discrimination for myocardial injury by single-layer vs. multiple-layer models generated areas (95%CI) under the receiver operating characteristic curve of: 0.70 (0.69–0.72) vs. 0.71 (0.70–0.73) with variables available before surgical referral, p < 0.001; 0.73 (0.72–0.75) vs. 0.75 (0.74–0.76) with additional variables available on admission, but before surgery, p < 0.001; and 0.76 (0.75–0.77) vs. 0.77 (0.76–0.78) with the addition of subsequent variables, p < 0.001. Discrimination for death by single-layer vs. multiple-layer models generated areas (95%CI) under the receiver operating characteristic curve of: 0.71 (0.66–0.76) vs. 0.74 (0.71–0.77) with variables available before surgical referral, p = 0.04; 0.78 (0.73–0.82) vs. 0.83 (0.79–0.86) with additional variables available on admission but before surgery, p = 0.01; and 0.87 (0.83–0.89) vs. 0.87 (0.85–0.90) with the addition of subsequent variables, p = 0.52. The accuracy of the multiple-layer model for myocardial injury and death with all variables was 70% and 89%, respectively.