Klinische Pharmakologie: Postoperative Übelkeit und Erbrechen

Zusammenfassung: Die "Dreierregel" beschreibt drei Schritte, die zur optimalen Kontrolle von postoperativer Übelkeit und Erbrechen ("postoperative nausea and vomiting", PONV) benötigt werden. Erstens sollte versucht werden, Hochrisikopatienten zu identifizieren. Risikofaktoren helfen mit, Patienten zu stratifizieren: Hochrisikopatienten profitieren am ehesten von einer Prävention; bei Niedrigrisikopatienten lohnt sich eine Prävention kaum. Zweitens sollte für Hochrisikopatienten eine Anästhesietechnik mit niedrigem emetogenen Potenzial gewählt werden. Und drittens sollten diese Patienten von einem präventiven antiemetischen Cocktail profitieren. Butyrophenone (z.B. Droperidol), 5-HT3-Rezeptoren-Antagonisten ("Setrone") und Steroide (z.B. Dexamethason) wirken am besten, wenn sie kombiniert werden. Sie gehören deshalb heute zu den logischen Komponenten eines antiemetischen Cocktails. Finanzielle Überlegungen können jedoch Zahl und Art der Antiemetika, die präventiv verabreicht werden sollen, beeinflussen. Die Identifizierung von Hochrisikopatienten bleibt der schwierigste Teil einer erfolgreichen PONV-Prävention. Zwar wurden Risikoscores vorgestellt, und diese wurden auch vielerorts in den klinischen Alltag integriert. Sensitivität und Spezifität dieser Scores sind jedoch ausgesprochen unbefriedigend, und ihre unkritische Anwendung bleibt somit unerwünscht. Solange keine zuverlässigeren Risikovoraussagen vorliegen, scheint bei manchen Patienten, eine aggressive Therapiestrategie sinnvoller und wahrscheinlich kosteneffizienter, als eine Präventio

Number Needed to Treat (or Harm)

The effect of a treatment versus controls may be expressed in relative or absolute terms. For rational decision-making, absolute measures are more meaningful. The number needed to treat, the reciprocal of the absolute risk reduction, is a powerful estimate of the effect of a treatment. It is particularly useful because it takes into account the underlying risk (what would happen without the intervention?). The number needed to treat tells us not only whether a treatment works but how well it works. Thus, it informs health care professionals about the effort needed to achieve a particular outcome. A number needed to treat should be accompanied by information about the experimental intervention, the control intervention against which the experimental intervention has been tested, the length of the observation period, the underlying risk of the study population, and an exact definition of the endpoint. A 95% confidence interval around the point estimate should be calculated. An isolated number needed to treat is rarely appropriate to summarize the usefulness of an intervention; multiple numbers needed to treat for benefit and harm are more helpful. Absolute risk reduction and number needed to treat should become standard summary estimates in randomized controlled trial

Model Extraction Warning in MLaaS Paradigm

Cloud vendors are increasingly offering machine learning services as part of their platform and services portfolios. These services enable the deployment of machine learning models on the cloud that are offered on a pay-per-query basis to application developers and end users. However recent work has shown that the hosted models are susceptible to extraction attacks. Adversaries may launch queries to steal the model and compromise future query payments or privacy of the training data. In this work, we present a cloud-based extraction monitor that can quantify the extraction status of models by observing the query and response streams of both individual and colluding adversarial users. We present a novel technique that uses information gain to measure the model learning rate by users with increasing number of queries. Additionally, we present an alternate technique that maintains intelligent query summaries to measure the learning rate relative to the coverage of the input feature space in the presence of collusion. Both these approaches have low computational overhead and can easily be offered as services to model owners to warn them of possible extraction attacks from adversaries. We present performance results for these approaches for decision tree models deployed on BigML MLaaS platform, using open source datasets and different adversarial attack strategies

Preprocessors Matter! Realistic Decision-Based Attacks on Machine Learning Systems

Decision-based adversarial attacks construct inputs that fool a machine-learning model into making targeted mispredictions by making only hard-label queries. For the most part, these attacks have been applied directly to isolated neural network models. However, in practice, machine learning models are just a component of a much larger system. By adding just a single preprocessor in front of a classifier, we find that state-of-the-art query-based attacks are as much as seven times less effective at attacking a prediction pipeline than attacking the machine learning model alone. Hence, attacks that are unaware of this invariance inevitably waste a large number of queries to re-discover or overcome it. We, therefore, develop techniques to first reverse-engineer the preprocessor and then use this extracted information to attack the end-to-end system. Our extraction method requires only a few hundred queries to learn the preprocessors used by most publicly available model pipelines, and our preprocessor-aware attacks recover the same efficacy as just attacking the model alone. The code can be found at https://github.com/google-research/preprocessor-aware-black-box-attack.Comment: Code can be found at https://github.com/google-research/preprocessor-aware-black-box-attac

Prevention of Bloodstream Infections With Central Venous Catheters Treated With Anti-Infective Agents Depends on Catheter Type and Insertion Time: Evidence From a Meta-Analysis

Objective: To test the evidence that the risk of infection related to central venous catheters (CVCs) is decreased by anti-infective coating or cuffing. Design: Systematic review of randomized, controlled trials comparing anti-infective with inactive (control) CVCs. Interventions: Average insertion times were taken as a measurement of the length of insertion. Dichotomous data were combined using a fixed effect model and expressed as odds ratio (OR) with 95% confidence interval (CI95). Results: Two trials on antibiotic coating (343 CVCs) had an average insertion time of 6 days; the risk of BSI decreased from 5.1% with control to 0% with anti-infective catheters. There were no trials with longer average insertion times. In three trials on silver collagen cuffs (422 CVCs), the average insertion time ranged from 5 to 8.2 days (median, 7 days); the risk of BSI was 5.6% with control and 3.2% with anti-infective catheters. In another trial on silver collagen cuffs (101 CVCs), the average insertion time was 38 days; the risk of BSI was 3.7% with control and 4.3% with anti-infective catheters. In five trials on chlorhexidine-silver sulfadiazine coating (1,269 CVCs), the average insertion time ranged from 5.2 to 7.5 days (median, 6 days); the risk of BSI decreased from 4.1% with control to 1.9% with anti-infective catheters. In five additional trials on chlorhexidine-silver sulfadiazine coating (1,544 CVCs), the average insertion time ranged from 7.8 to 20 days (median, 12 days); the risk of BSI was 4.5% with control and 4.2% with anti-infective catheters. Conclusions: Antibiotic and chlorhexidine-silver sulfadiazine coatings are anti-infective for short (approximately 1 week) insertion times. For longer insertion times, there are no data on antibiotic coating, and there is evidence of lack of effect for chlorhexidine-silver sulfadiazine coating. For silver-impregnated collagen cuffs, there is evidence of lack of effect for both short- and long-term insertio