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

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

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

Nefopam for the prevention of postoperative pain: quantitative systematic review

Nefopam, a centrally acting analgesic, has been used in the surgical setting in many countries since the mid-1970s. However, clinical trials provide contflicting results for its analgesic potency. We performed a systematic search (multiple databases, bibliographies, any language, to January 2008) for randomized, placebo-controlled trials of nefopam for the prevention of postoperative pain. Data were combined using classic methods of meta-analyses and were expressed as weighted mean difference (WMD), relative risk (RR), and number needed to treat/harm (NNT/H) with 95% confidence interval (CI). Nine trials (847 adult patients, 359 received nefopam) were included. Nefopam (cumulative doses, 20-160 mg) was given orally or i.v., as single or multiple doses, or as a continuous infusion. Compared with placebo, cumulative 24 h morphine consumption was decreased with nefopam: WMD −13 mg (95% CI −17.9 to −8.15). Pain intensity at 24 h was also decreased: on a 100 mm visual analogue scale, WMD −11.5 mm (95% CI −15.1 to −7.85). The incidence of tachycardia was increased with nefopam (RR 3.12, 95% CI 1.11-8.79; NNH 7), as was the incidence of sweating (RR 4.92, 95% CI 2.0-12.1; NNH 13). There is limited evidence from the published literature that nefopam may be a useful non-opioid analgesic in surgical patients. The analgesic potency seems to be similar to non-steroidal anti-inflammatory drugs. However, dose responsiveness and adverse effect profile remain unclear, and the role of nefopam as part of multimodal analgesia needs to be established. Data in children are lackin

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

How do authors of systematic reviews deal with research malpractice and misconduct in original studies? A cross-sectional analysis of systematic reviews and survey of their authors.

OBJECTIVES: To study whether systematic reviewers apply procedures to counter-balance some common forms of research malpractice such as not publishing completed research, duplicate publications, or selective reporting of outcomes, and to see whether they identify and report misconduct. DESIGN: Cross-sectional analysis of systematic reviews and survey of their authors. PARTICIPANTS: 118 systematic reviews published in four journals (Ann Int Med, BMJ, JAMA, Lancet), and the Cochrane Library, in 2013. MAIN OUTCOMES AND MEASURES: Number (%) of reviews that applied procedures to reduce the impact of: (1) publication bias (through searching of unpublished trials), (2) selective outcome reporting (by contacting the authors of the original studies), (3) duplicate publications, (4) sponsors' and (5) authors' conflicts of interest, on the conclusions of the review, and (6) looked for ethical approval of the studies. Number (%) of reviewers who suspected misconduct are reported. The procedures applied were compared across journals. RESULTS: 80 (68%) reviewers confirmed their data. 59 (50%) reviews applied three or more procedures; 11 (9%) applied none. Unpublished trials were searched in 79 (66%) reviews. Authors of original studies were contacted in 73 (62%). Duplicate publications were searched in 81 (69%). 27 reviews (23%) reported sponsors of the included studies; 6 (5%) analysed their impact on the conclusions of the review. Five reviews (4%) looked at conflicts of interest of study authors; none of them analysed their impact. Three reviews (2.5%) looked at ethical approval of the studies. Seven reviews (6%) suspected misconduct; only 2 (2%) reported it explicitly. Procedures applied differed across the journals. CONCLUSIONS: Only half of the systematic reviews applied three or more of the six procedures examined. Sponsors, conflicts of interest of authors and ethical approval remain overlooked. Research misconduct is sometimes identified, but rarely reported. Guidance on when, and how, to report suspected misconduct is needed