Update PONV – Was gibt es Neues bei der Prophylaxe und Therapie von postoperativer Übelkeit und postoperativem Erbrechen?

Die Anaesthesiologie - Auch wenn für Anästhesiologen über Jahrzehnte die Prophylaxe und Therapie postoperativer Schmerzen im Rahmen des postoperativen Patientenkomforts an vorderster...     

SALAD‐BAAR: A numerical risk score for hospital admission or emergency department presentation in ambulatory patients with cardiovascular disease

BackgroundWhile many interventions to reduce hospital admissions and emergency department (ED) visits for patients with cardiovascular disease have been developed, identifying ambulatory cardiac patients at high risk for admission can be challenging.HypothesisA computational model based on readily accessible clinical data can identify patients at risk for admission.MethodsElectronic health record (EHR) data from a tertiary referral center were used to generate decision tree and logistic regression models. International Classification of Disease (ICD) codes, labs, admissions, medications, vital signs, and socioenvironmental variables were used to model risk for ED presentation or hospital admission within 90 days following a cardiology clinic visit. Model training and testing were performed with a 70:30 data split. The final model was then prospectively validated.ResultsA total of 9326 patients and 46 465 clinic visits were analyzed. A decision tree model using 75 patient characteristics achieved an area under the curve (AUC) of 0.75 and a logistic regression model achieved an AUC of 0.73. A simplified 9‐feature model based on logistic regression odds ratios achieved an AUC of 0.72. A further simplified numerical score assigning 1 or 2 points to each variable achieved an AUC of 0.66, specificity of 0.75, and sensitivity of 0.58. Prospectively, this final model maintained its predictive performance (AUC 0.63–0.60).ConclusionNine patient characteristics from routine EHR data can be used to inform a highly specific model for hospital admission or ED presentation in cardiac patients. This model can be simplified to a risk score that is easily calculated and retains predictive performance.     

Ostriches and Obligations: Ethical Challenges Facing Research on Usual Care

In recent years, a robust body of scholarship has emerged that examines ethical challenges facing the learning health organization model. In “Bystander Ethics and Good Samaritanism,” James Sabin and colleagues make a valuable addition to this scholarship, identifying and exploring the important question of what researchers' obligations are to patients receiving “usual care” if “that care is seen as suboptimal.” The central issue that Sabin et al. faced was whether it would be acceptable for researchers to identify patients with untreated atrial fibrillation but then assign them to a control group that would not receive education about the importance of oral anticoagulation. The authors present this challenge as an issue of “bystander ethics.” To avoid being “bystanders” to identified instances of suboptimal care, the research team decided to instead identify a “delayed intervention” group for which they would not determine the members' anticoagulation status, thereby preventing them from knowing that specific patients met the criteria for oral anticoagulants but were not using them. This “workaround” approach strikes me as disingenuous.