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...     

Renal Neoplasia in Tuberous Sclerosis: A Study of 41 Patients

ObjectiveTo study the clinical features and identify unique renal neoplasia subtypes and their prognostic implications in individuals with tuberous sclerosis complex (TSC).Patients and MethodsThe Mayo Clinic nephrectomy registry included 37 patients with TSC diagnosed between 1970 and 2018. Four additional patients were identified from the pathology consultation and autopsy files. All available renal tumors were further characterized using immunohistochemistry and fluorescence in situ hybridization. Clinicopathologic features and follow-up were obtained from the medical record. The American Association for Cancer Research Project GENIE registry was accessed using cBioPortal for molecular profiling of angiomyolipoma (AML).ResultsA total of 276 renal tumors from 41 patients were analyzed. Renal tumors were classified into 9 distinct morphological subtypes, with AML predominating (238 [86%]). Interestingly, all these tumors acted in a benign fashion except one renal cell carcinoma with clear cells and fibromyomatous stroma and one epithelioid AML that metastasized. Molecular profiling studies revealed that epithelioid AMLs were enriched for alterations of TP53, RB1, and ATRX. Eight patients died of direct complications of TSC, including 3 of end-stage renal disease. To date, none have died of a renal epithelial neoplasm.ConclusionThe identification of unique renal neoplasia subtypes may provide important clues to establish a diagnosis of TSC, and in the somatic setting, this finding has important implications for accurate prognostication. These tumors tend to be indolent, and only 2 of 276 tumors in our study exhibited metastatic behavior. Our results support multidisciplinary management with a focus on preservation of renal function.     

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.