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Seroconversion rates following influenza vaccination in patients with hematologic malignancies after hematopoietic stem cell transplantation (HSCT) are known to be lower compared to healthy adults. The aim of our diagnostic study was to determine the rate of seroconversion after 1 or 2 doses of a novel split virion, inactivated, AS03-adjuvanted pandemic H1N1 influenza vaccine (A/California/7/2009) in HSCT recipients (ClinicalTrials.gov Identifier: NCT01017172). Blood samples were taken before and 21 days after a first dose and 21 days after a second dose of the vaccine. Antibody (AB) titers were determined by hemagglutination inhibition assay. Seroconversion was defined by either an AB titer of ≤1:10 before and ≥1:40 after or ≥1:10 before and ≥4-fold increase in AB titer 21 days after vaccination. Seventeen patients (14 allogeneic, 3 autologous HSCT) received 1 dose and 11 of these patients 2 doses of the vaccine. The rate of seroconversion was 41.2% (95% confidence interval [CI] 18.4-67.1) after the first and 81.8% (95% CI 48.2-97.7) after the second dose. Patients who failed to seroconvert after 1 dose of the vaccine were more likely to receive any immunosuppressive agent (P = .003), but time elapsed after or type of HSCT, age, sex, or chronic graft-versus-host disease was not different when compared to patients with seroconversion. In patients with hematologic malignancies after HSCT the rate of seroconversion after a first dose of an adjuvanted H1N1 influenza A vaccine was poor, but increased after a second dose.
Background: Intensive Care Resources are heavily utilized during the COVID-19 pandemic. However, risk stratification and prediction of SARS-CoV-2 patient clinical outcomes upon ICU admission remain inadequate. This study aimed to develop a machine learning model, based on retrospective & prospective clinical data, to stratify patient risk and predict ICU survival and outcomes. Methods: A Germany-wide electronic registry was established to pseudonymously collect admission, therapeutic and discharge information of SARS-CoV-2 ICU patients retrospectively and prospectively. Machine learning approaches were evaluated for the accuracy and interpretability of predictions. The Explainable Boosting Machine approach was selected as the most suitable method. Individual, non-linear shape functions for predictive parameters and parameter interactions are reported. Results: 1039 patients were included in the Explainable Boosting Machine model, 596 patients retrospectively collected, and 443 patients prospectively collected. The model for prediction of general ICU outcome was shown to be more reliable to predict “survival”. Age, inflammatory and thrombotic activity, and severity of ARDS at ICU admission were shown to be predictive of ICU survival. Patients’ age, pulmonary dysfunction and transfer from an external institution were predictors for ECMO therapy. The interaction of patient age with D-dimer levels on admission and creatinine levels with SOFA score without GCS were predictors for renal replacement therapy. Conclusions: Using Explainable Boosting Machine analysis, we confirmed and weighed previously reported and identified novel predictors for outcome in critically ill COVID-19 patients. Using this strategy, predictive modeling of COVID-19 ICU patient outcomes can be performed overcoming the limitations of linear regression models. Trial registration “ClinicalTrials” (clinicaltrials.gov) under NCT04455451.
Ultrasonic guided waves have been used successfully in structural health monitoring systems to detect damage in isotropic and composite materials with simple and complex geometry. A limitation of current research is given by a lack of freely available benchmark measurements to comparatively evaluate existing methods. This article introduces the extendable online platform Open Guided Waves (http://www.open-guided-waves.de) where high-quality and well-documented datasets for guided wave-based inspections are provided. In this article, we describe quasi-isotropic carbon-fiber-reinforced polymer plates with embedded piezoelectric transducers as a first benchmark structure. Intentionally, this is a structure of medium complexity to enable many researchers to apply their methods. In a first step, ultrasound and X-ray measurements were acquired to verify pristine conditions. Next, mechanical testing was done to determine the stiffness tensor and sample density based on standard test procedures. Guided wave measurements were divided into two parts: first, acoustic wave fields were acquired for a broad range of frequencies by three-dimensional scanning laser Doppler vibrometry. Second, structural health monitoring measurements in the carbon-fiber-reinforced polymer plate were collected at constant temperature using a distributed transducer network and a surface-mounted reversible defect model. Initial results serving as validation are presented and discussed.