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Background: To reduce smoking uptake in adolescents, the medical students' network Education Against Tobacco (EAT) has developed a school-based intervention involving a face-aging mobile app (Smokerface)., Methods: A two-arm cluster-randomized controlled trial was conducted, evaluating the 2016 EAT intervention, which employed the mobile app Smokerface and which was delivered by medical students. Schools were randomized to intervention or control group. Surveys were conducted at baseline (pre-intervention) and at 9, 16, and 24 months post-intervention via paper & pencil questionnaires. The primary outcome was the difference in within-group changes in smoking prevalence between intervention and control group at 24 months., Results: Overall, 144 German secondary schools comprising 11,286 pupils participated in the baseline survey, of which 100 schools participated in the baseline and at least one of the follow-up surveys, yielding 7437 pupils in the analysis sample. After 24 months, smoking prevalence was numerically lower in the intervention group compared to control group (12.9 % vs. 14.3 %); however, between-group differences in change in smoking prevalence between baseline and 24-months follow-up (OR=0.83, 95 %-CI: 0.64-1.09) were not statistically significant (p = 0.176). Intention to start smoking among baseline non-smokers declined non-significantly in the intervention group (p = 0.064), and remained essentially unchanged in the control group, but between-group differences in changes at the 24-months follow-up (OR=0.88, 0.64-1.21) were not statistically significant (p = 0.417)., Conclusion: While a trend towards beneficial effects of the intervention regarding smoking prevalence as well as intention to start smoking among baseline non-smokers was observed, our smoking prevention trial demonstrated no significant effect of the intervention., Competing Interests: Declaration of Competing Interest TJB programmed the Smokerface App and is therefore formally the owner/rights holder. In 2017, he was offered and accepted 500 Euro per year from another German tobacco prevention charity (ginko Stiftung für Prävention, Germany) to license the Smokerface App for their school-based prevention efforts,with no influence to this study. TJB is also the owner of Smart Health Heidelberg GmbH, a software company which develops digital utehealth apps. Werner Seeger received consulting fees from United Therapeutics, Tiakis Biotech AG, Liquidia, Pieris Pharmaceuticals, Abivax, Pfizer, Medspray BV, outside the submitted work. The other authors declare no conflict of interest., (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Objective: This study aimed to examine the relationship between the decrease in elective procedures and the need for blood donation during the novel coronavirus disease (COVID-19) pandemic at university hospitals., Background: The COVID-19 pandemic has immensely impacted transfusion medicine. By cancelling elective surgery, the German government hoped to increase the available resources for patients infected with COVID-19, especially in intensive care units, and prevent the shortage of blood products., Methods/materials: Over 26 weeks, from the 3rd of February 2020 to the 2nd of August 2020, during the first phase of the pandemic, we assessed the number of crossmatches, blood group typing, use of donated blood, and case mix indices by retrospectively analysing data from two major university hospitals' information systems in Essen and Hamburg, Germany. Data were pooled, analysed, and compared with that of the same period in the previous year., Results: Following the cessation of elective procedures, the number of requests for crossmatches and blood group typing significantly decreased in 2020 compared to that in 2019. However, the number of blood transfusions required was reduced to a lesser extent. The number of outpatient and inpatient cases significantly decreased, whereas the cases requiring transfusion decreased only., Conclusion: During the initial phase of the pandemic, transfusion medicine, especially in large institutions, faced an almost unchanged high demand for donated blood. This should be considered regarding personnel and blood donation allocations. Therefore, we developed a monitoring system to display the availability of blood products in real-time. The quick and easy display of in-stock and expiring blood products can optimise the use of this valuable resource., (© 2024 The Authors. Transfusion Medicine published by John Wiley & Sons Ltd on behalf of British Blood Transfusion Society.)

Introduction: An increasing shortage of donor blood is expected, considering the demographic change in Germany. Due to the short shelf life and varying daily fluctuations in consumption, the storage of platelet concentrates (PCs) becomes challenging. This emphasizes the need for reliable prediction of needed PCs for the blood bank inventories. Therefore, the objective of this study was to evaluate multimodal data from multiple source systems within a hospital to predict the number of platelet transfusions in 3 days on a per-patient level., Methods: Data were collected from 25,190 (42% female and 58% male) patients between 2017 and 2021. For each patient, the number of received PCs, platelet count blood tests, drugs causing thrombocytopenia, acute platelet diseases, procedures, age, gender, and the period of a patient's hospital stay were collected. Two models were trained on samples using a sliding window of 7 days as input and a day 3 target. The model predicts whether a patient will be transfused 3 days in the future. The model was trained with an excessive hyperparameter search using patient-level repeated 5-fold cross-validation to optimize the average macro F2-score., Results: The trained models were tested on 5,022 unique patients. The best-performing model has a specificity of 0.99, a sensitivity of 0.37, an area under the precision-recall curve score of 0.45, an MCC score of 0.43, and an F1-score of 0.43. However, the model does not generalize well for cases when the need for a platelet transfusion is recognized., Conclusion: A patient AI-based platelet forecast could improve logistics management and reduce blood product waste. In this study, we build the first model to predict patient individual platelet demand. To the best of our knowledge, we are the first to introduce this approach. Our model predicts the need for platelet units for 3 days in the future. While sensitivity underperforms, specificity performs reliably. The model may be of clinical use as a pretest for potential patients needing a platelet transfusion within the next 3 days. As sensitivity needs to be improved, further studies should introduce deep learning and wider patient characterization to the methodological multimodal, multisource data approach. Furthermore, a hospital-wide consumption of PCs could be derived from individual predictions., Competing Interests: The authors have no conflicts of interest to declare., (Copyright © 2023 by The Author(s). Published by S. Karger AG, Basel.)