57. Jahrestagung der Deutschen Gesellschaft für Transfusionsmedizin und Immunhämatologie

Dipl.-Ing.

State of Automation and Data Processing at the 57th Annual Meeting of the DGTI in Düsseldorf 2024

AI, Automation and Digitalization

Stand der Automation und Datenverarbeitung zur 57. Jahrestagung der DGTI in Düsseldorf 2024

The status of digitalization in hemotherapy and software implementation in laboratory medicine will be presented at the 57th annual congress of the DGTI in Düsseldorf.The aim is to use new solutions from practice to show examples that can be used for digitalization in hemotherapy and transfusion medicine while observing applicable guidelines.In addition, results from transfusion medicine laboratories and hemotherapy will be shown for data processing and software validation.The status of digitalization will be discussed for selected key topics.(1) Digitalization in hemotherapy for applications in transfusion medicine clinics and telemedicine (Falk Reinhardt (Plauen)) (2) Status of automation and data processing according to GAMP5 and GMP Guide Annex 15 in the Automation and data processing section (Validation and qualification examples in the DGTI member area) (Falk Reinhardt (Plauen), Wolfgang Steinke (Münster))(3) Data processing and software validation in-house laboratory solutions (Michael Schmidt (Frankfurt))(4) Archiving (reserve samples) in the central laboratory of the DRK BSD West (Katja Konietzko (Hagen), Lutz Pichl-(Hagen))(5) Automation of donor screening in the central laboratory of the DRK BSD West ( Regine Rietz (Hagen))With the involvement of external speakers, institutions, authorities, solution examples for the implementation of GAMP5 and GMP Guide in the routine for hospitals, transfusion facilities, and medical facilities are to be recorded, taking into account legally binding guidelines.Eurocode IBLS e.V. was founded 25 years ago on the recommendation of the DGTI Automation and Data Processing Section. Eurocode, DGTI 2024. The framework conditions for the EC's founding in 1998 as well as the first board structure of the Eurocode IBLS should be presented alongside the other highlights in a timeline with the relevant documents and specifications or the EU-relevant coordination and regulations in an abstract.The validations shown should be stored on the DGTI platform of the Automation and Data Processing section in the member area.This meeting of the Automation and Data Processing Section at the 57th Annual Meeting of the DGTI in Düsseldorf is also intended to help provide better support for IT staff in digitalization in hemotherapy, laboratory medicine, and transfusion medicine for future applications.keine

Poster

Dr. med.

Automation of donor screening in the central laboratory of a Blood Transfusion Service

Automatisierung des Spenderscreenings im Zentrallabor eines großen Blutspendedienstes

Introduction: In the production of blood products, safety is the first priority for blood donation services. Thus a safe and high-quality analysis, respectively, pre- and postanalysis of the blood samples is essential in our central laboratory. The process steps in pre- and postanalysis are manifold and – manually performed – would be one of the great timekeepers and sources of error in the laboratory. They are not only error-prone, they also mean a high degree of complexity and require many resources that can be better utilized elsewhere.Methods: In 2016 our Blood Donation Service has decided to install a new laboratory automation system for testing the infection-serology in our in-house central laboratory. The automated and standardized workflow to perform the testing on approximately 3000 donations per day is presented. It is shown which logistical, personnel and machine requirements have been coordinated to enable efficient and safe testing of our blood products.Results: Logistical, personnel and machine requirements have been coordinated to enable efficient and safe testing of our blood products.Conclusion: Laboratory automation was intended to simplify our lab-processes. This goal was achieved. Automation has optimized the performance of the laboratory and the safety of testing.No disclosures.

Prof. Dr.

A browser-based HLA epitope (mis)matching application in sensitizing events of transplantation and transfusion: epiTOol

Eine browserbasierte Anwendung zum HLA-Epitop-(Fehl-)Abgleich bei sensibilisierenden Ereignissen bei Transplantation und Transfusion: epiTOol

Handling large numbers of transplant donors and recipients&quot; data in diagnostics is often still a manual transfer process and a source for potentially fatal mishap. Moreover, allele matching, processing of antibody data, and epitope assignment requires separate tools with partially missing direct interfaces.Here we present &quot;epiTOol&quot;, an interactive analytics tool for all-in-one processing of HLA typing and antibody data. At first, data import functionalities create a database of individuals collecting HLA typing and antibody data. Large numbers of several thousand or tens of thousands of measurements can be loaded simultaneously. For the individually present alleles and antibodies, epitopes are then automatically assigned using incorporated data previously extracted from epregistry.com.br.Based on these epitopes, matches and mismatches between the recipient-donor pairs can be assessed. Thereby, two modes are available: a) Single individual mode: Comparison of a single donor to a list of potential recipients (i.e. one available organ to a waiting list of patients), or, vice versa, a single patient to a list of potential donors. b) Pair mode: Pairwise comparison of donors and assigned recipients. Both modes can be applied in the daily diagnostic tasks in immunology laboratories as well as for scientific retrospective evaluation. Matching and mismatching epitopes are displayed for each comparison by means of absolute numbers and of present allele, antibody and epitope information, respectively. Finally, export functionalities for spreadsheet and pdf files allow for easy and comfortable utilization of the evaluation results in routine processes such as issuing medical reports.As a proof of principle screening of a test cohort of 108 recipient-donor pairs led to first results, regarding epitope match and mismatch constellation, prevalence, and antibody production.none

Prediction of apheresis volume for CD34+ cell collection using Machine learning

Vorhersage des Apheresevolumens für die Sammlung von CD34+-Zellen mithilfe von Machine learning

Determination of the required apheresis volume in stem cell apheresis is a challenge. Prediction using the CE2 formula assumes a certain collection efficiency, which is often deliberately underestimated, in order to reliably reach the target CD34+ cell number. However, this exposes most donors longer to apheresis than necessary. The aim of this work is to use machine learning to generate a model that predicts apheresis volumes more accurately with the same reliability as the CE formula.We divided data from 3322 collections, which included CD34+ cells in the product, apheresis volume as well as 16 additional parameters, into a test and a main data set. We applied the main data in an optimization process to determine which machine learning model, which parameters and hyper parameters and which split of the main data set into training and validation data sets would generate the best model and which safety margin would lead to acceptable certainty to reach the desired CD34+ cell target. Finally, we tested the model with the test data set.The generated model has a probability of 98% to predict an apheresis volume to reach the desired CD34+ cell target. If only collections with true apheresis volumes of &lt; 15 L are considered, the mean standard error of the model is 606mL below that of the CE2 formula, with a collection efficiency that results in the same probability of 98% to collect the target amount of CD34+ cells. If all collections are considered, the difference is 3233mL.The generated model calculates apheresis volumes closer to the real throughput volumes needed with the same reliabilty as the CE2 formula. It therefore enables to shorten apheresis of stem cell donors without compromising safety or collection yield. The machine learning approach thus offers an advantage over previous prediction methods.There is no conflict of interest

Posterausstellung 14

Wintergartenrestaurant + Foyer

Posterausstellung

Posterausstellung 14
AI, Automation and Digitalization

Termin

Programm

Posterausstellung 14AI, Automation and Digitalization

Termin

Programm

Posterausstellung 14
AI, Automation and Digitalization