Prediction and impact of personalized donation intervals
| Autor: | Femmeke J. Prinsze, Jarkko Toivonen, Markus Heinonen, Esa Turkulainen, Pietro Della Briotta Parolo, Yrjö Koski, Mikko Arvas |
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| Přispěvatelé: | Department of Computer Science, Institute for Molecular Medicine Finland, Genomics of Neurological and Neuropsychiatric Disorders, Kernel Machines, Pattern Analysis and Computational Biology research group / Juho Rousu |
| Rok vydání: | 2021 |
| Předmět: |
donor health
Blood Donors 030204 cardiovascular system & hematology Hemoglobins 03 medical and health sciences 0302 clinical medicine Humans Cutoff Medicine Deferral Actual use haemoglobin measurement Hematologic Tests business.industry blood collection Anemia Hematology General Medicine Blood collection Hematologic Diseases Biobank 3. Good health 3121 General medicine internal medicine and other clinical medicine Donation Low haemoglobin Cohort 1182 Biochemistry cell and molecular biology Female business 030215 immunology Demography |
| Zdroj: | Vox Sanguinis. 117:504-512 |
| ISSN: | 1423-0410 0042-9007 |
| Popis: | Publisher Copyright: © 2021 The Authors. Vox Sanguinis published by John Wiley & Sons Ltd on behalf of International Society of Blood Transfusion. Background and Objectives: Deferral of blood donors due to low haemoglobin (Hb) is demotivating to donors, can be a sign for developing anaemia and incurs costs for blood establishments. The prediction of Hb deferral has been shown to be feasible in a number of studies based on demographic, Hb measurement and donation history data. The aim of this paper is to evaluate how state-of-the-art computational prediction tools can facilitate nationwide personalized donation intervals. Materials and Methods: Using donation history data from the last 20 years in Finland, FinDonor blood donor cohort data and blood service Biobank genotyping data, we built linear and non-linear predictors of Hb deferral. Based on financial data from the Finnish Red Cross Blood Service, we then estimated the economic impacts of deploying such predictors. Results: We discovered that while linear predictors generally predict Hb relatively well, they have difficulties in predicting low Hb values. Overall, we found that non-linear or linear predictors with or without genetic data performed only slightly better than a simple cutoff based on previous Hb. However, if any of our deferral prediction methods are used to assign temporary prolongations of donation intervals for females, then our calculations indicate cost savings while maintaining the blood supply. Conclusion: We find that even though the prediction accuracy is not very high, the actual use of any of our predictors in blood collection is still likely to bring benefits to blood donors and blood establishments alike. |
| Databáze: | OpenAIRE |
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