The use of predictive modelling to determine the likelihood of donor return during the COVID-19 pandemic.
COVID‐19
blood donor
predictive modelling
Journal
Transfusion medicine (Oxford, England)
ISSN: 1365-3148
Titre abrégé: Transfus Med
Pays: England
ID NLM: 9301182
Informations de publication
Date de publication:
08 Aug 2024
08 Aug 2024
Historique:
revised:
27
05
2024
received:
26
09
2023
accepted:
25
07
2024
medline:
8
8
2024
pubmed:
8
8
2024
entrez:
8
8
2024
Statut:
aheadofprint
Résumé
Artificial intelligence (AI) uses sophisticated algorithms to "learn" from large volumes of data. This could be used to optimise recruitment of blood donors through predictive modelling of future blood supply, based on previous donation and transfusion demand. We sought to assess utilisation of predictive modelling and AI blood establishments (BE) and conducted predictive modelling to illustrate its use. A BE survey of data modelling and AI was disseminated to the International Society of Blood transfusion members. Additional anonymzed data were obtained from Italy, Singapore and the United States (US) to build predictive models for each region, using January 2018 through August 2019 data to determine likelihood of donation within a prescribed number of months. Donations were from March 2020 to June 2021. Ninety ISBT members responded to the survey. Predictive modelling was used by 33 (36.7%) respondents and 12 (13.3%) reported AI use. Forty-four (48.9%) indicated their institutions do not utilise predictive modelling nor AI to predict transfusion demand or optimise donor recruitment. In the predictive modelling case study involving three sites, the most important variable for predicting donor return was number of previous donations for Italy and the US, and donation frequency for Singapore. Donation rates declined in each region during COVID-19. Throughout the observation period the predictive model was able to consistently identify those individuals who were most likely to return to donate blood. The majority of BE do not use predictive modelling and AI. The effectiveness of predictive model in determining likelihood of donor return was validated; implementation of this method could prove useful for BE operations.
Types de publication
Journal Article
Review
Langues
eng
Sous-ensembles de citation
IM
Informations de copyright
© 2024 British Blood Transfusion Society.
Références
Williamson LM, Devine DV. Challenges in the management of the blood supply. Lancet. 2013;381(9880):1866‐1875. doi:10.1016/S0140‐6736(13)60631‐5
Al‐Riyami AZ, Abdella YE, Badawi MA, et al. The impact of COVID‐19 pandemic on blood supplies and transfusion services in eastern Mediterranean region. Transfus Clin Biol. 2021;28(1):16‐24. doi:10.1016/J.TRACLI.2020.11.002
Loua A, Kasilo OMJ, Nikiema JB, Sougou AS, Kniazkov S, Annan EA. Impact of the COVID‐19 pandemic on blood supply and demand in the WHO African region. Vox Sang. 2021;116(7):774‐784. doi:10.1111/vox.13071
Choudhury N, Mathur A, Smit Sibinga CT, AATM & O behalf of. COVID‐19 pandemic – blood supply challenges and approaches in AATM member countries. ISBT Sci Ser. 2020;15(4):353‐361. doi:10.1111/voxs.12578
Wang Y, Han W, Pan L, et al. Impact of COVID‐19 on blood centres in Zhejiang province China. Vox Sang. 2020;115(6):502‐506. doi:10.1111/vox.12931
Reik R, Gammon RR, Carol N, et al. Rapid development of a de novo convalescent plasma program in response to a global pandemic: a large southeastern U.S. blood center's experience. J Clin Apher. 2021;36(4):533‐546. doi:10.1002/jca.21889
US Department of Health and Human Services. Food and Drug Administration, Center for Biologics Evaluation and Research. Maintaining a Safe and Adequate Blood Supply and Collecting Convalescent Plasma in the Context of the COVID‐19 Pandemic Interim Guidance. Food and Drug Administration; 2021.
Pandey HC, Coshic P, Chippy CS, Arcot PJ, Kumar K. Blood supply management in times of SARS‐CoV‐2 pandemic – challenges, strategies adopted, and the lessons learned from the experience of a hospital‐based blood centre. Vox Sang. 2021;116(5):497‐503. doi:10.1111/vox.13019
Hamet P, Tremblay J. Artificial intelligence in medicine. Metabolism. 2017;69:S36‐S40. doi:10.1016/j.metabol.2017.01.011
What is predictive analytics? PAT Research. Accessed November 30, 2022. https://www.predictiveanalyticstoday.com/what-is-predictive-analytics/
Franchini M, Farrugia A, Velati C, et al. The impact of the SARS‐CoV‐2 outbreak on the safety and availability of blood transfusions in Italy. Vox Sang. 2020;115(8):603‐605. doi:10.1111/vox.12928
Xue R, Chen Y, Wen J. Blood shortages and donation in China. Lancet. 2016;387(10031):1905. doi:10.1016/S0140‐6736(16)30416‐0
Kacker S, Katz LM, Ness PM, et al. Financial analysis of large‐volume delayed sampling to reduce bacterial contamination of platelets. Transfusion (Paris). 2020;60(5):997‐1002. doi:10.1111/trf.15773
Shander A, Van Aken H, Colomina MJ, et al. Patient blood management in Europe. Br J Anaesth. 2012;109(1):55‐68. doi:10.1093/bja/aes139
Goodnough LT, Shander A, Riou B. Patient Blood Management. Anesthesiology. 2012;116(6):1367‐1376. doi:10.1097/ALN.0b013e318254d1a3
Mueller MM, Van Remoortel H, Meybohm P, et al. Patient blood management: recommendations from the 2018 Frankfurt consensus conference. JAMA. 2019;321(10):983‐997. doi:10.1001/jama.2019.0554
Feng Y, Xu Z, Sun X, Wang D, Yu Y. Machine learning for predicting preoperative red blood cell demand. Transfus Med. 2021;31(4):262‐270. doi:10.1111/tme.12794
Nandi AK, Roberts DJ, Nandi AK. Prediction paradigm involving time series applied to total blood issues data from England. Transfusion. 2020;60(3):535‐543. doi:10.1111/trf.15705
Tinegate H, Chattree S, Iqbal A, Plews D, Whitehead J, Wallis JP. Ten‐year pattern of red blood cell use in the north of England. Transfusion. 2013;53(3):483‐489. doi:10.1111/j.1537‐2995.2012.03782.x
Blake JT, Hardy M. A generic modelling framework to evaluate network blood management policies: the Canadian Blood Services experience. Oper Res Health Care. 2014;3(3):116‐128. doi:10.1016/j.orhc.2014.05.002