A data-informed system to manage scarce blood product allocation in a randomized controlled trial of convalescent plasma.
convalescent plasma
data-driven optimization
demand forecasting
mathematical models
randomized controlled trials
resource allocation
scarce blood product
Journal
Transfusion
ISSN: 1537-2995
Titre abrégé: Transfusion
Pays: United States
ID NLM: 0417360
Informations de publication
Date de publication:
12 2022
12 2022
Historique:
revised:
19
09
2022
received:
28
07
2022
accepted:
26
09
2022
pubmed:
27
10
2022
medline:
15
12
2022
entrez:
26
10
2022
Statut:
ppublish
Résumé
Equitable allocation of scarce blood products needed for a randomized controlled trial (RCT) is a complex decision-making process within the blood supply chain. Strategies to improve resource allocation in this setting are lacking. We designed a custom-made, computerized system to manage the inventory and allocation of COVID-19 convalescent plasma (CCP) in a multi-site RCT, CONCOR-1. A hub-and-spoke distribution model enabled real-time inventory monitoring and assignment for randomization. A live CCP inventory system using REDCap was programmed for spoke sites to reserve, assign, and order CCP from hospital hubs. A data-driven mixed-integer programming model with supply and demand forecasting was developed to guide the equitable allocation of CCP at hubs across Canada (excluding Québec). 18/38 hospital study sites were hubs with a median of 2 spoke sites per hub. A total of 394.5 500-ml doses of CCP were distributed; 349.5 (88.6%) doses were transfused; 9.5 (2.4%) were wasted due to mechanical damage sustained to the blood bags; 35.5 (9.0%) were unused at the end of the trial. Due to supply shortages, 53/394.5 (13.4%) doses were imported from Héma-Québec to Canadian Blood Services (CBS), and 125 (31.7%) were transferred between CBS regional distribution centers to meet demand. 137/349.5 (39.2%) and 212.5 (60.8%) doses were transfused at hubs and spoke sites, respectively. The mean percentages of total unmet demand were similar across the hubs, indicating equitable allocation, using our model. Computerized tools can provide efficient and immediate solutions for equitable allocation decisions of scarce blood products in RCTs.
Sections du résumé
BACKGROUND
Equitable allocation of scarce blood products needed for a randomized controlled trial (RCT) is a complex decision-making process within the blood supply chain. Strategies to improve resource allocation in this setting are lacking.
METHODS
We designed a custom-made, computerized system to manage the inventory and allocation of COVID-19 convalescent plasma (CCP) in a multi-site RCT, CONCOR-1. A hub-and-spoke distribution model enabled real-time inventory monitoring and assignment for randomization. A live CCP inventory system using REDCap was programmed for spoke sites to reserve, assign, and order CCP from hospital hubs. A data-driven mixed-integer programming model with supply and demand forecasting was developed to guide the equitable allocation of CCP at hubs across Canada (excluding Québec).
RESULTS
18/38 hospital study sites were hubs with a median of 2 spoke sites per hub. A total of 394.5 500-ml doses of CCP were distributed; 349.5 (88.6%) doses were transfused; 9.5 (2.4%) were wasted due to mechanical damage sustained to the blood bags; 35.5 (9.0%) were unused at the end of the trial. Due to supply shortages, 53/394.5 (13.4%) doses were imported from Héma-Québec to Canadian Blood Services (CBS), and 125 (31.7%) were transferred between CBS regional distribution centers to meet demand. 137/349.5 (39.2%) and 212.5 (60.8%) doses were transfused at hubs and spoke sites, respectively. The mean percentages of total unmet demand were similar across the hubs, indicating equitable allocation, using our model.
CONCLUSION
Computerized tools can provide efficient and immediate solutions for equitable allocation decisions of scarce blood products in RCTs.
Types de publication
Randomized Controlled Trial
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
2525-2538Commentaires et corrections
Type : ErratumIn
Informations de copyright
© 2022 AABB.
Références
Williamson LM, Devine DV. Challenges in the management of the blood supply. Lancet. 2013;381(9880):1866-75. https://doi.org/10.1016/S0140-6736(13)60631-5
Torrado A, Barbosa-Póvoa A. Towards an optimized and sustainable blood supply chain network under uncertainty: a literature review. Clean Logist Supply Chain. 2022;3(100028):1-25. https://doi.org/10.1016/j.clscn.2022.100028
Yousefi Nejad Attari M, Pasandideh SHR, Akhavan Niaki ST. A hybrid robust stochastic programming for a bi-objective blood collection facilities problem (case study: Iranian blood transfusion network). J Ind Prod Eng. 2019;36(3):154-67. https://doi.org/10.1080/21681015.2019.1645747
Williams EP, Harper PR, Gartner D. Modeling of the collections process in the blood supply chain: a literature review. IISE Trans Healthc Syst Eng. 2020;10:200-11. https://doi.org/10.1080/24725579.2020.1776426
Soares HLF, Arruda EF, Bahiense L, Gartner D, Amorim FL. Optimisation and control of the supply of blood bags in hemotherapic centres via Markov decision process with discounted arrival rate. Artif Intell Med. 2020;104:101791. https://doi.org/10.1016/j.artmed.2020.101791
Li N, Chiang F, Down DG, Heddle NM. A decision integration strategy for short-term demand forecasting and ordering for red blood cell components. Oper Res Health Care. 2021;29:100290. https://doi.org/10.1016/j.orhc.2021.100290
Pirabán A, Guerrero WJ, Labadie N. Survey on blood supply chain management: models and methods. Comput Oper Res. 2019;112:104756. https://doi.org/10.1016/j.cor.2019.07.014
Zahiri B, Pishvaee MS. Blood supply chain network design considering blood group compatibility under uncertainty. Int J Prod Res. 2017;55(7):2013-33. https://doi.org/10.1080/00207543.2016.1262563
Canadian Blood Services. Inventory planning and management. Inventory best practices. doi:https://doi.org/10.1002/9781119203087.ch8
Stanger SHW, Yates N, Wilding R, Cotton S. Blood inventory management: hospital best practice. Transfus Med Rev. 2012;26(2):153-63. https://doi.org/10.1016/j.tmrv.2011.09.001
Jacka M, Nahirniak S. In critically ill adults, transfusion of fresh vs standard-issue red blood cells did not differ for 90-day mortality. Ann Intern Med. 2015;163(2):JC5. https://doi.org/10.7326/ACPJC-2015-163-2-005
Keir AK, Wilkinson D, Andersen C, Stark MJ. Washed versus unwashed red blood cells for transfusion for the prevention of morbidity and mortality in preterm infants. Cochrane Database Syst Rev. 2015;2015(1):91. https://doi.org/10.1002/14651858.CD011484
Heddle NM, Cook RJ, Arnold DM, Liu Y, Barty R, Crowther MA, et al. Effect of short-term vs. long-term blood storage on mortality after transfusion. N Engl J Med. 2016;375(20):1937-45. https://doi.org/10.1056/nejmoa1609014
Webert KE, Cook RJ, Couban S, Carruthers J, Lee KA, Blajchman MA, et al. A multicenter pilot-randomized controlled trial of the feasibility of an augmented red blood cell transfusion strategy for patients treated with induction chemotherapy for acute leukemia or stem cell transplantation. Transfusion. 2008;48(1):81-91. https://doi.org/10.1111/j.1537-2995.2007.01485.x
Sensebé L, Giraudeau B, Bardiaux L, Deconinck E, Schmidt A, Bidet M-L, et al. The efficiency of transfusing high doses of platelets in hematologic patients with thrombocytopenia: results of a prospective, randomized, open, blinded end point (PROBE) study. Blood. 2005;105(2):862-4. https://doi.org/10.1182/blood-2004-05-1841
Piechotta V, Iannizzi C, Chai KL, Valk SJ, Kimber C, Dorando E, et al. Convalescent plasma or hyperimmune immunoglobulin for people with COVID-19: a living systematic review. Cochrane Database Syst Rev. 2021;2021(5):CD013600. https://doi.org/10.1002/14651858.CD013600.pub4
Bégin P, Callum J, Jamula E, Cook R, Heddle NM, Tinmouth A, et al. Convalescent plasma for hospitalized patients with COVID-19: an open-label, randomized controlled trial. Nat Med. 2021;27:2012-24. https://doi.org/10.1038/s41591-021-01488-2
Bennett-Guerrero E, Romeiser JL, Talbot LR, Ahmed T, Mamone LJ, Singh SM, et al. Severe acute respiratory syndrome coronavirus 2 convalescent plasma versus standard plasma in coronavirus disease 2019 infected hospitalized patients in New York: a double-blind randomized trial. Crit Care Med. 2021;49(7):1015-25. https://doi.org/10.1097/CCM.0000000000005066
Diago-Sempere E, Bueno JL, Sancho-López A, Rubio EM, Torres F, de Molina RM, et al. Evaluation of convalescent plasma versus standard of care for the treatment of COVID-19 in hospitalized patients: study protocol for a phase 2 randomized, open-label, controlled, multicenter trial. Trials. 2021;22(1):70. https://doi.org/10.1186/s13063-020-05011-9
AlQahtani M, Abdulrahman A, Almadani A, Alali SY, al Zamrooni AM, Hejab AH, et al. Randomized controlled trial of convalescent plasma therapy against standard therapy in patients with severe COVID-19 disease. Sci Rep. 2021;11(1):9927. https://doi.org/10.1038/s41598-021-89444-5
O'Donnell MR, Grinsztejn B, Cummings MJ, Justman JE, Lamb MR, Eckhardt CM, et al. A randomized double-blind controlled trial of convalescent plasma in adults with severe COVID-19. J Clin Invest. 2021;131(13):e150646. https://doi.org/10.1172/JCI150646
Sullivan DJ, Gebo KA, Shoham S, Bloch EM, Lau B, Shenoy AG, et al. Randomized controlled trial of early outpatient COVID-19 treatment with high-titer convalescent plasma. medRxiv Prepr Serv Health Sci. 2021. https://doi.org/10.1101/2021.12.10.21267485
Masser BM, Ferguson E, Thorpe R, Lawrence C, Davison TE, Hoad V, et al. Motivators of and barriers to becoming a COVID-19 convalescent plasma donor: a survey study. Transfus Med. 2021;31(3):176-85. https://doi.org/10.1111/tme.12753
NCT04348656. CONvalescent plasma for hospitalized adults with COVID-19 respiratory illness (CONCOR-1). https://clinicaltrials.gov/show/NCT04348656. 2020. https://doi.org/10.1002/central/CN-02091713/full
Angus DC, Berry S, Lewis RJ, Al-Beidh F, Arabi Y, van Bentum-Puijk W, et al. The remap-cap (randomized embedded multifactorial adaptive platform for community-acquired pneumonia) study rationale and design. Ann Am Thorac Soc. 2020;17(7):879-91. https://doi.org/10.1513/AnnalsATS.202003-192SD
NCT04377568. Efficacy of human coronavirus-immune convalescent plasma for the treatment of COVID-19 disease in hospitalized children. https://clinicaltrials.gov/show/NCT04377568. 2020.
Abdinnour-Helm S. Network design in supply chain management. Int J Agil Manag Syst. 1999;1(2):99-106. https://doi.org/10.1108/14654659910280929
Calabrò RS, Manuli A, De Cola MC, Bramanti P. Innovation technology in neurorehabilitation: introducing a hub and spoke model to avoid patient “migration” in Sicily. J Health Organ Manag. 2020;34(2):207-14. https://doi.org/10.1108/JHOM-07-2019-0200
Elrod JK, Fortenberry JL. The hub-and-spoke organization design: an avenue for serving patients well. BMC Health Serv Res. 2017;17:457. https://doi.org/10.1186/s12913-017-2341-x
Harris PA, Taylor R, Minor BL, Elliott V, Fernandez M, O'Neal L, et al. The REDCap consortium: building an international community of software platform partners. J Biomed Inform. 2019;95:103208. https://doi.org/10.1016/j.jbi.2019.103208
CONCOR-1. Checking product inventory, reserving a unit, assigning a unit, and freeing a unit in REDCap. YouTube; 2020 https://www.youtube.com/watch?v=LIcWw6b0ylQ&ab_channel=CONCOR-1
Cooper I, Mondal A, Antonopoulos CG. A SIR model assumption for the spread of COVID-19 in different communities. Chaos Solitons Fract. 2020;139:110057. https://doi.org/10.1016/j.chaos.2020.110057
Weissman GE, Crane-Droesch A, Chivers C, Luong TB, Hanish A, Levy MZ, et al. Locally informed simulation to predict hospital capacity needs during the COVID-19 pandemic. Ann Intern Med. 2020;173(1):21-8. https://doi.org/10.7326/M20-1260
Akbari-Moghaddam M, Li N, Down DG, Arnold DM, Callum J, Bégin P, et al. Data-driven fair resource allocation for novel emerging epidemics: a COVID-19 convalescent plasma case study. arXiv. 2021;1-19. https://arxiv.org/abs/2106.14667
Friedman JH. Multivariate adaptive regression splines. The Annals of Statistics. 1991;19(1):1-67. https://doi.org/10.1214/aos/1176347963
Jarrett JE, Khumuwala SB. A study of forecast error and covariant time series to improve forecasting for financial decision making. Manag Financ. 1987;13(2):20-4. https://doi.org/10.1108/eb013583
Lu M, Chen Y. Improved estimation and forecasting through residual-based model error quantification. SPE Journal. 2020;25:951-68. doi:10.2118/199358-PA
Carpenter C. Model error estimation improves forecasting. J Petrol Tech. 2020;72(04):67-8. https://doi.org/10.2118/0420-0067-jpt
Ajmani PS. Blood group and immunology. In: Immunohematology and blood banking. Springer, Singapore. 2020;7-23. https://doi.org/10.1007/978-981-15-8435-0_2
Van Rossum G, Drake FL. Python 3 reference manual. CreateSpace. Scotts Valley, CA. 2009.
Canadian Blood Services. Blood types. Canadian Blood Services, Ottawa Ontario, Canada. 2014. Accessed May 22, 2022. https://www.blood.ca/en/blood/donating-blood/blood-types
Mansournia MA, Higgins JPT, Sterne JAC, Hernán MA. Biases in randomized trials. Epidemiology. 2017;28(1):54-9. https://doi.org/10.1097/EDE.0000000000000564
Chowdhury MEH, Rahman T, Khandakar A, Khandakar A, Al-Madeed S, Zughaier SM, et al. An early warning tool for predicting mortality risk of COVID-19 patients using machine learning. Cognit Comput. 2021;1-16.
Gong J, Ou J, Qiu X, Jie Y, Chen Y, Yuan L, et al. A tool for early prediction of severe coronavirus disease 2019 (covid-19): a multicenter study using the risk nomogram in Wuhan and Guangdong, China. Clin Infect Dis. 2020;71(15):833-40. https://doi.org/10.1093/cid/ciaa443
Tomar A, Gupta N. Prediction for the spread of COVID-19 in India and effectiveness of preventive measures. Sci Total Environ. 2020;728:138762. https://doi.org/10.1016/j.scitotenv.2020.138762
Khajanchi S, Sarkar K. Forecasting the daily and cumulative number of cases for the COVID-19 pandemic in India. Chaos. 2020;30(7):071101. https://doi.org/10.1063/5.0016240
Baron AF, Boulant O, Panico I, Vayatis N. A compartmental epidemiological model applied to the covid-19 epidemic. Image Process Line. 2021;11:105-19. https://doi.org/10.5201/IPOL.2021.323
Mwalili S, Kimathi M, Ojiambo V, Gathungu D, Mbogo R. SEIR model for COVID-19 dynamics incorporating the environment and social distancing. BMC Res Notes. 2020;13(1):352. https://doi.org/10.1186/s13104-020-05192-1
Silal SP, Little F, Barnes KI, White LJ. Sensitivity to model structure: a comparison of compartmental models in epidemiology. Health Syst. 2016;5(3):178-91. https://doi.org/10.1057/hs.2015.2
Nikolopoulos K, Punia S, Schäfers A, Tsinopoulos C, Vasilakis C. Forecasting and planning during a pandemic: COVID-19 growth rates, supply chain disruptions, and governmental decisions. Eur J Oper Res. 2021;290(1):99-115. https://doi.org/10.1016/j.ejor.2020.08.001
Ferstad JO, Gu A, Lee RY, Thapa I, Shin AY, Salomon JA, et al. A model to forecast regional demand for COVID-19 related hospital beds. medRxiv. 2020.
Box GEP, Tiao GC. Intervention analysis with applications to economic and environmental problems. J Am Stat Assoc. 1975;70(349):70-9. https://doi.org/10.1080/01621459.1975.10480264
Lin HH, Beck CL, Bloom MJ. On the use of multivariable piecewise-linear models for predicting human response to anesthesia. IEEE Trans Biomed Eng. 2004;51(11):1876-87. https://doi.org/10.1109/TBME.2004.831541
Brito BH, Finardi EC, Takigawa FYK. Mixed-integer nonseparable piecewise linear models for the hydropower production function in the unit commitment problem. Electr Power Syst Res. 2020;182:106234. https://doi.org/10.1016/j.epsr.2020.106234
Bekker R, uit het Broek M, Koole G. Modeling COVID-19 hospital admissions and occupancy in The Netherlands. Eur J Oper Res. 2023;304(1):207-18. https://doi.org/10.1016/j.ejor.2021.12.044
Segev R. Well-being and fairness in the distribution of scarce health resources. J Med Philos. 2005;30(3):231-60. https://doi.org/10.1080/03605310590960120
Pu L. Fairness of the Distribution of Public Medical and Health Resources. Front Public Health. 2021;9:768728. https://doi.org/10.3389/fpubh.2021.768728
Emanuel EJ, Persad G, Upshur R, Thome B, Parker M, Glickman A, et al. Fair allocation of scarce medical resources in the time of Covid-19. N Engl J Med. 2020;382(21):2049-55. https://doi.org/10.1056/nejmsb2005114