Multi-criteria decision analysis model for choosing the best hematopoietic stem cell donor
DOI:
https://doi.org/10.33448/rsd-v13i1.44678Keywords:
Hematopoietic stem cell transplantation; Donor; Multi-criteria decision; DEXi Software.Abstract
Allogeneic hematopoietic stem cell transplantation is employed in the treatment of hematologic and non-hematologic diseases, transferring stem cells from a healthy donor to the patient. Donor selection requires compatibility of the human leukocyte antigen, considering factors such as age, gender match, blood type, and cytomegalovirus serology, being an essential strategy for transplant success. This article proposes integrating two tools: REDCap (Research Electronic Data Capture) for electronic forms and DEXi for multi-criteria decision analysis. The goal is to address a complex decision-making process in choosing the best donor, considering multiple qualitative parameters. Relevant criteria were selected, and REDCap forms were constructed for data collection. A DEX model based on a hierarchized decision tree was developed, with values inserted by the author following predefined rules. The analysis involved four hypothetical donors. Results highlighted donor 1 for its strong HLA match with the recipient, assigning it higher hierarchical weight. The model indicated donor 1 as the preferred choice. The DEX methodology answered the decision question with visually accessible graphical results. The promising use of the REDCap platform and the positive evaluation of the DEXi model suggest it is a thriving technology for optimizing medical practice in transplantation.
References
Bohanec, M. (2017). Multi-criteria dex models: An overview and analysis. Proceedings of the 14th International Symposium on Operational Research, SOR 2017, 2017-Septe, 155–160.
Bohanec, M., Miljković, D., Valmarska, A., Mileva Boshkoska, B., Gasparoli, E., Gentile, G., Koutsikos, K., Marcante, A., Antonini, A., Gatsios, D., Rigas, G., Fotiadis, D. I., Tsiouris, K. M., & Konitsiotis, S. (2018). A decision support system for Parkinson disease management: expert models for suggesting medication change. Journal of Decision Systems, 27, 164–172. https://doi.org/10.1080/12460125.2018.1469320
Bohanec, M., Žnidaržič, M., Rajkovič, V., Bratko, I., & Zupan, B. (2013). DEX methodology: Three decades of qualitative multi-attribute modeling. Informatica (Slovenia), 37(1), 49–54.
Buturovic, L., Shelton, J., Spellman, S. R., Wang, T., Friedman, L., Loftus, D., Hesterberg, L., Woodring, T., Fleischhauer, K., Hsu, K. C., Verneris, M. R., Haagenson, M., & Lee, S. J. (2018). Evaluation of a Machine Learning-Based Prognostic Model for Unrelated Hematopoietic Cell Transplantation Donor Selection. Biology of Blood and Marrow Transplantation, 24(6), 1299–1306. https://doi.org/10.1016/j.bbmt.2018.01.038
Dehn, J., Spellman, S., Hurley, C. K., Shaw, B. E., Barker, J. N., Burns, L. J., Confer, D. L., Eapen, M., Fernandez-Vina, M., Hartzman, R., Maiers, M., Marino, S. R., Mueller, C., Perales, M. A., Rajalingam, R., & Pidala, J. (2020). Selection of unrelated donors and cord blood units for hematopoietic cell transplantation: guidelines from the NMDP/CIBMTR. Blood, 134(January), 924–934. https://doi.org/10.1182/blood.2019001212
Drnovšek, R., Milavec Kapun, M., & Rajkovič, U. (2021). Multi-criteria risk evaluation model for developing ventilator-associated pneumonia. Central European Journal of Operations Research, 29(3), 1021–1036. https://doi.org/10.1007/s10100-020-00720-7
Garcia, K. K. S., & Abrahão, A. A. (2021). Research development using redcap software. Healthcare Informatics Research, 27(4), 341–349. https://doi.org/10.4258/HIR.2021.27.4.341
Júnior, J. A. S., Martinho, G. H., Macedo, A. V. de, Verçosa, M. R., Nobre, V., & Teixeira, G. M. (2019). Assessing the impact of ABO incompatibility on major allogeneic hematopoietic stem cell transplant outcomes: a prospective, single-center, cohort study. Hematology, Transfusion and Cell Therapy, 41(1), 1–6. https://doi.org/10.1016/j.htct.2018.05.007
Kimura, F., Kanda, J., Ishiyama, K., Yabe, T., Yoshifuji, K., Fukuda, T., Ozawa, Y., Iwato, K., Eto, T., Mori, T., Uchida, N., Ota, S., Sakura, T., Ichinohe, T., Atsuta, Y., & Kanda, Y. (2019). ABO blood type incompatibility lost the unfavorable impact on outcome in unrelated bone marrow transplantation. Bone Marrow Transplantation, 54(10), 1676–1685. https://doi.org/10.1038/s41409-019-0496-2
Kollman, C., Howe, C. W. S., Anasetti, C., Antin, J. H., Davies, S. M., Filipovich, A. H., Hegland, J., Kamani, N., Keman, N. A., King, R., Ratanatharathorn, V., Weisdorf, D., & Confer, D. L. (2001). Donor characteristics as risk factors in recipients after transplantation of bone marrow from unrelated donors: The effect of donor age. Blood, 98(7), 2043–2051. https://doi.org/10.1182/blood.V98.7.2043
Kollman, C., Spellman, S. R., Zhang, M. J., Hassebroek, A., Anasetti, C., Antin, J. H., Champlin, R. E., Confer, D. L., DiPersio, J. F., Fernandez-Viña, M., Hartzman, R. J., Horowitz, M. M., Hurley, C. K., Karanes, C., Maiers, M., Mueller, C. R., Perales, M. A., Setterholm, M., Woolfrey, A. E., & Eapen, M. (2016). The effect of donor characteristics on survival after unrelated donor transplantation for hematologic malignancy. Blood, 127(2), 260–267. https://doi.org/10.1182/blood-2015-08-663823
Li, Y., Masiliune, A., Winstone, D., Gasieniec, L., Wong, P., Lin, H., Pawson, R., Parkes, G., & Hadley, A. (2020). Predicting the Availability of Hematopoietic Stem Cell Donors Using Machine Learning. Biology of Blood and Marrow Transplantation, 26(8), 1406–1413. https://doi.org/10.1016/j.bbmt.2020.03.026
Logan, B. R., Maiers, M. J., Sparapani, R. A., Laud, P. W., Spellman, S. R., McCulloch, R. E., & Shaw, B. E. (2021). Optimal Donor Selection for Hematopoietic Cell Transplantation Using Bayesian Machine Learning. JCO Clinical Cancer Informatics, 5, 494–507. https://doi.org/10.1200/cci.20.00185
Lown, R. N., Marsh, S. G. E., Switzer, G. E., Latham, K. A., Madrigal, J. A., & Shaw, B. E. (2014). Ethnicity, length of time on the register and sex predict donor availability at the confirmatory typing stage. Bone Marrow Transplantation, 49(4), 525–531. https://doi.org/10.1038/bmt.2013.206
Lown, R. N., & Shaw, B. E. (2013). Beating the odds: Factors implicated in the speed and availability of unrelated haematopoietic cell donor provision. Bone Marrow Transplantation, 48(2), 210–219. https://doi.org/10.1038/bmt.2012.54
Mühlbacher, A. C., & Kaczynski, A. (2016). Making Good Decisions in Healthcare with Multi-Criteria Decision Analysis: The Use, Current Research and Future Development of MCDA. Applied Health Economics and Health Policy, 14(1), 29–40. https://doi.org/10.1007/s40258-015-0203-4
Muhsen, I. N., Elhassan, T., & Hashmi, S. K. (2018). Artificial intelligence approaches in hematopoietic cell transplantation: A review of the current status and future directions. Turkish Journal of Hematology, 35(3), 152–157. https://doi.org/10.4274/tjh.2018.0123
Patridge, E. F., & Bardyn, T. P. (2018). Research electronic Patridge, E. F., & Bardyn, T. P. (2018). Research electronic data capture (REDCap). Journal of the Medical Library Association, 106(1), 142–144. https://doi.org/10.5195/jmla.2018.319data capture (REDCap). Journal of the Medical Library Association, 106(1), 142–144.
Pereira, S. C. M., Souza, A. M., Bouzas, L. F., & Oliveira, D. C. M. de. (2021). Donor selection: general aspects. JBMTCT, 2(1), 14–21. https://doi.org/10.46765/2675-374x.2021v4n1p14-21
Polomeni, A., Moreno, E., & Schulz-Kindermann, F. (2019). The EBMT Handbook. In E. Carreras, C. Dufour, M. Mohty, & N. Kröger (Eds.), The EBMT Handbook. Springer International Publishing. https://doi.org/10.1007/978-3-030-02278-5
Shaw, B. E., Mayor, N. P., Szydlo, R. M., Bultitude, W. P., Anthias, C., Kirkland, K., Perry, J., Clark, A., MacKinnon, S., Marks, D. I., Pagliuca, A., Potter, M. N., Russell, N. H., Thomson, K., Madrigal, J. A., & Marsh, S. G. E. (2017). Recipient/donor HLA and CMV matching in recipients of T-cell-depleted unrelated donor haematopoietic cell transplants. Bone Marrow Transplantation, 52(5), 717–725. https://doi.org/10.1038/bmt.2016.352
Shaw, Bronwen E., Logan, B. R., Spellman, S. R., Marsh, S. G. E., Robinson, J., Pidala, J., Hurley, C., Barker, J., Maiers, M., Dehn, J., Wang, H., Haagenson, M., Porter, D., Petersdorf, E. W., Woolfrey, A., Horowitz, M. M., Verneris, M., Hsu, K. C., Fleischhauer, K., & Lee, S. J. (2018). Development of an Unrelated Donor Selection Score Predictive of Survival after HCT: Donor Age Matters Most. Biology of Blood and Marrow Transplantation, 24(5), 1049–1056.https://doi.org/10.1016/j.bbmt.2018.02.006
Shouval, R., Fein, J. A., Labopin, M., Kröger, N., Duarte, R. F., Bader, P., Chabannon, C., Kuball, J., Basak, G. W., Dufour, C., Galimard, J. E., Polge, E., Lankester, A., Montoto, S., Snowden, J. A., Styczynski, J., Yakoub-Agha, I., Mohty, M., & Nagler, A. (2019). Outcomes of allogeneic haematopoietic stem cell transplantation from HLA-matched and alternative donors: a European Society for Blood and Marrow Transplantation registry retrospective analysis. The Lancet Haematology, 6(11), e573–e584. https://doi.org/10.1016/S2352-3026(19)30158-9
Simione, A. J., Neves, H. R. A. das, Silva, C. C., Sabaini, P. M. S., Geraldo, B. L. S. S., Pasquini, M., Vigorito, A. C., Ammi, M., Colturato, V., Nabhan, S., Seber, A., Silvério, A., Moreira, M. C. R., Barros, G. M. N., Astigarraga, C. C., Daudt, L. E., Macedo, M. C. M. de A., Chiattone, R., Novis, Y. A. S., … Barroso Duarte, F. (2022). Current use and outcomes of hematopoietic stem cell transplantation: Brazilian summary slides. Journal of Bone Marrow Transplantation and Cellular Therapy, 3(2), 171. https://doi.org/10.46765/2675-374x.2022v3n2p171
Trdin, N., & Bohanec, M. (2018). Extending the multi-criteria decision making method DEX with numeric attributes, value distributions and relational models. Central European Journal of Operations Research, 26(1), 1–41. https://doi.org/10.1007/s10100-017-0468-9
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