MULTIFACTOR REGRESSION MODEL FOR PREDICTION OF SECONDARY OSTEOPOROSIS IN PATIENTS WITH LYMPHOPROLIFERATIVE DISEASES
DOI:
https://doi.org/10.11603/1681-2786.2023.1.13755Keywords:
osteoporosis, bone mineral density, computed tomography, lymphoma, chemotherapy, prognosisAbstract
Purpose: to develop a mathematical model for assessing the risk of changes in the structural and functional state of bone tissue to study the probability of the development and progression of secondary osteoporosis in patients with non-Hodgkin's lymphoma.
Materials and Methods. With the help of regression analysis, a prognostic model of the risk of changes in the structural and functional state of bone tissue was built. 115 patients (group I) with non-Hodgkin's lymphoma (NHL) were examined to build a multivariate regression model for predicting the risk of osteoporotic disorders. To verify the prognostic value of the mathematical model, 105 patients (II group) were examined. The average age of the patients was 57.86±1.40 years, who were treated at the Ternopil Regional Oncology Dispensary in the period 2018–2022.
Results. Using logistic regression analysis, the most significant multicollinear risk factors for secondary osteoporosis were determined: age, gender, history of fractures, β2-microglobulin level in blood serum and structural and functional state of bone tissue at the diagnostic stage and after polychemotherapy according to the results of computed tomography. A correlation matrix was constructed with the calculation of regression coefficients, a mathematical model was created to determine the risk factor for the development of secondary osteoporosis (SO). Correspondence of the predicted results to the theoretically expected in the low-risk group was recorded in 97.14 %, in the medium-risk group – 96.12 %, in the high-risk group – 94.29 %, in the group with a critical degree of risk in 97.14 % of cases. The informativeness of the created mathematical model is 96.17 %, which indicates the high prognostic characteristics of the model.
Conclusions. The developed algorithm and mathematical model for predicting the development of secondary osteoporosis in patients with lymphoproliferative diseases are highly informative and allow to determine in advance the contingent of patients with a high probability of changes in the structural and functional state of bone tissue for the timely implementation of appropriate preventive measures.
References
Gruzeva, T.S., Lekhan, V.M., & Ognev, V.A. (2020). Biostatystyka [Biostatistics] [in Ukrainian].
Zhulkevych, I.V., & Chukur, P.A. (2023). Zminy strukturno-funktsionalnoho stanu kistkovoyi tkanyny u khvorykh na nekhodzhkinski limfomy v zalezhnosti vid typu ta tryvalosti khimioterapiyi [Changes in the structural and functional state of bone tissue in patients with non-Hodgkin’s lymphoma depending on the type and duration of chemotherapy]. Zdobutky klinichnoyi i eksperymentalnoyi medytsyny – Achievements of Clinical and Experimental Medicine, 1. 112-122 [in Ukrainian].
Zhulkevych, I.V., & Chukur, P.A. (2021). Otsinka mineralnoyi shchilnosti kistkovoyi tkanyny za danymy renthenivskoyi kompyuternoyi tomohrafiyi ta vyznachennya ryzykiv osteoporotychnykh perelomiv u khvorykh na dyfuznu B-velykoklitynnu limfomu [Assessment of bone tissue mineral density according computer tomography data and risk determination of osteoporotic fractures in patients with diffuse large b-cell cell lymphoma]. Zdobutky klinichnoyi i eksperymentalnoyi medytsyny – Achievements of Clinical and Experimental Medicine, 2, 68-76 [in Ukrainian].
Paccou, J., Merlusca, L., & Henry-Desailly, I. (2014). Alterations in bone mineral density and bone turnover markers in newly diagnosed adults with lymphoma receiving chemotherapy: a 1-year prospective pilot study. Annal. Oncol., 25, 481-486. DOI: https://doi.org/10.1093/annonc/mdt560
(1993).A predictive model for aggressive non-Hodgkin’s lymphoma. The New England Journal of Medicine, 329(14), 987-994. DOI: https://doi.org/10.1056/NEJM199309303291402
Baech, J., Hansen, S.M., & Jakobsen, L.H. (2020). Increased risk of osteoporosis following commonly used first-line treatments for lymphoma: a Danish Nationwide Cohort Study. Leukemia and Lymphoma, 61(6), 1345-1354. DOI: https://doi.org/10.1080/10428194.2020.1723015
Mancuso, S., Scaturro, D., & Santoro, M. (2021). Bone damage after chemotherapy for lymphoma: a real-world experience. BMC Musculoskeletal Disorders, 22(1), 1-13. DOI: https://doi.org/10.1186/s12891-021-04904-3
Rubin, K.H., Abrahamsen, B., & Friis-Holmberg, T. (2013). Comparison of different screening tools (FRAX®, OST, ORAI, OSIRIS, SCORE and age alone) to identify women with increased risk of fracture. A population-based prospective study. Bone, 56(1), 16-22. DOI: https://doi.org/10.1016/j.bone.2013.05.002
Sedrine, W.B., Chevallier, T., & Zegels, B. (2002). Development and assessment of the osteoporosis index of risk (OSIRIS) to facilitate selection of women for bone densitometry. Gynecological Endocrinology, 16(3), 245-250. DOI: https://doi.org/10.1080/gye.16.3.245.250
Federico, M., Bellei, M., & Marcheselli, L. (2009). Follicular lymphoma international prognostic index 2: A new prognostic index for follicular lymphoma developed by the international follicular lymphoma prognostic factor project. Journal of Clinical Oncology, 27(27), 4555-4562. DOI: https://doi.org/10.1200/JCO.2008.21.3991
FRAX ®Instrument otsinky ryzyku perelomiv – Fracture Risk Assessment Tool. Retrieved from: https://www.sheffield.ac.uk/FRAX/tool.aspx?country=66.
Bodden, J., Sun, D., & Joseph, G.B. (2021). Identification of non-Hodgkin lymphoma patients at risk for treatment-related vertebral density loss and fractures. Osteoporosis International, 32(2), 281-291. DOI: https://doi.org/10.1007/s00198-020-05577-9
Bellas, C., García, D., & Vicente, Y. (2014). Immunohistochemical and molecular characteristics with prognostic significance in diffuse large B-cell lymphoma. PloS one, 9(6), 98-169. DOI: https://doi.org/10.1371/journal.pone.0098169
Kanemasa, Y., Shimoyama, T., & Sasaki, Y. (2017). Beta-2 microglobulin as a significant prognostic factor and a new risk model for patients with diffuse large B-cell lymphoma. Hematological Oncology, 35(4), 440-446. DOI: https://doi.org/10.1002/hon.2312
Koh, L.K.H., Sedrine, W. Ben, & Torralba, T.P. (2001). A simple tool to identify Asian women at increased risk of osteoporosis. Osteoporosis International, 12 (8), 699-705. DOI: https://doi.org/10.1007/s001980170070
Anargyrou, K., Fotiou, D., & Vassilakopoulos, T.P. (2019). Low Bone Mineral Density and High Bone Turnover in Patients with Non-Hodgkin’s Lymphoma (NHL) Who Receive Frontline Therapy: Results of a Multicenter Prospective Study. HemaSphere, 3(6), 1-8. DOI: https://doi.org/10.1097/HS9.0000000000000303
Miyashita, K., Tomita, N., & Taguri, M. (2015). Beta-2 microglobulin is a strong prognostic factor in patients with DLBCL receiving R-CHOP therapy. Leukemia Research, 39(11), 1187-1191. DOI: https://doi.org/10.1016/j.leukres.2015.08.016
Chukur, O., Pasyechko, N., & Bob, A. (2022). Prediction of climacteric syndrome development in perimenopausal women with hypothyroidism. Przeglad menopauzalny = Menopause review, 21(4), 236-241. DOI: https://doi.org/10.5114/pm.2022.123522
Buttia, C., Llanaj, E., & Raeisi-Dehkordi, H. (2023). Prognostic models in COVID-19 infection that predict severity: a systematic review. European Journal of Epidemiology, 38(4), 355-372. DOI: https://doi.org/10.1007/s10654-023-00973-x
QFracture-2016. Retrieved from: https://qfracture.org/.
Lister, T.A., Crowther, D., & Sutcliffe, S.B. (1989). Report of a committee convened to discuss the evaluation and staging of patients with Hodgkin’s disease: Cotswolds meeting. Journal of Clinical Oncology, 7(11), 1630-1636. DOI: https://doi.org/10.1200/JCO.1989.7.11.1630
Cadarette, S.M., Jaglal, S.B., & Murray, T.M. (1999). Validation of the Simple Calculated Osteoporosis Risk Estimation (SCORE) for patient selection for bone densitometry. Osteoporosis International, 10(1), 85-90. DOI: https://doi.org/10.1007/s001980050199
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2023 Bulletin of Social Hygiene and Health Protection Organization of Ukraine
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish in this journal agree to the following terms:
1. The authors reserve the right to authorship of the work and pass the journal right of first publication of this work is licensed under a Creative Commons Attribution License, which allows others to freely distribute the work published with reference to the authors of the original work and the first publication of this magazine.
2. Authors are entitled to enter into a separate agreement on additional non-exclusive distribution of work in the form in which it was published in the magazine (eg work place in the electronic repository institution or publish monographs in part), provided that the reference to the first publication of this magazine.
3. Policy magazine allows and encourages authors placement on the Internet (eg, in storage facilities or on personal websites) manuscript of how to submit the manuscript to the editor and during his editorial processing, since it contributes to productive scientific discussion and positive impact on the efficiency and dynamics of citing published work (see. The Effect of Open Access).
