Development of prognostic models for bone mineral density disorders in patients with hodgkin lymphoma: a cross-sectional study

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Abstract

BACKGROUND: Assessment of the need for prevention of decreased bone mineral density in young individuals with Hodgkin lymphoma after pathogenetic therapy is relevant, as they face an increased risk of such long-term complications as frequent fractures.

AIM: This study aimed to develop prognostic models for assessing the need for prevention of decreased bone mineral density in young patients with Hodgkin lymphoma after standard pathogenetic therapy.

METHODS: This single-center cross-sectional study included 93 participants: 63 patients with an established diagnosis of Hodgkin lymphoma and 30 healthy volunteers aged 17–45 years. The study was conducted at the Department of Oncohematology, Chemotherapy, and Bone Marrow Transplantation of the Sverdlovsk Regional Clinical Hospital No. 1 (Yekaterinburg). Data were collected from 2018 to 2023. Each participant completed a questionnaire recommended by the Institute of Rheumatology to assess fracture risk factors. Dual-energy x-ray absorptiometry was performed in all patients to evaluate bone tissue status. The models were constructed using multivariate logistic regression. Model performance was assessed by the area under the ROC curve (AUC), as well as by accuracy, sensitivity, and specificity.

RESULTS: Two models were developed to assess the risk of osteoporosis and the need for early initiation of preventive measures for decreased bone mineral density in patients with Hodgkin lymphoma. The first model, based on questionnaire results, had an AUC of 0.727, accuracy of 71.4%, sensitivity of 75%, and specificity of 66.7%. The second model, which included densitometric parameters, demonstrated higher performance: ROC-AUC of 0.95, accuracy of 88.6%, sensitivity of 95%, and specificity of 80%.

CONCLUSION: Both models may be used as screening tools; however, the model based on densitometric parameters has higher diagnostic performance. The use of the model without densitometric data appears acceptable for preliminary identification of indications for preventive measures in patients with Hodgkin lymphoma.

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About the authors

Kseniya M. Badykova

State Scientific Center of the Russian Federation—Federal Medical Biophysical Center named after A.I. Burnazyan

Author for correspondence.
Email: badykova.gem@gmail.com
ORCID iD: 0009-0004-5049-1626
SPIN-code: 3800-4721

graduate student, Depart. of Therapy

Russian Federation, Moscow

Julija S. Kitaeva

State Scientific Center of the Russian Federation—Federal Medical Biophysical Center named after A.I. Burnazyan; Ural State Medical University

Email: JKhema@yandex.ru
ORCID iD: 0000-0002-4092-6305
SPIN-code: 2653-9401

MD, Cand. Sci. (Medicine), Assistant Lecturer, Depart. of Propaedeutics of Internal Diseases

Russian Federation, Moscow; Yekaterinburg

Evgenij A. Praskurnichij

Central State Medical Academy; The Russian National Research Medical University named after N.I. Pirogov

Email: praskurnichey@mail.ru
ORCID iD: 0000-0002-9523-5966
SPIN-code: 5628-1986

MD, Dr. Sci. (Medicine), Professor, Head, Depart. of Therapy

Russian Federation, Moscow; Moscow

Elena V. Kuznetsova

Ural State Medical University

Email: aliska@k66.ru
ORCID iD: 0000-0002-1631-486X
SPIN-code: 1178-7293

MD, Cand. Sci. (Medicine), Assistant Professor, Depart. of Propaedeutics of Internal Diseases

Russian Federation, Yekaterinburg

Amaliya R. Nagieva

State Scientific Center of the Russian Federation—Federal Medical Biophysical Center named after A.I. Burnazyan

Email: nagieva281@gmail.com
ORCID iD: 0009-0006-7653-6620
SPIN-code: 1521-9584

graduate student, Depart. of Therapy

Russian Federation, Moscow

References

  1. Galimzyanov HM, Trizno NN, Lopukhin YuM, et al. The predictive-preventive and personalied medicine as a new branch of health protection and its perspectives. Astrakhan medical journal. 2013;(1):64–68. EDN: QCYCQJ
  2. Tarasova S. Prognostication in the clinical medicine. Innovatsii v nauke. 2014;(30-2):57–61. EDN: RWVLRV
  3. Luchinin AS. Prognostic Models in Medicine. Clinical oncohematology. 2023;16(1):27–36. doi: 10.21320/2500-2139-2023-16-1-27-36 EDN: UZEQZU
  4. Karyakina OE, Dobrodeeva LK, Martynova NA, Karyakina TI. Use of mathematical models in clinical practice. Human Ecology. 2012;(7):55–64. EDN: OZLWTD
  5. Dimitriyev DA, Dimitriyev AD, Vorontsova GM. Use of the bayes classification for assessing an individual risk. Hygiene and sanitation, Russian journal. 2003;(4):64–66. EDN: OIWRJJ
  6. Kitaeva YS, Praskurnichy EA. Algorithm for assessment of risk factors for bone mineral density disorders in pathogenetic therapy for Hodgkin's lymphoma. Siberian Medical Review. 2023;(1):31–36. doi: 10.20333/25000136-2023-1-31-36 EDN: VDLHEQ
  7. Luchinin AS. Artificial Intelligence in Hematology. Clinical oncohematology. 2022;15(1):16–27. doi: 10.21320/2500-2139-2022-15-1-16-27 EDN: ISVFJA
  8. Bunimovich-Mendrazitsky S, Byrne H, Stone L. Mathematical model of pulsed immunotherapy for superficial bladder cancer. Bulletin of Mathematical Biology. 2008;70(7):2055–2076. doi: 10.1007/s11538-008-9344-z EDN: LSCFPX
  9. Ansell SM. Hodgkin lymphoma: 2023 update on diagnosis, risk-stratification, and management. Am J Hematol. 2022;97(11):1478–1488. doi: 10.1002/ajh.26717
  10. Seam P, Janik JE, Longo DL, Devita VT. The Role of Chemotherapy in Hodgkin's Lymphoma. Cancer. 2009;15(2):150–154. doi: 10.1097/PPO.0b013e3181a27018
  11. Rizzoli R, Body J-J, Brandi M-L, et al. Cancer-associated bone disease. Osteoporosis International. 2013;24(12):2929–2953. doi: 10.1007/s00198-013-2530-3 EDN: SOHFFF
  12. Kitaeva YS, Praskurnichiy EA. Predictors of Bone Mineral Density Reduction in Patients with Hodgkin's Lymphoma Associated with Pathogenetic Therapy. The Russian Archives of Internal Medicine. 2023;13(1):36–45. doi: 10.20514/2226-6704-2023-13-1-36-45 EDN: LHFKGU
  13. Voitko MS, Pospelova TI, Klimontov VV, Mezit EV. Factors influencing bone tissue remodeling in patients with Hodgkin's lymphoma (literature review). Siberian Scientific Medical Journal. 2018;38(4):65–72. doi: 10.15372/SSMJ20180408 EDN: XWBYOD
  14. Ofshenko N, Bercovich E, Mashiach T, Weiler-Sagie M. Reduction of the vertebral bone mineral density in patients with Hodgkin lymphoma correlates with their age and the treatment regimen they received. Cancers. 2022;14(3):495. doi: 10.3390/cancers14030495 EDN: KECSAX
  15. Belaya ZhE, Belova KYu, Biryukova EV, et al. Federal clinical guidelines for diagnosis, treatment and prevention of osteoporosis. Osteoporosis and Bone Diseases. 2021;24(2):4–47. doi: 10.14341/osteo12930 EDN: TUONYE
  16. Kiku PF, Zhigaev DS, Shiter NS, et al. The concept of risk factors for the health of the population. Bulletin of physiology and pathology of respiration. 2016;(62):101–109. doi: 10.12737/23260 EDN: XYEAZX
  17. Nikitinskaya OA, Toroptsova NV. Ocenka riska perelomov s ispol'zovaniem modeli FRAX® (retrospektivnoe desyatiletnee issledovanie). Almanac of Clinical Medicine. 2014;(32):50–55. EDN: SXYJPF
  18. Demina EA, Tumyan GS, Moiseeva TN, et al. Hodgkin Lymphoma. Clinical recommendations. Journal of Modern Oncology. 2020;22(2):6–33. doi: 10.26442/18151434.2020.2.200132 EDN: MWPSMO
  19. Skripnikova IA, Shcheplyagina LA, Novikov VE, et al. Vozmozhnosti kostnoy rentgenovskoy densitometrii vklinicheskoy praktike (Metodicheskie rekomendatsii). Osteoporosis and Bone Diseases. 2010;13(2):23–34. (In Russ.) EDN: OGEFDR
  20. Joseph VR. Optimal Ratio for Data Splitting. Statistical Analysis and Data Mining: The ASA Data Science Journal. 2022;15(4):531–538. doi: 10.1002/sam.11583 EDN: YFUIXT
  21. Kovalev AA, Kuznetsov BK, Yadchenko AA, Ignatenko VA. Assessment of the quality of a binary classifier in research. Health and Ecology Issues. 2020;(4):105–113. doi: 10.51523/2708-6011.2020-17-4-15 EDN: XPCLVS

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2. Fig. 1. ROC curve of the prognostic model for the need for osteoporosis prevention in patients with Hodgkin lymphoma.

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3. Fig. 2. ROC curve of the prognostic model for the need for osteoporosis prevention in patients with Hodgkin lymphoma considering densitometric parameters.

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