Development of a VTE prediction model based on automatically selected features in glioma patients
https://doi.org/10.18705/2782-3806-2024-4-6-517-530
EDN: SSZMPC
Abstract
Venous thromboembolism (VTE) is a serious threat to patients undergoing cancer treatment, especially in advanced and metastatic diseases. In neuro-oncology, the incidence of VTE depends on the location and stage of the tumor. Some primary and secondary brain tumors have an increased propensity for thrombotic events. In this study, we applied state-of-the-art machine learning methods, particularly XGBoost, to create models to search for predictors associated with the risk of VTE in glioma patients. By comparing the diagnostic accuracy of our XGBoost models with traditional logistic regression approaches, we aim to advance the understanding of VTE prediction in this patient population. Our results add to the growing body of research on thrombosis risk assessment in cancer patients and may help in the development of personalized prevention and treatment strategies to reduce the risk of VTE in hospitalized glioma patients.
About the Authors
S. S. Leontev
ITMO University
Russian Federation
Russian Federation
Leontev Sergei S., student
Saint Petersburg
M. A. Simakova
Almazov National Medical Research Centre, World-Class Research Centre for Personalized Medicine
Russian Federation
Russian Federation
Simakova Maria A., Candidate of Medical Sciences, Senior Researcher — Head of cardio-oncology Research Group
Saint Petersburg
V. L. Lukinov
Institute of Computational Mathematics and Mathematical Geophysics of the Siberian Branch of the Russian Academy of Sciences
Russian Federation
Russian Federation
Lukinov Vitaliy L., Lead researcher (Laboratory of numerical analysis of SDE)
Ac. Lavrentieva ave., 6, Novosibirsk, 630090
K. A. Pishchulov
Almazov National Medical Research Centre, World-Class Research Centre for Personalized Medicine
Russian Federation
Russian Federation
Pishchulov Konstantin A., Junior Research, Cardio-Oncology Research Group
Saint Petersburg
L. K. Abramyan
Almazov National Medical Research Centre, World-Class Research Centre for Personalized Medicine
Russian Federation
Russian Federation
Abramyan Levon K., Senior Data Scientist
Saint Petersburg
E. A. Ugolnikova
Institute of Computational Mathematics and Mathematical Geophysics of the Siberian Branch of the Russian Academy of Sciences
Russian Federation
Russian Federation
Ugolnikova Ekaterina A., Engineer (Laboratory of numerical analysis of SDE)
Ac. Lavrentieva ave., 6, Novosibirsk, 630090
References
1. Nicholson M, Chan N, Bhagirath V, et al. Prevention of venous thromboembolism in 2020 and beyond. J Clin Med. 2020;9(8):2467. Available from: http://dx.doi.org/10.3390/jcm9082467
2. Kearon C, Akl EA, Ornelas J, et al. Antithrombotic therapy for VTE disease: CHEST guideline and expert panel report. Chest. 2016;149(2):315–52. Available from: http://dx.doi.org/10.1016/j.chest.2015.11.026
3. Connors JM, Levy JH. COVID-19 and its implications for thrombosis and anticoagulation. Blood. 2020;135:2033–2040. DOI:10.1182/BLOOD.2020006000.
4. Xu Q, Lei H, Li X, et al. Machine learning predicts cancer-associated venous thromboembolism using clinically available variables in gastric cancer patients. Heliyon. 2023;9(1):e12681. Available from: http://dx.doi.org/10.1016/j.heliyon.2022.e12681.
5. He L, Luo L, Hou X, et al. Predicting venous thromboembolism in hospitalized trauma patients: a combination of the Caprini score and data-driven machine learning model. BMC Emerg Med. 2021;21(1):60. Available from: http://dx.doi.org/10.1186/s12873-021-00447-x.
6. Lin C-C, Chen C-C, Li C-I, et al. Derivation and validation of a clinical prediction model for risks of venous thromboembolism in diabetic and general populations. Medicine (Baltimore) [Internet]. 2021;100(39):e27367. Available from: http://dx.doi.org/10.1097/MD.0000000000027367.
7. Gerotziafas GT, Papageorgiou L, Salta S, et al. I. Updated clinical models for VTE prediction in hospitalized medical patients. Thromb Res. 2018;164 Suppl 1:S62–9. Available from: http://dx.doi.org/10.1016/j.thromres.2018.02.004.
8. Beal EW, Tumin D, Chakedis J, et al. Identification of patients at high risk for post-discharge venous thromboembolism after hepato-pancreato-biliary surgery: which patients benefit from extended thromboprophylaxis? HPB. 2018;20 (7):621–630. DOI:10.1016/j.hpb.2018.01.004.
9. Lee E-J, Chang C-H, Wang L-C, et al. Two primary brain tumors, meningioma and glioblastoma multiforme, in opposite hemispheres of the same patient. J Clin Neurosci. 2002;9(5):589–91. Available from: http://dx.doi.org/10.1054/jocn.2002.1086.
10. Farge D, Frere C, Connors JM, et al. 2022 international clinical practice guidelines for the treatment and prophylaxis of venous thromboembolism in patients with cancer, including patients with COVID-19. Lancet Oncol. 2022;23(7):e334–47. Available from: http://dx.doi.org/10.1016/S1470-2045(22)00160-7160-7.
Review
For citations:
Leontev S.S., Simakova M.A., Lukinov V.L., Pishchulov K.A., Abramyan L.K., Ugolnikova E.A. Development of a VTE prediction model based on automatically selected features in glioma patients. Russian Journal for Personalized Medicine. 2024;4(6):517-530. (In Russ.) https://doi.org/10.18705/2782-3806-2024-4-6-517-530. EDN: SSZMPC
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