1,396
Views
0
CrossRef citations to date
0
Altmetric
Surgery

Probabilistic graphical modelling using Bayesian networks for predicting clinical outcome after posterior decompression in patients with degenerative cervical myelopathy

, , , , , & show all
Article: 2232999 | Received 08 Dec 2021, Accepted 01 Jul 2023, Published online: 12 Jul 2023

References

  • Salem HM, Salem KM, Burget F, et al. Cervical spondylotic myelopathy: the prediction of outcome following surgical intervention in 93 patients using T1- and T2-weighted MRI scans. Eur Spine J. 2015;24(12):1–12. doi: 10.1007/s00586-015-4028-5.
  • Bond M, McIntosh G, Fisher C, et al. Treatment of mild cervical myelopathy: factors associated with decision for surgical intervention. Spine (Phila Pa 1976). 2019;44(22):1606–1612. doi: 10.1097/BRS.0000000000003124.
  • Sharifi B, McIntosh G, Fisher C, et al. Consultation and surgical wait times in cervical spondylotic myelopathy. Can J Neurol Sci. 2019;46(4):430–435. doi: 10.1017/cjn.2019.34.
  • Karadimas SK, Erwin WM, Ely CG, et al. Pathophysiology and natural history of cervical spondylotic myelopathy. Spine (Phila Pa 1976). 2013;38(22 Suppl. 1):S21–S36. doi: 10.1097/BRS.0b013e3182a7f2c3.
  • Sadasivan KK, Reddy RP, Albright JA. The natural history of cervical spondylotic myelopathy. Yale J Biol Med. 1993;66(3):235–242.
  • Fehlings MG, Arvin B. Surgical management of cervical degenerative disease: the evidence related to indications, impact, and outcome. J Neurosurg Spine. 2009;11(2):97–100. doi: 10.3171/2009.5.SPINE09210.
  • Nagai T, Takahashi Y, Endo K, et al. Analysis of spastic gait in cervical myelopathy: linking compression ratio to spatiotemporal and pedobarographic parameters. Gait Posture. 2018;59:152–156. doi: 10.1016/j.gaitpost.2017.10.013.
  • Son DK, Son DW, Song GS, et al. Effectiveness of the laminoplasty in the elderly patients with cervical spondylotic myelopathy. Korean J Spine. 2014;11(2):39–44. doi: 10.14245/kjs.2014.11.2.39.
  • Sun LQ, Li M, Li YM. Predictors for surgical outcome of laminoplasty for cervical spondylotic myelopathy. World Neurosurg. 2016;94:89–96. doi: 10.1016/j.wneu.2016.06.092.
  • Tetreault L, Palubiski LM, Kryshtalskyj M, et al. Significant predictors of outcome following surgery for the treatment of degenerative cervical myelopathy: a systematic review of the literature. Neurosurg Clin N Am. 2018;29(1):115–127.e35. doi: 10.1016/j.nec.2017.09.020.
  • Tetreault LA, Cote P, Kopjar B, et al. A clinical prediction model to assess surgical outcome in patients with cervical spondylotic myelopathy: internal and external validations using the prospective multicenter AOSpine North American and International Datasets of 743 patients. Spine J. 2015;15(3):388–397. doi: 10.1016/j.spinee.2014.12.145.
  • Shen C, Xu H, Xu B, et al. Value of conventional MRI and diffusion tensor imaging parameters in predicting surgical outcome in patients with degenerative cervical myelopathy. J Back Musculoskelet Rehabil. 2018;31(3):525–532. doi: 10.3233/BMR-170972.
  • Takenaka S, Aono H. Prediction of postoperative clinical recovery of drop foot attributable to lumbar degenerative diseases, via a Bayesian network. Clin Orthop Relat Res. 2017;475(3):872–880. doi: 10.1007/s11999-016-5180-x.
  • Harrell FEJr., Lee KL, Mark DB. Multivariable prognostic models: issues in developing models, evaluating assumptions and adequacy, and measuring and reducing errors. Stat Med. 1996;15(4):361–387. doi: 10.1002/(SICI)1097-0258(19960229)15:4<361::AID-SIM168>3.0.CO;2-4.
  • Arora P, Boyne D, Slater JJ, et al. Bayesian networks for risk prediction using real-world data: a tool for precision medicine. Value Health. 2019;22(4):439–445. doi: 10.1016/j.jval.2019.01.006.
  • Onisko A, Druzdzel MJ, Austin RM. How to interpret the results of medical time series data analysis: classical statistical approaches versus dynamic Bayesian network modeling. J Pathol Inform. 2016;7:50. doi: 10.4103/2153-3539.197191.
  • Yoo C, Ramirez L, Liuzzi J. Big data analysis using modern statistical and machine learning methods in medicine. Int Neurourol J. 2014;18(2):50–57. doi: 10.5213/inj.2014.18.2.50.
  • Suzuki T, Shimoda T, Takahashi N, et al. Factors affecting bone mineral density among snowy region residents in Japan: analysis using multiple linear regression and Bayesian network model. Interact J Med Res. 2018;7(1):e10. doi: 10.2196/ijmr.8555.
  • Tetreault L, Wilson JR, Kotter MR, et al. Predicting the minimum clinically important difference in patients undergoing surgery for the treatment of degenerative cervical myelopathy. Neurosurg Focus. 2016;40(6):E14. doi: 10.3171/2016.3.FOCUS1665.
  • Park SB, Chung CK, Gonzalez E, et al. Causal inference network of genes related with bone metastasis of breast cancer and osteoblasts using causal Bayesian networks. J Bone Metab. 2018;25(4):251–266. doi: 10.11005/jbm.2018.25.4.251.
  • Pumberger M, Schmidt H, Putzier M. Spinal deformity surgery: a critical review of alignment and balance. Asian Spine J. 2018;12(4):775–783. doi: 10.31616/asj.2018.12.4.775.
  • Agostinho NB, Machado KS, Werhli AV. Inference of regulatory networks with a convergence improved MCMC sampler. BMC Bioinformatics. 2015;16:306. doi: 10.1186/s12859-015-0734-6.
  • Khachatryan T, Robinson JS. The possible impact of cervical stenosis on cephalad neuronal dysfunction. Med Hypotheses. 2018;118:13–18. doi: 10.1016/j.mehy.2018.06.008.
  • Wolf K, Reisert M, Beltrán SF, et al. Spinal cord motion in degenerative cervical myelopathy: the level of the stenotic segment and gender cause altered pathodynamics. J Clin Med. 2021;10(17):3788. doi: 10.3390/jcm10173788.
  • Yang S, Nguyen ND, Center JR, et al. Association between hypertension and fragility fracture: a longitudinal study. Osteoporos Int. 2014;25(1):97–103. doi: 10.1007/s00198-013-2457-8.
  • Nouri A, Martin AR, Kato S, et al. The relationship between MRI signal intensity changes, clinical presentation, and surgical outcome in degenerative cervical myelopathy: analysis of a global cohort. Spine (Phila Pa 1976). 2017;42(24):1851–1858. doi: 10.1097/BRS.0000000000002234.