Predictive modeling of ambulatory outcomes after spinal cord injury using machine learning

van Middendorp JJ, Goss B, Urquhart S, Atresh S, Williams RP, Schuetz M. Diagnosis and Prognosis of Traumatic Spinal Cord Injury. Glob Spine J. 2011;1:001–7.

Article  Google Scholar 

Qiu Z, Wang F, Hong Y, Zhang J, Tang H, Li X, et al. Clinical Predictors of Neurological Outcome within 72 h after Traumatic Cervical Spinal Cord Injury. Sci Rep. 2016;6:38909.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Phan P, Budhram B, Zhang Q, Rivers CS, Noonan VK, Plashkes T, et al. Highlighting discrepancies in walking prediction accuracy for patients with traumatic spinal cord injury: an evaluation of validated prediction models using a Canadian Multicenter Spinal Cord Injury Registry. Spine J. 2019;19:703–10.

Article  PubMed  Google Scholar 

Jean S, Mac-Thiong JM, Jean MC, Dionne A, Bégin J, Richard-Denis A. Early Clinical Prediction of Independent Outdoor Functional Walking Capacity in a Prospective Cohort of Traumatic Spinal Cord Injury Patients. Am J Phys Med Rehabil. 2021;100:1034–41.

Article  PubMed  Google Scholar 

Draganich C, Weber KA, Thornton WA, Berliner JC, Sevigny M, Charlifue S, et al. Predicting Outdoor Walking 1 Year After Spinal Cord Injury: A Retrospective, Multisite External Validation Study. J Neurol Phys Ther. 2023. Available from: https://journals.lww.com/10.1097/NPT.0000000000000428.

Wilson JR, Grossman RG, Frankowski RF, Kiss A, Davis AM, Kulkarni AV, et al. A Clinical Prediction Model for Long-Term Functional Outcome after Traumatic Spinal Cord Injury Based on Acute Clinical and Imaging Factors. J Neurotrauma. 2012;29:2263–71.

Article  PubMed  PubMed Central  Google Scholar 

Belliveau T, Jette AM, Seetharama S, Axt J, Rosenblum D, Larose D, et al. Developing Artificial Neural Network Models to Predict Functioning One Year After Traumatic Spinal Cord Injury. Arch Phys Med Rehabil. 2016;97:1663–.e3.

Article  PubMed  Google Scholar 

Denis AR, Feldman D, Thompson C, Mac-Thiong JM. Prediction of functional recovery six months following traumatic spinal cord injury during acute care hospitalization. J Spinal Cord Med. 2018;41:309–17.

Article  PubMed  Google Scholar 

Waters RL, Adkins R, Yakura J, Vigil D. Prediction of ambulatory performance based on motor scores derived from standards of the American Spinal Injury Association. Arch Phys Med Rehabil. 1994;75:756–60.

Article  CAS  PubMed  Google Scholar 

Oleson CV, Burns AS, Ditunno JF, Geisler FH, Coleman WP. Prognostic Value of Pinprick Preservation in Motor Complete, Sensory Incomplete Spinal Cord Injury. Arch Phys Med Rehabil. 2005;86:988–92.

Article  PubMed  Google Scholar 

Kaminski L, Cordemans V, Cernat E, M’Bra KI, Mac-Thiong JM. Functional Outcome Prediction after Traumatic Spinal Cord Injury Based on Acute Clinical Factors. J Neurotrauma. 2017;34:2027–33.

Article  PubMed  Google Scholar 

Kay ED, Deutsch A, Wuermser LA. Predicting Walking at Discharge From Inpatient Rehabilitation After a Traumatic Spinal Cord Injury. Arch Phys Med Rehabil. 2007;88:745–50.

Article  PubMed  Google Scholar 

van Middendorp JJ, Hosman AJ, Donders ART, Pouw MH, Ditunno JF, Curt A, et al. A clinical prediction rule for ambulation outcomes after traumatic spinal cord injury: a longitudinal cohort study. Lancet. 2011;377:1004–10.

Article  PubMed  Google Scholar 

Hicks KE, Zhao Y, Fallah N, Rivers CS, Noonan VK, Plashkes T, et al. A simplified clinical prediction rule for prognosticating independent walking after spinal cord injury: a prospective study from a Canadian multicenter spinal cord injury registry. Spine J. 2017;17:1383–92.

Article  PubMed  Google Scholar 

Inoue T, Ichikawa D, Ueno T, Cheong M, Inoue T, Whetstone WD, et al. XGBoost, a Machine Learning Method, Predicts Neurological Recovery in Patients with Cervical Spinal Cord Injury. Neurotrauma Rep. 2020;1:8–16.

Article  PubMed  PubMed Central  Google Scholar 

Khan O, Badhiwala JH, Wilson JRF, Jiang F, Martin AR, Fehlings MG. Predictive Modeling of Outcomes After Traumatic and Nontraumatic Spinal Cord Injury Using Machine Learning: Review of Current Progress and Future Directions. Neurospine. 2019;16:678–85.

Article  PubMed  PubMed Central  Google Scholar 

Hodel J, Stucki G, Prodinger B. The potential of prediction models of functioning remains to be fully exploited: A scoping review in the field of spinal cord injury rehabilitation. J Clin Epidemiol. 2021;139:177–90.

Article  PubMed  Google Scholar 

Kirshblum S, Millis S, McKinley W, Tulsky D. Late neurologic recovery after traumatic spinal cord injury11No commercial party having a direct financial interest in the results of the research supporting this article has or will confer a benefit upon the author(s) or upon any organization with which the author(s) is/are associated. Arch Phys Med Rehabil. 2004;85:1811–7.

Article  PubMed  Google Scholar 

Collins GS, Reitsma JB, Altman DG, Moons KGM. Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD statement. BMJ. 2015;350:g7594–g7594.

Article  PubMed  Google Scholar 

Moons KGM, Altman DG, Reitsma JB, Ioannidis JPA, Macaskill P, Steyerberg EW, et al. Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD): Explanation and Elaboration. Ann Intern Med. 2015;162:W1–73.

Article  PubMed  Google Scholar 

R Core Team. R: A language and environment for statistical computing. R Found Stat Comput Viennea Austria. 2020; Available from: https://www.R-project.org/.

Van Buuren S, Groothuis-Oudshoorn K. Mice: Multivariate Imputation by Chained Equations. R J Stat Softw. 2011;45:1–67.

Google Scholar 

Friedman J, Hastie T, Tibshirani R. Regularization Paths for Generalized Linear Models via Coordinate Descent. J Stat Softw. 2010;33. Available from: http://www.jstatsoft.org/v33/i01/.

Fiedman J, Ribshirani R, Hastie T. The Elements of Statistical Learning: Data Mining, Inference, and Prediction. 2nd ed. Netherlands: Springer New York; 2008.

Chen T, Guestrin C XGBoost: A scalable tree boosting system. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York, NY United States: SIGMOD; 2016.

Wilson S. ParBayesianOptimization: Parallel Bayesian Optimization of Hyperparameters. Available from: https://CRAN.R-project.org/package=ParBayesianOptimization.

Allaire J, Chollet F. keras: R Interface to ‘Keras’. 2022. Available from: https://CRAN.R-project.org/package=keras.

Allaire J. tfruns: Training Run Tools for ‘TensorFlow’. 2022. Available from: https://CRAN.R-project.org/package=tfruns.

Mandrekar JN. Receiver Operating Characteristic Curve in Diagnostic Test Assessment. J Thorac Oncol. 2010;5:1315–6.

Article  PubMed  Google Scholar 

Thiele C, Hirschfeld G. cutpointr: Improved Estimation and Validation of Optimal Cutpoints in R. J Stat Softw. 2021;98. Available from: http://www.jstatsoft.org/v98/i11/.

Greenwell BM, Boehmke BC. Variable Importance Plots—An Introduction to the vip Package. R J. 2020;12:343.

Article  Google Scholar 

DeVries Z, Hoda M, Rivers CS, Maher A, Wai E, Moravek D, et al. Development of an unsupervised machine learning algorithm for the prognostication of walking ability in spinal cord injury patients. Spine J. 2020;20:213–24.

Article  PubMed  Google Scholar 

Rowald A, Komi S, Demesmaeker R, Baaklini E, Hernandez-Charpak SD, Paoles E, et al. Activity-dependent spinal cord neuromodulation rapidly restores trunk and leg motor functions after complete paralysis. Nat Med. 2022;28:260–71.

Article  CAS  PubMed  Google Scholar 

Boakye M, Ball T, Dietz N, Sharma M, Angeli C, Rejc E, et al. Spinal cord epidural stimulation for motor and autonomic function recovery after chronic spinal cord injury: A case series and technical note. Surg Neurol Int. 2023;14:87.

Article  PubMed  PubMed Central  Google Scholar 

Wagner FB, Mignardot JB, Le Goff-Mignardot CG, Demesmaeker R, Komi S, Capogrosso M, et al. Targeted neurotechnology restores walking in humans with spinal cord injury. Nature. 2018;563:65–71.

Article  CAS  PubMed  Google Scholar 

Smith AC, Weber KA, Parrish TB, Hornby TG, Tysseling VM, McPherson JG, et al. Ambulatory function in motor incomplete spinal cord injury: a magnetic resonance imaging study of spinal cord edema and lower extremity muscle morphometry. Spinal Cord. 2017;55:672–8.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Berliner JC, O’Dell DR, Albin SR, Dungan D, Sevigny M, Elliott JM, et al. The influence of conventional T 2 MRI indices in predicting who will walk outside one year after spinal cord injury. J Spinal Cord Med. 2023;46:501–7.

Article  PubMed  Google Scholar 

Smith AC, Draganich C, Thornton WA, Berliner JC, Lennarson PJ, Rejc E, et al. A Single Dermatome Clinical Prediction Rule for Independent Walking 1 Year After Spinal Cord Injury. Arch Phys Med Rehabil. 2023 Jul;S000399932300374X.

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