Application of a novel nested ensemble algorithm in predicting motor function recovery in patients with traumatic cervical spinal cord injury

Huang, Y.-H. et al. The prognosis of acute blunt cervical spinal cord injury. J. Trauma 66, 1441–1445 (2009).PubMed 

Google Scholar 
Burns, A. S., Marino, R. J., Flanders, A. E. & Flett, H. Clinical diagnosis and prognosis following spinal cord injury. Handb. Clin. Neurol. 109, 47–62 (2012).Article 
PubMed 

Google Scholar 
Kamp, O. et al. Survival among patients with severe high cervical spine injuries—a TraumaRegister DGU® database study. Scand. J. Trauma Resusc. Emerg. Med. 29, 1 (2021).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
James, S. L. et al. Global, regional, and national burden of traumatic brain injury and spinal cord injury, 1990–2016: A systematic analysis for the Global Burden of Disease Study 2016. Lancet Neurol. 18, 56–87 (2019).Article 

Google Scholar 
Rupp, R. et al. International standards for neurological classification of spinal cord injury. Top Spinal Cord. Inj. Rehabil. 27, 1–22 (2021).Article 
PubMed 
PubMed Central 

Google Scholar 
Ditunno, J. F., Young, W., Donovan, W. H. & Creasey, G. The international standards booklet for neurological and functional classification of spinal cord injury. Spinal Cord. 32, 70–80 (1994).Article 

Google Scholar 
Hubli, M. & Dietz, V. The physiological basis of neurorehabilitation—locomotor training after spinal cord injury. J. Neuroeng. Rehabil. 10, 5 (2013).Article 
PubMed 
PubMed Central 

Google Scholar 
Kirshblum, S., Snider, B., Eren, F. & Guest, J. Characterizing natural recovery after traumatic spinal cord injury. J. Neurotrauma 38, 1267–1284 (2021).Article 
PubMed 
PubMed Central 

Google Scholar 
Chay, W. & Kirshblum, S. Predicting outcomes after spinal cord injury. Phys. Med. Rehab. Clin. N. Am. 31, 331–343 (2020).Article 

Google Scholar 
Nadeau, M. et al. Patient perspective: Diagnosis and prognosis of acute spinal cord injuries. Spinal Cord 59, 865–873 (2021).Article 
PubMed 

Google Scholar 
Wang, T. Y. et al. Management of acute traumatic spinal cord injury: A review of the literature. Front. Surg. 8, 698736 (2021).Article 
PubMed 
PubMed Central 

Google Scholar 
Nakajima, H. et al. Prognostic factors and optimal management for patients with cervical spinal cord injury without major bone injury. J. Orthop. Sci. 24, 230–236 (2019).Article 
PubMed 

Google Scholar 
Kirshblum, S. C. & O’Connor, K. C. Predicting neurologic recovery in traumatic cervical spinal cord injury. Arch. Phys. Med. Rehabil. 79, 1456–1466 (1998).Article 
CAS 
PubMed 

Google Scholar 
Kirshblum, S. C. et al. Patterns of sacral sparing components on neurologic recovery in newly injured persons with traumatic spinal cord injury. Arch. Phys. Med. Rehabil. 97, 1647–1655 (2016).Article 
PubMed 

Google Scholar 
Khorasanizadeh, M. et al. Neurological recovery following traumatic spinal cord injury: A systematic review and meta-analysis. J. Neurosurg. Spine 30, 683–699 (2019).Article 
PubMed 

Google Scholar 
Kalyani, P. et al. Prediction of patient’s neurological recovery from cervical spinal cord injury through XGBoost learning approach. Eur. Spine J. 32, 2140–2148 (2023).Article 
CAS 
PubMed 

Google Scholar 
Wang, K., Lu, J., Liu, A., Zhang, G. & Xiong, L. Evolving gradient boost: A pruning scheme based on loss improvement ratio for learning under concept drift. IEEE Trans. Cybern. 53, 2110–2123 (2023).Article 
PubMed 

Google Scholar 
Vong, C.-M. & Du, J. Accurate and efficient sequential ensemble learning for highly imbalanced multi-class data. Neural Netw. 128, 268–278 (2020).Article 
PubMed 

Google Scholar 
Sun, X., Liu, Y. & An, L. Ensemble dimensionality reduction and feature gene extraction for single-cell RNA-seq data. Nat. Commun. 11, 5853 (2020).Article 
ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Shin, J. Random subspace ensemble learning for functional near-infrared spectroscopy brain-computer interfaces. Front. Hum. Neurosci. 14, 236 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Fehlings, M. G. et al. A clinical practice guideline for the management of patients with acute spinal cord injury and central cord syndrome: Recommendations on the timing (≤24 hours versus >24 hours) of decompressive surgery. Global Spine J. 7, 195S-202S (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
Badhiwala, J. H. et al. The influence of timing of surgical decompression for acute spinal cord injury: A pooled analysis of individual patient data. Lancet Neurol. 20, 117–126 (2021).Article 
CAS 
PubMed 

Google Scholar 
ter Wengel, P. V. et al. Early surgical decompression improves neurological outcome after complete traumatic cervical spinal cord injury: A meta-analysis. J. Neurotrauma 36, 835–844 (2019).Article 
PubMed 

Google Scholar 
Mattiassich, G. et al. Functional Outcomes in Individuals Undergoing Very Early (< 5 h) and Early (5–24 h) Surgical Decompression in Traumatic Cervical Spinal Cord Injury: Analysis of Neurological Improvement from the Austrian Spinal Cord Injury Study. J. Neurotrauma 34, 3362–3371 (2017).Article 
PubMed 

Google Scholar 
Fehlings, M. G. et al. Early versus delayed decompression for traumatic cervical spinal cord injury: Results of the Surgical Timing in Acute Spinal Cord Injury Study (STASCIS). PLoS One 7, e32037 (2012).Article 
ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Furlan, J. C., Noonan, V., Cadotte, D. W. & Fehlings, M. G. Timing of decompressive surgery of spinal cord after traumatic spinal cord injury: An evidence-based examination of pre-clinical and clinical studies. J. Neurotrauma 28, 1371–1399 (2011).Article 
PubMed 
PubMed Central 

Google Scholar 
Geisler, F. H., Coleman, W. P., Grieco, G., Poonian, D., & Sygen Study Group. The Sygen multicenter acute spinal cord injury study. Spine (Phila Pa 1976) 26, S87–98 (2001).Bracken, M. B. et al. Administration of methylprednisolone for 24 or 48 hours or tirilazad mesylate for 48 hours in the treatment of acute spinal cord injury. Results of the Third National Acute Spinal Cord Injury Randomized Controlled Trial. National Acute Spinal Cord Injury Study. JAMA 277, 1597–1604 (1997).Jug, M. et al. Neurological recovery after traumatic cervical spinal cord injury is superior if surgical decompression and instrumented fusion are performed within 8 hours versus 8 to 24 hours after injury: A single center experience. J. Neurotrauma 32, 1385–1392 (2015).Article 
PubMed 

Google Scholar 
Chang, C.-C. & Lin, C.-J. Training v-support vector classifiers: Theory and algorithms. Neural Comput. 13, 2119–2147 (2001).Article 
CAS 
PubMed 

Google Scholar 
Suits, D. B. Use of dummy variables in regression equations. J. Am. Stat. Assoc. 52, 548–551 (1957).Article 

Google Scholar 
Joshi, M. V., Agarwal, R. C. & Kumar, V. Predicting rare classes: can boosting make any weak learner strong? In Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining 297–306 (Association for Computing Machinery, New York, NY, USA, 2002). https://doi.org/10.1145/775047.775092.Hastie, T., Rosset, S., Zhu, J. & Zou, H. Multi-class AdaBoost. Stat. Interface 2, 349–360 (2009).Article 
MathSciNet 

Google Scholar 
Erickson, B. J. & Kitamura, F. Magician’s Corner: 9. Performance metrics for machine learning models. Radiol. Artif. Intell. 3, e200126 (2021).Yang, T. et al. Ensemble learning models based on noninvasive features for type 2 diabetes screening: Model development and validation. JMIR Med. Inform. 8, e15431 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Asghari Varzaneh, Z., Shanbehzadeh, M. & Kazemi-Arpanahi, H. Prediction of successful aging using ensemble machine learning algorithms. BMC Med. Inform. Decis. Mak. 22, 258 (2022).Article 
PubMed 
PubMed Central 

Google Scholar 
Guo, Y., Xiong, X., Liu, Y., Xu, L. & Li, Q. A novel speech emotion recognition method based on feature construction and ensemble learning. PLoS One 17, e0267132 (2022).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
OSCIS investigators et al. Effect of early vs delayed surgical treatment on motor recovery in incomplete cervical spinal cord injury with preexisting cervical stenosis: A randomized clinical trial. JAMA Netw. Open 4, e2133604 (2021).Nedumaran, L., Rebekah, G., Tharion, E. & Tharion, G. Initial autonomic parameters and subsequent short-term neurological recovery after inpatient rehabilitation, in traumatic cervical spinal cord injury patients. Neurorehabil. Neural Repair. 36, 269–273 (2022).Article 
PubMed 

Google Scholar 
Qiu, Z. et al. Clinical predictors of neurological outcome within 72 h after traumatic cervical spinal cord injury. Sci. Rep. 6, 38909 (2016).Article 
ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
R, K., H, V., S, N. & S, C. Predicting the Role of Preoperative Intramedullary Lesion Length and Early Decompressive Surgery in ASIA Impairment Scale Grade Improvement Following Subaxial Traumatic Cervical Spinal Cord Injury. Journal of neurological surgery. Part A, Central Eur. Neurosurg. 84, (2023).Mora-Boga, R. et al. Prognostic value of early magnetic resonance imaging in the morbidity and mortality of traumatic spinal cord injury. Med. Intens. (Engl Ed) 47, 157–164 (2023).Article 

Google Scholar 
Fan, G. et al. Early prognostication of critical patients with spinal cord injury: A machine learning study with 1485 cases. Spine (Phila Pa 1976). https://doi.org/10.1097/BRS.0000000000004861 (2023).Okimatsu, S. et al. Determining the short-term neurological prognosis for acute cervical spinal cord injury using machine learning. J. Clin. Neurosci. 96, 74–79 (2022).Article 
PubMed 

Google Scholar 
Fallah, N. et al. Development of a machine learning algorithm for predicting in-hospital and 1-year mortality after traumatic spinal cord injury. Spine J. 22, 329–336 (2022).Article 
PubMed 

Google Scholar 
Dietz, N. et al. Machine learning in clinical diagnosis, prognostication, and management of acute traumatic spinal cord injury (SCI): A systematic review. J. Clin. Orthop. Trauma 35, 102046 (2022).Article 
PubMed 
PubMed Central 

Google Scholar 
Maki, S. et al. Machine learning web application for predicting functional outcomes in patients with traumatic spinal cord injury following inpatient rehabilitation. J. Neurotrauma https://doi.org/10.1089/neu.2022.0383 (2023).Article 
PubMed 

Google Scholar 
Sizheng, Z., Boxuan, H., Feng, X. & Dianying, Z. A functional outcome prediction model of acute traumatic spinal cord injury based on extreme gradient boost. J. Orthopaed. Surg. Res. 17, 451 (2022).Article 

Google Scholar 
Dong, X., Yu, Z., Cao, W., Shi, Y. & Ma, Q. A survey on ensemble learning. Front. Comput. Sci. 14, 241–258 (2020).Article 

Google Scholar 
Kato, C., Uemura, O., Sato, Y. & Tsuji, T. Functional outcome prediction after spinal cord injury using ensemble machine learning. Arch. Phys. Med. Rehab. 105, 95–100 (2024).Article 

Google Scholar 
Sharif, S. & Jazaib Ali, M. Y. Outcome prediction in spinal cord injury: Myth or reality. World Neurosurg. 140, 574–590 (2020).Article 
PubMed 

Google Scholar 
Burns, A. S., Lee, B. S., Ditunno, J. F. & Tessler, A. Patient selection for clinical trials: The reliability of the early spinal cord injury examination. J. Neurotrauma 20, 477–482 (2003).Article 
PubMed 

Google Scholar 
Brown, P. J., Marino, R. J., Herbison, G. J. & Ditunno, J. F. The 72-hour examination as a predictor of recovery in motor complete quadriplegia. Arch. Phys. Med. Rehabil. 72, 546–548 (1991).CAS 
PubMed 

Google Scholar 
Herbison, G. J., Zerby, S. A., Cohen, M. E., Marino, R. J. & Ditunno, J. F. Motor power differences within the first two weeks post-SCI in cervical spinal cord-injured quadriplegic subjects. J. Neurotrauma 9, 373–380 (1992).Article 
CAS 
PubMed 

Google Scholar 
Marino, R. J. et al. Upper- and lower-extremity motor recovery after traumatic cervical spinal cord injury: An update from the national spinal cord injury database. Arch. Phys Med. Rehabil. 92, 369–375 (2011).Article 
PubMed 

Google Scholar 
Fawcett, J. W. et al. Guidelines for the conduct of clinical trials for spinal cord injury as developed by the ICCP panel: Spontaneous recovery after spinal cord injury and statistical power needed for therapeutic clinical trials. Spinal Cord. 45, 190–205 (2007).Article 
CAS 
PubMed 

Google Scholar