A functional outcome prediction model of acute traumatic spinal cord injury based on extreme gradient boost

Zhan Sizheng, Huang Boxuan, Xue Feng, Zhang Dianying, Zhan Sizheng, Huang Boxuan, Xue Feng, Zhang Dianying

Abstract

Objective: We aimed to construct a nonlinear regression model through Extreme Gradient Boost (XGBoost) to predict functional outcome 1 year after surgical decompression for patients with acute spinal cord injury (SCI) and explored the importance of predictors in predicting the functional outcome.

Methods: We prospectively enrolled 249 patients with acute SCI from 5 primary orthopedic centers from June 1, 2016, to June 1, 2020. We identified a total of 6 predictors with three aspects: (1) clinical characteristics, including age, American Spinal Injury Association (ASIA) Impairment Scale (AIS) at admission, level of injury and baseline ASIA motor score (AMS); (2) MR imaging, mainly including Brain and Spinal Injury Center (BASIC) score; (3) surgical timing, specifically comparing whether surgical decompression was received within 24 h or not. We assessed the SCIM score at 1 year after the operation as the functional outcome index. XGBoost was used to build a nonlinear regression prediction model through the method of boosting integrated learning.

Results: We successfully constructed a nonlinear regression prediction model through XGBoost and verified the credibility. There is no significant difference between actual SCIM and nonlinear prediction model (t = 0.86, P = 0.394; Mean ± SD: 3.31 ± 2.8). The nonlinear model is superior to the traditional linear model (t = 6.57, P < 0.001). AMS and age played the most important roles in constructing predictive models. There is an obvious correlation between AIS, AMS and BASIC score.

Conclusion: We verified the feasibility of using XGBoost to construct a nonlinear regression prediction model for the functional outcome of patients with acute SCI, and proved that the predictive performance of the nonlinear model is better than the traditional linear regression prediction model. Age and baseline AMS play the most important role in predicting the functional outcome. We also found a significant correlation between AIS at admission, baseline AMS and BASIC score.

Trial registration: ClinicalTrials.gov identifier: NCT03103516.

Keywords: Acute spinal cord injury; Extreme gradient boost; Prediction model; Spinal cord independence measure.

Conflict of interest statement

SZ.Z, BX.H, F.X and DY.Z declare that they have no conflicting interests.

© 2022. The Author(s).

Figures

Fig. 1
Fig. 1
Patients flow
Fig. 2
Fig. 2
Validation of predictive model. Comparison between actual value, nonlinear model and linear model predicted value
Fig. 3
Fig. 3
Rank of features importance
Fig. 4
Fig. 4
Correlation of the 6 predictors

References

    1. Jazayeri SB, Beygi S, Shokraneh F, Hagen EM, Rahimi-Movaghar V. Incidence of traumatic spinal cord injury worldwide: a systematic review. Eur Spine J. 2015;24(5):905–918. doi: 10.1007/s00586-014-3424-6.
    1. Kumar R, Lim J, Mekary RA, et al. Traumatic spinal injury: global epidemiology and worldwide volume. World neurosurgery. 2018;113:e345–e363. doi: 10.1016/j.wneu.2018.02.033.
    1. Kriz J, Kulakovska M, Davidova H, Silova M, Kobesova A. Incidence of acute spinal cord injury in the Czech Republic: a prospective epidemiological study 2006–2015. Spinal cord. 2017;55(9):870–874. doi: 10.1038/sc.2017.20.
    1. van den Berg ME, Castellote JM, de Pedro-Cuesta J, Mahillo-Fernandez I. Survival after spinal cord injury: a systematic review. J Neurotrauma. 2010;27(8):1517–1528. doi: 10.1089/neu.2009.1138.
    1. Singh H, Shah M, Flett HM, Craven BC, Verrier MC, Musselman KE. Perspectives of individuals with sub-acute spinal cord injury after personalized adapted locomotor training. Disabil Rehabil. 2018;40(7):820–828. doi: 10.1080/09638288.2016.1277395.
    1. Wilson JR, Grossman RG, Frankowski RF, 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(13):2263–2271. doi: 10.1089/neu.2012.2417.
    1. 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(12):2027–2033. doi: 10.1089/neu.2016.4955.
    1. Tanaka C, Tagami T, Kaneko J, et al. Early versus late surgery after cervical spinal cord injury: a Japanese nationwide trauma database study. J Orthop Surg Res. 2019;14(1):302. doi: 10.1186/s13018-019-1341-4.
    1. Wilson JR, Witiw CD, Badhiwala J, Kwon BK, Fehlings MG, Harrop JS. Early surgery for traumatic spinal cord injury: where are we now? Glob Spine J. 2020;10(1 Suppl):84s–91s. doi: 10.1177/2192568219877860.
    1. Badhiwala JH, Wilson JR, Witiw CD, et al. The influence of timing of surgical decompression for acute spinal cord injury: a pooled analysis of individual patient data. Lancet Neurol. 2021;20(2):117–126. doi: 10.1016/S1474-4422(20)30406-3.
    1. Fehlings MG, Tetreault LA, Wilson JR, 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. 2017;7(3 Suppl):195s–202s. doi: 10.1177/2192568217706367.
    1. Fehlings MG, Vaccaro A, Wilson JR, 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. 2012;7(2):e32037. doi: 10.1371/journal.pone.0032037.
    1. XGBoost: A Scalable Tree Boosting System. Paper presented at: the 22nd ACM SIGKDD International Conference 2016.
    1. Li Y, Li M, Li C, Liu Z. Forest aboveground biomass estimation using Landsat 8 and Sentinel-1A data with machine learning algorithms. Sci Rep. 2020;10(1):9952. doi: 10.1038/s41598-020-67024-3.
    1. Torlay L, Perrone-Bertolotti M, Thomas E, Baciu MJBI. Machine learning–XGBoost analysis of language networks to classify patients with epilepsy. 2017.
    1. Ke J, Chen Y, Wang X, et al. Machine learning-based in-hospital mortality prediction models for patients with acute coronary syndrome. Am J Emerg Med. 2022;53:127–134. doi: 10.1016/j.ajem.2021.12.070.
    1. Hurlbert RJ, Hadley MN, Walters BC, et al. Pharmacological therapy for acute spinal cord injury. Neurosurgery. 2013;72(Suppl 2):93–105. doi: 10.1227/NEU.0b013e31827765c6.
    1. Wang T, Jiang B. Road traffic mortality in China: good prospect and arduous undertaking. Lancet Public Health. 2019;4(5):e214–e215. doi: 10.1016/S2468-2667(19)30063-5.
    1. Talbott JF, Whetstone WD, Readdy WJ, et al. The Brain and Spinal Injury Center score: a novel, simple, and reproducible method for assessing the severity of acute cervical spinal cord injury with axial T2-weighted MRI findings. J Neurosurg Spine. 2015;23(4):495–504. doi: 10.3171/2015.1.SPINE141033.
    1. Mabray MC, Talbott JF, Whetstone WD, et al. Multidimensional analysis of magnetic resonance imaging predicts early impairment in thoracic and thoracolumbar spinal cord injury. J Neurotrauma. 2016;33(10):954–962. doi: 10.1089/neu.2015.4093.
    1. Haefeli J, Mabray MC, Whetstone WD, et al. Multivariate analysis of MRI biomarkers for predicting neurologic impairment in cervical spinal cord injury. AJNR Am J Neuroradiol. 2017;38(3):648–655. doi: 10.3174/ajnr.A5021.
    1. Catz A, Itzkovich M, Agranov E, Ring H, Tamir A. SCIM–spinal cord independence measure: a new disability scale for patients with spinal cord lesions. Spinal Cord. 1997;35(12):850–856. doi: 10.1038/sj.sc.3100504.
    1. Roberts TT, Leonard GR, Cepela DJ. Classifications In Brief: American Spinal Injury Association (ASIA) Impairment Scale. Clin Orthop Relat Res. 2017;475(5):1499–1504. doi: 10.1007/s11999-016-5133-4.
    1. Itzkovich M, Gelernter I, Biering-Sorensen F, et al. The Spinal cord independence measure (SCIM) version III: reliability and validity in a multi-center international study. Disabil Rehabil. 2007;29(24):1926–1933. doi: 10.1080/09638280601046302.
    1. Middleton JW, Harvey LA, Batty J, Cameron I, Quirk R, Winstanley J. Five additional mobility and locomotor items to improve responsiveness of the FIM in wheelchair-dependent individuals with spinal cord injury. Spinal Cord. 2006;44(8):495–504. doi: 10.1038/sj.sc.3101872.
    1. Fehlings MG, Martin AR, Tetreault LA, et al. A clinical practice guideline for the management of patients with acute spinal cord injury: recommendations on the role of baseline magnetic resonance imaging in clinical decision making and outcome prediction. Global Spine J. 2017;7(3 Suppl):221S–230S. doi: 10.1177/2192568217703089.
    1. Khera R, Haimovich J, Hurley NC, et al. Use of machine learning models to predict death after acute myocardial infarction. JAMA Cardiol. 2021;6(6):633–641. doi: 10.1001/jamacardio.2021.0122.
    1. Tseng PY, Chen YT, Wang CH, et al. Prediction of the development of acute kidney injury following cardiac surgery by machine learning. Crit Care (London, England) 2020;24(1):478. doi: 10.1186/s13054-020-03179-9.
    1. Hou N, Li M, He L, et al. Predicting 30-days mortality for MIMIC-III patients with sepsis-3: a machine learning approach using XGboost. J Transl Med. 2020;18(1):462. doi: 10.1186/s12967-020-02620-5.
    1. Kalsi-Ryan S, Wilson J, Yang JM, Fehlings MG. Neurological grading in traumatic spinal cord injury. World Neurosurgery. 2014;82(3–4):509–518. doi: 10.1016/j.wneu.2013.01.007.
    1. Wutte C, Becker J, Klein B, et al. Early decompression (<8 hours) improves functional bladder outcome and mobility after traumatic thoracic spinal cord Injury. World Neurosurg. 2020;134:e847–e854. doi: 10.1016/j.wneu.2019.11.015.
    1. Wilson JR, Davis AM, Kulkarni AV, et al. Defining age-related differences in outcome after traumatic spinal cord injury: analysis of a combined, multicenter dataset. Spine J. 2014;14(7):1192–1198. doi: 10.1016/j.spinee.2013.08.005.
    1. Oleson CV, Marino RJ, Leiby BE, Ditunno JF. Influence of age alone, and age combined with pinprick, on recovery of walking function in motor complete, sensory incomplete spinal cord injury. Arch Phys Med Rehabil. 2016;97(10):1635–1641. doi: 10.1016/j.apmr.2016.01.024.
    1. Penrod LE, Hegde SK, Ditunno JF., Jr Age effect on prognosis for functional recovery in acute, traumatic central cord syndrome. Arch Phys Med Rehabil. 1990;71(12):963–968.
    1. Engel-Haber E, Zeilig G, Haber S, Worobey L, Kirshblum S. The effect of age and injury severity on clinical prediction rules for ambulation among individuals with spinal cord injury. Spine J. 2020;20(10):1666–1675. doi: 10.1016/j.spinee.2020.05.551.
    1. van Middendorp JJ, Hosman AJ, Donders AR, et al. A clinical prediction rule for ambulation outcomes after traumatic spinal cord injury: a longitudinal cohort study. Lancet (London, England) 2011;377(9770):1004–1010. doi: 10.1016/S0140-6736(10)62276-3.

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