Use of artificial neural networks to predict surgical satisfaction in patients with lumbar spinal canal stenosis

Clinical article

View More View Less
  • 1 Department of Neurosurgery, Shahid Beheshti University of Medical Sciences, Tehran, Iran; and
  • 2 Department of Neurosurgery, Cleveland Clinic Foundation, Cleveland, Ohio
Restricted access

Purchase Now

USD  $45.00

Spine - 1 year subscription bundle (Individuals Only)

USD  $369.00

JNS + Pediatrics + Spine - 1 year subscription bundle (Individuals Only)

USD  $600.00
Print or Print + Online

Object

The purpose of this study was to develop an artificial neural network (ANN) model for predicting 2-year surgical satisfaction, and to compare the new model with traditional predictive tools in patients with lumbar spinal canal stenosis.

Methods

The 2 prediction models included an ANN and a logistic regression (LR) model. The patient age, sex, duration of symptoms, walking distance, visual analog scale scores of leg pain or numbness, the Japanese Orthopaedic Association score, the Neurogenic Claudication Outcome Score, and the stenosis ratio values were determined as the input variables for the ANN and LR models that were developed. Patient surgical satisfaction was recorded using a standardized measure. The ANNs were fed patient data to predict 2-year surgical satisfaction based on several input variables. Sensitivity analysis was applied to the ANN model to identify the important variables. The receiver operating characteristic–area under curve (ROC-AUC), Hosmer-Lemeshow statistics, and accuracy rate were calculated for evaluating the 2 models.

Results

A total of 168 patients (59 male, 109 female; mean age 59.8 ± 11.6 years) were divided into training (n = 84), testing (n = 42), and validation (n = 42) data sets. Postsurgical satisfaction was 88.7% at 2-year follow-up. The stenosis ratio was the important variable selected by the ANN. The ANN model displayed a better accuracy rate in 96.9% of patients, a better Hosmer-Lemeshow statistic in 42.4% of patients, and a better ROC-AUC in 80% of patients, compared with the LR model.

Conclusions

The findings show that an ANN can predict 2-year surgical satisfaction for use in clinical application and is more accurate compared with an LR model.

Abbreviations used in this paper:ANN = artificial neural network; JOA = Japanese Orthopaedic Association; LBP = low-back pain; LR = logistic regression; LSCS = lumbar spinal canal stenosis; MLP = multilayer perceptron; NCOS = Neurogenic Claudication Outcome Score; ROC-AUC = receiver operating characteristic–area under curve; SR = stenosis ratio; VAS = visual analog scale.

Spine - 1 year subscription bundle (Individuals Only)

USD  $369.00

JNS + Pediatrics + Spine - 1 year subscription bundle (Individuals Only)

USD  $600.00

Contributor Notes

Address correspondence to: Parisa Azimi, M.D., Functional Neurosurgery Research Center of Shohada Tajrish Hospital, Shahid Beheshti University of Medical Sciences, Sharadari St., Tajrish Square, Tehran 1989934148, Iran. email: parisa.azimi@gmail.com.

Please include this information when citing this paper: published online January 17, 2014; DOI: 10.3171/2013.12.SPINE13674.

  • 1.

    Azimi P, , Mohammadi HR, & Montazeri A: An outcome measure of functionality and pain in patients with lumbar disc herniation: a validation study of the Japanese Orthopedic Association (JOA) score. J Orthop Sci 17:341345, 2012

    • Search Google Scholar
    • Export Citation
  • 2.

    Azimi P, , Mohammadi HR, & Montazeri A: An outcome measure of functionality in patients with lumber spinal stenosis: a validation study of the Iranian version of Neurogenic Claudication Outcome Score (NCOS). BMC Neurol 12:101, 2012

    • Search Google Scholar
    • Export Citation
  • 3.

    Cross SS, , Harrison RF, & Kennedy RL: Introduction to neural networks. Lancet 346:10751079, 1995

  • 4.

    Gevirtz C: Update on treatment of lumbar spinal stenosis. Part 1: defining the problem, diagnosis, and appropriate imaging. Topics in Pain Management 25:15, 2010

    • Search Google Scholar
    • Export Citation
  • 5.

    Hamanishi C, , Matukura N, , Fujita M, , Tomihara M, & Tanaka S: Cross-sectional area of the stenotic lumbar dural tube measured from the transverse views of magnetic resonance imaging. J Spinal Disord 7:388393, 1994

    • Search Google Scholar
    • Export Citation
  • 6.

    Laurencin CT, , Lipson SJ, , Senatus P, , Botchwey E, , Jones TR, & Koris M, : The stenosis ratio: a new tool for the diagnosis of degenerative spinal stenosis. Int J Surg Investig 1:127131, 1999

    • Search Google Scholar
    • Export Citation
  • 7.

    Li YC, , Liu L, , Chiu WT, & Jian WS: Neural network modeling for surgical decisions on traumatic brain injury patients. Int J Med Inform 57:19, 2000

    • Search Google Scholar
    • Export Citation
  • 8.

    Markman JD, & Gaud KG: Lumbar spinal stenosis in older adults: current understanding and future directions. Clin Geriatr Med 24:369388, viii, 2008

    • Search Google Scholar
    • Export Citation
  • 9.

    Patel JL, & Goyal RK: Applications of artificial neural networks in medical science. Curr Clin Pharmacol 2:217226, 2007

  • 10.

    Price DD, , McGrath PA, , Rafii A, & Buckingham B: The validation of visual analogue scales as ratio scale measures for chronic and experimental pain. Pain 17:4556, 1983

    • Search Google Scholar
    • Export Citation
  • 11.

    Rughani AI, , Dumont TM, , Lu Z, , Bongard J, , Horgan MA, & Penar PL, : Use of an artificial neural network to predict head injury outcome. Clinical article. J Neurosurg 113:585590, 2010

    • Search Google Scholar
    • Export Citation
  • 12.

    Weiner BK, , Patel NM, & Walker MA: Outcomes of decompression for lumbar spinal canal stenosis based upon preoperative radiographic severity. J Orthop Surg 2:3, 2007

    • Search Google Scholar
    • Export Citation

Metrics

All Time Past Year Past 30 Days
Abstract Views 235 166 16
Full Text Views 858 39 4
PDF Downloads 278 43 1
EPUB Downloads 0 0 0