A Predictive Model for Assessment of Successful Outcome in Posterior Spinal Fusion Surgery
International Clinical Neuroscience Journal,
Vol. 4 No. 4 (2017),
8 October 2017
,
Page 143-151
Abstract
Background: Low back pain is a common problem in many people. Neurosurgeons recommend Posterior Spinal Fusion Surgery (PSF) as one of the therapeutic strategies to the patients with low back pain. Due to the high risk of this type of surgery and the critical importance of making the right decision, accurate prediction of the surgical outcome is one of the main concerns for the neurosurgeons.
Methods: In this study, 12 types of Multi-Layer perceptron networks (MLP) and 66 Radial Basis Function (RBF) networks as the types of artificial neural network methods and a Logistic Regression model created and compared to predict the satisfaction with PSF surgery as one of the most well-known spinal surgeries.
Results: The most important clinical and radiologic features as twenty-seven factors for 480 patients (150 males, 330 females; mean age 52.32 ± 8.39 years) were considered as the model inputs that included: age, sex, type of disorder, duration of symptoms, job, walking distance without pain, walking distance without sensory disorders, visual analog scale scores, Japanese Orthopaedic Association score, diabetes, smoking, knee pain, pelvic pain, osteoporosis, spinal deformity and etc. The indexes such as receiver operating characteristic–area under curve (ROC-AUC), positive predictive value, negative predictive value and accuracy calculated to determine the best model. Postsurgical satisfaction was 77.5% at 6 months follow-up. The patients divided into the training, testing, and validation data sets.
Conclusion: The findings showed that the MLP model performed better in comparison with RBF and LR models for prediction of PSF surgery.
- Posterior spinal fusion surgery (PSF)
- Prediction
- Surgical satisfaction
- Multi-Layer Perceptron (MLP)
- Logistic regression (LR)
How to Cite
References
Azimi P, Benzel E, Shahzadi S, Azhdari S, Mohammadi HR. The Prediction of successful surgery outcome in lumbar disc herniation based on artificial neural networks. J Neurosurg Sci. 2015; 60(2): 173-177.
Azimi P, Benzel E, Shahzadi S, Azhdari S, Mohammadi HR. Use of artificial neural networks to predict surgical satisfaction in patients with lumbar spinal canal stenosis. J Neurosurg Spine. 2014;20(3): 300–305.
Spine center Atlanta. Available at: http://spinecenteratlanta.com/surgical-treatment/anteriorposterior-spine-fusion/ accessed September 7, 2017.
Barba M, Cicione C, Bernardini C, Campana V, Pagano E, Michetti F, et al. Spinal Fusion in the Next Generation: Gene and Cell Therapy Approaches. Scientific World Journal 406159:9, 2014.
Matis GK, Chryson OI, Silva D, Karanikas MA, Baltsavias G, Lyratzopoulos N, et al. Prediction of Lumbar Disc Herniation Patients’ Satisfaction with the Aid of an Artificial Neural Network. Turk Neurosurg. 2016; 26(2): 253-259.
Eftekhar B, Mohammad K, Ardebili HE, Ghodsi M, Ketabchi E. Comparison of artificial neural network and logistic regression models for prediction of mortality in head trauma based on initial clinical data. BMC Med Inform Decis Mak. 2005;5(3).
Li YC, Liu Li, Chiu WT, Jian WS. Neural network modeling for surgical decisions on traumatic brain injury patients. Int J Med Inform. 2000; 57: 1–9.
Rughani AI, Dumont TM, Lu Z, Bongard J, Horgan MA, Penar PL, et al. Use of an artificial neural network to predict head injury outcome. Neurosurgery. 2010; 113(3): 585-590.
Shi HY, Hwang SL, Lee KT, Lin CL.In-hospital mortality after traumatic brain injury surgery: a nationwide population-based comparison of mortality predictors used in artificial neural network and logistic regression models. Neurosurgery. 2013;118:746–52.
Mala K, Sadasivam V, Alagappan S. Neural network based texture analysis of CT images for fatty and cirrhosis liver classification. Applied Soft Computing. 2015; 32: 80-86.
Oermann EK, Kress MA, Collins BT, Collins SP, Morris D, Ahalt SC, et al. Predicting survival in patients with brain metastases treated with radiosurgery using artificial neural networks. Neurosurgery. 2013; 72(6):944-951.
Parsaeian M, Mohammad K, Mahmoudi M, Zeraati H. Comparison of logistic regression and artificial neural network in low back pain prediction: second national health survey. Iran J Public Health. 2012; 41(6): 86–92.
Bishop JB, Szpalski M, Ananthraman SK, McIntyre DR, Pope MH. Classification of low back pain from dynamic motion characteristics using an artificial neural network. Spine. 1997; 22(24):2991-8.
Tao C, Li H, Wang J, You C. Predictors of Surgical Results in Patients with Primary Pontine Hemorrhage. Turk Neurosurg. 2016; 26(1):77-83.
Zhao G, Liang JC, Wang WM, Wu Hx, Li L, Qin ZH, et al. Long-term Effects of Gamma-knife Radiosurgery for Cerebral Arteriovenous Malformation. Neurosurg Quarterly. 2008; 18:126–129.
Green M, Björk J, Forberg J, Ekelund U, Edenbrandt L, Ohlsson M. Comparison between neural networks and multiple logistic regression to predict acute coronary syndrome in the emergency room. Artif Intell Med. 2006;38(3): 305–318.
Tseng CJ, Lu CJ, Chang CC, Chen GD. Application of machine learning to predict the recurrence - proneness for cervical cancer. Neural Comput Appl. 2014; 24(6):1311–1316.
Li Z, Quan Z, Zhang N, Zhao J, Shen D. Comparison Between Intraventricular and Intraparenchymal Intracranial Pressure Monitoring in Asian Patients with Severe Traumatic Brain Injury. Neurosurg Quarterly. 2016; 26:120–124.
Drotár P, Mekyska J, Rektorová I, Masarova L, Smékal Z, Faundez-Zanuy M. Evaluation of handwriting kinematics and pressure for differential diagnosis of Parkinson's disease. Artif Intell Med. 2016;67: 39–46.
Bhagya Shree SR, Sheshadri HS. Diagnosis of Alzheimer's disease using Naive Bayesian Classifier. Neural Comput Appl. 2016; 1-10.
Bidiwala S, Pittman T. Neural network classification of pediatric posterior fossa tumors using clinical and imaging data. Pediatr Neurosurg. 2004;40(1):8-15.
Arjmand N, Ekrami O, Shirazi-Adl A, Plamondon A, Parnianpour M. relative performances of artificial neural network and regression mapping tools in evaluation of spinal loads and muscle forces during static lifting. J Biomech. 2013 May 31;46(8):1454-62.
Azimi P, Mohammadi HR, Benzel E, Shahzadi S, Azhdari S. Artificial neural networks in neurosurgery. J Neurol Neurosurg Psychiatry. 2015; 86(3): 251-256.
Price DD, McGrath PA, Rafii A, Buckingham B. The validation of visual analogue scales as ratio scale measures for chronic and experimental. Pain. 1983; 17(1):45–56.
Fujimori T, Okuda S, Iwasaki M, Yamasaki R, Maeno T, Yamashita T, et al. Validity of the Japanese Orthopaedic Association scoring system based on patient-reported improvement after posterior lumbar interbody fusion. Spine. 2016; 16(6): 728–736.
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 Spin Dis. 1994; 7(5): 388–393.
Winn HR. Youmans and Winn Neurological Surgery. Elsevier, 2017. pp 2272.
Winn HR: Youmans and Winn Neurological Surgery. Elsevier, 2017. pp 2289.
Azimi P, Shahzadi S, Safdari H, Ghandehari S, Sadeghi S, Azhari S, et al. Severity of symptoms, physical functioning and satisfaction in patients with lumbar spinal stenosis: a validation study of the Iranian version of the Swiss Spinal Stenosis Score (SSS). J Neurosurg Sci. 2014; 58(3):177-182.
Heidari E, Sobati MA, Movahedirad S. Accurate prediction of nanofluid viscosity using a multilayer perceptron artificial neural network (MLP-ANN). Chemometr Intell Lab Syst. 2016;155:73-85.
Mashaly AF, Alazba AA. MLP and MLR models for instantaneous thermal efficiency prediction of solar still under hyper-arid environment. Comput Electron Agric. 2016;122: 146–155.
Azadeh A, Saberi M, Anvari M. An Integrated Artificial Neural Network Fuzzy C- Means -Normalization Algorithm for performance assessment of decision - making units: The cases of auto industry and power plant. Computers & Industrial Engineering. 2011; 60: 328–340.
Kheirkhah A, Azadeh A, Saberi M, Azaron A, Shakouri H. Improved estimation of electricity demand function by using of artificial neural network principal component analysis and data envelopment analysis. Computers & Industrial Engineering. 2013; 64:425–441.
Chiu MC, Chiou JY. Technical service platform planning based on a company’s competitive advantage and future market trends: A case study of an IC foundry. Computers & Industrial Engineering. 2016; 99: 503–517.
De Menezes FS, Liska GR, Cirillo MA, Vivanco MJF. Data classification with binary response through the Boosting algorithm and logistic regression. Expert Syst Appl. 2017;69: 62–73.
Kurt I, Ture M, Kurum AT. Comparing performances of logistic regression, classification and regression tree, and neural networks for predicting coronary artery disease. Expert Syst Appl. 2008; 34(1):366–374.
Gretchen GM, Frescino TS. Comparing five modeling techniques for predicting forest characteristics. Ecological Modeling. 2002; 157(2-3): 209- 225.
- Abstract Viewed: 554 times
- PDF Downloaded: 371 times