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A points-based algorithm for prognosticating clinical outcome of Chiari malformation Type I with syringomyelia: results from a predictive model analysis of 82 surgically managed adult patients.
J Neurosurg Spine. 2018 01; 28(1):23-32.JN

Abstract

OBJECTIVE

Although various predictors of postoperative outcome have been previously identified in patients with Chiari malformation Type I (CMI) with syringomyelia, there is no known algorithm for predicting a multifactorial outcome measure in this widely studied disorder. Using one of the largest preoperative variable arrays used so far in CMI research, the authors attempted to generate a formula for predicting postoperative outcome.

METHODS

Data from the clinical records of 82 symptomatic adult patients with CMI and altered hindbrain CSF flow who were managed with foramen magnum decompression, C-1 laminectomy, and duraplasty over an 8-year period were collected and analyzed. Various preoperative clinical and radiological variables in the 57 patients who formed the study cohort were assessed in a bivariate analysis to determine their ability to predict clinical outcome (as measured on the Chicago Chiari Outcome Scale [CCOS]) and the resolution of syrinx at the last follow-up. The variables that were significant in the bivariate analysis were further analyzed in a multiple linear regression analysis. Different regression models were tested, and the model with the best prediction of CCOS was identified and internally validated in a subcohort of 25 patients.

RESULTS

There was no correlation between CCOS score and syrinx resolution (p = 0.24) at a mean ± SD follow-up of 40.29 ± 10.36 months. Multiple linear regression analysis revealed that the presence of gait instability, obex position, and the M-line-fourth ventricle vertex (FVV) distance correlated with CCOS score, while the presence of motor deficits was associated with poor syrinx resolution (p ≤ 0.05). The algorithm generated from the regression model demonstrated good diagnostic accuracy (area under curve 0.81), with a score of more than 128 points demonstrating 100% specificity for clinical improvement (CCOS score of 11 or greater). The model had excellent reliability (κ = 0.85) and was validated with fair accuracy in the validation cohort (area under the curve 0.75).

CONCLUSIONS

The presence of gait imbalance and motor deficits independently predict worse clinical and radiological outcomes, respectively, after decompressive surgery for CMI with altered hindbrain CSF flow. Caudal displacement of the obex and a shorter M-line-FVV distance correlated with good CCOS scores, indicating that patients with a greater degree of hindbrain pathology respond better to surgery. The proposed points-based algorithm has good predictive value for postoperative multifactorial outcome in these patients.

Authors+Show Affiliations

1Department of Neurological Sciences, Sri Sathya Sai Institute of Higher Medical Sciences, Bangalore; and.1Department of Neurological Sciences, Sri Sathya Sai Institute of Higher Medical Sciences, Bangalore; and.2Department of Psychiatry, Christian Medical College, Vellore, India.1Department of Neurological Sciences, Sri Sathya Sai Institute of Higher Medical Sciences, Bangalore; and.1Department of Neurological Sciences, Sri Sathya Sai Institute of Higher Medical Sciences, Bangalore; and.1Department of Neurological Sciences, Sri Sathya Sai Institute of Higher Medical Sciences, Bangalore; and.1Department of Neurological Sciences, Sri Sathya Sai Institute of Higher Medical Sciences, Bangalore; and.1Department of Neurological Sciences, Sri Sathya Sai Institute of Higher Medical Sciences, Bangalore; and.

Pub Type(s)

Journal Article

Language

eng

PubMed ID

29125433

Citation

Thakar, Sumit, et al. "A Points-based Algorithm for Prognosticating Clinical Outcome of Chiari Malformation Type I With Syringomyelia: Results From a Predictive Model Analysis of 82 Surgically Managed Adult Patients." Journal of Neurosurgery. Spine, vol. 28, no. 1, 2018, pp. 23-32.
Thakar S, Sivaraju L, Jacob KS, et al. A points-based algorithm for prognosticating clinical outcome of Chiari malformation Type I with syringomyelia: results from a predictive model analysis of 82 surgically managed adult patients. J Neurosurg Spine. 2018;28(1):23-32.
Thakar, S., Sivaraju, L., Jacob, K. S., Arun, A. A., Aryan, S., Mohan, D., Sai Kiran, N. A., & Hegde, A. S. (2018). A points-based algorithm for prognosticating clinical outcome of Chiari malformation Type I with syringomyelia: results from a predictive model analysis of 82 surgically managed adult patients. Journal of Neurosurgery. Spine, 28(1), 23-32. https://doi.org/10.3171/2017.5.SPINE17264
Thakar S, et al. A Points-based Algorithm for Prognosticating Clinical Outcome of Chiari Malformation Type I With Syringomyelia: Results From a Predictive Model Analysis of 82 Surgically Managed Adult Patients. J Neurosurg Spine. 2018;28(1):23-32. PubMed PMID: 29125433.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - A points-based algorithm for prognosticating clinical outcome of Chiari malformation Type I with syringomyelia: results from a predictive model analysis of 82 surgically managed adult patients. AU - Thakar,Sumit, AU - Sivaraju,Laxminadh, AU - Jacob,Kuruthukulangara S, AU - Arun,Aditya Atal, AU - Aryan,Saritha, AU - Mohan,Dilip, AU - Sai Kiran,Narayanam Anantha, AU - Hegde,Alangar S, Y1 - 2017/11/10/ PY - 2017/11/11/pubmed PY - 2018/6/21/medline PY - 2017/11/11/entrez KW - AUC = area under the curve KW - CCOS = Chicago Chiari outcome scale KW - CMI = Chiari malformation Type I KW - Chiari Type I malformation KW - FM = foramen magnum KW - FVV = fourth ventricle vertex KW - ICV = intracranial volume KW - PFV = posterior fossa volume KW - PSM = predictive statistical modeling KW - ROC = receiver operating characteristic KW - algorithm KW - congenital KW - outcome KW - postsurgical improvement KW - predictive model analysis KW - syringomyelia SP - 23 EP - 32 JF - Journal of neurosurgery. Spine JO - J Neurosurg Spine VL - 28 IS - 1 N2 - OBJECTIVE Although various predictors of postoperative outcome have been previously identified in patients with Chiari malformation Type I (CMI) with syringomyelia, there is no known algorithm for predicting a multifactorial outcome measure in this widely studied disorder. Using one of the largest preoperative variable arrays used so far in CMI research, the authors attempted to generate a formula for predicting postoperative outcome. METHODS Data from the clinical records of 82 symptomatic adult patients with CMI and altered hindbrain CSF flow who were managed with foramen magnum decompression, C-1 laminectomy, and duraplasty over an 8-year period were collected and analyzed. Various preoperative clinical and radiological variables in the 57 patients who formed the study cohort were assessed in a bivariate analysis to determine their ability to predict clinical outcome (as measured on the Chicago Chiari Outcome Scale [CCOS]) and the resolution of syrinx at the last follow-up. The variables that were significant in the bivariate analysis were further analyzed in a multiple linear regression analysis. Different regression models were tested, and the model with the best prediction of CCOS was identified and internally validated in a subcohort of 25 patients. RESULTS There was no correlation between CCOS score and syrinx resolution (p = 0.24) at a mean ± SD follow-up of 40.29 ± 10.36 months. Multiple linear regression analysis revealed that the presence of gait instability, obex position, and the M-line-fourth ventricle vertex (FVV) distance correlated with CCOS score, while the presence of motor deficits was associated with poor syrinx resolution (p ≤ 0.05). The algorithm generated from the regression model demonstrated good diagnostic accuracy (area under curve 0.81), with a score of more than 128 points demonstrating 100% specificity for clinical improvement (CCOS score of 11 or greater). The model had excellent reliability (κ = 0.85) and was validated with fair accuracy in the validation cohort (area under the curve 0.75). CONCLUSIONS The presence of gait imbalance and motor deficits independently predict worse clinical and radiological outcomes, respectively, after decompressive surgery for CMI with altered hindbrain CSF flow. Caudal displacement of the obex and a shorter M-line-FVV distance correlated with good CCOS scores, indicating that patients with a greater degree of hindbrain pathology respond better to surgery. The proposed points-based algorithm has good predictive value for postoperative multifactorial outcome in these patients. SN - 1547-5646 UR - https://www.unboundmedicine.com/medline/citation/29125433/A_points_based_algorithm_for_prognosticating_clinical_outcome_of_Chiari_malformation_Type_I_with_syringomyelia:_results_from_a_predictive_model_analysis_of_82_surgically_managed_adult_patients_ L2 - https://thejns.org/doi/10.3171/2017.5.SPINE17264 DB - PRIME DP - Unbound Medicine ER -