Development of a pre-operative scoring system for predicting risk of post-operative paediatric cerebellar mutism syndrome. (2nd January 2018)

• Record Type: Journal Article
• Authors: Liu, Jo-Fen; Dineen, Robert A.; Avula, Shivaram; Chambers, Tom; Dutta, Manali; Jaspan, Tim; MacArthur, Donald C.; Howarth, Simon; Soria, Daniele; Quinlan, Philip; Harave, Srikrishna; Ong, Chan Chang; Mallucci, Conor L.; Kumar, Ram; Pizer, Barry; Walker, David A.
• Is Part Of: British journal of neurosurgery. Volume 32:Number 1(2018)
• Page Start: 18
• Page End: 27
• Publication Date: 2018-01-02
• Subjects: Cerebellar mutism -- posterior fossa syndrome -- pre-operative risk assessment -- paediatric brain tumours -- posterior fossa tumours; Nervous system -- Surgery -- Periodicals; 617.48
• DOI: 10.1080/02688697.2018.1431204
• Languages: English
• ISSNs: 0268-8697

Abstract

Abstract: Background: Despite previous identification of pre-operative clinical and radiological predictors of post-operative paediatric cerebellar mutism syndrome (CMS), a unifying pre-operative risk stratification model for use during surgical consent is currently lacking. The aim of the project is to develop a simple imaging-based pre-operative risk scoring scheme to stratify patients in terms of post-operative CMS risk. Methods: Pre-operative radiological features were recorded for a retrospectively assembled cohort of 89 posterior fossa tumour patients from two major UK treatment centers (age 2–23yrs; gender 28 M, 61 F; diagnosis: 38 pilocytic astrocytoma, 32 medulloblastoma, 12 ependymoma, 1 high grade glioma, 1 pilomyxoid astrocytoma, 1 atypical teratoid rhabdoid tumour, 1 hemangioma, 1 neurilemmoma, 2 oligodendroglioma). Twenty-six (29%) developed post-operative CMS. Based upon results from univariate analysis and C4.5 decision tree, stepwise logistic regression was used to develop the optimal model and generate risk scores. Results: Univariate analysis identified five significant risk factors and C4.5 decision tree analysis identified six predictors. Variables included in the final model are MRI primary location, bilateral middle cerebellar peduncle involvement (invasion and/or compression), dentate nucleus invasion and age at imaging >12.4 years. This model has an accuracy of 88.8% (79/89). Using risk score cut-off of 203 and 238, respectively, allowed discrimination into low (38/89, predicted CMS probability <3%), intermediate (17/89, predicted CMS probability 3–52%) and high-risk (34/89, predicted CMS probability ≥52%). Conclusions: A risk stratification model for post-operative paediatric CMS could flag patients at increased or reduced risk pre-operatively which may influence strategies for surgical treatment of cerebellar tumours. Following future testing and prospective validation, this risk scoring scheme will be proposed for use during the surgical consenting process.