Dineen, R.A. and Avula, S. and Chambers, T. and Dutta, M. and Liu, J.F. and Soria, D. and Quinlan, P. and MacArthur, D. and Howart, S. and Harave, S. and Ong, C. and Mallucci, C. and Kumar, R. and Pizer, B. and Walker, D.A.
Development and clinical acceptability of a pre-operative risk
stratification tool of cerebellar mutism syndrome in children with posterior fossa tumour.
In: 2016 Royal College of Paediatrics and Child Health annual conference, 26-28 April 2016, Liverpool, Great Britain.
Aims: Despite identification of numerous pre-operative cerebellar mutism syndrome (CMS) clinical and radiological predictors, a unifying pre-operative risk stratification model for use during surgical consent is currently lacking. The aims of the project are (1) to develop a simple, easy to implemented risk scoring scheme to flag patients at higher risk of post-operative CMS; and (2) to assess its clinical acceptability amongst medical professionals.
Methods: The combined cohort consists of 89 patients from two major treatment centres (age: 2-23yrs, gender 28M,61F, MRI pathology estimate 36 medulloblastoma, 40 pilocytic astrocytoma, 12 ependymoma, 1 non-committal); 26 (29%) of whom developed post-operative CMS. Post-operative CMS status was ascertained from clinical notes and pre-operative MRI scans, blinded to CMS status, underwent structured evaluation for 21 tightly-defined candidate imaging risk markers based on prior literature. All variables were first screened based upon results from univariate analysis and C4.5 decision tree. Stepwise logistic regression was then used to develop the optimal model, and multiple logistic regression coefficients for the predictors were converted into risk scores.
Results: Univariate analysis identified five significant risks and C4.5 decision tree identified six predictors. The final model (Table 1) has an accuracy of 88.8% (79/89), with a sensitivity of 96.2% (25/26) and specificity of 85.7% (54/63). Using risk score cut-offs 203 and 238 permit discrimination into low (38/89, predicted probability < 3%), intermediate (17/89, predicted probability 3–52%) and high-risk (34/89, predicted probability 52%), respectively (Figure 1). Three illustrative cases from these categories will be used to collect clinicians’ opinion on surgical treatment decision and the acceptability of using this risk stratification for decision making and surgical consenting process. A web-based voting app will be used.
Conclusions: A risk stratification model for post-operative CMS could flag patients at increased risk pre-operatively and may influence strategies for surgical treatment of cerebellar tumours. Following future testing and prospective validation, this risk scoring scheme may be utilised during the surgical consenting process.
Conference or Workshop Item
||Published in: Archives of Disease in Childhood,2016, v. 101, Suppl. 1, p. A8. doi:10.1136/archdischild-2016-310863.12
||University of Nottingham UK Campus > Faculty of Medicine and Health Sciences > School of Medicine
University of Nottingham UK Campus > Faculty of Science > School of Computer Science
||01 Jul 2016 09:00
||14 Sep 2016 03:32
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