Phase-contrast magnetic resonance imaging-based predictive modelling for surgical outcomes in patients with Chiari...

Prospective outcome prediction plays a crucial role in guiding preoperative decision-making in patients with Chiari malformation type I (CM-Ⅰ) with syringomyelia. Here, we aimed to develop a predictive model for postoperative outcomes in patients with CM-Ⅰ with syringomyelia by integrating clinical and radiological parameters.

We retrospectively analysed the data of 151 adult patients diagnosed with CM-I with syringomyelia who underwent posterior fossa decompression surgery. Clinical outcomes were assessed using the Chicago Chiari Outcome Scale (CCOS). Predictors were investigated using bivariate and multiple linear regression analyses. Five factors were used to build seven independent machine learning (ML) models: Cat Boost classifier (CatBoost), random forest, light gradient boosting machine, decision tree classifier, logistic regression, K neighbours classifier, and support vector machine. The dataset was randomly divided into training (n = 121, 80%) and test (n = 30, 20%) sets. Model performance was evaluated using precision, recall, F-1 score, and area under the curve (AUC). Shapley additive explanations (SHAP) was used to interpret the feature significance.

The best independent model was the CatBoost model, with an AUC of 0.9583 and an accuracy of 0.9097. The cross-validation results indicated that the accuracy of the CatBoost model was 0.8667. The SHAP plot revealed the important ranking of the features affecting the CCOS score as syrinx diameter, preoperative symptom duration, gait instability, peak diastolic velocity at the foramen magnum, and age.

We successfully developed a model to predict the prognosis of patients with CM-Ⅰ with syringomyelia after posterior fossa decompression.