Surgical treatment of Chiari syrinx: goals and outcome assessment

The technical goals of surgery for Chiari malformation Type 1 (CM1) with syringomyelia  are to reverse symptoms associated with tonsillar impaction, to eliminate the mechanism that causes syringomyelia and to arrest progression of neurological deficits. Surgery is considered ‘successful’ when there is evidence of resolution of the syrinx and its related symptoms.

Technically, this occurs by eliminating the obstruction to free, pulsatile subarachnoid CSF flow at the craniovertebral junction: a goal that is achieved by any procedure that allows for an increase in the space at the foramen magnum. Radiologically, this technical ‘success’ is reflected as restoration of CSF pathways and syrinx resolution. Assessment of clinical and functional improvement is however, multi-dimensional and complex. While surgery may have achieved the radiological goal, the desired clinical outcome may still remain incompletely addressed.

Although improvement in clinical symptoms occurs in a majority of the patients, resolution of these symptoms is often incomplete and many patients continue to experience varying degrees of disability and dissatisfaction.

Various preoperative factors have been analyzed in relation to outcomes after CMI surgery. Results from such studies have been diverse, and sometimes even conflicting. Given the present focus on value-based individualized medicine, there is a strong need for a robust outcome assessment approach in CM1. This entails moving away from gestalt evaluations and physician-centric assessments towards validated patient-centric outcome measures. Predictive analysis based algorithms would allow for objective and evidence-based preoperative counseling, enhanced patient-perceived satisfaction and in turn, improved overall outcomes after surgery. This is of added relevance in CM1 that has been identified to have psychological and cognitive connotations.

This lecture reviews the technical goals of CM1 surgery and discusses various outcome measures and predictors identified so far. It also elucidates the findings and relevance of two novel algorithms that we have developed to predict post-surgical clinical improvement.

Going forward, machine learning based algorithms that predict patient-perceived outcomes may effectively address the clinical and functional goals of CM1 surgery.