OBJECTIVE : The 19-item Epilepsy Surgery Satisfaction Questionnaire (ESSQ-19) is a validated and reliable post hoc means of assessing patient satisfaction with epilepsy surgery. Prediction models building on these data can be used to counsel patients.
METHODS : The ESSQ-19 was derived and validated on 229 patients recruited from Canada and Sweden. We isolated 201 (88%) patients with complete clinical data for this analysis. These patients were adults (≥18 years old) who underwent epilepsy surgery 1 year or more prior to answering the questionnaire. We extracted each patient's ESSQ-19 score (scale is 0-100; 100 represents complete satisfaction) and relevant clinical variables that were standardized prior to the analysis. We used machine learning (linear kernel support vector regression [SVR]) to predict satisfaction and assessed performance using the R2 calculated following threefold cross-validation. Model parameters were ranked to infer the importance of each clinical variable to overall satisfaction with epilepsy surgery.
RESULTS : Median age was 41 years (interquartile range [IQR] = 32-53), and 116 (57%) were female. Median ESSQ-19 global score was 68 (IQR = 59-75), and median time from surgery was 5.4 years (IQR = 2.0-8.9). Linear kernel SVR performed well following threefold cross-validation, with an R2 of .44 (95% confidence interval = .36-.52). Increasing satisfaction was associated with postoperative self-perceived quality of life, seizure freedom, and reductions in antiseizure medications. Self-perceived epilepsy disability, age, and increasing frequency of seizures that impair awareness were associated with reduced satisfaction.
SIGNIFICANCE : Machine learning applied postoperatively to the ESSQ-19 can be used to predict surgical satisfaction. This algorithm, once externally validated, can be used in clinical settings by fixing immutable clinical characteristics and adjusting hypothesized postoperative variables, to counsel patients at an individual level on how satisfied they will be with differing surgical outcomes.
Josephson Colin B, Engbers Jordan D T, Sajobi Tolulope T, Wahby Sandra, Lawal Oluwaseyi A, Keezer Mark R, Nguyen Dang K, Malmgren Kristina, Atkinson Mark J, Hader Walter J, Macrodimitris Sophia, Patten Scott B, Pillay Neelan, Sharma Ruby, Singh Shaily, Starreveld Yves, Wiebe Samuel
epilepsy surgery, machine learning, patient satisfaction, patient-reported outcomes, questionnaire