Use of a machine learning algorithm with a focus on spinopelvic parameters to predict development of symptomatic
Maria A Punchak1, Kamila M Bond1, Connor A Wathen1
11Department of Neurosurgery, University of Pennsylvania Health System, Philadelphia, Pennsylvania; and.
Objective:
Among patients with a history of prior lipomyelomeningocele repair, an association between increased lumbosacral angle (LSA) and cord retethering has been described. The authors sought to build a predictive algorithm to determine which complex tethered cord patients will develop the symptoms of spinal cord retethering after initial surgical repair with a focus on spinopelvic parameters.
Methods:
An electronic medical record database was reviewed to identify patients with complex tethered cord (e.g., lipomyelomeningocele, lipomyeloschisis, myelocystocele) who underwent detethering before 12 months of age between January 1, 2008, and June 30, 2022. Descriptive statistics were used to characterize the patient population. The Caret package in R was used to develop a machine learning model that predicted symptom development by using spinopelvic parameters.
Results:
A total of 72 patients were identified (28/72 [38.9%] were male). The most commonly observed dysraphism was lipomyelomeningocele (41/72 [56.9%]). The mean ± SD age at index MRI was 2.1 ± 2.2 months, at which time 87.5% of patients (63/72) were asymptomatic. The mean ± SD lumbar lordosis at the time of index MRI was 23.8° ± 11.1°, LSA was 36.5° ± 12.3°, sacral inclination was 30.4° ± 11.3°, and sacral slope was 23.0° ± 10.5°. Overall, 39.6% (25/63) of previously asymptomatic patients developed new symptoms during the mean ± SD follow-up period of 44.9 ± 47.2 months. In the recursive partitioning model, patients whose LSA increased at a rate ≥ 5.84°/year remained asymptomatic, whereas those with slower rates of LSA change experienced neurological decline (sensitivity 77.5%, specificity 84.9%, positive predictive value 88.9%, and negative predictive value 70.9%).
Conclusions:
This is the first study to build a machine learning algorithm to predict symptom development of spinal cord retethering after initial surgical repair. The authors found that, after initial surgery, patients who demonstrate a slower rate of LSA change per year may be at risk of developing neurological symptoms.
