Study objectives: The aim of this study is to generate and validate supervised machine learning algorithms to detect patients with Chiari malformation (CM) 1 or 1.5 at high risk of the development of sleep-related breathing disorders (SRBD) using clinical and neuroradiological parameters.
Methods: We prospectively included two independent datasets. A training dataset (n = 90) was used to obtain the best model, whereas a second dataset was used to validate it (n = 74). In both cohorts, the same clinical, neuroradiological, and sleep studies were carried out. We used two supervised machine learning approaches, multiple logistic regression (MLR) and the unbiased recursive partitioning technique conditional inference tree (URP-CTREE), to detect patients at high risk of SRBD. We then compared the accuracy, sensitivity, and specificity of the two prediction models.
Results: Age (odds ratio [OR] 1.1 95% confidence interval [CI] 1.05-1.17), sex (OR 0.19 95% CI 0.05-0.67), CM type (OR 4.36 95% CI 1.14-18.5), and clivus length (OR 1.14 95% CI 1.01-1.31) were the significant predictor variables for a respiratory disturbance index (RDI) cutoff that was ≥ 10 events/h using MLR. The URP-CTREE model predicted that patients with CM-1 who were age 52 years or older and males with CM-1 who were older than 29 years had a high risk of SRBD. The accuracy of predicting patients with an RDI ≥ 10 events/h was similar in the two cohorts but in the URP-CTREE model, specificity was significantly greater when compared to MLR in both study groups.
Conclusions: Both MLR and URP-CTREE predictive models are useful for the diagnosis of SRBD in patients with CM. However, URP-CTREE is easier to apply and interpret in clinical practice.
Keywords: Chiari malformation type 1; craniovertebral junction malformation; logistic regression; machine learning; magnetic resonance imaging; morphometric analysis; posterior cranial fossa; sleep apnea; sleep disorders; sleep-related breathing disorders.
© 2019 American Academy of Sleep Medicine.