Neurosurgery 90:407–418, 2022
Machine learning (ML) approaches can signiﬁcantly improve the classical Rout -based evaluation of the lumbar infusion test (LIT) and the clinical management of the normal pressure hydrocephalus.
OBJECTIVE: To develop a ML model that accurately identiﬁes patients as candidates for permanent cerebral spinal ﬂuid shunt implantation using only intracranial pressure and electrocardiogram signals recorded throughout LIT.
METHODS: This was a single-center cohort study of prospectively collected data of 96 patients who underwent LIT and 5-day external lumbar cerebral spinal ﬂuid drainage (external lumbar drainage) as a reference diagnostic method. A set of selected 48 intracranial pressure/ electrocardiogram complex signal waveform features describing nonlinear behavior, wavelet transform spectral signatures, or recurrent map patterns were calculated for each patient. After applying a leave-one-out cross-validation training–testing split of the data set, we trained and evaluated the performance of various state-of-the-art ML algorithms.
RESULTS: The highest performing ML algorithm was the eXtreme Gradient Boosting. This model showed a good calibration and discrimination on the testing data, with an area under the receiver operating characteristic curve of 0.891 (accuracy: 82.3%, sensitivity: 86.1%, and speciﬁcity: 73.9%) obtained for 8 selected features. Our ML model clearly outperforms the classical Rout based manual classiﬁcation commonly used in clinical practice with an accuracy of 62.5%.
CONCLUSION: This study successfully used the ML approach to predict the outcome of a 5-day external lumbar drainage and hence which patients are likely to beneﬁt from permanent shunt implantation. Our automated ML model thus enhances the diagnostic utility ofLIT in management.