Prediction of Shunt Responsiveness in Suspected Patients With Normal Pressure Hydrocephalus Using the Lumbar Infusion Test: A Machine Learning Approach
| العنوان: | Prediction of Shunt Responsiveness in Suspected Patients With Normal Pressure Hydrocephalus Using the Lumbar Infusion Test: A Machine Learning Approach |
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| المؤلفون: | Arnošt Mládek, Václav Gerla, Petr Skalický, Aleš Vlasák, Awista Zazay, Lenka Lhotská, Vladimír Beneš, Ondřej Bradáč |
| المصدر: | Neurosurgery. 90:407-418 |
| بيانات النشر: | Ovid Technologies (Wolters Kluwer Health), 2022. |
| سنة النشر: | 2022 |
| مصطلحات موضوعية: | Cohort Studies, Machine Learning, Intracranial Pressure, Humans, Surgery, Neurology (clinical), Cerebrospinal Fluid Shunts, Hydrocephalus, Normal Pressure |
| الوصف: | 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.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.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.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%.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 of LIT in management. |
| تدمد: | 1524-4040 0148-396X |
| الوصول الحر: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f21b9b26ffe65fb36d86ba215eea78e6Test https://doi.org/10.1227/neu.0000000000001838Test |
| رقم الانضمام: | edsair.doi.dedup.....f21b9b26ffe65fb36d86ba215eea78e6 |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 15244040 0148396X |
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