BACKGROUND: 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 of LIT in management.

Czech Technical University Prague Czech Republic

Department of Cognitive Systems and Neurosciences Czech Institute of Informatics Robotics and Cybernetics Czech Technical University Prague Czech Republic

Department of Natural Sciences Faculty of Biomedical Engineering Czech Technical University Prague Czech Republic

Department of Neurosurgery and Neurooncology Military University Hospital 1st Faculty of Medicine Charles University Prague Prague Czech Republic

Department of Neurosurgery Motol University Hospital 2nd Faculty of Medicine Charles University Prague Prague Czech Republic

Institute of Pathological Physiology 1st Faculty of Medicine Charles University Prague Prague Czech Republic

Zobrazit více v PubMed

Andersson J, Rosell M, Kockum K, Lilja-Lund O, Söderström L, Laurell K. Prevalence of idiopathic normal pressure hydrocephalus: a prospective, population-based study. PLoS One. 2019;14(5):e0217705.

Hakim S, Adams RD. The special clinical problem of symptomatic hydrocephalus with normal cerebrospinal fluid pressure. Observations on cerebrospinal fluid hydrodynamics. J Neurol Sci. 1965;2(4):307-327.

Hashimoto M, Ishikawa M, Mori E, Kuwana N; Study of INPH on neurological improvement (SINPHONI). Diagnosis of idiopathic normal pressure hydrocephalus is supported by MRI-based scheme: a prospective cohort study. Cerebrospinal Fluid Res. 2010;7:18.

Reddy GK, Bollam P, Caldito G. Long-term outcomes of ventriculoperitoneal shunt surgery in patients with hydrocephalus. World Neurosurg. 2014;81(2):404-410.

Kiefer M, Unterberg A. The differential diagnosis and treatment of normal-pressure hydrocephalus. Dtsch Arztebl Int. 2012;109(1-2):15-26.

Czepko R, Cieslicki K. Repeated assessment of suspected normal pressure hydrocephalus in non-shunted cases. A prospective study based on the constant rate lumbar infusion test. Acta Neurochir (Wien). 2016;158(5):855-863.

Hebb AO, Cusimano MD. Idiopathic normal pressure hydrocephalus: a systematic review of diagnosis and outcome. Neurosurgery. 2001;49(5):1166-1184.

Skalický P, Mládek A, Vlasák A, De Lacy P, Beneš V, Bradáč O. Normal pressure hydrocephalus-an overview of pathophysiological mechanisms and diagnostic procedures. Neurosurg Rev. 2020;43(6):1451-1464.

Marmarou A, Bergsneider M, Klinge P, Relkin N, Black PM. The value of supplemental prognostic tests for the preoperative assessment of idiopathic normal-pressure hydrocephalus. Neurosurgery. 2005;57(3 suppl):S17-S28.

Ryding E, Kahlon B, Reinstrup P. Improved lumbar infusion test analysis for normal pressure hydrocephalus diagnosis. Brain Behav. 2018;8(11):e01125.

Kahlon B, Sundbärg G, Rehncrona S. Comparison between the lumbar infusion and CSF tap tests to predict outcome after shunt surgery in suspected normal pressure hydrocephalus. J Neurol Neurosurg Psychiatry. 2002;73(6):721-726.

Kahlon B, Sundbärg G, Rehncrona S. Lumbar infusion test in normal pressure hydrocephalus. Acta Neurol Scand. 2005;111(6):379-384.

Governale LS, Fein N, Logsdon J, Black PM. Techniques and complications of external lumbar drainage for normal pressure hydrocephalus. Neurosurgery. 2008;63:379-384.

Walchenbach R, Geiger E, Thomeer RT, Vanneste JA. The value of temporary external lumbar CSF drainage in predicting the outcome of shunting on normal pressure hydrocephalus. J Neurol Neurosurg Psychiatry. 2002;72(5):503-506.

Yasaka K, Abe O. Deep learning and artificial intelligence in radiology: current applications and future directions. PLoS Med. 2018;15(11):e1002707.

Mei J, Desrosiers C, Frasnelli J. Machine learning for the diagnosis of Parkinson’s disease: a review of literature. Front Aging Neurosci. 2021;13:633752.

Senders JT, Zaki MM, Karhade AV, et al. An introduction and overview of machine learning in neurosurgical care. Acta Neurochir (Wien). 2018;160(1):29-38.

Segato A, Marzullo A, Calimeri F, De Momi E. Artificial intelligence for brain diseases: a systematic review. APL Bioeng. 2020;4(4):041503.

Deo RC. Machine learning in medicine. Circulation. 2015;132(20):1920-1930.

Buchlak QD, Esmaili N, Leveque JC, et al. Machine learning applications to clinical decision support in neurosurgery: an artificial intelligence augmented systematic review. Neurosurg Rev. 2020;43(5):1235-1253.

Staartjes VE, Stumpo V, Kernbach JM, et al. Machine learning in neurosurgery: a global survey. Acta Neurochir (Wien). 2020;162(12):3081-3091.

Celtikci E. A systematic review on machine learning in neurosurgery: the future of decision-making in patient care. Turk Neurosurg. 2018;28(2):167-173.

Booth TC, Williams M, Luis A, Cardoso J, Ashkan K, Shuaib H. Machine learning and glioma imaging biomarkers. Clin Radiol. 2020;75(1):20-32.

Savarraj JPJ, Hergenroeder GW, Zhu L, et al. Machine learning to predict delayed cerebral ischemia and outcomes in subarachnoid hemorrhage. Neurology. 2021;96(4):e553-e562.

Rau A, Kim S, Yang S, et al. SVM-based normal pressure hydrocephalus detection. Clin Neuroradiol. 2021;31(4):1029-1035.

Shao M, Han S, Carass A, et al. Brain ventricle parcellation using a deep neural network: application to patients with ventriculomegaly. Neuroimage Clin. 2019;23:101871.

Maass F, Michalke B, Willkommen D, et al. Elemental fingerprint: reassessment of a cerebrospinal fluid biomarker for Parkinson's disease. Neurobiol Dis. 2020;134:104677.

Santamarta D, González-Martínez E, Fernández J, Mostaza A. The prediction of shunt response in idiopathic normal-pressure hydrocephalus based on intracranial pressure monitoring and lumbar infusion. Acta Neurochir Suppl. 2016;122:267-274.

Muscas G, Matteuzzi T, Becattini E, et al. Development of machine learning models to prognosticate chronic shunt-dependent hydrocephalus after aneurysmal subarachnoid hemorrhage. Acta Neurochir (Wien). 2020;162(12):3093-3105.

Klimont M, Flieger M, Rzeszutek J, Stachera J, Zakrzewska A, Jończyk-Potoczna K. Automated ventricular system segmentation in paediatric patients treated for hydrocephalus using deep learning methods. Biomed Res Int. 2019;2019:3059170.

Collins GS, Reitsma JB, Altman DG, Moons KG. Transparent reporting of a multivariable prediction model for individual Prognosis or diagnosis (TRIPOD): the TRIPOD statement. Ann Intern Med. 2015;162(1):55-63.

Luo W, Phung D, Tran T, et al. Guidelines for developing and reporting machine learning predictive models in biomedical research: a multidisciplinary view. J Med Internet Res. 2016;18(12):e323.

Relkin N, Marmarou A, Klinge P, et al. Diagnosing idiopathic normal-pressure hydrocephalus. Neurosurgery. 2005;57(3 suppl):S4-S16.

Mori E, Ishikawa M, Kato T, et al. Guidelines for management of idiopathic normal pressure hydrocephalus: second edition. Neurol Med Chir (Tokyo). 2012;52(11):775-809.

Ravdin LD, Katzen HL, Jackson AE, Tsakanikas D, Assuras S, Relkin NR. Features of gait most responsive to tap test in normal pressure hydrocephalus. Clin Neurol Neurosurg. 2008;110(5):455-461.

Boon AJ, Tans JT, Delwel EJ, et al. Dutch Normal-Pressure Hydrocephalus Study: randomized comparison of low- and medium-pressure shunts. J Neurosurg. 1998, 88(3):490-495.

Nakajima M, Yamada S, Miyajima M, et al. Guidelines for management of idiopathic normal pressure hydrocephalus (third edition): endorsed by the Japanese society of normal pressure hydrocephalus. Neurol Med Chir (Tokyo). 2021;61(2):63-97.

Craven CL, Toma AK, Mostafa T, Patel N, Watkins LD. The predictive value of DESH for shunt responsiveness in idiopathic normal pressure hydrocephalus. J Clin Neurosci. 2016;34:294-298.

Meier U, Bartels P. The importance of the intrathecal infusion test in the diagnostic of normal-pressure hydrocephalus. Eur Neurol. 2001;46(4):178-186.

Børgesen SE, Gjerris F. Relationships between intracranial pressure, ventricular size, and resistance to CSF outflow. J Neurosurg. 1987;67(4):535-539.

Kim DJ, Kim H, Kim YT, et al. Thresholds of resistance to CSF outflow in predicting shunt responsiveness. Neurol Res. 2015;37(4):332-340.

Higuchi T. Approach to an irregular time series on the basis of the fractal theory. Phys Nonlinear Phenom. 1988;31(2):277-283.

Shannon CE. A mathematical theory of communication. Bell Syst Tech J. 1948;27(3):379-423.

Dai H, Jia X, Pahren L, Lee J, Foreman B. Intracranial pressure monitoring signals after traumatic brain injury: a narrative overview and conceptual data science framework. Front Neurol. 2020;11:959.

Esteller R, Vachtsevanos G, Echauz J, Litt B. A comparison of waveform fractal dimension algorithms. IEEE. 2001;48(2):177-183.

Yang H. Multiscale recurrence quantification analysis of spatial cardiac vectorcardiogram signals. IEEE Trans Biomed Eng. 2011;58(2):339-347.

Pedregosa F, Varoquaux G, Gramfort A, et al. Scikit-learn: machine learning in Python. J Mach Learn Res. 2011;12:2825-2830.

Chotai S, Medel R, Herial NA, Medhkour A. External lumbar drain: a pragmatic test for prediction of shunt outcomes in idiopathic normal pressure hydrocephalus. Surg Neurol Int. 2014;5(1):12.

Giordan E, Palandri G, Lanzino G, Murad MH, Elder BD. Outcomes and complications of different surgical treatments for idiopathic normal pressure hydrocephalus: a systematic review and meta-analysis. J Neurosurg. 2019;131:1024-1036.

Malm J, Graff-Radford NR, Ishikawa M, et al. Influence of comorbidities in idiopathic normal pressure hydrocephalus — research and clinical care. A report of the ISHCSF task force on comorbidities in INPH. Fluids Barriers CNS. 2013;10(1):22.

Krahulik D, Vaverka M, Hrabalek L, et al. Ventriculoperitoneal shunt in treating of idiopathic normal pressure hydrocephalus-single-center study. Acta Neurochir (Wien). 2020;162(1):1-7.

Molinaro AM, Simon R, Pfeiffer RM. Prediction error estimation: a comparison of resampling methods. Bioinformatics. 2005;21(15):3301-3307.

Wong T-T. Performance evaluation of classification algorithms by k-fold and leave-one-out cross validation. Pattern Recognit. 2015;48(9):2839-2846.

Arlot S, Celisse A. A survey of cross-validation procedures for model selection. Stat Surv. 2010;4:40-79.

Review of the lumbar infusion test use in pediatric populations: state-of-the-art and future perspectives