Tag: Lumbar infusion test

Posted on

The diagnostic value of the lumbar infusion test to predict symptomatic improvement after shunting for normal pressure hydrocephalus. A meta‑analysis

Acta Neurochirurgica (2025) 167:180

This meta-analysis found that the lumbar infusion test (LIT) has poor negative predictive value for predicting non-response to shunting in normal pressure hydrocephalus. LIT cannot reliably exclude patients from shunt surgery and should not be used for this purpose in its current form.

• The lumbar infusion test (LIT) is used to predict which normal pressure hydrocephalus (NPH) patients will benefit from CSF shunting.

• This meta-analysis pooled sensitivity, specificity, and negative predictive value (NPV) of LIT from 10 studies.

• Sensitivity of LIT (using Rout) was moderate (77–82%) at lower cutoffs (12–14 mmHg/ml/min), but specificity was low (34–37%).

• Higher Rout cutoff (18 mmHg/ml/min) increased specificity (78%) but decreased sensitivity (37%).

• NPV was consistently low for all cutoffs (≤33%), meaning LIT is poor at ruling out shunt responders.

• No other LIT parameters (e.g., plateau pressure, CSF amplitude) showed better diagnostic value.

• The test cannot be used to exclude patients from shunt implantation due to high false negative rates.

• Future research should focus on improving the negative predictive value of LIT for NPH.

Posted on

Prediction of Shunt Responsiveness in Suspected Patients With Normal Pressure Hydrocephalus Using the Lumbar Infusion Test: A Machine Learning Approach

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.

Proudly powered by WordPress