Congenital internal carotid artery hypoplasia: A systematic review.

This review evaluates the current evidence for the clinical management of congenital internal carotid artery hypoplasia (CICAH). We summarise clinical presentations diagnostic standards, imaging recommendations, treatment and follow-up. The review was prompted by a case of CICAH in a 50-year-old female who presented to our neurosurgery clinic with an acute episode of vertigo. The patient underwent CT angiogram, which showed an unusually low right carotid bifurcation. The right internal carotid artery (ICA) was hypoplastic, and the A1 segment of the anterior cerebral artery (ACA) was absent. Skull base CT showed an ipsilateral hypoplastic carotid canal. To summarise current evidence for clinical management of CICAH we followed PRISMA guidelines to identify papers meeting our predefined inclusion criteria. We searched three databases using the terms 'ICA' and 'Hypoplasia'. We reviewed 41 papers meeting our criteria. 34 were clinical reports. We performed a data extraction and quality appraisal on these reports. We found that CICAH may be less rare than previously described. Blood pressure control in CICAH is crucial due to the increased risk of stroke and aneurysm formation. Follow-up imaging is strongly recommended. Carotid doppler sonography is a powerful and underutilised diagnostic tool, and carotid canal hypoplasia is not a pathognomic sign. In conclusion, clinicians should be alert to anatomic variations such as CICAH because these produce haemodynamic changes that may have serious clinical consequences. We recommend a central registry of patients with CICAH in order to understand the longer-term natural history of the condition.

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

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.