Background: The use of intracranial catheters is a common procedure used for neurosurgical patients with a variety of pathologies. Despite its frequency of use, shunt failure and revision have been reported to be a common problem. Given that depth of insertion can significantly affect the catheter tip position, a single institution retrospective chart review was performed to examine the accuracy of shunt and external ventricular drain (EVD) placement.
Methods: Computed tomography (CT) images of the head following shunt or ventriculostomy insertion were analyzed to determine the delta between the final length of the intracranial catheter and the intended depth described in the operative notes.
Results: We found that there was a statistically significant difference in the accuracy of placement when comparing EVDs to shunts. The most used EVDs at our institution are marked with a solid black line in increments spaced 2 cm apart. The most used ventricular shunt catheter has a marking at 5 cm and 10 cm from the tip of the catheter. We believe that the visual confirmation that is afforded by metric unit markings on the EVD allows for better final placement of the catheter at the outer table of the calvarium.
Conclusion: The addition of regular millimeter metric unit markings by the manufacturer is imperative in decreasing the chances of error in the insertion of ventricular catheters and preventing potential neurovascular injury to the surrounding structures.
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http://dx.doi.org/10.25259/SNI_577_2024 | DOI Listing |
Ann Neurol
January 2025
Research Unit of Neurology, Neurophysiology and Neurobiology, Department of Medicine and Surgery, Università Campus Bio-Medico di Roma, Rome, Italy.
Objective: Despite diagnostic criteria refinements, Parkinson's disease (PD) clinical diagnosis still suffers from a not satisfying accuracy, with the post-mortem examination as the gold standard for diagnosis. Seminal clinicopathological series highlighted that a relevant number of patients alive-diagnosed with idiopathic PD have an alternative post-mortem diagnosis. We evaluated the diagnostic accuracy of PD comparing the in-vivo clinical diagnosis with the post-mortem diagnosis performed through the pathological examination in 2 groups.
View Article and Find Full Text PDFJ Neurointerv Surg
January 2025
Department of Neurology, UTHealth Houston McGovern Medical School, Houston, Texas, USA
Background: Automated machine learning (ML)-based large vessel occlusion (LVO) detection algorithms have been shown to improve in-hospital workflow metrics including door-to-groin time (DTG). The degree to which care team engagement and interaction are required for these benefits remains incompletely characterized.
Methods: This analysis was conducted as a pre-planned post-hoc analysis of a randomized prospective clinical trial.
JMIR Res Protoc
January 2025
South African Medical Research Council/University of Johannesburg Pan African Centre for Epidemics Research Extramural Unit, Faculty of Health Sciences, University of Johannesburg, Johannesburg, South Africa.
Background: HIV testing is the cornerstone of HIV prevention and a pivotal step in realizing the Joint United Nations Program on HIV/AIDS (UNAIDS) goal of ending AIDS by 2030. Despite the availability of relevant survey data, there exists a research gap in using machine learning (ML) to analyze and predict HIV testing among adults in South Africa. Further investigation is needed to bridge this knowledge gap and inform evidence-based interventions to improve HIV testing.
View Article and Find Full Text PDFJAMA Intern Med
January 2025
Department of Clinical Medicine, Aarhus University, Aarhus, Denmark.
Importance: There are no validated decision rules for terminating resuscitation during in-hospital cardiac arrest. Decision rules may guide termination and prevent inappropriate early termination of resuscitation.
Objective: To develop and validate termination of resuscitation rules for in-hospital cardiac arrest.
ACS Omega
January 2025
School of Computer Science and Technology, Zhejiang Sci-Tech University, Hangzhou 310018, China.
Accurate drug-target binding affinity (DTA) prediction is crucial in drug discovery. Recently, deep learning methods for DTA prediction have made significant progress. However, there are still two challenges: (1) recent models always ignore the correlations in drug and target data in the drug/target representation process and (2) the interaction learning of drug-target pairs always is by simple concatenation, which is insufficient to explore their fusion.
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