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Rubinos C, Kwon SB, Megjhani M, Terilli K, Wong B, Cespedes L, Ford J, Reyes R, Kirsch H, Alkhachroum A, Velazquez A, Roh D, Agarwal S, Claassen J, Connolly ES, Park S. Predicting Shunt Dependency from the Effect of Cerebrospinal Fluid Drainage on Ventricular Size. Neurocrit Care 2022; 37:670-677. [PMID: 35750930 PMCID: PMC9847349 DOI: 10.1007/s12028-022-01538-8] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2022] [Accepted: 05/19/2022] [Indexed: 01/21/2023]
Abstract
BACKGROUND Prolonged external ventricular drainage (EVD) in patients with subarachnoid hemorrhage (SAH) leads to morbidity, whereas early removal can have untoward effects related to recurrent hydrocephalus. A metric to help determine the optimal time for EVD removal or ventriculoperitoneal shunt (VPS) placement would be beneficial in preventing the prolonged, unnecessary use of EVD. This study aimed to identify whether dynamics of cerebrospinal fluid (CSF) biometrics can temporally predict VPS dependency after SAH. METHODS This was a retrospective analysis of a prospective, single-center, observational study of patients with aneurysmal SAH who required EVD placement for hydrocephalus. Patients were divided into VPS-dependent (VPS+) and non-VPS dependent groups. We measured the bicaudate index (BCI) on all available computed tomography scans and calculated the change over time (ΔBCI). We analyzed the relationship of ΔBCI with CSF output by using Pearson's correlation. A k-nearest neighbor model of the relationship between ΔBCI and CSF output was computed to classify VPS. RESULTS Fifty-eight patients met inclusion criteria. CSF output was significantly higher in the VPS+ group in the 7 days post EVD placement. There was a negative correlation between delta BCI and CSF output in the VPS+ group (negative delta BCI means ventricles become smaller) and a positive correlation in the VPS- group starting from days four to six after EVD placement (p < 0.05). A weighted k-nearest neighbor model for classification had a sensitivity of 0.75, a specificity of 0.70, and an area under the receiver operating characteristic curve of 0.80. CONCLUSIONS The correlation of ΔBCI and CSF output is a reliable intraindividual biometric for VPS dependency after SAH as early as days four to six after EVD placement. Our machine learning model leverages this relationship between ΔBCI and cumulative CSF output to predict VPS dependency. Early knowledge of VPS dependency could be studied to reduce EVD duration in many centers (intensive care unit length of stay).
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Affiliation(s)
- Clio Rubinos
- Department of Neurology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Department of Neurology, Columbia University, 177 Fort Washington Avenue, MHB 8 Center, Room 300, New York, NY, 10032, USA
| | - Soon Bin Kwon
- Department of Neurology, Columbia University, 177 Fort Washington Avenue, MHB 8 Center, Room 300, New York, NY, 10032, USA
- Program for Hospital and Intensive Care Informatics, Department of Neurology, Columbia University, New York, NY, USA
| | - Murad Megjhani
- Department of Neurology, Columbia University, 177 Fort Washington Avenue, MHB 8 Center, Room 300, New York, NY, 10032, USA
- Program for Hospital and Intensive Care Informatics, Department of Neurology, Columbia University, New York, NY, USA
| | - Kalijah Terilli
- Department of Neurology, Columbia University, 177 Fort Washington Avenue, MHB 8 Center, Room 300, New York, NY, 10032, USA
- Program for Hospital and Intensive Care Informatics, Department of Neurology, Columbia University, New York, NY, USA
| | - Brenda Wong
- NewYork-Presbyterian Hospital, Columbia University Irving Medical Center, New York, NY, USA
| | - Lizbeth Cespedes
- NewYork-Presbyterian Hospital, Columbia University Irving Medical Center, New York, NY, USA
| | - Jenna Ford
- Department of Neurology, Columbia University, 177 Fort Washington Avenue, MHB 8 Center, Room 300, New York, NY, 10032, USA
| | - Renz Reyes
- NewYork-Presbyterian Hospital, Columbia University Irving Medical Center, New York, NY, USA
| | - Hannah Kirsch
- Department of Neurology, Columbia University, 177 Fort Washington Avenue, MHB 8 Center, Room 300, New York, NY, 10032, USA
| | - Ayham Alkhachroum
- Department of Neurology, Columbia University, 177 Fort Washington Avenue, MHB 8 Center, Room 300, New York, NY, 10032, USA
| | - Angela Velazquez
- Department of Neurology, Columbia University, 177 Fort Washington Avenue, MHB 8 Center, Room 300, New York, NY, 10032, USA
| | - David Roh
- Department of Neurology, Columbia University, 177 Fort Washington Avenue, MHB 8 Center, Room 300, New York, NY, 10032, USA
- NewYork-Presbyterian Hospital, Columbia University Irving Medical Center, New York, NY, USA
| | - Sachin Agarwal
- Department of Neurology, Columbia University, 177 Fort Washington Avenue, MHB 8 Center, Room 300, New York, NY, 10032, USA
- NewYork-Presbyterian Hospital, Columbia University Irving Medical Center, New York, NY, USA
| | - Jan Claassen
- Department of Neurology, Columbia University, 177 Fort Washington Avenue, MHB 8 Center, Room 300, New York, NY, 10032, USA
- NewYork-Presbyterian Hospital, Columbia University Irving Medical Center, New York, NY, USA
| | - E Sander Connolly
- NewYork-Presbyterian Hospital, Columbia University Irving Medical Center, New York, NY, USA
- Department of Neurosurgery, Columbia University, New York, NY, USA
| | - Soojin Park
- Department of Neurology, Columbia University, 177 Fort Washington Avenue, MHB 8 Center, Room 300, New York, NY, 10032, USA.
- Program for Hospital and Intensive Care Informatics, Department of Neurology, Columbia University, New York, NY, USA.
- NewYork-Presbyterian Hospital, Columbia University Irving Medical Center, New York, NY, USA.
- Department of Biomedical Informatics, Columbia University, New York, NY, USA.
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