Murad Megjhani1,2, Kalijah Terilli1,2, Lakshman Kalasapudi3, Justine Chen1,4, John Carlson1,2, Serenity Miller5, Neeraj Badjatia3, Peter Hu5, Angela Velazquez1, David J Roh1,4, Sachin Agarwal1,4, Jan Claassen1,4, E S Connolly4,6, Xiao Hu7,8, Nicholas Morris3, Soojin Park9,10,11. 1. Department of Neurology, Columbia University Vagelos College of Physicians and Surgeons, 177 Fort Washington Ave, 8 Milstein - 300 Center, New York, NY, USA. 2. Program for Hospital and Intensive Care Informatics, Department of Neurology, Columbia University Vagelos College of Physicians and Surgeons, New York, NY, USA. 3. Department of Neurology, Program in Trauma, University of Maryland School of Medicine, Baltimore, MD, USA. 4. New York Presbyterian Hospital, Columbia University Irving Medical Center, New York, NY, USA. 5. Department of Anesthesia, Program in Trauma, University of Maryland School of Medicine, Baltimore, MD, USA. 6. Department of Neurosurgery, Columbia University, New York, NY, USA. 7. School of Nursing, Duke University, Durham, NC, USA. 8. Departments of Electrical and Computer Engineering, Biostatistics and Bioinformatics, Surgery, Neurology, Duke University, Durham, NC, USA. 9. Department of Neurology, Columbia University Vagelos College of Physicians and Surgeons, 177 Fort Washington Ave, 8 Milstein - 300 Center, New York, NY, USA. spark@columbia.edu. 10. Program for Hospital and Intensive Care Informatics, Department of Neurology, Columbia University Vagelos College of Physicians and Surgeons, New York, NY, USA. spark@columbia.edu. 11. New York Presbyterian Hospital, Columbia University Irving Medical Center, New York, NY, USA. spark@columbia.edu.
Abstract
BACKGROUND: Intracranial pressure waveform morphology reflects compliance, which can be decreased by ventriculitis. We investigated whether morphologic analysis of intracranial pressure dynamics predicts the onset of ventriculitis. METHODS: Ventriculitis was defined as culture or Gram stain positive cerebrospinal fluid, warranting treatment. We developed a pipeline to automatically isolate segments of intracranial pressure waveforms from extraventricular catheters, extract dominant pulses, and obtain morphologically similar groupings. We used a previously validated clinician-supervised active learning paradigm to identify metaclusters of triphasic, single-peak, or artifactual peaks. Metacluster distributions were concatenated with temperature and routine blood laboratory values to create feature vectors. A L2-regularized logistic regression classifier was trained to distinguish patients with ventriculitis from matched controls, and the discriminative performance using area under receiver operating characteristic curve with bootstrapping cross-validation was reported. RESULTS: Fifty-eight patients were included for analysis. Twenty-seven patients with ventriculitis from two centers were identified. Thirty-one patients with catheters but without ventriculitis were selected as matched controls based on age, sex, and primary diagnosis. There were 1590 h of segmented data, including 396,130 dominant pulses in patients with ventriculitis and 557,435 pulses in patients without ventriculitis. There were significant differences in metacluster distribution comparing before culture-positivity versus during culture-positivity (p < 0.001) and after culture-positivity (p < 0.001). The classifier demonstrated good discrimination with median area under receiver operating characteristic 0.70 (interquartile range 0.55-0.80). There were 1.5 true alerts (ventriculitis detected) for every false alert. CONCLUSIONS: Intracranial pressure waveform morphology analysis can classify ventriculitis without cerebrospinal fluid sampling.
BACKGROUND: Intracranial pressure waveform morphology reflects compliance, which can be decreased by ventriculitis. We investigated whether morphologic analysis of intracranial pressure dynamics predicts the onset of ventriculitis. METHODS: Ventriculitis was defined as culture or Gram stain positive cerebrospinal fluid, warranting treatment. We developed a pipeline to automatically isolate segments of intracranial pressure waveforms from extraventricular catheters, extract dominant pulses, and obtain morphologically similar groupings. We used a previously validated clinician-supervised active learning paradigm to identify metaclusters of triphasic, single-peak, or artifactual peaks. Metacluster distributions were concatenated with temperature and routine blood laboratory values to create feature vectors. A L2-regularized logistic regression classifier was trained to distinguish patients with ventriculitis from matched controls, and the discriminative performance using area under receiver operating characteristic curve with bootstrapping cross-validation was reported. RESULTS: Fifty-eight patients were included for analysis. Twenty-seven patients with ventriculitis from two centers were identified. Thirty-one patients with catheters but without ventriculitis were selected as matched controls based on age, sex, and primary diagnosis. There were 1590 h of segmented data, including 396,130 dominant pulses in patients with ventriculitis and 557,435 pulses in patients without ventriculitis. There were significant differences in metacluster distribution comparing before culture-positivity versus during culture-positivity (p < 0.001) and after culture-positivity (p < 0.001). The classifier demonstrated good discrimination with median area under receiver operating characteristic 0.70 (interquartile range 0.55-0.80). There were 1.5 true alerts (ventriculitis detected) for every false alert. CONCLUSIONS: Intracranial pressure waveform morphology analysis can classify ventriculitis without cerebrospinal fluid sampling.
Authors: Nils Schweingruber; Marius Marc Daniel Mader; Anton Wiehe; Frank Röder; Jennifer Göttsche; Stefan Kluge; Manfred Westphal; Patrick Czorlich; Christian Gerloff Journal: Brain Date: 2022-08-27 Impact factor: 15.255