ArticleNeurocritical care2022
Dynamic Intracranial Pressure Waveform Morphology Predicts Ventriculitis.
Article in Neurocritical care, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed, 8 citations in OpenAlex.
- Review
- Classification of intracranial pressure epochs using a novel machine learning framework.NPJ digital medicine · 2025Article
- Machine learning approach for noninvasive intracranial pressure estimation using pulsatile cranial expansion waveforms.NPJ digital medicine · 2025Article
- A preliminary study on the application of electrical impedance tomography based on cerebral perfusion monitoring to intracranial pressure changes.Frontiers in neuroscience · 2024Article
- Automatic identification of intracranial pressure waveform during external ventricular drainage clamping: segmentation via wavelet analysis.Physiological measurement · 2023Article
- A recurrent machine learning model predicts intracranial hypertension in neurointensive care patients.Brain : a journal of neurology · 2022Article
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Authors and funding
16 authors at 5 institutions in 1 country.
Funding
Abstract
backgroundIntracranial 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.
methodsVentriculitis 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.
resultsFifty-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.
conclusionsIntracranial pressure waveform morphology analysis can classify ventriculitis without cerebrospinal fluid sampling.
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