Dynamic Intracranial Pressure Waveform Morphology Predicts Ventriculitis

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.

Keywords: Clustering; External ventricular drainage; ICP waveform; Machine learning; Neurocritical care; Ventriculitis.

Figure 1:

Overview of the approach.

Figure 2:

Steps for the bag-of-words (BoW) feature representation and constructing classifiers using BoW features for ventriculitis.

Figure 3:

Illustrates the changes in the distribution of the ICP waveforms. (A) Distribution of meta-clusters (green: triphasic; yellow: single-peak; red: artifacts) in control vs before, during, after the culture or Gram-stain positive stage of ventriculitis. (B) Displays 311 leaf nodes, green color indicates triphasic, yellow single-peak and red artifacts.

Figure 4:

Illustrates the performance of the machine learning model. (A) Performance of the logistic regression model, median Area under the receiver operating characteristic curve. (B) Confusion matrix.