Dynamic Intracranial Pressure Waveform Morphology Predicts Ventriculitis

Megjhani, Murad; Terilli, Kalijah; Kalasapudi, Lakshman; Chen, Justine; Carlson, John; Miller, Steven; Badjatia, Neeraj; Hu, Peter; Velázquez, Ángela; Roh, David; Agarwal, Sachin; Claassen, Jan; Connolly, E. Sander; Hu, Xiao; Morris, Nicholas; Park, Soo‐Jin

doi:10.1007/s12028-021-01303-3

Cited by 4 publications

(3 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“… 35 On the other hand the ICP signal itself can be used to predict ventriculitis with machine learning. 36 Recurrent machine learning in contrast to other machine learning methods, is better suited to learn time-dependent information since the output is fed back into the input of the model during training. 37 …”

Section: Introductionmentioning

confidence: 99%

A recurrent machine learning model predicts intracranial hypertension in neurointensive care patients

Schweingruber

Mader

Wiehe

et al. 2022

Brain

View full text Add to dashboard Cite

The evolution of intracranial pressure (ICP) of critically ill patients admitted to a neurointensive care unit (ICU) is difficult to predict. Besides the underlying disease and compromised intracranial space, ICP is affected by a multitude of factors, many of which are monitored on the ICU, but the complexity of the resulting patterns limits their clinical use. This paves the way for new machine learning (ML) techniques to assist clinical management of patients undergoing invasive ICP monitoring independent of the underlying disease. An institutional cohort (ICP-ICU) of patients with invasive ICP monitoring (n = 1346) was used to train recurrent ML models to predict the occurrence of ICP increases of ≥ 22mmHg over a long (> 2 hours) time period in the upcoming hours. External validation was performed on patients undergoing invasive ICP measurement in two publicly available datasets (Medical Information Mart for Intensive Care (MIMIC, n = 998) and eICU Collaborative Research Database (eICU, n = 1634)). Different distances (1h-24 h) between prediction time point and upcoming critical phase were evaluated, demonstrating a decrease in performance but still robust AUC-ROC with larger distances (24 h AUC-ROC: ICP-ICU 0.826 ± 0.0071, MIMIC 0.836 ± 0.0063, eICU 0.779 ± 0.0046, 1 h AUC-ROC: ICP-ICU 0.982 ± 0.0008, MIMIC 0.965 ± 0.0010, eICU 0.941 ± 0.0025). The model operates on sparse hourly data and is stable in handling variable input lengths and missingness through its nature of recurrence and internal memory. Calculation of gradient-based feature importance revealed individual underlying decisions for our Long Short Time Memory (LSTM) based model and thereby provided improved clinical interpretability. Recurrent ML models have the potential to be an effective tool for the prediction of ICP increases with high translational potential.

show abstract

Section: Introductionmentioning

confidence: 99%

A recurrent machine learning model predicts intracranial hypertension in neurointensive care patients

Schweingruber

Mader

Wiehe

et al. 2022

Brain

View full text Add to dashboard Cite

show abstract

“…However, changes in the position of the ventricular catheter may reduce the accuracy of ICP measurement. In addition, placement of ventricular catheters can lead to severe bleeding and complications of infection (ventriculitis and meningitis) ( Megjhani et al, 2022 ). The second commonly used device is the intraparenchymal transducer.…”

Section: Introductionmentioning

confidence: 99%

A preliminary study on the application of electrical impedance tomography based on cerebral perfusion monitoring to intracranial pressure changes

Yan,

Wang,

et al. 2024

Front. Neurosci.

View full text Add to dashboard Cite

BackgroundIn intracranial pathologic conditions of intracranial pressure (ICP) disturbance or hemodynamic instability, maintaining appropriate ICP may reduce the risk of ischemic brain injury. The change of ICP is often accompanied by the change of intracranial blood status. As a non-invasive functional imaging technique, the sensitivity of electrical impedance tomography (EIT) to cerebral hemodynamic changes has been preliminarily confirmed. However, no team has conducted a feasibility study on the dynamic detection of ICP by EIT technology from the perspective of non-invasive whole-brain blood perfusion monitoring. In this study, human brain EIT image sequence was obtained by in vivo measurement, from which a variety of indicators that can reflect the tidal changes of the whole brain impedance were extracted, in order to establish a new method for non-invasive monitoring of ICP changes from the level of cerebral blood perfusion monitoring.MethodsValsalva maneuver (VM) was used to temporarily change the cerebral blood perfusion status of volunteers. The electrical impedance information of the brain during this process was continuously monitored by EIT device and real-time imaging was performed, and the hemodynamic indexes of bilateral middle cerebral arteries were monitored by transcranial Doppler (TCD). The changes in monitoring information obtained by the two techniques were compared and observed.ResultsThe EIT imaging results indicated that the image sequence showed obvious tidal changes with the heart beating. Perfusion indicators of vascular pulsation obtained from EIT images decreased significantly during the stabilization phase of the intervention (PAC: 242.94 ± 100.83, p < 0.01); perfusion index which reflects vascular resistance increased significantly in the stable stage of intervention (PDT: 79.72 ± 18.23, p < 0.001). After the intervention, the parameters gradually returned to the baseline level before compression. The changes of EIT indexes in the whole process are consistent with the changes of middle cerebral artery velocity related indexes shown in TCD results.ConclusionThe EIT image combined with the blood perfusion index proposed in this paper can reflect the decrease of cerebral blood flow under the condition of increased ICP in real time and intuitively. With the advantages of high time resolution and high sensitivity, EIT provides a new idea for non-invasive bedside measurement of ICP.

show abstract

“…As a result, investigators seek to process the ICP waveform to engineer informative features that can be presented in real-time for decision support. In prior work, we sought to leverage these changes in ICP waveform morphology to detect ventriculitis (Megjhani et al 2022), but found that there were no automated techniques that could identify the segments of ICP waveform data seamlessly when EVD is clamped. Manual segmentation of 1590 h of ICP data from 18 648 monitoring hours would be prohibitive and would not result in a useful discovery if implementation is desired.…”

Section: Introductionmentioning

confidence: 99%

Automatic identification of intracranial pressure waveform during external ventricular drainage clamping: segmentation via wavelet analysis

et al. 2023

Self Cite

View full text Add to dashboard Cite

Objective: The objective of this study is to develop and validate a method for automatically identifying segments of intracranial pressure (ICP) waveform data from external ventricular drainage (EVD) recordings during intermittent drainage and closure.Methods: The proposed method uses time-frequency analysis through wavelets to distinguish periods of ICP waveform in EVD data. By comparing the frequency compositions of the ICP signals (when the EVD system is clamped) and the artifacts (when the system is open), the algorithm can detect short, uninterrupted segments of ICP waveform from the longer periods of non-measurement data. The method involves applying a wavelet transform, calculating the absolute power in a specific range, using Otsu thresholding to automatically identify a threshold, and performing a morphological operation to remove small segments. Two investigators manually graded the same randomly selected one-hour segments of the resulting processed data. Performance metrics were calculated as a percentage. Results: The study analyzed data from 229 patients who had EVD placed following subarachnoid hemorrhage between June 2006 and December 2012. Of these, 155 (67.7%) were female and 62 (27%) developed delayed cerebral ischemia. A total of 45,150 hours of data were segmented. 2044 one-hour segments were randomly selected and evaluated by two investigators (MM and DN). Of those, the evaluators agreed on the classification of 1,556 one-hour segments. The algorithm was able to correctly identify 86% (1338 hours) of ICP waveform data. 8.2%(128 hours) of the time the algorithm either partially or fully failed to segment the ICP waveform. 5.4 % (84 hours) of data, artifacts were mistakenly identified as ICP waveforms (false positives). Conclusion: The proposed algorithm automates the identification of valid ICP waveform segments of waveform in EVD data and thus enables the inclusion in real-time data analysis for decision support. It also standardizes and makes research data management more efficient.

show abstract

Dynamic Intracranial Pressure Waveform Morphology Predicts Ventriculitis

Cited by 4 publications

References 28 publications

A recurrent machine learning model predicts intracranial hypertension in neurointensive care patients

A recurrent machine learning model predicts intracranial hypertension in neurointensive care patients

A preliminary study on the application of electrical impedance tomography based on cerebral perfusion monitoring to intracranial pressure changes

Automatic identification of intracranial pressure waveform during external ventricular drainage clamping: segmentation via wavelet analysis

Contact Info

Product

Resources

About