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Vector Angle Analysis of Multimodal Neuromonitoring Data for Continuous Prediction of Delayed Cerebral Ischemia

  • Big Data in Neurocritical Care
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Abstract

Background

Dysfunctional cerebral autoregulation often precedes delayed cerebral ischemia (DCI). Currently, there are no data-driven techniques that leverage this information to predict DCI in real time. Our hypothesis is that information using continuous updated analyses of multimodal neuromonitoring and cerebral autoregulation can be deployed to predict DCI.

Methods

Time series values of intracranial pressure, brain tissue oxygenation, cerebral perfusion pressure (CPP), optimal CPP (CPPOpt), ΔCPP (CPP − CPPOpt), mean arterial pressure, and pressure reactivity index were combined and summarized as vectors. A validated temporal signal angle measurement was modified into a classification algorithm that incorporates hourly data. The time-varying temporal signal angle measurement (TTSAM) algorithm classifies DCI at varying time points by vectorizing and computing the angle between the test and reference time signals. The patient is classified as DCI+ if the error between the time-varying test vector and DCI+ reference vector is smaller than that between the time-varying test vector and DCI− reference vector. Finally, prediction at time point t is calculated as the majority voting over all the available signals. The leave-one-patient-out cross-validation technique was used to train and report the performance of the algorithms. The TTSAM and classifier performance was determined by balanced accuracy, F1 score, true positive, true negative, false positive, and false negative over time.

Results

One hundred thirty-one patients with aneurysmal subarachnoid hemorrhage who underwent multimodal neuromonitoring were identified from two centers (Columbia University: 52 [39.7%], Aachen University: 79 [60.3%]) and included in the analysis. Sixty-four (48.5%) patients had DCI, and DCI was diagnosed 7.2 ± 3.3 days after hemorrhage. The TTSAM algorithm achieved a balanced accuracy of 67.3% and an F1 score of 0.68 at 165 h (6.9 days) from bleed day with a true positive of 0.83, false positive of 0.16, true negative of 0.51, and false negative of 0.49.

Conclusions

A TTSAM algorithm using multimodal neuromonitoring and cerebral autoregulation calculations shows promise to classify DCI in real time.

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Funding

This study was funded by the National Institutes of Health [Grants R21NS113055 (SP), K01-ES026833 (SP)] and the American Heart Association [Grant 20POST35210653 (MM)].

Author information

Authors and Affiliations

  1. Department of Neurology, Columbia University, 177 Fort Washington Ave, 8 Milstein – 300 Center, New York, NY, USA

    Murad Megjhani, Soon Bin Kwon, Jenna Ford, Daniel Nametz, Angela Velazquez, David Roh, Sachin Agarwal, Jan Claassen & Soojin Park

  2. Program for Hospital and Intensive Care Informatics, Department of Neurology, Columbia University, New York, NY, USA

    Murad Megjhani, Soon Bin Kwon, Jenna Ford, Daniel Nametz, Nick Kastenholz, Hart Fogel & Soojin Park

  3. Department of Neurosurgery, RWTH Aachen University, Aachen, Germany

    Miriam Weiss, Nick Kastenholz & Gerrit A. Schubert

  4. Department of Neurosurgery, Kantonsspital Aarau AG, Aarau, Switzerland

    Miriam Weiss & Gerrit A. Schubert

  5. NewYork-Presbyterian Hospital, Columbia University Irving Medical Center, New York, NY, USA

    David Roh, Sachin Agarwal, E. Sander Connolly Jr., Jan Claassen & Soojin Park

  6. Department of Neurosurgery, Columbia University, New York, NY, USA

    E. Sander Connolly Jr.

  7. Department of Biomedical Informatics, Columbia University, New York, NY, USA

    Soojin Park

Authors
  1. Murad Megjhani
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  2. Miriam Weiss
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  3. Soon Bin Kwon
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  4. Jenna Ford
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  5. Daniel Nametz
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  6. Nick Kastenholz
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  7. Hart Fogel
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  8. Angela Velazquez
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  9. David Roh
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  10. Sachin Agarwal
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  11. E. Sander Connolly Jr.
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  12. Jan Claassen
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  13. Gerrit A. Schubert
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  14. Soojin Park
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Contributions

Data collection: MW, JF, DN, NK, HF, AV, DR, SA, ESC, JC, GS, analysis: MM, MW, SK GS, SP, writing: MM, SP, MW, editing: all.

Corresponding author

Correspondence to Soojin Park.

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The study was approved by the institutional review boards at the respective centers.

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Megjhani, M., Weiss, M., Kwon, S.B. et al. Vector Angle Analysis of Multimodal Neuromonitoring Data for Continuous Prediction of Delayed Cerebral Ischemia. Neurocrit Care 37 (Suppl 2), 230–236 (2022). https://doi.org/10.1007/s12028-022-01481-8

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  • DOI: https://doi.org/10.1007/s12028-022-01481-8

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