ArticleNeurocritical care2022
Vector Angle Analysis of Multimodal Neuromonitoring Data for Continuous Prediction of Delayed Cerebral Ischemia.
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 7 papers, 2 of them syntheses that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
7 citing papers in PubMed, 2 syntheses or guidelines pooled it, 10 citations in OpenAlex.
- Machine Learning for the Early Prediction of Delayed Cerebral Ischemia in Patients With Subarachnoid Hemorrhage: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2025Pooled it
- Intracranial multimodal monitoring in neurocritical care (Neurocore-iMMM): an open, decentralized consensus.Critical care (London, England) · 2024Guideline
- Evaluating the Machine Learning Models in Predicting Intensive Care Unit Discharge for Neurosurgical Patients Undergoing Craniotomy: A Big Data Analysis.Neurocritical care · 2025Article
- Individualized autoregulation-guided arterial blood pressure management in neurocritical care.Neurotherapeutics : the journal of the American Society for Experimental NeuroTherapeutics · 2025Review
- Time-Series Modeling and Forecasting of Cerebral Pressure-Flow Physiology: A Scoping Systematic Review of the Human and Animal Literature.Sensors (Basel, Switzerland) · 2024Article
- The Role of Brain Tissue Oxygenation Monitoring in the Management of Subarachnoid Hemorrhage: A Scoping Review.Neurocritical care · 2023Article
- Optimal Cerebral Perfusion Pressure and Brain Tissue Oxygen in Aneurysmal Subarachnoid Hemorrhage.Stroke · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
14 authors at 3 institutions in 3 countries.
Funding
Abstract
backgroundDysfunctional 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.
methodsTime 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.
resultsOne 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.
conclusionsA TTSAM algorithm using multimodal neuromonitoring and cerebral autoregulation calculations shows promise to classify DCI in real time.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.