Dataset Information

Support Vector Machine Based Monitoring of Cardio-Cerebrovascular Reserve during Simulated Hemorrhage.

ABSTRACT: Introduction: In the initial phase of hypovolemic shock, mean blood pressure (BP) is maintained by sympathetically mediated vasoconstriction rendering BP monitoring insensitive to detect blood loss early. Late detection can result in reduced tissue oxygenation and eventually cellular death. We hypothesized that a machine learning algorithm that interprets currently used and new hemodynamic parameters could facilitate in the detection of impending hypovolemic shock. Method: In 42 (27 female) young [mean (sd): 24 (4) years], healthy subjects central blood volume (CBV) was progressively reduced by application of -50 mmHg lower body negative pressure until the onset of pre-syncope. A support vector machine was trained to classify samples into normovolemia (class 0), initial phase of CBV reduction (class 1) or advanced CBV reduction (class 2). Nine models making use of different features were computed to compare sensitivity and specificity of different non-invasive hemodynamic derived signals. Model features included: volumetric hemodynamic parameters (stroke volume and cardiac output), BP curve dynamics, near-infrared spectroscopy determined cortical brain oxygenation, end-tidal carbon dioxide pressure, thoracic bio-impedance, and middle cerebral artery transcranial Doppler (TCD) blood flow velocity. Model performance was tested by quantifying the predictions with three methods: sensitivity and specificity, absolute error, and quantification of the log odds ratio of class 2 vs. class 0 probability estimates. Results: The combination with maximal sensitivity and specificity for classes 1 and 2 was found for the model comprising volumetric features (class 1: 0.73-0.98 and class 2: 0.56-0.96). Overall lowest model error was found for the models comprising TCD curve hemodynamics. Using probability estimates the best combination of sensitivity for class 1 (0.67) and specificity (0.87) was found for the model that contained the TCD cerebral blood flow velocity derived pulse height. The highest combination for class 2 was found for the model with the volumetric features (0.72 and 0.91). Conclusion: The most sensitive models for the detection of advanced CBV reduction comprised data that describe features from volumetric parameters and from cerebral blood flow velocity hemodynamics. In a validated model of hemorrhage in humans these parameters provide the best indication of the progression of central hypovolemia.

SUBMITTER: van der Ster BJP

PROVIDER: S-EPMC5761201 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature

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Support Vector Machine Based Monitoring of Cardio-Cerebrovascular Reserve during Simulated Hemorrhage.

van der Ster Björn J P BJP Bennis Frank C FC Delhaas Tammo T Westerhof Berend E BE Stok Wim J WJ van Lieshout Johannes J JJ

Frontiers in physiology 20180105

<b>Introduction:</b> In the initial phase of hypovolemic shock, mean blood pressure (BP) is maintained by sympathetically mediated vasoconstriction rendering BP monitoring insensitive to detect blood loss early. Late detection can result in reduced tissue oxygenation and eventually cellular death. We hypothesized that a machine learning algorithm that interprets currently used and new hemodynamic parameters could facilitate in the detection of impending hypovolemic shock. <b>Method:</b> In 42 (2 ...[more]

PMID: 29354062

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Project description:Background and purposeLoss of hemodynamic reserve in intracranial cerebrovascular disease reduces blood oxygenation level-dependent activation by fMRI and increases asymmetry in MTT measured by provocative DSC perfusion MR imaging before and after vasodilation with intravenous acetazolamide. The concordance for detecting hemodynamic reserve integrity has been compared.Materials and methodsPatients (n = 40) with intracranial cerebrovascular disease and technically adequate DSA, fMRI and provocative DSC perfusion studies were retrospectively grouped into single vessels proximal to and distal from the circle of Willis, multiple vessels, and Moyamoya disease. The vascular territories were classified as having compromised hemodynamic reserve if the expected fMRI blood oxygenation level-dependent activation was absent or if MTT showed increased asymmetry following vasodilation. Concordance was examined in compromised and uncompromised vascular territories of each group with the Fischer exact test and proportions of agreement.ResultsExtensive leptomeningeal collateral circulation was present in all cases. Decreased concordance between the methods was found in vascular territories with stenosis distal to but not proximal to the circle of Willis. Multivessel and Moyamoya diseases also showed low concordance. A model of multiple temporally displaced arterial inputs from leptomeningeal collateral flow demonstrated that the resultant lengthening MTT mimicked compromised hemodynamic reserve despite being sufficient to support blood oxygenation level-dependent contrast.ConclusionsDecreased concordance between the 2 methods for assessment of hemodynamic reserve for vascular disease distal to the circle of Willis is posited to be due to well-developed leptomeningeal collateral circulation providing multiple temporally displaced arterial input functions that bias the perfusion analysis toward hemodynamic reserve compromise while blood oxygenation level-dependent activation remains detectable.

Dataset Information

Support Vector Machine Based Monitoring of Cardio-Cerebrovascular Reserve during Simulated Hemorrhage.

Publications

Support Vector Machine Based Monitoring of Cardio-Cerebrovascular Reserve during Simulated Hemorrhage.

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