Method for Prediction of Acute Hypotensive Episodes

Hypotension is type of secondary insult and it is related to poor outcome. The ability to predict adverse hypotensive events, where a patient's arterial blood pressure drops to abnormally low levels, would be of major benefit to the fields of primary and secondary health care. The aim of the paper is to present the novel method for predicting of acute hypotensive episodes, based on ECG analysis by the complex system theory approach. 45 patients (in four neurointensive care facilities throughout Europe) data were selected for the analysis. 11 patients had EUSIG-defined hypotensive events. The method includes determining of time varying biomarkers corresponding to plurality of physiological processes in patient's organism as a non-linear dynamic complex system and generating an acute hypotension prediction classifier. The calculations of biomarkers are based on complex system approach and algebraic matrix analysis of ECG parameters. The classifier is based on the comparison of biomarkers behaviour in 3D images. It is demonstrated that the presented method allows us to predict arterial hypotension events 40-50 minutes ahead with a sensitivity of 81 %, specificity 94 %. This result was obtained from prospective real-time data collection in a live clinical intensive care environment

Intraorbital pressure-volume characteristics in a piglet model: In vivo pilot study.

Intracranial pressure measurement is frequently used for diagnosis in neurocritical care but cannot always accurately predict neurological deterioration. Intracranial compliance plays a significant role in maintaining cerebral blood flow, cerebral perfusion pressure, and intracranial pressure. This study's objective was to investigate the feasibility of transferring external pressure into the eye orbit in a large-animal model while maintaining a clinically acceptable pressure gradient between intraorbital and external pressures. The experimental system comprised a specifically designed pressure applicator that can be placed and tightly fastened onto the eye. A pressure chamber made from thin, elastic, non-allergenic film was attached to the lower part of the applicator and placed in contact with the eyelid and surrounding tissues of piglets' eyeballs. External pressure was increased from 0 to 20 mmHg with steps of 1 mmHg, from 20 to 30 mmHg with steps of 2 mmHg, and from 30 to 50 mmHg with steps of 5 mmHg. An invasive pressure sensor was used to measure intraorbital pressure directly. An equation was derived from measured intraorbital and external pressures (intraorbital pressure = 0.82 × external pressure + 3.12) and demonstrated that external pressure can be linearly transferred to orbit tissues with a bias (systematic error) of 3.12 mmHg. This is close to the initial intraorbital pressure within the range of pressures tested. We determined the relationship between intraorbital compliance and externally applied pressure. Our findings indicate that intraorbital compliance can be controlled across a wide range of 1.55 to 0.15 ml/mmHg. We observed that external pressure transfer into the orbit can be achieved while maintaining a clinically acceptable pressure gradient between intraorbital and external pressures

Feasibility of the optimal cerebral perfusion pressure value identification without a delay that is too long

Optimal cerebral perfusion pressure (CPPopt)-targeted treatment of traumatic brain injury (TBI) patients requires 2-8 h multi-modal monitoring data accumulation to identify CPPopt value for individual patient. Minimizing the time required for monitoring data accumulation is needed to improve the efficacy of CPPopt-targeted therapy. A retrospective analysis of multimodal physiological monitoring data from 87 severe TBI patients was performed by separately representing cerebrovascular autoregulation (CA) indices in relation to CPP, arterial blood pressure (ABP), and intracranial pressure (ICP) to improve the existing CPPopt identification algorithms. Machine learning (ML)-based algorithms were developed for automatic identification of informative data segments that were used for reliable CPPopt, ABPopt, ICPopt and the lower/upper limits of CA (LLCA/ULCA) identification. The reference datasets of the informative data segments and, artifact-distorted segments, and the datasets of different clinical situations were used for training the ML-based algorithms, allowing us to choose the appropriate individualized CPP-, ABP- or ICP-guided management for 79% of the full monitoring time for the studied population. The developed ML-based algorithms allow us to recognize informative physiological ABP/ICP variations within 24 min intervals with an accuracy up to 79% (compared to the initial accuracy of 74%) and use these segments for timely optimal value identification or CA limits determination in CPP, ABP or ICP data. Prospective clinical studies are needed to prove the efficiency of the developed algorithms

Typical heart rate variation.

<p>(A) A heart rate variation in a healthy volunteer. (B) A pressure increase of 4 mmHg per pressure Pe(t) step (time period of approximately 30 sec each) was used on the ocular globe from 0 mmHg to 48 mmHg.</p

Example spectrograms of pulse waves of blood flow in the ophthalmic artery collected by a transcranial Doppler using different external pressure steps.

<p>(A) 0 mmHg; (B) 48 mmHg. HR—heart rate.</p

Schematic representation of the non-invasive intracranial pressure (ICP) measurement equipment Vittamed 205.

<p>(A) Relevant orbit and brain anatomy in contact with the ICP measurement device. (B) Block diagram of the system control unit. ICA—internal carotid artery; IOA—intracranial part of the ophthalmic artery; EOA—extracranial part of the ophthalmic artery; TCD—transcranial Doppler; Pe—external pressure applied to the ocular globe.</p

Histogram of HR_diff (parameter for the detection of the oculocardiac reflex) for subjects from all groups and 870 external pressure steps applied during all of the non-invasive intracranial pressure measurements.

<p>Histogram of HR_diff (parameter for the detection of the oculocardiac reflex) for subjects from all groups and 870 external pressure steps applied during all of the non-invasive intracranial pressure measurements.</p

Heart rate (HR) variation at every external pressure step (Pe) applied on the ocular globe in the case of a 10% decrease in the MHR compared to the BHR.

<p>(A) Healthy subject. (B) Glaucoma patient. (C) Healthy subject. (D) Healthy subject.</p

Protocol parameters for the non-invasive intracranial pressure measurement according to group.

<p>Protocol parameters for the non-invasive intracranial pressure measurement according to group.</p

Demographic data of the included subjects in this retrospective study.

<p>Demographic data of the included subjects in this retrospective study.</p