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Gritti P, Bonfanti M, Zangari R, Farina A, Longhi L, Rasulo FA, Bertuetti R, Biroli A, Biroli F, Lorini FL. Evaluation and Application of Ultra-Low-Resolution Pressure Reactivity Index in Moderate or Severe Traumatic Brain Injury. J Neurosurg Anesthesiol 2023; 35:313-321. [PMID: 35499152 DOI: 10.1097/ana.0000000000000847] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2021] [Accepted: 03/24/2022] [Indexed: 11/26/2022]
Abstract
BACKGROUND The pressure reactivity index (PRx) has emerged as a surrogate method for the continuous bedside estimation of cerebral autoregulation and a predictor of unfavorable outcome after traumatic brain injury (TBI). However, calculation of PRx require continuous high-resolution monitoring currently limited to specialized intensive care units. The aim of this study was to evaluate a new index, the ultra-low-frequency PRx (UL-PRx) sampled at ∼0.0033 Hz at ∼5 minutes periods, and to investigate its association with outcome. METHODS Demographic data, admission Glasgow coma scale, in-hospital mortality and Glasgow outcome scale extended at 12 months were extracted from electronic records. The filtering and preparation of time series of intracranial pressure (ICP), mean arterial pressure and cerebral perfusion pressure (CPP), and calculation of the indices (UL-PRx, Δ-optimal CPP), were performed in MATLAB using an in-house algorithm. RESULTS A total of 164 TBI patients were included in the study; in-hospital and 12-month mortality was 29.3% and 38.4%, respectively, and 64% of patients had poor neurological outcome at 12 months. On univariate analysis, ICP, CPP, UL-PRx, and ΔCPPopt were associated with 12-month mortality. After adjusting for age, Glasgow coma scale, ICP and CPP, mean UL-PRx and UL-PRx thresholds of 0 and +0.25 remained associated with 12-month mortality. Similar findings were obtained for in-hospital mortality. For mean UL-PRx, the area under the receiver operating characteristic curves for in-hospital and 12-month mortality were 0.78 (95% confidence interval [CI]: 0.69-0.87; P <0.001) and 0.70 (95% CI: 0.61-0.79; P <0.001), respectively, and 0.65 (95% CI: 0.57-0.74; P =0.001) for 12-month neurological outcome. CONCLUSIONS Our findings indicate that ultra-low-frequency sampling might provide sufficient resolution to derive information about the state of cerebrovascular autoregulation and prediction of 12-month outcome in TBI patients.
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Affiliation(s)
- Paolo Gritti
- Department of Anesthesia and Critical Care Medicine
| | | | - Rosalia Zangari
- FROM Research Foundation, Papa Giovanni XXIII Hospital, Bergamo
| | | | - Luca Longhi
- Department of Anesthesia and Critical Care Medicine
| | - Frank A Rasulo
- Department of Surgical Specialties, Radiological Sciences and Public Health, University of Brescia, Brescia, Italy
| | - Rita Bertuetti
- Department of Surgical Specialties, Radiological Sciences and Public Health, University of Brescia, Brescia, Italy
| | - Antonio Biroli
- Department of Surgical Specialties, Radiological Sciences and Public Health, University of Brescia, Brescia, Italy
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Szmelter AH, Venturini G, Abbed RJ, Acheampong MO, Eddington DT. Emulating clinical pressure waveforms in cell culture using an Arduino-controlled millifluidic 3D-printed platform for 96-well plates. LAB ON A CHIP 2023; 23:793-802. [PMID: 36727452 PMCID: PMC9979247 DOI: 10.1039/d2lc00970f] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/18/2023]
Abstract
High blood pressure is the primary risk factor for heart disease, the leading cause of death globally. Despite this, current methods to replicate physiological pressures in vitro remain limited in sophistication and throughput. Single-chamber exposure systems allow for only one pressure condition to be studied at a time and the application of dynamic pressure waveforms is currently limited to simple sine, triangular, or square waves. Here, we introduce a high-throughput hydrostatic pressure exposure system for 96-well plates. The platform can deliver a fully-customizable pressure waveform to each column of the plate, for a total of 12 simultaneous conditions. Using clinical waveform data, we are able to replicate real patients' blood pressures as well as other medically-relevant pressures within the body and have assembled a small patient-derived waveform library of some key physiological locations. As a proof of concept, human umbilical vein endothelial cells (HUVECs) survived and proliferated for 3 days under a wide range of static and dynamic physiologic pressures ranging from 10 mm Hg to 400 mm Hg. Interestingly, pathologic and supraphysiologic pressure exposures did not inhibit cell proliferation. By integrating with, rather than replacing, ubiquitous lab cultureware it is our hope that this device will facilitate the incorporation of hydrostatic pressure into standard cell culture practice.
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Affiliation(s)
- Adam H Szmelter
- Department of Biomedical Engineering, University of Illinois at Chicago, 835 S. Wolcott Ave., Chicago, IL, USA.
| | - Giulia Venturini
- Department of Biomedical Engineering, University of Illinois at Chicago, 835 S. Wolcott Ave., Chicago, IL, USA.
| | - Rana J Abbed
- Department of Biomedical Engineering, University of Illinois at Chicago, 835 S. Wolcott Ave., Chicago, IL, USA.
| | - Manny O Acheampong
- Department of Biomedical Engineering, University of Illinois at Chicago, 835 S. Wolcott Ave., Chicago, IL, USA.
| | - David T Eddington
- Department of Biomedical Engineering, University of Illinois at Chicago, 835 S. Wolcott Ave., Chicago, IL, USA.
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Evaluation and Application of Ultra-Low-Resolution Pressure Reactivity Index in Moderate or Severe Traumatic Brain Injury. J Neurosurg Anesthesiol 2022. [DOI: 10.1097/ana.0000000000000847 10.1097/ana.0000000000000847] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/01/2023]
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Ganzer PD, Loeian MS, Roof SR, Teng B, Lin L, Friedenberg DA, Baumgart IW, Meyers EC, Chun KS, Rich A, Tsao AL, Muir WW, Weber DJ, Hamlin RL. Dynamic detection and reversal of myocardial ischemia using an artificially intelligent bioelectronic medicine. SCIENCE ADVANCES 2022; 8:eabj5473. [PMID: 34985951 PMCID: PMC8730601 DOI: 10.1126/sciadv.abj5473] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/12/2023]
Abstract
Myocardial ischemia is spontaneous, frequently asymptomatic, and contributes to fatal cardiovascular consequences. Importantly, myocardial sensory networks cannot reliably detect and correct myocardial ischemia on their own. Here, we demonstrate an artificially intelligent and responsive bioelectronic medicine, where an artificial neural network (ANN) supplements myocardial sensory networks, enabling reliable detection and correction of myocardial ischemia. ANNs were first trained to decode spontaneous cardiovascular stress and myocardial ischemia with an overall accuracy of ~92%. ANN-controlled vagus nerve stimulation (VNS) significantly mitigated major physiological features of myocardial ischemia, including ST depression and arrhythmias. In contrast, open-loop VNS or ANN-controlled VNS following a caudal vagotomy essentially failed to reverse cardiovascular pathophysiology. Last, variants of ANNs were used to meet clinically relevant needs, including interpretable visualizations and unsupervised detection of emerging cardiovascular stress. Overall, these preclinical results suggest that ANNs can potentially supplement deficient myocardial sensory networks via an artificially intelligent bioelectronic medicine system.
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Affiliation(s)
- Patrick D. Ganzer
- Medical Devices and Neuromodulation, Battelle Memorial Institute, 505 King Ave., Columbus, OH 43201, USA
- Department of Biomedical Engineering, University of Miami, 1320 S Dixie Hwy., Coral Gables, FL 33146, USA
- The Miami Project to Cure Paralysis, University of Miami, 1095 NW 14th Terrace #48, Miami, FL 33136, USA
- Corresponding author.
| | - Masoud S. Loeian
- Medical Devices and Neuromodulation, Battelle Memorial Institute, 505 King Ave., Columbus, OH 43201, USA
| | - Steve R. Roof
- QTest Labs, 6456 Fiesta Dr., Columbus, OH 43235, USA
| | - Bunyen Teng
- QTest Labs, 6456 Fiesta Dr., Columbus, OH 43235, USA
| | - Luan Lin
- Health Analytics, Battelle Memorial Institute, 505 King Ave., Columbus, OH 43201, USA
| | - David A. Friedenberg
- Health Analytics, Battelle Memorial Institute, 505 King Ave., Columbus, OH 43201, USA
| | - Ian W. Baumgart
- Medical Devices and Neuromodulation, Battelle Memorial Institute, 505 King Ave., Columbus, OH 43201, USA
| | - Eric C. Meyers
- Medical Devices and Neuromodulation, Battelle Memorial Institute, 505 King Ave., Columbus, OH 43201, USA
| | - Keum S. Chun
- Medical Devices and Neuromodulation, Battelle Memorial Institute, 505 King Ave., Columbus, OH 43201, USA
| | - Adam Rich
- Health Analytics, Battelle Memorial Institute, 505 King Ave., Columbus, OH 43201, USA
| | - Allison L. Tsao
- Cardiovascular Section, Department of Medicine, VA Boston Healthcare System, Boston, MA 02130, USA
- Cardiovascular Division, Department of Medicine, Brigham and Women’s Hospital, Boston, MA 02115, USA
| | - William W. Muir
- QTest Labs, 6456 Fiesta Dr., Columbus, OH 43235, USA
- College of Veterinary Medicine, Lincoln Memorial University, 6965 Cumberland Gap Parkway, Harrogate, TN 37752, USA
| | - Doug J. Weber
- Department of Mechanical Engineering and Neuroscience, Carnegie Mellon University, 5000 Forbes Ave., Pittsburgh, PA 15213, USA
| | - Robert L. Hamlin
- QTest Labs, 6456 Fiesta Dr., Columbus, OH 43235, USA
- Department of Veterinary Biosciences, The Ohio State University, 1900 Coffey Road, Columbus, OH 43201, USA
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Ye G, Balasubramanian V, Li JKJ, Kaya M. Machine Learning-Based Continuous Intracranial Pressure Prediction for Traumatic Injury Patients. IEEE JOURNAL OF TRANSLATIONAL ENGINEERING IN HEALTH AND MEDICINE 2022; 10:4901008. [PMID: 35795876 PMCID: PMC9252333 DOI: 10.1109/jtehm.2022.3179874] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/23/2021] [Revised: 04/06/2022] [Accepted: 05/24/2022] [Indexed: 11/18/2022]
Abstract
Structured Abstract—Objective: Abnormal elevation of intracranial pressure (ICP) can cause dangerous or even fatal outcomes. The early detection of high intracranial pressure events can be crucial in saving lives in an intensive care unit (ICU). Despite many applications of machine learning (ML) techniques related to clinical diagnosis, ML applications for continuous ICP detection or short-term predictions have been rarely reported. This study proposes an efficient method of applying an artificial recurrent neural network on the early prediction of ICP evaluation continuously for TBI patients. Methods: After ICP data preprocessing, the learning model is generated for thirteen patients to continuously predict the ICP signal occurrence and classify events for the upcoming 10 minutes by inputting the previous 20-minutes of the ICP signal. Results: As the overall model performance, the average accuracy is 94.62%, the average sensitivity is 74.91%, the average specificity is 94.83%, and the average root mean square error is approximately 2.18 mmHg. Conclusion: This research addresses a significant clinical problem with the management of traumatic brain injury patients. The machine learning model data enables early prediction of ICP continuously in a real-time fashion, which is crucial for appropriate clinical interventions. The results show that our machine learning-based model has high adaptive performance, accuracy, and efficiency.
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Affiliation(s)
- Guochang Ye
- Department of Biomedical and Chemical Engineering and Sciences, Florida Institute of Technology, Melbourne, FL, USA
| | - Vignesh Balasubramanian
- Department of Biomedical and Chemical Engineering and Sciences, Florida Institute of Technology, Melbourne, FL, USA
| | - John K-J. Li
- Department of Biomedical Engineering, Rutgers University, New Brunswick, NJ, USA
| | - Mehmet Kaya
- Department of Biomedical and Chemical Engineering and Sciences, Florida Institute of Technology, Melbourne, FL, USA
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Non-Invasive Hemodynamics Monitoring System Based on Electrocardiography via Deep Convolutional Autoencoder. SENSORS 2021; 21:s21186264. [PMID: 34577471 PMCID: PMC8469191 DOI: 10.3390/s21186264] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/10/2021] [Revised: 09/13/2021] [Accepted: 09/15/2021] [Indexed: 01/09/2023]
Abstract
This study evaluates cardiovascular and cerebral hemodynamics systems by only using non-invasive electrocardiography (ECG) signals. The Massachusetts General Hospital/Marquette Foundation (MGH/MF) and Cerebral Hemodynamic Autoregulatory Information System Database (CHARIS DB) from the PhysioNet database are used for cardiovascular and cerebral hemodynamics, respectively. For cardiovascular hemodynamics, the ECG is used for generating the arterial blood pressure (ABP), central venous pressure (CVP), and pulmonary arterial pressure (PAP). Meanwhile, for cerebral hemodynamics, the ECG is utilized for the intracranial pressure (ICP) generator. A deep convolutional autoencoder system is applied for this study. The cross-validation method with Pearson’s linear correlation (R), root mean squared error (RMSE), and mean absolute error (MAE) are measured for the evaluations. Initially, the ECG is used to generate the cardiovascular waveform. For the ABP system—the systolic blood pressure (SBP) and diastolic blood pressures (DBP)—the R evaluations are 0.894 ± 0.004 and 0.881 ± 0.005, respectively. The MAE evaluations for SBP and DBP are, respectively, 6.645 ± 0.353 mmHg and 3.210 ± 0.104 mmHg. Furthermore, for the PAP system—the systolic and diastolic pressures—the R evaluations are 0.864 ± 0.003 mmHg and 0.817 ± 0.006 mmHg, respectively. The MAE evaluations for systolic and diastolic pressures are, respectively, 3.847 ± 0.136 mmHg and 2.964 ± 0.181 mmHg. Meanwhile, the mean CVP evaluations are 0.916 ± 0.001, 2.220 ± 0.039 mmHg, and 1.329 ± 0.036 mmHg, respectively, for R, RMSE, and MAE. For the mean ICP evaluation in cerebral hemodynamics, the R and MAE evaluations are 0.914 ± 0.003 and 2.404 ± 0.043 mmHg, respectively. This study, as a proof of concept, concludes that the non-invasive cardiovascular and cerebral hemodynamics systems can be potentially investigated by only using the ECG signal.
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Near-infrared Spectroscopy-derived Cerebral Autoregulation Indices Independently Predict Clinical Outcome in Acutely Ill Comatose Patients. J Neurosurg Anesthesiol 2021; 32:234-241. [PMID: 30864999 PMCID: PMC6732251 DOI: 10.1097/ana.0000000000000589] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
OBJECTIVE Outcome prediction in comatose patients with acute brain injury remains challenging. Regional cerebral oxygenation (rSO2) derived from near-infrared spectroscopy (NIRS) is a surrogate for cerebral blood flow and can be used to calculate cerebral autoregulation (CA) continuously at the bedside from the derived cerebral oximetry index (COx). We hypothesized that COx derived thresholds for CA are associated with outcomes in patients with acute coma from neurological injury. METHODS A prospective cohort study was conducted in 88 acutely comatose adults with heterogenous brain injury diagnoses who were continuously monitored with COx for up to 3 consecutive days. Multivariable logistic regression was performed to investigate association between averaged COx and short (in-hospital and 3 mo) and long-term (6 mo) outcomes. RESULTS Six month mortality rate was 62%. Median COx in nonsurvivors at hospital discharge was 0.082 [interquartile range, IQR: 0.045 to 0.160] compared with 0.042 [IQR: -0.005 to 0.110] in survivors (P=0.012). At 6 months, median COx was 0.075 [IQR: 0.27 to 0.158] in nonsurvivors compared with 0.029 [IQR: -0.015 to 0.077] in survivors (P=0.02). In the multivariable logistic regression model adjusted for confounders, average COx ≥0.05 was associated with both in-hospital mortality (adjusted odds ratio [OR]=2.9, 95% confidence interval [CI]=1.15-7.33, P=0.02), mortality at 6 months (adjusted OR=4.4, 95% CI=1.41-13.7, P=0.01), and severe disability (modified Rankin Score ≥4) at 6 months (adjusted OR=4.4, 95% CI=1.07-17.8, P=0.04). Area under the receiver operating characteristic curve for predicting mortality and severe disability at 6 months were 0.783 and 0.825, respectively. CONCLUSIONS Averaged COx ≥0.05 is independently associated with short and long-term mortality and long-term severe disability in acutely comatose adults with neurological injury. We propose that COx ≥0.05 represents an accurate threshold to predict long-term functional outcome in acutely comatose adults.
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Martinez-Ríos E, Montesinos L, Alfaro-Ponce M, Pecchia L. A review of machine learning in hypertension detection and blood pressure estimation based on clinical and physiological data. Biomed Signal Process Control 2021. [DOI: 10.1016/j.bspc.2021.102813] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
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Stroh JN, Bennett TD, Kheyfets V, Albers D. Clinical Decision Support for Traumatic Brain Injury: Identifying a Framework for Practical Model-Based Intracranial Pressure Estimation at Multihour Timescales. JMIR Med Inform 2021; 9:e23215. [PMID: 33749613 PMCID: PMC8077603 DOI: 10.2196/23215] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2020] [Revised: 01/21/2021] [Accepted: 01/22/2021] [Indexed: 01/27/2023] Open
Abstract
BACKGROUND The clinical mitigation of intracranial hypertension due to traumatic brain injury requires timely knowledge of intracranial pressure to avoid secondary injury or death. Noninvasive intracranial pressure (nICP) estimation that operates sufficiently fast at multihour timescales and requires only common patient measurements is a desirable tool for clinical decision support and improving traumatic brain injury patient outcomes. However, existing model-based nICP estimation methods may be too slow or require data that are not easily obtained. OBJECTIVE This work considers short- and real-time nICP estimation at multihour timescales based on arterial blood pressure (ABP) to better inform the ongoing development of practical models with commonly available data. METHODS We assess and analyze the effects of two distinct pathways of model development, either by increasing physiological integration using a simple pressure estimation model, or by increasing physiological fidelity using a more complex model. Comparison of the model approaches is performed using a set of quantitative model validation criteria over hour-scale times applied to model nICP estimates in relation to observed ICP. RESULTS The simple fully coupled estimation scheme based on windowed regression outperforms a more complex nICP model with prescribed intracranial inflow when pulsatile ABP inflow conditions are provided. We also show that the simple estimation data requirements can be reduced to 1-minute averaged ABP summary data under generic waveform representation. CONCLUSIONS Stronger performance of the simple bidirectional model indicates that feedback between the systemic vascular network and nICP estimation scheme is crucial for modeling over long intervals. However, simple model reduction to ABP-only dependence limits its utility in cases involving other brain injuries such as ischemic stroke and subarachnoid hemorrhage. Additional methodologies and considerations needed to overcome these limitations are illustrated and discussed.
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Affiliation(s)
- J N Stroh
- Department of Bioengineering, University of Colorado Denver
- Anschutz Medical Campus, Aurora, CO, United States
| | - Tellen D Bennett
- Department of Pediatrics, University of Colorado School of Medicine, Aurora, CO, United States
| | - Vitaly Kheyfets
- Department of Bioengineering, University of Colorado Denver
- Anschutz Medical Campus, Aurora, CO, United States
| | - David Albers
- Department of Bioengineering, University of Colorado Denver
- Anschutz Medical Campus, Aurora, CO, United States.,Department of Pediatrics, University of Colorado School of Medicine, Aurora, CO, United States
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Hüser M, Kündig A, Karlen W, De Luca V, Jaggi M. Forecasting intracranial hypertension using multi-scale waveform metrics. Physiol Meas 2020; 41:014001. [PMID: 31851948 DOI: 10.1088/1361-6579/ab6360] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
Abstract
OBJECTIVE Acute intracranial hypertension is an important risk factor of secondary brain damage after traumatic brain injury. Hypertensive episodes are often diagnosed reactively, leading to late detection and lost time for intervention planning. A pro-active approach that predicts critical events several hours ahead of time could assist in directing attention to patients at risk. APPROACH We developed a prediction framework that forecasts onsets of acute intracranial hypertension in the next 8 h. It jointly uses cerebral auto-regulation indices, spectral energies and morphological pulse metrics to describe the neurological state of the patient. One-minute base windows were compressed by computing signal metrics, and then stored in a multi-scale history, from which physiological features were derived. MAIN RESULTS Our model predicted events up to 8 h in advance with an alarm recall rate of 90% at a precision of 30% in the MIMIC-III waveform database, improving upon two baselines from the literature. We found that features derived from high-frequency waveforms substantially improved the prediction performance over simple statistical summaries of low-frequency time series, and each of the three feature classes contributed to the performance gain. The inclusion of long-term history up to 8 h was especially important. SIGNIFICANCE Our results highlight the importance of information contained in high-frequency waveforms in the neurological intensive care unit. They could motivate future studies on pre-hypertensive patterns and the design of new alarm algorithms for critical events in the injured brain.
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Affiliation(s)
- Matthias Hüser
- Biomedical Informatics Group, Institute of Machine Learning, Department of Computer Science, ETH Zürich, 8092 Zürich, Switzerland
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Discrepancy Between Internal and External Intracranial Pressure Transducers: Quantification of an Old Source of Error in EVDs? World Neurosurg 2019; 133:e18-e25. [PMID: 31394360 DOI: 10.1016/j.wneu.2019.07.213] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2019] [Revised: 07/28/2019] [Accepted: 07/29/2019] [Indexed: 11/22/2022]
Abstract
BACKGROUND Intracranial pressure monitoring remains the foundation for prevention of secondary injury after traumatic brain injury and is most commonly performed using an external ventricular drain or intraparenchymal pressure monitor. The Integra Flex ventricular catheter combines an external ventricular catheter with a pressure transducer embedded in the tip of the catheter to allow continuous pressure readings while simultaneously draining cerebrospinal fluid. Discrepancies between measurements from the continuously reported internal pressure transducer and intermittently assessed and externally transduced ventricular drain prompted an analysis and characterization of pressures transduced from the same ventricular source. METHODS More than 500 hours of high-resolution (125 Hz) continuous recordings were manually reviewed to identify 73 hours of simultaneous measurements (clamped external ventricular drain) from internal and external transducers in patients with traumatic brain injury. RESULTS A significant positive bias was found in pressure readings obtained from external relative to internal measurements. The 2 methods of measurement generally correlated poorly with each other and variably. Although proportional bias was found with Bland-Altman analysis, coherence revealed rare shifts in the external transducer as a major source of discrepancy. Infrequent changes in the 0-level of the external transducer were found to be the primary source of discrepancy. Relative to the observed differences, no significant trend was observed over time between the 2 modalities. CONCLUSIONS This study suggests that the internal pressure transducer may be a more reliable estimate of intracranial pressure relative to bedside external transducers due to the inherent behavioral requirement of leveling.
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Simjanoska M, Gjoreski M, Gams M, Madevska Bogdanova A. Non-Invasive Blood Pressure Estimation from ECG Using Machine Learning Techniques. SENSORS (BASEL, SWITZERLAND) 2018; 18:E1160. [PMID: 29641430 PMCID: PMC5949031 DOI: 10.3390/s18041160] [Citation(s) in RCA: 62] [Impact Index Per Article: 8.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/24/2018] [Revised: 03/13/2018] [Accepted: 03/26/2018] [Indexed: 12/14/2022]
Abstract
BACKGROUND Blood pressure (BP) measurements have been used widely in clinical and private environments. Recently, the use of ECG monitors has proliferated; however, they are not enabled with BP estimation. We have developed a method for BP estimation using only electrocardiogram (ECG) signals. METHODS Raw ECG data are filtered and segmented, and, following this, a complexity analysis is performed for feature extraction. Then, a machine-learning method is applied, combining a stacking-based classification module and a regression module for building systolic BP (SBP), diastolic BP (DBP), and mean arterial pressure (MAP) predictive models. In addition, the method allows a probability distribution-based calibration to adapt the models to a particular user. RESULTS Using ECG recordings from 51 different subjects, 3129 30-s ECG segments are constructed, and seven features are extracted. Using a train-validation-test evaluation, the method achieves a mean absolute error (MAE) of 8.64 mmHg for SBP, 18.20 mmHg for DBP, and 13.52 mmHg for the MAP prediction. When models are calibrated, the MAE decreases to 7.72 mmHg for SBP, 9.45 mmHg for DBP and 8.13 mmHg for MAP. CONCLUSION The experimental results indicate that, when a probability distribution-based calibration is used, the proposed method can achieve results close to those of a certified medical device for BP estimation.
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Affiliation(s)
- Monika Simjanoska
- Faculty of Computer Science and Engineering, Ss. Cyril and Methodius University, Rugjer Boshkovikj 16, 1000 Skopje, Macedonia.
| | - Martin Gjoreski
- Department of Intelligent Systems, Jožef Stefan Institute, Jožef Stefan International Postgraduate School, Jamova cesta 39, 1000 Ljubljana, Slovenia.
| | - Matjaž Gams
- Department of Intelligent Systems, Jožef Stefan Institute, Jožef Stefan International Postgraduate School, Jamova cesta 39, 1000 Ljubljana, Slovenia.
| | - Ana Madevska Bogdanova
- Faculty of Computer Science and Engineering, Ss. Cyril and Methodius University, Rugjer Boshkovikj 16, 1000 Skopje, Macedonia.
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Copplestone S, Welbourne J. A narrative review of the clinical application of pressure reactiviy indices in the neurocritical care unit. Br J Neurosurg 2018; 32:4-12. [PMID: 29298527 DOI: 10.1080/02688697.2017.1416063] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
Pressure reactivity indices are used in clinical research as a surrogate marker of the ability of the cerebrovasculature to maintain cerebral autoregulation. The use of pressure reactivity indices in patients with neurological injury represents a potential to move away from population-based physiological targets used in guidelines to individualized physiological targets. The aim of this review is to describe the underlying principles and development of pressure reactivity indices, alongside a critique of how they have been used in clinical research, including their limitations. The primary source literature was identified from a database search of PUBMed and OVID online using the search terms "pressure reactivity index" and "pressure reactivity indices". The evidence base regarding pressure reactivity indices currently remains Level III. Pressure reactivity indices rely on the correlation (-1 to +1) between the arterial blood pressure and intracranial pressure, with negative values indicating intact cerebral autoregulation and positive values indicating dysfunctional cerebral autoregulation. Meaningful data is taken from summary measures and trends. The traumatic brain injury population feature most prominently in the literature. There is limited description of the potential confounding factors that may affect pressure reactivity indices, including physiological parameters and therapeutic interventions. Plotting a pressure reactivity index against a cerebral perfusion pressure can indicate an optimal cerebral perfusion pressure to individualise patient care. There is potential to over interpret optimal cerebral perfusion pressure targets when the values of pressure reactivity indices are close to zero. There is an association between pressure reactivity indices and neurological outcomes, however the use of pressure reactivity indices as a prognostication tool is to be challenged. Average values of cerebral perfusion pressure that are not close to averaged values of optimal cerebral perfusion pressure are also associated with poor outcome. Further research is required to ascertain whether targeting an optimal cerebral perfusion pressure may alter outcome.
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Affiliation(s)
- Stephen Copplestone
- a Advanced trainee in Intensive Care Medicine and Anaesthesia , Plymouth Hospitals NHS Trust , Plymouth , UK
| | - Jessie Welbourne
- b Consultant in Intensive Care Medicine and Neuroanaesthesia, Department of Intensive Care Medicine , Plymouth Hospitals NHS Trust , Plymouth , UK
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Pineda B, Kosinski C, Kim N, Danish S, Craelius W. Assessing Cerebral Hemodynamic Stability After Brain Injury. ACTA NEUROCHIRURGICA. SUPPLEMENT 2018; 126:297-301. [PMID: 29492578 DOI: 10.1007/978-3-319-65798-1_58] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
OBJECTIVE Following brain injury, unstable cerebral hemodynamics can be characterized by abnormal rises in intracranial pressure (ICP). This behavior has been quantified by the RAP index: the correlation (R) between ICP pulse amplitude (A) and mean (P). While RAP could be a valuable indicator of autoregulatory processes, its prognostic ability is not well established and its validity has been questioned due to potential errors in measurement. Here, we test (1) whether RAP is a consistent measure of intracranial hemodynamics and (2) whether RAP has prognostic value in predicting hemodynamic instability following brain injury. MATERIALS AND METHODS RAP was tested in seven brain injured patients treated in a surgical intensive care unit. A sample of ICP data was randomly chosen and segmented into 1 hour periods. Hours were then categorized as either stable, which contained no sharp rises in ICP, or unstable, which contained ≥1 sharp rise-where a sharp rise is defined as ICP exceeding a mean slope of 0.15 mmHg/s. Equal numbers of stable and unstable segments were then selected for each patient. RAP was calculated as the Pearson's correlation coefficient between ICP pulse amplitude (AMP) and mean (mICP), determined in 6 second windows, according to established methods. RESULTS Results showed that (1) average AMP and ICP levels were similar between stable and unstable periods and (2) unstable periods were identified by RAP values exceeding 0.6 with an average positive predictive value of 74%. CONCLUSIONS We conclude that RAP can provide a valid measure of ICP dynamics, is not affected by sensor drift, and can better distinguish periods of instability than ICP or AMP alone.
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Affiliation(s)
- Bianca Pineda
- Department of Biomedical Engineering, Rutgers, The State University of New Jersey, Piscataway, NJ, USA
| | - Colin Kosinski
- Department of Biomedical Engineering, Rutgers, The State University of New Jersey, Piscataway, NJ, USA
| | - Nam Kim
- Department of Biomedical Engineering, Rutgers, The State University of New Jersey, Piscataway, NJ, USA
| | - Shabbar Danish
- Department of Neurosurgery, Rutgers, Robert Wood Johnson Medical School, New Brunswick, NJ, USA
| | - William Craelius
- Department of Biomedical Engineering, Rutgers, The State University of New Jersey, Piscataway, NJ, USA.
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Scheeren TWL, Saugel B. Journal of clinical monitoring and computing 2016 end of year summary: monitoring cerebral oxygenation and autoregulation. J Clin Monit Comput 2017; 31:241-246. [PMID: 28120178 PMCID: PMC5346134 DOI: 10.1007/s10877-017-9980-7] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2017] [Accepted: 01/03/2017] [Indexed: 11/24/2022]
Abstract
In the perioperative and critical care setting, monitoring of cerebral oxygenation (ScO2) and cerebral autoregulation enjoy increasing popularity in recent years, particularly in patients undergoing cardiac surgery. Monitoring ScO2 is based on near infrared spectroscopy, and attempts to early detect cerebral hypoperfusion and thereby prevent cerebral dysfunction and postoperative neurologic complications. Autoregulation of cerebral blood flow provides a steady flow of blood towards the brain despite variations in mean arterial blood pressure (MAP) and cerebral perfusion pressure, and is effective in a MAP range between approximately 50–150 mmHg. This range of intact autoregulation may, however, vary considerably between individuals, and shifts to higher thresholds have been observed in elderly and hypertensive patients. As a consequence, intraoperative hypotension will be poorly tolerated, and might cause ischemic events and postoperative neurological complications. This article summarizes research investigating technologies for the assessment of ScO2 and cerebral autoregulation published in the Journal of Clinical Monitoring and Computing in 2016.
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Affiliation(s)
- Thomas W L Scheeren
- Department of Anesthesiology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands.
| | - Bernd Saugel
- Department of Anesthesiology, Centre of Anesthesiology and Intensive Care Medicine, University Medical Centre Hamburg-Eppendorf, Martinistrasse 52, 20246, Hamburg, Germany
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