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Intracranial-Pressure-Monitoring-Assisted Management Associated with Favorable Outcomes in Moderate Traumatic Brain Injury Patients with a GCS of 9-11. J Clin Med 2022; 11:jcm11226661. [PMID: 36431137 PMCID: PMC9694446 DOI: 10.3390/jcm11226661] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2022] [Revised: 11/01/2022] [Accepted: 11/02/2022] [Indexed: 11/13/2022] Open
Abstract
Objective: With a mortality rate of 10−30%, a moderate traumatic brain injury (mTBI) is one of the most variable traumas. The indications for intracranial pressure (ICP) monitoring in patients with mTBI and the effects of ICP on patients’ outcomes are uncertain. The purpose of this study was to examine the indications of ICP monitoring (ICPm) and its effects on the long-term functional outcomes of mTBI patients. Methods: Patients with Glasgow Coma Scale (GCS) scores of 9−11 at Tangdu hospital, between January 2015 and December 2021, were enrolled and treated in this retrospective cohort study. We assessed practice variations in ICP interventions using the therapy intensity level (TIL). Six-month mortality and a Glasgow Outcome Scale Extended (GOS-E) score were the main outcomes. The secondary outcome was neurological deterioration (ND) events. The indication and the estimated impact of ICPm on the functional outcome were investigated by using binary regression analyses. Results: Of the 350 patients, 145 underwent ICP monitoring-assisted management, and the other 205 patients received a standard control based on imaging or clinical examinations. A GCS ≤ 10 (OR 1.751 (95% CI 1.216−3.023), p = 0.003), midline shift (mm) ≥ 2.5 (OR 3.916 (95% CI 2.076−7.386) p < 0.001), and SDH (OR 1.772 (95% CI 1.065−2.949) p = 0.028) were predictors of ICP. Patients who had ICPm (14/145 (9.7%)) had a decreased 6-month mortality rate compared to those who were not monitored (40/205 (19.5%), p = 0.011). ICPm was linked to both improved neurological outcomes at 6 months (OR 0.815 (95% CI 0.712−0.933), p = 0.003) and a lower ND rate (2 = 11.375, p = 0.010). A higher mean ICP (17.32 ± 3.52, t = −6.047, p < 0.001) and a more significant number of ICP > 15 mmHg (27 (9−45.5), Z = −5.406, p < 0.001) or ICP > 20 mmHg (5 (0−23), Z = −4.635, p < 0.001) 72 h after injury were associated with unfavorable outcomes. The best unfavorable GOS-E cutoff value of different ICP characteristics showed that the mean ICP was >15.8 mmHg (AUC 0.698; 95% CI, 0.606−0.789, p < 0.001), the number of ICP > 15 mmHg was >25.5 (AUC 0.681; 95% CI, 0.587−0.774, p < 0.001), and the number of ICP > 20 mmHg was >6 (AUC 0.660; 95% CI, 0.561−0.759, p < 0.001). The total TIL score during the first 72 h post-injury in the non-ICP group (9 (8, 11)) was lower than that of the ICP group (13 (9, 17), Z = −8.388, p < 0.001), and was associated with unfavorable outcomes. Conclusion: ICPm-assisted management was associated with better clinical outcomes six months after discharge and lower incidences of ND for seven days post-injury. A mean ICP > 15.8 mmHg, the number of ICP > 15 mmHg > 25.5, or the number of ICP > 20 mmHg > 6 implicate an unfavorable long-term prognosis after 72 h of an mTBI.
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Godoy DA, Seifi A, Chi G, Paredes Saravia L, Rabinstein AA. Intracranial Pressure Monitoring in Moderate Traumatic Brain Injury: A Systematic Review and Meta-Analysis. Neurocrit Care 2022; 37:514-522. [PMID: 35610529 DOI: 10.1007/s12028-022-01533-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2022] [Accepted: 05/03/2022] [Indexed: 01/01/2023]
Abstract
BACKGROUND The principal aim of this study was to determine the prevalence of intracranial pressure (ICP) monitoring and intracranial hypertension (IHT) in patients treated for moderate traumatic brain injury (TBI). A secondary objective was to assess factors associated with ICP monitoring. METHODS We conducted a systematic review of the literature to identify studies that assessed ICP monitoring in moderate TBI. The meta-analysis was performed by using a random-effects model. RESULTS A total of 13 studies comprising 116,714 patients were pooled to estimate the overall prevalence of ICP monitoring and IHT (one episode or more of ICP > 20 mm Hg) after moderate TBI. The prevalence rate for ICP monitoring was 18.3% (95% confidence interval 8.1-36.1%), whereas the proportion of IHT was 44% (95% confidence interval 33.8-54.7%). Three studies were pooled to estimate the prevalence of ICP monitoring according to Glasgow Coma Scale (GCS) (≤ 10 vs. > 10). ICP monitoring was performed in 32.2% of patients with GCS ≤ 10 versus 15.2% of patients with GCS > 10 (p = 0.59). Both subgroups were highly heterogeneous. We found no other variables associated with ICP monitoring or IHT. CONCLUSIONS The prevalence of ICP monitoring in moderate TBI is low, but the prevalence of IHT is high among patients undergoing ICP monitoring. Current literature is limited in size and quality and does not identify factors associated with ICP monitoring or IHT. Further research is needed to guide the optimal use of ICP monitoring in moderate TBI.
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Affiliation(s)
- Daniel Agustin Godoy
- Critical Care Department, Neurointensive Care Unit, Sanatorio Pasteur, Chacabuco 675 , 4700, Catamarca, Argentina.
| | - Ali Seifi
- Department of Neurosurgery, Division of Neurocritical Care, University of Texas Health Science Center at San Antonio, School of Medicine, San Antonio, TX, USA
| | - Gerald Chi
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
| | | | - Alejandro A Rabinstein
- Department of Neurology, Neuroscience Intensive Care Unit, Mayo Clinic, Rochester, MN, USA
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Zeng L, Li G, Zhang M, Zhu R, Chen J, Li M, Yin S, Bai Z, Zhuang W, Sun J. A noninvasive and comprehensive method for continuous assessment of cerebral blood flow pulsation based on magnetic induction phase shift. PeerJ 2022; 10:e13002. [PMID: 35228911 PMCID: PMC8881914 DOI: 10.7717/peerj.13002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2021] [Accepted: 02/03/2022] [Indexed: 01/11/2023] Open
Abstract
Cerebral blood flow (CBF) monitoring is of great significance for treating and preventing strokes. However, there has not been a fully accepted method targeting continuous assessment in clinical practice. In this work, we built a noninvasive continuous assessment system for cerebral blood flow pulsation (CBFP) that is based on magnetic induction phase shift (MIPS) technology and designed a physical model of the middle cerebral artery (MCA). Physical experiments were carried out through different simulations of CBF states. Four healthy volunteers were enrolled to perform the MIPS and ECG synchronously monitoring trials. Then, the components of MIPS related to the blood supply level and CBFP were investigated by signal analysis in time and frequency domain, wavelet decomposition and band-pass filtering. The results show that the time-domain baseline of MIPS increases with blood supply level. A pulse signal was identified in the spectrum (0.2-2 Hz in 200-2,000 ml/h groups, respectively) of MIPS when the simulated blood flow rate was not zero. The pulsation frequency with different simulated blood flow rates is the same as the squeezing frequency of the feeding pump. Similar to pulse waves, the MIPS signals on four healthy volunteers all had periodic change trends with obvious peaks and valleys. Its frequency is close to that of the ECG signal and there is a certain time delay between them. These results indicate that the CBFP component can effectively be extracted from MIPS, through which different blood supply levels can be distinguished. This method has the potential to become a new solution for non-invasive and comprehensive monitoring of CBFP.
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Affiliation(s)
- Lingxi Zeng
- School of Pharmacy and Bioengineering, Chongqing University of Technology, Chongqing, China
| | - Gen Li
- School of Pharmacy and Bioengineering, Chongqing University of Technology, Chongqing, China
| | - Maoting Zhang
- College of Biomedical Engineering, Army Medical University, Chongqing, China
| | - Rui Zhu
- School of Pharmacy and Bioengineering, Chongqing University of Technology, Chongqing, China
| | - Jingbo Chen
- College of Biomedical Engineering, Army Medical University, Chongqing, China
| | - Mingyan Li
- College of Artificial Intelligence, Chongqing University of Technology, Chongqing, China
| | - Shengtong Yin
- School of Pharmacy and Bioengineering, Chongqing University of Technology, Chongqing, China
| | - Zelin Bai
- College of Biomedical Engineering, Army Medical University, Chongqing, China
| | - Wei Zhuang
- College of Biomedical Engineering, Army Medical University, Chongqing, China
| | - Jian Sun
- College of Biomedical Engineering, Army Medical University, Chongqing, China
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Pérez-Sánchez J, Carrillo de Gea JM, Rodríguez Barceló S, Toval Á, Fernández-Alemán JL, García-Berná JA, Popović M, Toval A. Intracranial pressure analysis software: A mapping study and proposal. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2021; 209:106334. [PMID: 34450483 DOI: 10.1016/j.cmpb.2021.106334] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/28/2021] [Accepted: 07/28/2021] [Indexed: 06/13/2023]
Abstract
Introduction Intracranial pressure (ICP) monitoring and analysis are techniques that are, each year, applied to millions of patients with pathologies with million of patients annually. The detection of the so called A and B-waves, and the analysis of subtle changes in C-waves, which are present in ICP waveform, may indicate decreased intracranial compliance, and may improve the clinical outcome. Despite the advances in the field of computerized data analysis, the visual screening of ICP continues to be the means principally employed to detect these waves. To the best of our knowledge, no review study has addressed automated ICP analysis in sufficient detail and a need to research the state of the art of ICP analysis has, therefore, been identified. Methodology This paper presents a systematic mapping study to provide answers to 7 research questions: publication time, venue and source trends, medical tasks undertaken, research methods used, computational systems developed, validation methodology, tools and systems employed for evaluation and research problems identified. An ICP software prototype is presented and evaluated as a consequence of the results. Results A total of 23 papers, published between 1990 and 2020, were selected from 6 online databases. After analyzing these papers, the following information was obtained: diagnosis and monitoring medical tasks were addressed to the same extent, and the main research method used was evaluation research. Several computational systems were identified in the papers, the main one being image classification, while the main analysis objective was single pulse analysis. Correlation with expert analysis was the most frequent validation method, and few of the papers stated the use of a published dataset. Few authors referred to the tools used to build or evaluate the proposed solutions. The most frequent research problem was the need for new analysis methods. These results have inspired us to propose a software prototype with which provide an automated solution that integrates ICP analysis and monitoring techniques. Conclusions The papers in this study were selected and classified with regard to ICP automated analysis methods. Several research gaps were identified, which the authors of this study have employed as a based on which to recommend future work. Furthermore, this study has identified the need for an empirical comparison between methods, which will require the use and development of certain standard metrics. An in-depth analysis conducted by means of systematic literature review is also required. The software prototype evaluation provided positive results, showing that the prototype may be a reliable system for A-wave detection.
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Affiliation(s)
- Juanjo Pérez-Sánchez
- Department of Informatics and Systems, Faculty of Computer Science, University of Murcia, Murcia, Spain.
| | - Juan M Carrillo de Gea
- Department of Informatics and Systems, Faculty of Computer Science, University of Murcia, Murcia, Spain.
| | | | - Ángel Toval
- Department of Human Anatomy and Psychobiology, Faculty of Medicine, University of Murcia, Murcia, Spain; Institute of Biomedical Research of Murcia, Virgen de la Arrixaca University Hospital, University of Murcia, Murcia, Spain.
| | - José L Fernández-Alemán
- Department of Informatics and Systems, Faculty of Computer Science, University of Murcia, Murcia, Spain.
| | - José A García-Berná
- Department of Informatics and Systems, Faculty of Computer Science, University of Murcia, Murcia, Spain.
| | - Miroljub Popović
- Department of Human Anatomy and Psychobiology, Faculty of Medicine, University of Murcia, Murcia, Spain; Institute of Biomedical Research of Murcia, Virgen de la Arrixaca University Hospital, University of Murcia, Murcia, Spain.
| | - Ambrosio Toval
- Department of Informatics and Systems, Faculty of Computer Science, University of Murcia, Murcia, Spain.
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Schmid W, Fan Y, Chi T, Golanov E, Regnier-Golanov AS, Austerman RJ, Podell K, Cherukuri P, Bentley T, Steele CT, Schodrof S, Aazhang B, Britz GW. Review of wearable technologies and machine learning methodologies for systematic detection of mild traumatic brain injuries. J Neural Eng 2021; 18. [PMID: 34330120 DOI: 10.1088/1741-2552/ac1982] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2021] [Accepted: 07/30/2021] [Indexed: 12/16/2022]
Abstract
Mild traumatic brain injuries (mTBIs) are the most common type of brain injury. Timely diagnosis of mTBI is crucial in making 'go/no-go' decision in order to prevent repeated injury, avoid strenuous activities which may prolong recovery, and assure capabilities of high-level performance of the subject. If undiagnosed, mTBI may lead to various short- and long-term abnormalities, which include, but are not limited to impaired cognitive function, fatigue, depression, irritability, and headaches. Existing screening and diagnostic tools to detect acute andearly-stagemTBIs have insufficient sensitivity and specificity. This results in uncertainty in clinical decision-making regarding diagnosis and returning to activity or requiring further medical treatment. Therefore, it is important to identify relevant physiological biomarkers that can be integrated into a mutually complementary set and provide a combination of data modalities for improved on-site diagnostic sensitivity of mTBI. In recent years, the processing power, signal fidelity, and the number of recording channels and modalities of wearable healthcare devices have improved tremendously and generated an enormous amount of data. During the same period, there have been incredible advances in machine learning tools and data processing methodologies. These achievements are enabling clinicians and engineers to develop and implement multiparametric high-precision diagnostic tools for mTBI. In this review, we first assess clinical challenges in the diagnosis of acute mTBI, and then consider recording modalities and hardware implementation of various sensing technologies used to assess physiological biomarkers that may be related to mTBI. Finally, we discuss the state of the art in machine learning-based detection of mTBI and consider how a more diverse list of quantitative physiological biomarker features may improve current data-driven approaches in providing mTBI patients timely diagnosis and treatment.
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Affiliation(s)
- William Schmid
- Department of Electrical and Computer Engineering and Neuroengineering Initiative (NEI), Rice University, Houston, TX 77005, United States of America
| | - Yingying Fan
- Department of Electrical and Computer Engineering and Neuroengineering Initiative (NEI), Rice University, Houston, TX 77005, United States of America
| | - Taiyun Chi
- Department of Electrical and Computer Engineering and Neuroengineering Initiative (NEI), Rice University, Houston, TX 77005, United States of America
| | - Eugene Golanov
- Department of Neurosurgery, Houston Methodist Hospital, Houston, TX 77030, United States of America
| | | | - Ryan J Austerman
- Department of Neurosurgery, Houston Methodist Hospital, Houston, TX 77030, United States of America
| | - Kenneth Podell
- Department of Neurology, Houston Methodist Hospital, Houston, TX 77030, United States of America
| | - Paul Cherukuri
- Institute of Biosciences and Bioengineering (IBB), Rice University, Houston, TX 77005, United States of America
| | - Timothy Bentley
- Office of Naval Research, Arlington, VA 22203, United States of America
| | - Christopher T Steele
- Military Operational Medicine Research Program, US Army Medical Research and Development Command, Fort Detrick, MD 21702, United States of America
| | - Sarah Schodrof
- Department of Athletics-Sports Medicine, Rice University, Houston, TX 77005, United States of America
| | - Behnaam Aazhang
- Department of Electrical and Computer Engineering and Neuroengineering Initiative (NEI), Rice University, Houston, TX 77005, United States of America
| | - Gavin W Britz
- Department of Neurosurgery, Houston Methodist Hospital, Houston, TX 77030, United States of America
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Chen J, Li G, Liang H, Zhao S, Sun J, Qin M. An amplitude-based characteristic parameter extraction algorithm for cerebral edema detection based on electromagnetic induction. Biomed Eng Online 2021; 20:74. [PMID: 34344370 PMCID: PMC8335876 DOI: 10.1186/s12938-021-00913-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2021] [Accepted: 07/26/2021] [Indexed: 11/10/2022] Open
Abstract
Background Cerebral edema is a common condition secondary to any type of neurological injury. The early diagnosis and monitoring of cerebral edema is of great importance to improve the prognosis. In this article, a flexible conformal electromagnetic two-coil sensor was employed as the electromagnetic induction sensor, associated with a vector network analyzer (VNA) for signal generation and receiving. Measurement of amplitude data over the frequency range of 1–100 MHz is conducted to evaluate the changes in cerebral edema. We proposed an Amplitude-based Characteristic Parameter Extraction (Ab-CPE) algorithm for multi-frequency characteristic analysis over the frequency range of 1–100 MHz and investigated its performance in electromagnetic induction-based cerebral edema detection and distinction of its acute/chronic phase. Fourteen rabbits were enrolled to establish cerebral edema model and the 24 h real-time monitoring experiments were carried out for algorithm verification. Results The proposed Ab-CPE algorithm was able to detect cerebral edema with a sensitivity of 94.1% and specificity of 95.4%. Also, in the early stage, it can detect cerebral edema with a sensitivity of 85.0% and specificity of 87.5%. Moreover, the Ab-CPE algorithm was able to distinguish between acute and chronic phase of cerebral edema with a sensitivity of 85.0% and specificity of 91.0%. Conclusion The proposed Ab-CPE algorithm is suitable for multi-frequency characteristic analysis. Combined with this algorithm, the electromagnetic induction method has an excellent performance on the detection and monitoring of cerebral edema.
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Affiliation(s)
- Jingbo Chen
- College of Biomedical Engineering, Third Military Medical University (Army Medical University), Chongqing, China
| | - Gen Li
- School of Pharmacy and Bioengineering, Chongqing University of Technology, Chongqing, China.
| | - Huayou Liang
- China Aerodynamics Research and Development Center Low Speed Aerodynamic Institute, Mianyang, Sichuan, China
| | - Shuanglin Zhao
- College of Biomedical Engineering, Third Military Medical University (Army Medical University), Chongqing, China
| | - Jian Sun
- College of Biomedical Engineering, Third Military Medical University (Army Medical University), Chongqing, China
| | - Mingxin Qin
- College of Biomedical Engineering, Third Military Medical University (Army Medical University), Chongqing, China.
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