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Su L, Liu S, Long Y, Chen C, Chen K, Chen M, Chen Y, Cheng Y, Cui Y, Ding Q, Ding R, Duan M, Gao T, Gu X, He H, He J, Hu B, Hu C, Huang R, Huang X, Jiang H, Jiang J, Lan Y, Li J, Li L, Li L, Li W, Li Y, Lin J, Luo X, Lyu F, Mao Z, Miao H, Shang X, Shang X, Shang Y, Shen Y, Shi Y, Sun Q, Sun W, Tang Z, Wang B, Wang H, Wang H, Wang L, Wang L, Wang S, Wang Z, Wang Z, Wei D, Wu J, Wu Q, Xing X, Yang J, Yang X, Yu J, Yu W, Yu Y, Yuan H, Zhai Q, Zhang H, Zhang L, Zhang M, Zhang Z, Zhao C, Zheng R, Zhong L, Zhou F, Zhu W. Chinese experts' consensus on the application of intensive care big data. Front Med (Lausanne) 2024; 10:1174429. [PMID: 38264049 PMCID: PMC10804886 DOI: 10.3389/fmed.2023.1174429] [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: 02/26/2023] [Accepted: 11/09/2023] [Indexed: 01/25/2024] Open
Abstract
The development of intensive care medicine is inseparable from the diversified monitoring data. Intensive care medicine has been closely integrated with data since its birth. Critical care research requires an integrative approach that embraces the complexity of critical illness and the computational technology and algorithms that can make it possible. Considering the need of standardization of application of big data in intensive care, Intensive Care Medicine Branch of China Health Information and Health Care Big Data Society, Standard Committee has convened expert group, secretary group and the external audit expert group to formulate Chinese Experts' Consensus on the Application of Intensive Care Big Data (2022). This consensus makes 29 recommendations on the following five parts: Concept of intensive care big data, Important scientific issues, Standards and principles of database, Methodology in solving big data problems, Clinical application and safety consideration of intensive care big data. The consensus group believes this consensus is the starting step of application big data in the field of intensive care. More explorations and big data based retrospective research should be carried out in order to enhance safety and reliability of big data based models of critical care field.
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Affiliation(s)
- Longxiang Su
- Department of Critical Care Medicine, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, China
| | - Shengjun Liu
- Department of Critical Care Medicine, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, China
| | - Yun Long
- Department of Critical Care Medicine, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, China
| | - Chaodong Chen
- Department of Surgical Intensive Critical Unit, Beijing Chao-yang Hospital, Capital Medical University, Beijing, China
| | - Kai Chen
- Department of Critical Care Medicine, Fujian Provincial Key Laboratory of Critical Care Medicine, Shengli Clinical Medical College of Fujian Medical University, Fujian Provincial Hospital, Fujian Provincial Center for Critical Care Medicine, Fuzhou, Fujian, China
| | - Ming Chen
- Department of Critical Care Medicine, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing, Jiangsu, China
| | - Yaolong Chen
- Evidence-based Medicine Center, School of Basic Medical Sciences, Lanzhou University, Lanzhou, China
| | - Yisong Cheng
- Department of Critical Care Medicine, West China Hospital of Sichuan University, Chengdu, China
| | - Yating Cui
- Department of Critical Care Medicine, The First Medical Center, Chinese PLA General Hospital, Beijing, China
| | - Qi Ding
- Department of Surgical Intensive Critical Unit, Beijing Chao-yang Hospital, Capital Medical University, Beijing, China
| | - Renyu Ding
- Department of Intensive Care Unit, The First Hospital of China Medical University, Shenyang, Liaoning, China
| | - Meili Duan
- Department of Critical Care Medicine, Beijing Friendship Hospital, Capital Medical University, Beijing, China
| | - Tao Gao
- Department of Critical Care Medicine, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing, Jiangsu, China
| | - Xiaohua Gu
- Department of Critical Care Medicine, Northern Jiangsu People’s Hospital; Clinical Medical College, Yangzhou University, Yangzhou, China
| | - Hongli He
- Intensive Care Unit, Sichuan Academy of Medical Sciences & Sichuan Provincial People’s Hospital, School of Medicine of University of Electronic Science and Technology, Chengdu, China
| | - Jiawei He
- Department of Critical Care Medicine, Beijing Friendship Hospital, Capital Medical University, Beijing, China
| | - Bo Hu
- Department of Critical Care Medicine, Zhongnan Hospital of Wuhan University, Wuhan, Hubei, China
| | - Chang Hu
- Department of Critical Care Medicine, Zhongnan Hospital of Wuhan University, Wuhan, Hubei, China
| | - Rui Huang
- Department of Critical Care Medicine, The Second Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, China
| | - Xiaobo Huang
- Intensive Care Unit, Sichuan Academy of Medical Sciences & Sichuan Provincial People’s Hospital, School of Medicine of University of Electronic Science and Technology, Chengdu, China
| | - Huizhen Jiang
- Department of Information Center, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, China
| | - Jing Jiang
- Department of Critical Care Medicine, Chongqing General Hospital, Chongqing, China
| | - Yunping Lan
- Intensive Care Unit, Sichuan Academy of Medical Sciences & Sichuan Provincial People’s Hospital, School of Medicine of University of Electronic Science and Technology, Chengdu, China
| | - Jun Li
- Department of Critical Care Medicine, Fujian Provincial Key Laboratory of Critical Care Medicine, Shengli Clinical Medical College of Fujian Medical University, Fujian Provincial Hospital, Fujian Provincial Center for Critical Care Medicine, Fuzhou, Fujian, China
| | - Linfeng Li
- Medical Data Research Institute, Chongqing Medical University, Chongqing, China
| | - Lu Li
- Department of Critical Care Medicine, Zhongnan Hospital of Wuhan University, Wuhan, Hubei, China
| | - Wenxiong Li
- Department of Surgical Intensive Critical Unit, Beijing Chao-yang Hospital, Capital Medical University, Beijing, China
| | - Yongzai Li
- Information Network Center, QiLu Hospital, ShanDong University, Jinan, China
| | - Jin Lin
- Department of Critical Care Medicine, Beijing Friendship Hospital, Capital Medical University, Beijing, China
| | - Xufei Luo
- Evidence-based Medicine Center, School of Basic Medical Sciences, Lanzhou University, Lanzhou, China
| | - Feng Lyu
- Department of Computer Science and Engineering, Central South University, Changsha, China
| | - Zhi Mao
- Department of Critical Care Medicine, The First Medical Center, Chinese PLA General Hospital, Beijing, China
| | - He Miao
- Department of Intensive Care Unit, The First Hospital of China Medical University, Shenyang, Liaoning, China
| | - Xiaopu Shang
- Department of Information Management, Beijing Jiaotong University, Beijing, China
| | - Xiuling Shang
- Department of Critical Care Medicine, Fujian Provincial Key Laboratory of Critical Care Medicine, Shengli Clinical Medical College of Fujian Medical University, Fujian Provincial Hospital, Fujian Provincial Center for Critical Care Medicine, Fuzhou, Fujian, China
| | - You Shang
- Department of Critical Care Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Yuwen Shen
- Intensive Care Unit of Cardiovascular Surgery Department, Qilu Hospital of Shandong University, Jinan, China
| | - Yinghuan Shi
- National Institute of Healthcare Data Science, Nanjing University, Nanjing, China
| | - Qihang Sun
- British Chinese Society of Health Informatics, Beijing, China
| | - Weijun Sun
- Faculty of Automation, Guangdong University of Technology, Guangzhou, China
| | - Zhiyun Tang
- Department of Intensive Care Unit, Zhejiang Provincial People’s Hospital, Affiliated People’s Hospital, Emergency and Intensive Care Unit Center, Hangzhou Medical College, Hangzhou, Zhejiang, China
| | - Bo Wang
- Department of Critical Care Medicine, West China Hospital of Sichuan University, Chengdu, China
| | - Haijun Wang
- Department of Intensive Care Unit, National Cancer Center/National Clinical Research Center, Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Hongliang Wang
- Department of Critical Care Medicine, The Second Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, China
| | - Li Wang
- Department of Epidemiology and Biostatistics, Institute of Basic Medical Sciences Chinese Academy of Medical Sciences; School of Basic Medicine Peking Union Medical College, Beijing, China
| | - Luhao Wang
- Department of Critical Care Medicine, Sun Yat-Sen University First Affiliated Hospital, Guangzhou, China
| | - Sicong Wang
- Department of Critical Care Medicine, The Second Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, China
| | - Zhanwen Wang
- Intensive Care Unit, XiangYa Hospital, Central South University, Changsha, China
- National Clinical Research Center for Geriatric Disorders, Xiang Ya Hospital, Central South University, Changsha, China
- Hunan Provincial Clinical Research Center for Critical Care Medicine, Xiang Ya Hospital, Central South University, Changsha, China
| | - Zhong Wang
- Department of Intensive Care Unit, The First Hospital of China Medical University, Shenyang, Liaoning, China
| | - Dong Wei
- National Institute of Healthcare Data Science, Nanjing University, Nanjing, China
| | - Jianfeng Wu
- Intensive Care Unit, XiangYa Hospital, Central South University, Changsha, China
| | - Qin Wu
- Department of Critical Care Medicine, West China Hospital of Sichuan University, Chengdu, China
| | - Xuezhong Xing
- Department of Epidemiology and Biostatistics, Institute of Basic Medical Sciences Chinese Academy of Medical Sciences; School of Basic Medicine Peking Union Medical College, Beijing, China
| | - Jin Yang
- Department of Critical Care Medicine, Chongqing General Hospital, Chongqing, China
| | - Xianghong Yang
- Department of Intensive Care Unit, National Cancer Center/National Clinical Research Center, Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Jiangquan Yu
- Department of Critical Care Medicine, Northern Jiangsu People’s Hospital; Clinical Medical College, Yangzhou University, Yangzhou, China
| | - Wenkui Yu
- Department of Critical Care Medicine, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing, Jiangsu, China
| | - Yuan Yu
- Intensive Care Unit of Cardiovascular Surgery Department, Qilu Hospital of Shandong University, Jinan, China
| | - Hao Yuan
- Department of Critical Care Medicine, Sun Yat-Sen University First Affiliated Hospital, Guangzhou, China
| | - Qian Zhai
- National Institute of Healthcare Data Science, Nanjing University, Nanjing, China
| | - Hao Zhang
- Department of Intensive Care Unit, National Cancer Center/National Clinical Research Center, Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Lina Zhang
- Intensive Care Unit, XiangYa Hospital, Central South University, Changsha, China
- National Clinical Research Center for Geriatric Disorders, Xiang Ya Hospital, Central South University, Changsha, China
- Hunan Provincial Clinical Research Center for Critical Care Medicine, Xiang Ya Hospital, Central South University, Changsha, China
| | - Meng Zhang
- Department of Critical Care Medicine, Chongqing General Hospital, Chongqing, China
| | - Zhongheng Zhang
- Department of Emergency Medicine, Key Laboratory of Precision Medicine in Diagnosis and Monitoring Research of Zhejiang Province, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Chunguang Zhao
- Intensive Care Unit, XiangYa Hospital, Central South University, Changsha, China
- National Clinical Research Center for Geriatric Disorders, Xiang Ya Hospital, Central South University, Changsha, China
- Hunan Provincial Clinical Research Center for Critical Care Medicine, Xiang Ya Hospital, Central South University, Changsha, China
| | - Ruiqiang Zheng
- Department of Critical Care Medicine, Northern Jiangsu People’s Hospital; Clinical Medical College, Yangzhou University, Yangzhou, China
| | - Lei Zhong
- Department of Intensive Care Unit, National Cancer Center/National Clinical Research Center, Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Feihu Zhou
- Department of Critical Care Medicine, The First Medical Center, Chinese PLA General Hospital, Beijing, China
| | - Weiguo Zhu
- Department of General Medicine, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, China
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Mainali S, Cardim D, Sarwal A, Merck LH, Yeatts SD, Czosnyka M, Shutter L. Prolonged Automated Robotic TCD Monitoring in Acute Severe TBI: Study Design and Rationale. Neurocrit Care 2022; 37:267-275. [PMID: 35381966 DOI: 10.1007/s12028-022-01483-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2021] [Accepted: 03/01/2022] [Indexed: 10/18/2022]
Abstract
BACKGROUND Transcranial Doppler ultrasonography (TCD) is a portable, bedside, noninvasive diagnostic tool used for the real-time assessment of cerebral hemodynamics. Despite the evident utility of TCD and the ability of this technique to function as a stethoscope to the brain, its use has been limited to specialized centers because of the dearth of technical and clinical expertise required to acquire and interpret the cerebrovascular parameters. Additionally, the conventional pragmatic episodic TCD monitoring protocols lack dynamic real-time feedback to guide time-critical clinical interventions. Fortunately, with the recent advent of automated robotic TCD technology in conjunction with the automated software for TCD data processing, we now have the technology to automatically acquire TCD data and obtain clinically relevant information in real-time. By obviating the need for highly trained clinical personnel, this technology shows great promise toward a future of widespread noninvasive monitoring to guide clinical care in patients with acute brain injury. METHODS Here, we describe a proposal for a prospective observational multicenter clinical trial to evaluate the safety and feasibility of prolonged automated robotic TCD monitoring in patients with severe acute traumatic brain injury (TBI). We will enroll patients with severe non-penetrating TBI with concomitant invasive multimodal monitoring including, intracranial pressure, brain tissue oxygenation, and brain temperature monitoring as part of standard of care in centers with varying degrees of TCD availability and experience. Additionally, we propose to evaluate the correlation of pertinent TCD-based cerebral autoregulation indices such as the critical closing pressure, and the pressure reactivity index with the brain tissue oxygenation values obtained invasively. CONCLUSIONS The overarching goal of this study is to establish safety and feasibility of prolonged automated TCD monitoring for patients with TBI in the intensive care unit and identify clinically meaningful and pragmatic noninvasive targets for future interventions.
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Affiliation(s)
- Shraddha Mainali
- Department of Neurology, Virginial Commonwealth University, Richmond, VA, USA.
| | - Danilo Cardim
- Department of Neurology, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Aarti Sarwal
- Department of Neurology, Wake Forest School of Medicine, Winston Salem, NC, USA
| | - Lisa H Merck
- Departments of Emergency Medicine and Neurology, University of Florida College of Medicine, Gainesville, FL, USA
| | - Sharon D Yeatts
- Department of Public Health Sciences, Medical University of South Carolina, Charleston, SC, USA
| | - Marek Czosnyka
- Brain Physics Laboratory, Neurosurgical Unit, Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
| | - Lori Shutter
- Department of Critical Care Medicine, Neurology, and Neurosurgery, University of Pittsburgh, Pittsburgh, PA, USA
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Abstract
In this work, we introduce a deep learning architecture for evaluation on multimodal electroencephalographic (EEG) and functional near-infrared spectroscopy (fNIRS) recordings from 40 epileptic patients. Long short-term memory units and convolutional neural networks are integrated within a multimodal sequence-to-sequence autoencoder. The trained neural network predicts fNIRS signals from EEG, sans a priori, by hierarchically extracting deep features from EEG full spectra and specific EEG frequency bands. Results show that higher frequency EEG ranges are predictive of fNIRS signals with the gamma band inputs dominating fNIRS prediction as compared to other frequency envelopes. Seed based functional connectivity validates similar patterns between experimental fNIRS and our model's fNIRS reconstructions. This is the first study that shows it is possible to predict brain hemodynamics (fNIRS) from encoded neural data (EEG) in the resting human epileptic brain based on power spectrum amplitude modulation of frequency oscillations in the context of specific hypotheses about how EEG frequency bands decode fNIRS signals.
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McCredie VA, Chavarría J, Baker AJ. How do we identify the crashing traumatic brain injury patient - the intensivist's view. Curr Opin Crit Care 2021; 27:320-327. [PMID: 33852501 PMCID: PMC8240643 DOI: 10.1097/mcc.0000000000000825] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
PURPOSE OF REVIEW Over 40% of patients with severe traumatic brain injury (TBI) show clinically significant neurological worsening within the acute admission period. This review addresses the importance of identifying the crashing TBI patient, the difficulties appreciating clinical neurological deterioration in the comatose patient and how neuromonitoring may provide continuous real-time ancillary information to detect physiologic worsening. RECENT FINDINGS The latest editions of the Brain Trauma Foundation's Guidelines omitted management algorithms for adult patients with severe TBI. Subsequently, three consensus-based management algorithms were published using a Delphi method approach to provide a bridge between the evidence-based guidelines and integration of the individual treatment modalities at the bedside. These consensus statements highlight the serious situation of critical deterioration requiring emergent evaluation and guidance on sedation holds to obtain a neurological examination while balancing the potential risks of inducing a stress response. SUMMARY One of the central tenets of neurocritical care is to detect the brain in trouble. The first and most fundamental neurological monitoring tool is the clinical exam. Ancillary neuromonitoring data may provide early physiologic biomarkers to help anticipate, prevent or halt secondary brain injury processes. Future research should seek to understand how data integration and visualization technologies may reduce the cognitive workload to improve timely detection of neurological deterioration.
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Affiliation(s)
- Victoria A. McCredie
- Interdepartmental Division of Critical Care Medicine, University of Toronto
- Toronto Western Hospital, University Health Network
- Krembil Research Institute, Toronto Western Hospital
- Department of Critical Care Medicine, Sunnybrook Health Sciences Centre
| | - Javier Chavarría
- Interdepartmental Division of Critical Care Medicine, University of Toronto
| | - Andrew J. Baker
- Interdepartmental Division of Critical Care Medicine, University of Toronto
- Department of Critical Care, St. Michael's Hospital Toronto, University of Toronto
- Department of Anesthesia, Keenan Research Centre for Biomedical Science, St. Michael's Hospital Toronto, University of Toronto, Toronto, Ontario, Canada
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Waller RG, Wright MC, Segall N, Nesbitt P, Reese T, Borbolla D, Del Fiol G. Novel displays of patient information in critical care settings: a systematic review. J Am Med Inform Assoc 2020; 26:479-489. [PMID: 30865769 DOI: 10.1093/jamia/ocy193] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2018] [Revised: 09/28/2018] [Accepted: 01/02/2019] [Indexed: 12/14/2022] Open
Abstract
OBJECTIVE Clinician information overload is prevalent in critical care settings. Improved visualization of patient information may help clinicians cope with information overload, increase efficiency, and improve quality. We compared the effect of information display interventions with usual care on patient care outcomes. MATERIALS AND METHODS We conducted a systematic review including experimental and quasi-experimental studies of information display interventions conducted in critical care and anesthesiology settings. Citations from January 1990 to June 2018 were searched in PubMed and IEEE Xplore. Reviewers worked independently to screen articles, evaluate quality, and abstract primary outcomes and display features. RESULTS Of 6742 studies identified, 22 studies evaluating 17 information displays met the study inclusion criteria. Information display categories included comprehensive integrated displays (3 displays), multipatient dashboards (7 displays), physiologic and laboratory monitoring (5 displays), and expert systems (2 displays). Significant improvement on primary outcomes over usual care was reported in 12 studies for 9 unique displays. Improvement was found mostly with comprehensive integrated displays (4 of 6 studies) and multipatient dashboards (5 of 7 studies). Only 1 of 5 randomized controlled trials had a positive effect in the primary outcome. CONCLUSION We found weak evidence suggesting comprehensive integrated displays improve provider efficiency and process outcomes, and multipatient dashboards improve compliance with care protocols and patient outcomes. Randomized controlled trials of physiologic and laboratory monitoring displays did not show improvement in primary outcomes, despite positive results in simulated settings. Important research translation gaps from laboratory to actual critical care settings exist.
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Affiliation(s)
- Rosalie G Waller
- Department of Biomedical Informatics, University of Utah, Salt Lake City, Utah, USA
| | - Melanie C Wright
- Trinity Health and Saint Alphonsus Regional Medical Center, Boise, ID, USA
| | - Noa Segall
- Department of Anesthesiology, Duke University Medical Center, Durham, North Carolina, USA
| | - Paige Nesbitt
- Trinity Health and Saint Alphonsus Regional Medical Center, Boise, ID, USA
| | - Thomas Reese
- Department of Biomedical Informatics, University of Utah, Salt Lake City, Utah, USA
| | - Damian Borbolla
- Department of Biomedical Informatics, University of Utah, Salt Lake City, Utah, USA
| | - Guilherme Del Fiol
- Department of Biomedical Informatics, University of Utah, Salt Lake City, Utah, USA
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Baldassano SN, Hill CE, Shankar A, Bernabei J, Khankhanian P, Litt B. Big data in status epilepticus. Epilepsy Behav 2019; 101:106457. [PMID: 31444029 PMCID: PMC6944751 DOI: 10.1016/j.yebeh.2019.106457] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/30/2019] [Accepted: 07/26/2019] [Indexed: 12/23/2022]
Abstract
Status epilepticus care and treatment are already being touched by the revolution in data science. New approaches designed to leverage the tremendous potential of "big data" in the clinical sphere are enabling researchers and clinicians to extract information from sources such as administrative claims data, the electronic medical health record, and continuous physiologic monitoring data streams. Algorithmic methods of data extraction also offer potential to fuse multimodal data (including text-based documentation, imaging data, and time-series data) to improve patient assessment and stratification beyond the manual capabilities of individual physicians. Still, the potential of data science to impact the diagnosis, treatment, and minute-to-minute care of patients with status epilepticus is only starting to be appreciated. In this brief review, we discuss how data science is impacting the field and draw examples from the following three main areas: (1) analysis of insurance claims from large administrative datasets to evaluate the impact of continuous electroencephalogram (EEG) monitoring on clinical outcomes; (2) natural language processing of the electronic health record to find, classify, and stratify patients for prognostication and treatment; and (3) real-time systems for data analysis, data reduction, and multimodal data fusion to guide therapy in real time. While early, it is our hope that these examples will stimulate investigators to leverage data science, computer science, and engineering methods to improve the care and outcome of patients with status epilepticus and other neurological disorders. This article is part of the Special Issue "Proceedings of the 7th London-Innsbruck Colloquium on Status Epilepticus and Acute Seizures".
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Affiliation(s)
- Steven N. Baldassano
- Department of Bioengineering, University of Pennsylvania, 210 South 33rd Street, Philadelphia, PA 19104, United States,Center for Neuroengineering and Therapeutics, University of Pennsylvania, 240 South 33rd Street, Philadelphia, PA 19104, United States
| | - Chloé E. Hill
- Department of Neurology, University of Michigan, 1500 East Medical Center Drive, Ann Arbor, MI 48109, United States
| | - Arjun Shankar
- Department of Bioengineering, University of Pennsylvania, 210 South 33rd Street, Philadelphia, PA 19104, United States,Center for Neuroengineering and Therapeutics, University of Pennsylvania, 240 South 33rd Street, Philadelphia, PA 19104, United States
| | - John Bernabei
- Department of Bioengineering, University of Pennsylvania, 210 South 33rd Street, Philadelphia, PA 19104, United States,Center for Neuroengineering and Therapeutics, University of Pennsylvania, 240 South 33rd Street, Philadelphia, PA 19104, United States
| | - Pouya Khankhanian
- Department of Neurology, University of Michigan, 1500 East Medical Center Drive, Ann Arbor, MI 48109, United States,Department of Neurology, Penn Epilepsy Center, University of Pennsylvania, 3400 Spruce Street, Philadelphia, PA 19104, United States
| | - Brian Litt
- Department of Bioengineering, University of Pennsylvania, 210 South 33rd Street, Philadelphia, PA 19104, United States,Center for Neuroengineering and Therapeutics, University of Pennsylvania, 240 South 33rd Street, Philadelphia, PA 19104, United States,Department of Neurology, Penn Epilepsy Center, University of Pennsylvania, 3400 Spruce Street, Philadelphia, PA 19104, United States
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Wright MC, Borbolla D, Waller RG, Del Fiol G, Reese T, Nesbitt P, Segall N. Critical care information display approaches and design frameworks: A systematic review and meta-analysis. J Biomed Inform 2019; 3:100041. [PMID: 31423485 PMCID: PMC6696941 DOI: 10.1016/j.yjbinx.2019.100041] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2018] [Revised: 06/10/2019] [Accepted: 06/16/2019] [Indexed: 11/06/2022]
Abstract
OBJECTIVE To systematically review original user evaluations of patient information displays relevant to critical care and understand the impact of design frameworks and information presentation approaches on decision-making, efficiency, workload, and preferences of clinicians. METHODS We included studies that evaluated information displays designed to support real-time care decisions in critical care or anesthesiology using simulated tasks. We searched PubMed and IEEExplore from 1/1/1990 to 6/30/2018. The search strategy was developed iteratively with calibration against known references. Inclusion screening was completed independently by two authors. Extraction of display features, design processes, and evaluation method was completed by one and verified by a second author. RESULTS Fifty-six manuscripts evaluating 32 critical care and 22 anesthesia displays were included. Primary outcome metrics included clinician accuracy and efficiency in recognizing, diagnosing, and treating problems. Implementing user-centered design (UCD) processes, especially iterative evaluation and redesign, resulted in positive impact in outcomes such as accuracy and efficiency. Innovative display approaches that led to improved human-system performance in critical care included: (1) improving the integration and organization of information, (2) improving the representation of trend information, and (3) implementing graphical approaches to make relationships between data visible. CONCLUSION Our review affirms the value of key principles of UCD. Improved information presentation can facilitate faster information interpretation and more accurate diagnoses and treatment. Improvements to information organization and support for rapid interpretation of time-based relationships between related quantitative data is warranted. Designers and developers are encouraged to involve users in formal iterative design and evaluation activities in the design of electronic health records (EHRs), clinical informatics applications, and clinical devices.
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Affiliation(s)
- Melanie C. Wright
- Trinity Health, Livonia, MI, USA
- Saint Alphonsus Regional Medical Center, Boise, ID, USA
| | - Damian Borbolla
- Biomedical Informatics, University of Utah, Salt Lake City, UT, USA
| | | | | | - Thomas Reese
- Biomedical Informatics, University of Utah, Salt Lake City, UT, USA
| | - Paige Nesbitt
- Saint Alphonsus Regional Medical Center, Boise, ID, USA
| | - Noa Segall
- Anesthesiology, Duke University, Durham, NC, USA
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Szatala A, Young B. Implementation of a Data Acquisition and Integration Device in the Neurologic Intensive Care Unit. AACN Adv Crit Care 2019; 30:40-47. [PMID: 30842072 DOI: 10.4037/aacnacc2019188] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/01/2022]
Abstract
The neurologic intensive care unit has evolved into a data-rich, complex arena. Various neurologic monitors, collectively referred to as multimodality monitoring, provide clinicians with a plethora of real-time information about a comatose patient's condition. The time and cognitive burden required to synthesize the available data and reach meaningful clinical conclusions can be overwhelming. The Moberg Component Neuromonitoring System (Moberg Research, Inc) is a data acquisition and integration device that collects data from multiple monitors, displaying them on a single screen in a way that highlights physiological trends throughout a patient's clinical course. Implementation of the Moberg Component Neuromonitoring System in the neurologic intensive care unit can improve understanding of a patient's neurophysiology, enhance clinical decision-making, and improve quality of care. Use of a staged process of implementation including exploration, installation, initial implementation, and full implementation can bring technology to the bedside in a sustainable fashion.
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Affiliation(s)
- Amanda Szatala
- Amanda Szatala is Clinical Nurse Specialist, Neurointensive and Progressive Care Unit, Penn Presbyterian Medical Center, 51 N 39th St, Philadelphia, PA 19104 . Bethany Young is Clinical Nurse Specialist, Neurointensive Care Unit, Hospital of the University of Pennsylvania, Philadelphia
| | - Bethany Young
- Amanda Szatala is Clinical Nurse Specialist, Neurointensive and Progressive Care Unit, Penn Presbyterian Medical Center, 51 N 39th St, Philadelphia, PA 19104 . Bethany Young is Clinical Nurse Specialist, Neurointensive Care Unit, Hospital of the University of Pennsylvania, Philadelphia
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Abstract
PURPOSE OF REVIEW This article focuses on the multiple neuromonitoring devices that can be used to collect bedside data in the neurocritical care unit and the methodology to integrate them into a multimodality monitoring system. The article describes how to apply the collected data to appreciate the physiologic changes and develop therapeutic approaches to prevent secondary injury. RECENT FINDINGS The neurologic examination has served as the primary monitor for secondary brain injury in patients admitted to the neurocritical care unit. However, the International Multidisciplinary Consensus Conference on Multimodality Monitoring in Neurocritical Care concluded that frequent bedside examinations are not sufficient to detect and prevent secondary brain injury and that integration of multimodality monitoring with advanced informatics tools will most likely enhance our assessments compared to the clinical examinations alone. This article reviews the invasive and noninvasive technologies used to monitor focal and global neurophysiologic cerebral alterations. SUMMARY Multimodal monitoring is still in the early stages of development. Research is still needed to establish more advanced monitors with the bioinformatics to identify useful trends from data gathered to predict clinical outcome or prevent secondary brain injury.
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Chen S, Gallagher MJ, Hogg F, Papadopoulos MC, Saadoun S. Visibility Graph Analysis of Intraspinal Pressure Signal Predicts Functional Outcome in Spinal Cord Injured Patients. J Neurotrauma 2018; 35:2947-2956. [DOI: 10.1089/neu.2018.5775] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022] Open
Affiliation(s)
- Suliang Chen
- Academic Neurosurgery Unit, St. George's, University of London, London, United Kingdom
| | - Mathew J. Gallagher
- Academic Neurosurgery Unit, St. George's, University of London, London, United Kingdom
| | - Florence Hogg
- Academic Neurosurgery Unit, St. George's, University of London, London, United Kingdom
| | | | - Samira Saadoun
- Academic Neurosurgery Unit, St. George's, University of London, London, United Kingdom
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11
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Maas AIR, Menon DK, Adelson PD, Andelic N, Bell MJ, Belli A, Bragge P, Brazinova A, Büki A, Chesnut RM, Citerio G, Coburn M, Cooper DJ, Crowder AT, Czeiter E, Czosnyka M, Diaz-Arrastia R, Dreier JP, Duhaime AC, Ercole A, van Essen TA, Feigin VL, Gao G, Giacino J, Gonzalez-Lara LE, Gruen RL, Gupta D, Hartings JA, Hill S, Jiang JY, Ketharanathan N, Kompanje EJO, Lanyon L, Laureys S, Lecky F, Levin H, Lingsma HF, Maegele M, Majdan M, Manley G, Marsteller J, Mascia L, McFadyen C, Mondello S, Newcombe V, Palotie A, Parizel PM, Peul W, Piercy J, Polinder S, Puybasset L, Rasmussen TE, Rossaint R, Smielewski P, Söderberg J, Stanworth SJ, Stein MB, von Steinbüchel N, Stewart W, Steyerberg EW, Stocchetti N, Synnot A, Te Ao B, Tenovuo O, Theadom A, Tibboel D, Videtta W, Wang KKW, Williams WH, Wilson L, Yaffe K, Adams H, Agnoletti V, Allanson J, Amrein K, Andaluz N, Anke A, Antoni A, van As AB, Audibert G, Azaševac A, Azouvi P, Azzolini ML, Baciu C, Badenes R, Barlow KM, Bartels R, Bauerfeind U, Beauchamp M, Beer D, Beer R, Belda FJ, Bellander BM, Bellier R, Benali H, Benard T, Beqiri V, Beretta L, Bernard F, Bertolini G, Bilotta F, Blaabjerg M, den Boogert H, Boutis K, Bouzat P, Brooks B, Brorsson C, Bullinger M, Burns E, Calappi E, Cameron P, Carise E, Castaño-León AM, Causin F, Chevallard G, Chieregato A, Christie B, Cnossen M, Coles J, Collett J, Della Corte F, Craig W, Csato G, Csomos A, Curry N, Dahyot-Fizelier C, Dawes H, DeMatteo C, Depreitere B, Dewey D, van Dijck J, Đilvesi Đ, Dippel D, Dizdarevic K, Donoghue E, Duek O, Dulière GL, Dzeko A, Eapen G, Emery CA, English S, Esser P, Ezer E, Fabricius M, Feng J, Fergusson D, Figaji A, Fleming J, Foks K, Francony G, Freedman S, Freo U, Frisvold SK, Gagnon I, Galanaud D, Gantner D, Giraud B, Glocker B, Golubovic J, Gómez López PA, Gordon WA, Gradisek P, Gravel J, Griesdale D, Grossi F, Haagsma JA, Håberg AK, Haitsma I, Van Hecke W, Helbok R, Helseth E, van Heugten C, Hoedemaekers C, Höfer S, Horton L, Hui J, Huijben JA, Hutchinson PJ, Jacobs B, van der Jagt M, Jankowski S, Janssens K, Jelaca B, Jones KM, Kamnitsas K, Kaps R, Karan M, Katila A, Kaukonen KM, De Keyser V, Kivisaari R, Kolias AG, Kolumbán B, Kolundžija K, Kondziella D, Koskinen LO, Kovács N, Kramer A, Kutsogiannis D, Kyprianou T, Lagares A, Lamontagne F, Latini R, Lauzier F, Lazar I, Ledig C, Lefering R, Legrand V, Levi L, Lightfoot R, Lozano A, MacDonald S, Major S, Manara A, Manhes P, Maréchal H, Martino C, Masala A, Masson S, Mattern J, McFadyen B, McMahon C, Meade M, Melegh B, Menovsky T, Moore L, Morgado Correia M, Morganti-Kossmann MC, Muehlan H, Mukherjee P, Murray L, van der Naalt J, Negru A, Nelson D, Nieboer D, Noirhomme Q, Nyirádi J, Oddo M, Okonkwo DO, Oldenbeuving AW, Ortolano F, Osmond M, Payen JF, Perlbarg V, Persona P, Pichon N, Piippo-Karjalainen A, Pili-Floury S, Pirinen M, Ple H, Poca MA, Posti J, Van Praag D, Ptito A, Radoi A, Ragauskas A, Raj R, Real RGL, Reed N, Rhodes J, Robertson C, Rocka S, Røe C, Røise O, Roks G, Rosand J, Rosenfeld JV, Rosenlund C, Rosenthal G, Rossi S, Rueckert D, de Ruiter GCW, Sacchi M, Sahakian BJ, Sahuquillo J, Sakowitz O, Salvato G, Sánchez-Porras R, Sándor J, Sangha G, Schäfer N, Schmidt S, Schneider KJ, Schnyer D, Schöhl H, Schoonman GG, Schou RF, Sir Ö, Skandsen T, Smeets D, Sorinola A, Stamatakis E, Stevanovic A, Stevens RD, Sundström N, Taccone FS, Takala R, Tanskanen P, Taylor MS, Telgmann R, Temkin N, Teodorani G, Thomas M, Tolias CM, Trapani T, Turgeon A, Vajkoczy P, Valadka AB, Valeinis E, Vallance S, Vámos Z, Vargiolu A, Vega E, Verheyden J, Vik A, Vilcinis R, Vleggeert-Lankamp C, Vogt L, Volovici V, Voormolen DC, Vulekovic P, Vande Vyvere T, Van Waesberghe J, Wessels L, Wildschut E, Williams G, Winkler MKL, Wolf S, Wood G, Xirouchaki N, Younsi A, Zaaroor M, Zelinkova V, Zemek R, Zumbo F. Traumatic brain injury: integrated approaches to improve prevention, clinical care, and research. Lancet Neurol 2017; 16:987-1048. [DOI: 10.1016/s1474-4422(17)30371-x] [Citation(s) in RCA: 822] [Impact Index Per Article: 117.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2016] [Revised: 07/06/2017] [Accepted: 09/27/2017] [Indexed: 12/11/2022]
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Lin YL, Guerguerian AM, Tomasi J, Laussen P, Trbovich P. "Usability of data integration and visualization software for multidisciplinary pediatric intensive care: a human factors approach to assessing technology". BMC Med Inform Decis Mak 2017; 17:122. [PMID: 28806954 PMCID: PMC5557066 DOI: 10.1186/s12911-017-0520-7] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2017] [Accepted: 08/04/2017] [Indexed: 11/17/2022] Open
Abstract
Background Intensive care clinicians use several sources of data in order to inform decision-making. We set out to evaluate a new interactive data integration platform called T3™ made available for pediatric intensive care. Three primary functions are supported: tracking of physiologic signals, displaying trajectory, and triggering decisions, by highlighting data or estimating risk of patient instability. We designed a human factors study to identify interface usability issues, to measure ease of use, and to describe interface features that may enable or hinder clinical tasks. Methods Twenty-two participants, consisting of bedside intensive care physicians, nurses, and respiratory therapists, tested the T3™ interface in a simulation laboratory setting. Twenty tasks were performed with a true-to-setting, fully functional, prototype, populated with physiological and therapeutic intervention patient data. Primary data visualization was time series and secondary visualizations were: 1) shading out-of-target values, 2) mini-trends with exaggerated maxima and minima (sparklines), and 3) bar graph of a 16-parameter indicator. Task completion was video recorded and assessed using a use error rating scale. Usability issues were classified in the context of task and type of clinician. A severity rating scale was used to rate potential clinical impact of usability issues. Results Time series supported tracking a single parameter but partially supported determining patient trajectory using multiple parameters. Visual pattern overload was observed with multiple parameter data streams. Automated data processing using shading and sparklines was often ignored but the 16-parameter data reduction algorithm, displayed as a persistent bar graph, was visually intuitive. However, by selecting or automatically processing data, triggering aids distorted the raw data that clinicians use regularly. Consequently, clinicians could not rely on new data representations because they did not know how they were established or derived. Conclusions Usability issues, observed through contextual use, provided directions for tangible design improvements of data integration software that may lessen use errors and promote safe use. Data-driven decision making can benefit from iterative interface redesign involving clinician-users in simulated environments. This study is a first step in understanding how software can support clinicians’ decision making with integrated continuous monitoring data. Importantly, testing of similar platforms by all the different disciplines who may become clinician users is a fundamental step necessary to understand the impact on clinical outcomes of decision aids. Electronic supplementary material The online version of this article (doi:10.1186/s12911-017-0520-7) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Ying Ling Lin
- Institute of Biomaterials and Biomedical Engineering, University of Toronto, Rosebrugh Building (RS), 164 College Street, Room 407, Toronto, ON, M5S 3G9, Canada.,Department of Critical Care Medicine, The Hospital for Sick Children, Canada, 555 University Ave., 2nd Floor, Atrium - Room 2830A, Toronto, ON, M5G 1X8, Canada
| | - Anne-Marie Guerguerian
- Institute of Biomaterials and Biomedical Engineering, University of Toronto, Rosebrugh Building (RS), 164 College Street, Room 407, Toronto, ON, M5S 3G9, Canada.,Department of Critical Care Medicine, The Hospital for Sick Children, Canada, 555 University Ave., 2nd Floor, Atrium - Room 2830A, Toronto, ON, M5G 1X8, Canada.,Neurosciences and Mental Health Research, The Hospital for Sick Children Research Institute, Peter Gilgan Centre for Research & Learning, 686 Bay Street, Toronto, ON, M5G 0A4, Canada
| | - Jessica Tomasi
- Institute of Biomaterials and Biomedical Engineering, University of Toronto, Rosebrugh Building (RS), 164 College Street, Room 407, Toronto, ON, M5S 3G9, Canada
| | - Peter Laussen
- Department of Critical Care Medicine, The Hospital for Sick Children, Canada, 555 University Ave., 2nd Floor, Atrium - Room 2830A, Toronto, ON, M5G 1X8, Canada
| | - Patricia Trbovich
- Institute of Biomaterials and Biomedical Engineering, University of Toronto, Rosebrugh Building (RS), 164 College Street, Room 407, Toronto, ON, M5S 3G9, Canada. .,Institute of Health Policy, Management and Evaluation, University of Toronto, 155 College St., Suite 425, Toronto, ON, M5T 3M6, Canada. .,Research and Innovation, North York General Hospital, 4001 Leslie Street, Toronto, ON, M2K 1E1, Canada.
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13
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Abstract
PURPOSE OF REVIEW Big data is the new hype in business and healthcare. Data storage and processing has become cheap, fast, and easy. Business analysts and scientists are trying to design methods to mine these data for hidden knowledge. Neurocritical care is a field that typically produces large amounts of patient-related data, and these data are increasingly being digitized and stored. This review will try to look beyond the hype, and focus on possible applications in neurointensive care amenable to Big Data research that can potentially improve patient care. RECENT FINDINGS The first challenge in Big Data research will be the development of large, multicenter, and high-quality databases. These databases could be used to further investigate recent findings from mathematical models, developed in smaller datasets. Randomized clinical trials and Big Data research are complementary. Big Data research might be used to identify subgroups of patients that could benefit most from a certain intervention, or can be an alternative in areas where randomized clinical trials are not possible. SUMMARY The processing and the analysis of the large amount of patient-related information stored in clinical databases is beyond normal human cognitive ability. Big Data research applications have the potential to discover new medical knowledge, and improve care in the neurointensive care unit.
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Fartoumi S, Emeriaud G, Roumeliotis N, Brossier D, Sawan M. Computerized Decision Support System for Traumatic Brain Injury Management. J Pediatr Intensive Care 2016; 5:101-107. [PMID: 31110893 DOI: 10.1055/s-0035-1569997] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2015] [Accepted: 10/09/2015] [Indexed: 10/22/2022] Open
Abstract
Mortality and morbidity related to traumatic brain injury (TBI) present a major health care burden. Patients with severe TBI must be managed rapidly and efficiently to minimize secondary brain injury potentially leading to permanent sequelae. This is especially important in young patients, whose brain is still in development, making them particularly susceptible to secondary insults. The complexity of both brain injury pathophysiology and the intensive care unit environment makes the management of these patients challenging, with a risk of delayed response and/or patient instability contributing to worsened outcome. Computerized assistance in TBI appears likely to improve patient management, by helping clinicians quickly analyze and respond to ongoing clinical changes and optimizing patient status by guiding management. Currently, computerized decision support systems (CDSSs) do not feature continuous medical assistance with individualized treatment plans. This review presents new developments in CDSSs specialized in TBI. We also present the framework for future CDSSs needed to improve TBI management in real time, taking into account individual patient characteristics.
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Affiliation(s)
- Sina Fartoumi
- Polystim Neurotechnology Laboratory, Department of Electrical Engineering, Polytechnique Montreal, Quebec, Canada.,Pediatric Intensive Care Unit, CHU Sainte-Justine, Université de Montréal, Quebec, Canada
| | - Guillaume Emeriaud
- Pediatric Intensive Care Unit, CHU Sainte-Justine, Université de Montréal, Quebec, Canada
| | - Nadia Roumeliotis
- Pediatric Intensive Care Unit, CHU Sainte-Justine, Université de Montréal, Quebec, Canada
| | - David Brossier
- Pediatric Intensive Care Unit, CHU Sainte-Justine, Université de Montréal, Quebec, Canada
| | - Mohamad Sawan
- Polystim Neurotechnology Laboratory, Department of Electrical Engineering, Polytechnique Montreal, Quebec, Canada
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15
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Abstract
The challenges posed by acute brain injury (ABI) involve the management of the initial insult in addition to downstream inflammation, edema, and ischemia that can result in secondary brain injury (SBI). SBI is often subclinical, but can be detected through physiologic changes. These changes serve as a surrogate for tissue injury/cell death and are captured by parameters measured by various monitors that measure intracranial pressure (ICP), cerebral blood flow (CBF), brain tissue oxygenation (PbtO2), cerebral metabolism, and electrocortical activity. In the ideal setting, multimodality monitoring (MMM) integrates these neurological monitoring parameters with traditional hemodynamic monitoring and the physical exam, presenting the information needed to clinicians who can intervene before irreversible damage occurs. There are now consensus guidelines on the utilization of MMM, and there continue to be new advances and questions regarding its use. In this review, we examine these recommendations, recent evidence for MMM, and future directions for MMM.
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Affiliation(s)
- David Roh
- Department of Neurology and Neurocritical Care, Columbia University, 177 Fort Washington Ave, New York, NY 10032, USA
| | - Soojin Park
- Department of Neurology and Neurocritical Care, Columbia University, 177 Fort Washington Ave, New York, NY 10032, USA
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