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Kalaiarasan VV, Miller M, Han X, Foreman B, Jia X. A Novel Methodology for Intracranial Pressure Subpeak Identification Enabling Morphological Feature Analysis. IEEE Trans Biomed Eng 2025; 72:1288-1297. [PMID: 39527430 DOI: 10.1109/tbme.2024.3495542] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2024]
Abstract
OBJECTIVE The objective of this study is to propose a novel methodology for intracranial pressure (ICP) waveform subpeak identification by incorporating arterial blood pressure (ABP) and electrocardiogram (ECG) signals from patients who have undergone traumatic brain injury (TBI). METHODS This approach consisted of 1) multimodal signal pre-processing and initial manual ICP waveform morphology labeling, 2) semi-supervised training of a support vector machine (SVM) ICP waveform morphological classifier, and 3) a dynamic time warping barycenter averaging (DBA) based ICP waveform template generation and derivative dynamic time warping (DDTW)-driven ICP waveform subpeak mapping from template to incoming processed waveforms. RESULTS This proposed framework was evaluated on 30,000 ICP waveforms and resulted in an overall subpeak identification accuracy score of 98.2%. CONCLUSION The results showcased an improvement over existing methodologies and showed resilience to variations in ICP waveform morphologies from patient to patient due to the incorporation of a subject matter expert (SME) to accommodate new and unseen ICP morphologies. SIGNIFICANCE The robustness of this comprehensive approach enabled the analysis of ICP morphological features over time to provide clinicians with crucial insights regarding the development of secondary pathologies in patients and facilitate monitoring their physiological state.
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Brasil S, Godoy DA, Videtta W, Rubiano AM, Solla D, Taccone FS, Robba C, Rasulo F, Aries M, Smielewski P, Meyfroidt G, Battaglini D, Hirzallah MI, Amorim R, Sampaio G, Moulin F, Deana C, Picetti E, Kolias A, Hutchinson P, Hawryluk GW, Czosnyka M, Panerai RB, Shutter LA, Park S, Rynkowski C, Paranhos J, Silva THS, Malbouisson LMS, Paiva WS. A Comprehensive Perspective on Intracranial Pressure Monitoring and Individualized Management in Neurocritical Care: Results of a Survey with Global Experts. Neurocrit Care 2024; 41:880-892. [PMID: 38811514 PMCID: PMC11599332 DOI: 10.1007/s12028-024-02008-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2024] [Accepted: 05/01/2024] [Indexed: 05/31/2024]
Abstract
BACKGROUND Numerous trials have addressed intracranial pressure (ICP) management in neurocritical care. However, identifying its harmful thresholds and controlling ICP remain challenging in terms of improving outcomes. Evidence suggests that an individualized approach is necessary for establishing tolerance limits for ICP, incorporating factors such as ICP waveform (ICPW) or pulse morphology along with additional data provided by other invasive (e.g., brain oximetry) and noninvasive monitoring (NIM) methods (e.g., transcranial Doppler, optic nerve sheath diameter ultrasound, and pupillometry). This study aims to assess current ICP monitoring practices among experienced clinicians and explore whether guidelines should incorporate ancillary parameters from NIM and ICPW in future updates. METHODS We conducted a survey among experienced professionals involved in researching and managing patients with severe injury across low-middle-income countries (LMICs) and high-income countries (HICs). We sought their insights on ICP monitoring, particularly focusing on the impact of NIM and ICPW in various clinical scenarios. RESULTS From October to December 2023, 109 professionals from the Americas and Europe participated in the survey, evenly distributed between LMIC and HIC. When ICP ranged from 22 to 25 mm Hg, 62.3% of respondents were open to considering additional information, such as ICPW and other monitoring techniques, before adjusting therapy intensity levels. Moreover, 77% of respondents were inclined to reassess patients with ICP in the 18-22 mm Hg range, potentially escalating therapy intensity levels with the support of ICPW and NIM. Differences emerged between LMIC and HIC participants, with more LMIC respondents preferring arterial blood pressure transducer leveling at the heart and endorsing the use of NIM techniques and ICPW as ancillary information. CONCLUSIONS Experienced clinicians tend to personalize ICP management, emphasizing the importance of considering various monitoring techniques. ICPW and noninvasive techniques, particularly in LMIC settings, warrant further exploration and could potentially enhance individualized patient care. The study suggests updating guidelines to include these additional components for a more personalized approach to ICP management.
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Affiliation(s)
- Sérgio Brasil
- Division of Neurosurgery, Department of Neurology, School of Medicine University of São Paulo, Av. Dr. Eneas de Carvalho Aguiar 255, São Paulo, Brazil.
| | | | - Walter Videtta
- Intensive Care Unit, Hospital Posadas, Buenos Aires, Argentina
| | | | - Davi Solla
- Division of Neurosurgery, Department of Neurology, School of Medicine University of São Paulo, Av. Dr. Eneas de Carvalho Aguiar 255, São Paulo, Brazil
| | - Fabio Silvio Taccone
- Department of Intensive Care, Hôpital Universitaire de Bruxelles, Université Libre de Bruxelles, Brussels, Belgium
| | - Chiara Robba
- Anesthesia and Intensive Care, Scientific Institute for Research, Hospitalization and Healthcare, Policlínico San Martino, Genoa, Italy
| | - Frank Rasulo
- Neuroanesthesia, Neurocritical and Postoperative Care, Spedali Civili University Affiliated Hospital of Brescia, Brescia, Italy
| | - Marcel Aries
- Department of Intensive Care, Maastricht University Medical Center, Maastricht, The Netherlands
- School of Mental Health and Neurosciences, University Maastricht, Maastricht, The Netherlands
| | - Peter Smielewski
- Department of Clinical Neurosciences, Addenbrookes Hospital, University of Cambridge, Cambridge, UK
| | - Geert Meyfroidt
- Department and Laboratory of Intensive Care Medicine, University Hospitals Leuven, Leuven, Belgium
| | - Denise Battaglini
- Anesthesia and Intensive Care, Scientific Institute for Research, Hospitalization and Healthcare, Policlínico San Martino, Genoa, Italy
| | - Mohammad I Hirzallah
- Departments of Neurology, Neurosurgery, and Center for Space Medicine, Baylor College of Medicine, Houston, TX, USA
| | - Robson Amorim
- Division of Neurosurgery, Department of Neurology, School of Medicine University of São Paulo, Av. Dr. Eneas de Carvalho Aguiar 255, São Paulo, Brazil
| | - Gisele Sampaio
- Neurology Department, São Paulo Federal University Medical School, São Paulo, Brazil
| | - Fabiano Moulin
- Neurology Department, São Paulo Federal University Medical School, São Paulo, Brazil
| | - Cristian Deana
- Department of Anesthesia and Intensive Care, Health Integrated Agency of Friuli Centrale, Udine, Italy
| | - Edoardo Picetti
- Department of Anesthesia and Intensive Care, Parma University Hospital, Parma, Italy
| | | | | | - Gregory W Hawryluk
- Cleveland Clinic Neurological Institute, Akron General Hospital, Fairlawn, OH, USA
- Uniformed Services University, Bethesda, USA
- Brain Trauma Foundation, New York, USA
| | - Marek Czosnyka
- Division of Neurosurgery, Addenbrooke's Hospital, Cambridge, UK
| | - Ronney B Panerai
- Cerebral Haemodynamics in Ageing and Stroke Medicine Group, Department of Cardiovascular Sciences, University of Leicester, Leicester, UK
| | - Lori A Shutter
- Departments of Critical Care Medicine, Neurology and Neurosurgery, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Soojin Park
- Departments of Neurology and Biomedical Informatics, Columbia University Vagelos College of Physicians and Surgeons, New York-Presbyterian Hospital, New York, NY, USA
| | - Carla Rynkowski
- Department of Urgency and Trauma, Medical Faculty, Federal University of Health Sciences of Porto Alegre, Porto Alegre, Brazil
| | - Jorge Paranhos
- Intensive Care and Neuroemergency, Santa Casa de Misericórdia, São João del Rei, Brazil
| | - Thiago H S Silva
- Department of Intensive Care, School of Medicine University of São Paulo, São Paulo, Brazil
| | - Luiz M S Malbouisson
- Department of Intensive Care, School of Medicine University of São Paulo, São Paulo, Brazil
| | - Wellingson S Paiva
- Division of Neurosurgery, Department of Neurology, School of Medicine University of São Paulo, Av. Dr. Eneas de Carvalho Aguiar 255, São Paulo, Brazil
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Li Z, Xu F, Zhang T, Zhao B, Cai Y, Yang H, Li D, Chen M, Zhao T, Zhang X, Zhao L, Ge S, Qu Y. A Nomogram to Predict Intracranial Hypertension in Moderate Traumatic Brain Injury Patients. World Neurosurg 2024; 191:e1-e19. [PMID: 38996962 DOI: 10.1016/j.wneu.2024.04.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2024] [Revised: 03/31/2024] [Accepted: 04/01/2024] [Indexed: 07/14/2024]
Abstract
OBJECTIVE Patients with moderate traumatic brain injury (mTBI) are under the threat of intracranial hypertension (IHT). However, it is unclear which mTBI patient will develop IHT and should receive intracranial pressure (ICP)-lowering treatment or invasive ICP monitoring after admission. The purpose of the present study was to develop and validate a prediction model that estimates the risk of IHT in mTBI patients. METHODS Baseline data collected on admission of 296 mTBI patients with Glasgow Coma Scale (GCS) score of 9-11 was collected and analyzed. Multivariable logistic regression modeling with backward stepwise elimination was used to develop a prediction model for IHT. The discrimination efficacy, calibration efficacy, and clinical utility of the prediction model were evaluated. Finally, the prediction model was validated in a separate cohort of 122 patients from 3 hospitals. RESULTS Four independent prognostic factors for IHT were identified: GCS score, Marshall head computed tomography score, injury severity score, and location of contusion. The C-statistic of the prediction model in internal validation was 84.30% (95% CI: 0.794-0.892). The area under the curve for the prediction model in external validation was 82.80% (95% CI: 0.747-0.909). CONCLUSIONS A prediction model based on baseline parameters was found to be highly sensitive in distinguishing mTBI patients with GCS score of 9-11 who would suffer IHT. The high discriminative ability of the prediction model supports its use in identifying mTBI patients with GCS score of 9-11 who need ICP-lowering therapy or invasive ICP monitoring.
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Affiliation(s)
- Zhihong Li
- Department of Neurosurgery, Tangdu Hospital, Air Force Medical University, Xi'an, China
| | - Feifei Xu
- Department of Foreign Languages, Air Force Medical University, Xi'an, China
| | - Taihui Zhang
- School of Aerospace Medicine, Air Force Medicinal University, Xi'an, China
| | - Baocheng Zhao
- Department of Internal Medicine, Central Medical District of Chinese PLA General Hospital, Beijing, China
| | - Yaning Cai
- Department of Neurosurgery, Tangdu Hospital, Air Force Medical University, Xi'an, China
| | - Haigui Yang
- Department of Neurosurgery, Yanan People's Hospital, Yanan, China
| | - Dongbo Li
- Department of Neurosurgery, Ankang Central Hospital, Ankang, China
| | - Mingsheng Chen
- Department of Neurosurgery, Tangdu Hospital, Air Force Medical University, Xi'an, China
| | - Tianzhi Zhao
- Department of Neurosurgery, Tangdu Hospital, Air Force Medical University, Xi'an, China
| | - Xingye Zhang
- Department of Neurosurgery, Tangdu Hospital, Air Force Medical University, Xi'an, China
| | - Lanfu Zhao
- Department of Neurosurgery, Tangdu Hospital, Air Force Medical University, Xi'an, China
| | - Shunnan Ge
- Department of Neurosurgery, Tangdu Hospital, Air Force Medical University, Xi'an, China
| | - Yan Qu
- Department of Neurosurgery, Tangdu Hospital, Air Force Medical University, Xi'an, China.
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Zoerle T, Beqiri E, Åkerlund CAI, Gao G, Heldt T, Hawryluk GWJ, Stocchetti N. Intracranial pressure monitoring in adult patients with traumatic brain injury: challenges and innovations. Lancet Neurol 2024; 23:938-950. [PMID: 39152029 DOI: 10.1016/s1474-4422(24)00235-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2023] [Revised: 05/15/2024] [Accepted: 05/28/2024] [Indexed: 08/19/2024]
Abstract
Intracranial pressure monitoring enables the detection and treatment of intracranial hypertension, a potentially lethal insult after traumatic brain injury. Despite its widespread use, robust evidence supporting intracranial pressure monitoring and treatment remains sparse. International studies have shown large variations between centres regarding the indications for intracranial pressure monitoring and treatment of intracranial hypertension. Experts have reviewed these two aspects and, by consensus, provided practical approaches for monitoring and treatment. Advances have occurred in methods for non-invasive estimation of intracranial pressure although, for now, a reliable way to non-invasively and continuously measure intracranial pressure remains aspirational. Analysis of the intracranial pressure signal can provide information on brain compliance (ie, the ability of the cranium to tolerate volume changes) and on cerebral autoregulation (ie, the ability of cerebral blood vessels to react to changes in blood pressure). The information derived from the intracranial pressure signal might allow for more individualised patient management. Machine learning and artificial intelligence approaches are being increasingly applied to intracranial pressure monitoring, but many obstacles need to be overcome before their use in clinical practice could be attempted. Robust clinical trials are needed to support indications for intracranial pressure monitoring and treatment. Progress in non-invasive assessment of intracranial pressure and in signal analysis (for targeted treatment) will also be crucial.
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Affiliation(s)
- Tommaso Zoerle
- Neuroscience Intensive Care Unit, Department of Anesthesia and Critical Care, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy; Department of Pathophysiology and Transplantation, University of Milan, Milan, Italy.
| | - Erta Beqiri
- Brain Physics Laboratory, Division of Neurosurgery, Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
| | - Cecilia A I Åkerlund
- Department of Physiology and Pharmacology, Section of Perioperative Medicine and Intensive Care, Karolinska Institutet, Stockholm, Sweden; Function Perioperative Medicine and Intensive Care, Karolinska University Hospital, Stockholm, Sweden
| | - Guoyi Gao
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Thomas Heldt
- Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Gregory W J Hawryluk
- Cleveland Clinic Akron General Hospital, Uniformed Services University, Cleveland, OH, USA
| | - Nino Stocchetti
- Neuroscience Intensive Care Unit, Department of Anesthesia and Critical Care, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy; Department of Pathophysiology and Transplantation, University of Milan, Milan, Italy
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van Hal ST, van der Jagt M, van Genderen ME, Gommers D, Veenland JF. Using Artificial Intelligence to Predict Intracranial Hypertension in Patients After Traumatic Brain Injury: A Systematic Review. Neurocrit Care 2024; 41:285-296. [PMID: 38212559 PMCID: PMC11335950 DOI: 10.1007/s12028-023-01910-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2023] [Accepted: 12/01/2023] [Indexed: 01/13/2024]
Abstract
Intracranial hypertension (IH) is a key driver of secondary brain injury in patients with traumatic brain injury. Lowering intracranial pressure (ICP) as soon as IH occurs is important, but a preemptive approach would be more beneficial. We systematically reviewed the artificial intelligence (AI) models, variables, performances, risks of bias, and clinical machine learning (ML) readiness levels of IH prediction models using AI. We conducted a systematic search until 12-03-2023 in three databases. Only studies predicting IH or ICP in patients with traumatic brain injury with a validation of the AI model were included. We extracted type of AI model, prediction variables, model performance, validation type, and prediction window length. Risk of bias was assessed with the Prediction Model Risk of Bias Assessment Tool, and we determined the clinical ML readiness level. Eleven out of 399 nonduplicate publications were included. A gaussian processes model using ICP and mean arterial pressure was most common. The maximum reported area under the receiver operating characteristic curve was 0.94. Four studies conducted external validation, and one study a prospective clinical validation. The prediction window length preceding IH varied between 30 and 60 min. Most studies (73%) had high risk of bias. The highest clinical ML readiness level was 6 of 9, indicating "real-time model testing" stage in one study. Several IH prediction models using AI performed well, were externally validated, and appeared ready to be tested in the clinical workflow (clinical ML readiness level 5 of 9). A Gaussian processes model was most used, and ICP and mean arterial pressure were frequently used variables. However, most studies showed a high risk of bias. Our findings may help position AI for IH prediction on the path to ultimate clinical integration and thereby guide researchers plan and design future studies.
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Affiliation(s)
- S T van Hal
- Department of Adult Intensive Care, Erasmus MC University Medical Center, Room Ne-413, Doctor Molewaterplein 40, Rotterdam, 3015 GD, The Netherlands.
| | - M van der Jagt
- Department of Adult Intensive Care, Erasmus MC University Medical Center, Room Ne-413, Doctor Molewaterplein 40, Rotterdam, 3015 GD, The Netherlands
| | - M E van Genderen
- Department of Adult Intensive Care, Erasmus MC University Medical Center, Room Ne-413, Doctor Molewaterplein 40, Rotterdam, 3015 GD, The Netherlands
| | - D Gommers
- Department of Adult Intensive Care, Erasmus MC University Medical Center, Room Ne-413, Doctor Molewaterplein 40, Rotterdam, 3015 GD, The Netherlands
| | - J F Veenland
- Department of Medical Informatics, Erasmus MC University Medical Center, Rotterdam, The Netherlands
- Department of Radiology, Erasmus MC University Medical Center, Rotterdam, The Netherlands
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Uparela-Reyes MJ, Villegas-Trujillo LM, Cespedes J, Velásquez-Vera M, Rubiano AM. Usefulness of Artificial Intelligence in Traumatic Brain Injury: A Bibliometric Analysis and Mini-review. World Neurosurg 2024; 188:83-92. [PMID: 38759786 DOI: 10.1016/j.wneu.2024.05.065] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2024] [Accepted: 05/10/2024] [Indexed: 05/19/2024]
Abstract
BACKGROUND Traumatic brain injury (TBI) has become a major source of disability worldwide, increasing the interest in algorithms that use artificial intelligence (AI) to optimize the interpretation of imaging studies, prognosis estimation, and critical care issues. In this study we present a bibliometric analysis and mini-review on the main uses that have been developed for TBI in AI. METHODS The results informing this review come from a Scopus database search as of April 15, 2023. The bibliometric analysis was carried out via the mapping bibliographic metrics method. Knowledge mapping was made in the VOSviewer software (V1.6.18), analyzing the "link strength" of networks based on co-occurrence of key words, countries co-authorship, and co-cited authors. In the mini-review section, we highlight the main findings and contributions of the studies. RESULTS A total of 495 scientific publications were identified from 2000 to 2023, with 9262 citations published since 2013. Among the 160 journals identified, The Journal of Neurotrauma, Frontiers in Neurology, and PLOS ONE were those with the greatest number of publications. The most frequently co-occurring key words were: "machine learning", "deep learning", "magnetic resonance imaging", and "intracranial pressure". The United States accounted for more collaborations than any other country, followed by United Kingdom and China. Four co-citation author clusters were found, and the top 20 papers were divided into reviews and original articles. CONCLUSIONS AI has become a relevant research field in TBI during the last 20 years, demonstrating great potential in imaging, but a more modest performance for prognostic estimation and neuromonitoring.
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Affiliation(s)
- Maria José Uparela-Reyes
- Neurosurgery Section, School of Medicine, Universidad del Valle, Cali, Colombia; Neurosurgery Section, Hospital Universitario del Valle, Cali, Colombia.
| | - Lina María Villegas-Trujillo
- Neurosurgery Section, School of Medicine, Universidad del Valle, Cali, Colombia; School of Biomedical Sciences, Universidad del Valle, Cali, Colombia
| | - Jorge Cespedes
- Comprehensive Epilepsy Center, Yale University, New Haven, Connecticut, USA
| | - Miguel Velásquez-Vera
- Neurosurgery Section, School of Medicine, Universidad del Valle, Cali, Colombia; Neurosurgery Section, Hospital Universitario del Valle, Cali, Colombia
| | - Andrés M Rubiano
- Neurosurgery Section, School of Medicine, Universidad del Valle, Cali, Colombia; Neurosurgery Section, Hospital Universitario del Valle, Cali, Colombia; INUB-Meditech Research Group, Neurosciences Institute, Universidad El Bosque, Bogotá, Colombia
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Zhu J, Shan Y, Li Y, Xu X, Wu X, Xue Y, Gao G. Random forest-based prediction of intracranial hypertension in patients with traumatic brain injury. Intensive Care Med Exp 2024; 12:58. [PMID: 38954280 PMCID: PMC11219663 DOI: 10.1186/s40635-024-00643-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2024] [Accepted: 06/17/2024] [Indexed: 07/04/2024] Open
Abstract
BACKGROUND Treatment and prevention of intracranial hypertension (IH) to minimize secondary brain injury are central to the neurocritical care management of traumatic brain injury (TBI). Predicting the onset of IH in advance allows for a more aggressive prophylactic treatment. This study aimed to develop random forest (RF) models for predicting IH events in TBI patients. METHODS We analyzed prospectively collected data from patients admitted to the intensive care unit with invasive intracranial pressure (ICP) monitoring. Patients with persistent ICP > 22 mmHg in the early postoperative period (first 6 h) were excluded to focus on IH events that had not yet occurred. ICP-related data from the initial 6 h were used to extract linear (ICP, cerebral perfusion pressure, pressure reactivity index, and cerebrospinal fluid compensatory reserve index) and nonlinear features (complexity of ICP and cerebral perfusion pressure). IH was defined as ICP > 22 mmHg for > 5 min, and severe IH (SIH) as ICP > 22 mmHg for > 1 h during the subsequent ICP monitoring period. RF models were then developed using baseline characteristics (age, sex, and initial Glasgow Coma Scale score) along with linear and nonlinear features. Fivefold cross-validation was performed to avoid overfitting. RESULTS The study included 69 patients. Forty-three patients (62.3%) experienced an IH event, of whom 30 (43%) progressed to SIH. The median time to IH events was 9.83 h, and to SIH events, it was 11.22 h. The RF model showed acceptable performance in predicting IH with an area under the curve (AUC) of 0.76 and excellent performance in predicting SIH (AUC = 0.84). Cross-validation analysis confirmed the stability of the results. CONCLUSIONS The presented RF model can forecast subsequent IH events, particularly severe ones, in TBI patients using ICP data from the early postoperative period. It provides researchers and clinicians with a potentially predictive pathway and framework that could help triage patients requiring more intensive neurological treatment at an early stage.
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Affiliation(s)
- Jun Zhu
- Department of Neurosurgery, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 201600, China
| | - Yingchi Shan
- Department of Neurosurgery, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 201600, China
| | - Yihua Li
- Department of Neurosurgery, Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, 200092, China
| | - Xuxu Xu
- Department of Neurosurgery, Minhang Hospital Fudan University, Shanghai, 201199, China
| | - Xiang Wu
- Department of Neurosurgery, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 201600, China
| | - Yajun Xue
- Department of Neurosurgery, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 201600, China.
| | - Guoyi Gao
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, 100070, China.
- Neurotrauma Laboratory, Beijing Neurosurgical Institute, Capital Medical University, Beijing, 100070, China.
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Smielewski P, Beqiri E, Mataczynski C, Placek M, Kazimierska A, Hutchinson P, Czosnyka M, Kasprowicz M. Advanced neuromonitoring powered by ICM+ and its place in the Brand New AI World, reflections at the 20th anniversary boundary. BRAIN & SPINE 2024; 4:102835. [PMID: 39071453 PMCID: PMC11278591 DOI: 10.1016/j.bas.2024.102835] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/30/2023] [Revised: 05/06/2024] [Accepted: 05/17/2024] [Indexed: 07/30/2024]
Abstract
Introduction Adoption of the ICM+® brain monitoring software by clinical research centres worldwide has been continuously growing over the past 20 years. This has necessitated ongoing updates to accommodate evolving neuromonitoring research needs, including recent explosion of artificial intelligence (AI). Research question We sought to provide an update on the current features of the software. In particular, we aimed to highlight the new options of integrating AI models. Material and methods We reviewed all currently available ICM+ analytical areas and discussed potential AI based extensions in each. We tested a proof-of-concept integration of an AI model and evaluated its performance for real-time data processing. Results ICM+ current analytical tools serve both real-time (bed-side) and offline (file based) analysis, including the calculation engine, Signal Calculator, Custom Statistics, Batch tools, ScriptLab and charting. The ICM+ Python plugin engine allows to execute custom Python scripts and take advantage of complex AI frameworks. For the proof-of-concept, we used a neural network convolutional model with 207,000 trainable parameters that classifies morphology of intracranial pressure (ICP) pulse waveform into 5 pulse categories (normal to pathological plus artefactual). When evaluated within ICM+ plugin script on a Windows 10 laptop the classification of a 5 min ICP waveform segment took only 0.19s with a 2.3s of initial, one-off, model loading time required. Conclusions Modernised ICM+ analytical tools, reviewed in this manuscript, include integration of custom AI models allowing them to be shared and run in real-time, facilitating rapid prototyping and validating of new AI ideas at the bed-side.
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Affiliation(s)
- P. Smielewski
- Brain Physics Laboratory, Division of Neurosurgery, Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
| | - E. Beqiri
- Brain Physics Laboratory, Division of Neurosurgery, Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
| | - C. Mataczynski
- Department of Computer Engineering, Faculty of Information and Communication Technology, Wroclaw University of Science and Technology, Wroclaw, Poland
| | - M. Placek
- Brain Physics Laboratory, Division of Neurosurgery, Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
| | - A. Kazimierska
- Department of Biomedical Engineering, Faculty of Fundamental Problems of Technology, Wroclaw University of Science and Technology, Wroclaw, Poland
| | - P.J. Hutchinson
- Brain Physics Laboratory, Division of Neurosurgery, Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
- Department of Computer Engineering, Faculty of Information and Communication Technology, Wroclaw University of Science and Technology, Wroclaw, Poland
- Department of Biomedical Engineering, Faculty of Fundamental Problems of Technology, Wroclaw University of Science and Technology, Wroclaw, Poland
- Neurosurgery Department, Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK
| | - M. Czosnyka
- Brain Physics Laboratory, Division of Neurosurgery, Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
| | - M. Kasprowicz
- Department of Biomedical Engineering, Faculty of Fundamental Problems of Technology, Wroclaw University of Science and Technology, Wroclaw, Poland
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Jeffcote T, Battistuzzo CR, Plummer MP, McNamara R, Anstey J, Bellapart J, Roach R, Chow A, Westerlund T, Delaney A, Bihari S, Bowen D, Weeden M, Trapani A, Reade M, Jeffree RL, Fitzgerald M, Gabbe BJ, O'Brien TJ, Nichol AD, Cooper DJ, Bellomo R, Udy A. PRECISION-TBI: a study protocol for a vanguard prospective cohort study to enhance understanding and management of moderate to severe traumatic brain injury in Australia. BMJ Open 2024; 14:e080614. [PMID: 38387978 PMCID: PMC10882309 DOI: 10.1136/bmjopen-2023-080614] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/06/2023] [Accepted: 02/13/2024] [Indexed: 02/24/2024] Open
Abstract
INTRODUCTION Traumatic brain injury (TBI) is a heterogeneous condition in terms of pathophysiology and clinical course. Outcomes from moderate to severe TBI (msTBI) remain poor despite concerted research efforts. The heterogeneity of clinical management represents a barrier to progress in this area. PRECISION-TBI is a prospective, observational, cohort study that will establish a clinical research network across major neurotrauma centres in Australia. This network will enable the ongoing collection of injury and clinical management data from patients with msTBI, to quantify variations in processes of care between sites. It will also pilot high-frequency data collection and analysis techniques, novel clinical interventions, and comparative effectiveness methodology. METHODS AND ANALYSIS PRECISION-TBI will initially enrol 300 patients with msTBI with Glasgow Coma Scale (GCS) <13 requiring intensive care unit (ICU) admission for invasive neuromonitoring from 10 Australian neurotrauma centres. Demographic data and process of care data (eg, prehospital, emergency and surgical intervention variables) will be collected. Clinical data will include prehospital and emergency department vital signs, and ICU physiological variables in the form of high frequency neuromonitoring data. ICU treatment data will also be collected for specific aspects of msTBI care. Six-month extended Glasgow Outcome Scores (GOSE) will be collected as the key outcome. Statistical analysis will focus on measures of between and within-site variation. Reports documenting performance on selected key quality indicators will be provided to participating sites. ETHICS AND DISSEMINATION Ethics approval has been obtained from The Alfred Human Research Ethics Committee (Alfred Health, Melbourne, Australia). All eligible participants will be included in the study under a waiver of consent (hospital data collection) and opt-out (6 months follow-up). Brochures explaining the rationale of the study will be provided to all participants and/or an appropriate medical treatment decision-maker, who can act on the patient's behalf if they lack capacity. Study findings will be disseminated by peer-review publications. TRIAL REGISTRATION NUMBER NCT05855252.
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Affiliation(s)
- Toby Jeffcote
- Department of Intensive Care, The Alfred Hospital, Melbourne, Victoria, Australia
- Australian and New Zealand Intensive Care Research Centre, Monash University, Melbourne, Victoria, Australia
| | - Camila R Battistuzzo
- Australian and New Zealand Intensive Care Research Centre, Monash University, Melbourne, Victoria, Australia
| | - Mark P Plummer
- Department of Intensive Care, Royal Adelaide Hospital, Adelaide, South Australia, Australia
| | - Robert McNamara
- Department of Intensive Care Medicine, Royal Perth Hospital, Perth, Western Australia, Australia
| | - James Anstey
- Department of Intensive Care, The Royal Melbourne Hospital, Melbourne, Victoria, Australia
| | - Judith Bellapart
- Royal Brisbane and Women's Hospital, Brisbane, Queensland, Australia
| | - Rebecca Roach
- Department of Intensive Care, The Alfred Hospital, Melbourne, Victoria, Australia
| | - Andrew Chow
- Department of Intensive Care Medicine, John Hunter Hospital, Newcastle, New South Wales, Australia
| | - Torgeir Westerlund
- Department of Intensive Care Medicine, John Hunter Hospital, Newcastle, New South Wales, Australia
| | - Anthony Delaney
- The George Institute for Global Health, Sydney, New South Wales, Australia
- Department of Intensive Care Medicine, Royal North Shore Hospital, Sydney, New South Wales, Australia
| | - Shailesh Bihari
- Flinders Medical Centre, Bedford Park, South Australia, Australia
| | - David Bowen
- Westmead Hospital, Sydney, New South Wales, Australia
| | - Mark Weeden
- Intensive Care Unit, St George Hospital, Sydney, New South Wales, Australia
| | - Anthony Trapani
- Australian and New Zealand Intensive Care Research Centre, Monash University, Melbourne, Victoria, Australia
| | - Michael Reade
- Department of Intensive Care Medicine, Royal Brisbane and Women's Hospital, Brisbane, Queensland, Australia
- Faculty of Medicine, Medical School, University of Queensland, Brisbane, Queensland, Australia
| | - Rosalind L Jeffree
- Faculty of Medicine, Medical School, University of Queensland, Brisbane, Queensland, Australia
- Neurosurgery, Royal Brisbane and Women's Hospital, Brisbane, Queensland, Australia
| | - Melinda Fitzgerald
- Curtin Health Innovation Research Institute, Curtin University Faculty of Health Sciences, Perth, Western Australia, Australia
- Perron Institute for Neurological and Translational Sciences, Nedlands, Western Australia, Australia
| | - Belinda J Gabbe
- Epidemiology and Preventive Medicine, Monash University, Melbourne, Victoria, Australia
| | - Terence J O'Brien
- Department of Neurology, Alfred Hospital, Melbourne, Victoria, Australia
- Department of Neuroscience, Monash University Central Clinical School, Melbourne, Victoria, Australia
| | - Alistair D Nichol
- Department of Intensive Care, The Alfred Hospital, Melbourne, Victoria, Australia
- Australian and New Zealand Intensive Care Research Centre, Monash University, Melbourne, Victoria, Australia
| | - D James Cooper
- Australian and New Zealand Intensive Care Research Centre, Monash University, Melbourne, Victoria, Australia
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia
| | - Rinaldo Bellomo
- Australian and New Zealand Intensive Care Research Centre, Monash University, Melbourne, Victoria, Australia
- Department of Intensive Care, Austin Hospital, Heidelberg, Victoria, Australia
| | - Andrew Udy
- Department of Intensive Care, The Alfred Hospital, Melbourne, Victoria, Australia
- Australian and New Zealand Intensive Care Research Centre, Monash University, Melbourne, Victoria, Australia
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Lazaridis C, Foreman B. Management Strategies Based on Multi-Modality Neuromonitoring in Severe Traumatic Brain Injury. Neurotherapeutics 2023; 20:1457-1471. [PMID: 37491682 PMCID: PMC10684466 DOI: 10.1007/s13311-023-01411-2] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/14/2023] [Indexed: 07/27/2023] Open
Abstract
Secondary brain injury after neurotrauma is comprised of a host of distinct, potentially concurrent and interacting mechanisms that may exacerbate primary brain insult. Multimodality neuromonitoring is a method of measuring multiple aspects of the brain in order to understand the signatures of these different pathomechanisms and to detect, treat, or prevent potentially reversible secondary brain injuries. The most studied invasive parameters include intracranial pressure (ICP), cerebral perfusion pressure (CPP), autoregulatory indices, brain tissue partial oxygen tension, and tissue energy and metabolism measures such as the lactate pyruvate ratio. Understanding the local metabolic state of brain tissue in order to infer pathology and develop appropriate management strategies is an area of active investigation. Several clinical trials are underway to define the role of brain tissue oxygenation monitoring and electrocorticography in conjunction with other multimodal neuromonitoring information, including ICP and CPP monitoring. Identifying an optimal CPP to guide individualized management of blood pressure and ICP has been shown to be feasible, but definitive clinical trial evidence is still needed. Future work is still needed to define and clinically correlate patterns that emerge from integrated measurements of metabolism, pressure, flow, oxygenation, and electrophysiology. Pathophysiologic targets and precise critical care management strategies to address their underlying causes promise to mitigate secondary injuries and hold the potential to improve patient outcome. Advancements in clinical trial design are poised to establish new standards for the use of multimodality neuromonitoring to guide individualized clinical care.
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Affiliation(s)
- Christos Lazaridis
- Division of Neurocritical Care, Departments of Neurology and Neurosurgery, University of Chicago Medical Center, 5841 S. Maryland Avenue, Chicago, IL, 60637, USA.
| | - Brandon Foreman
- Division of Neurocritical Care, Department of Neurology and Rehabilitation Medicine, University of Cincinnati, Cincinnati, OH, USA
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11
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Li T, Tang Q, Xu J, Ye X, Chen K, Zhong J, Zhu J, Lu S, Zhu T. Apelin-Overexpressing Neural Stem Cells in Conjunction with a Silk Fibroin Nanofiber Scaffold for the Treatment of Traumatic Brain Injury. Stem Cells Dev 2023; 32:539-553. [PMID: 37261998 DOI: 10.1089/scd.2023.0008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/03/2023] Open
Abstract
Traumatic brain injury (TBI), especially moderate or severe TBI, is one of the most devastating injuries to the nervous system, as the existing therapies for neurological defect repair have difficulty achieving satisfactory results. Neural stem cells (NSCs) therapy is a potentially effective treatment option, especially after specific genetic modifications and when used in combination with biomimetic biological scaffolds. In this study, tussah silk fibroin (TSF) scaffolds with interconnected nanofibrous structures were fabricated using a top-down method. We constructed the apelin-overexpressing NSCs that were cocultured with a TSF nanofiber scaffold (TSFNS) that simulated the extracellular matrix in vitro. To verify the therapeutic efficacy of engineered NSCs in vivo, we constructed TBI models and randomized the C57BL/6 mice into three groups: a control group, an NSC-ctrl group (transplantation of NSCs integrated on TSFNS), and an NSC-apelin group (transplantation of apelin-overexpressing NSCs integrated on TSFNS). The neurological functions of the model mice were evaluated in stages. Specimens were obtained 24 days after transplantation for immunohistochemistry, immunofluorescence, and western blot experiments, and statistical analysis was performed. The results showed that the combination of the TSFNS and apelin overexpression guided extension and elevated the proliferation and differentiation of NSCs both in vivo and in vitro. Moreover, the transplantation of TSFNS-NSCs-Apelin reduced lesion volume, enhanced angiogenesis, inhibited neuronal apoptosis, reduced blood-brain barrier damage, and mitigated neuroinflammation. In summary, TSFNS-NSC-Apelin therapy could build a microenvironment that is more conducive to neural repair to promote the recovery of injured neurological function.
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Affiliation(s)
- Tianwen Li
- Department of Neurosurgery, Huashan Hospital, Shanghai Medical College, Fudan University, National Center for Neurological Disorders, National Key Laboratory for Medical Neurobiology, Institutes of Brain Science, Shanghai Key Laboratory of Brain Function and Regeneration, Institute of Neurosurgery, MOE Frontiers Center for Brain Science, Shanghai, China
| | - Qisheng Tang
- Department of Neurosurgery, Huashan Hospital, Shanghai Medical College, Fudan University, National Center for Neurological Disorders, National Key Laboratory for Medical Neurobiology, Institutes of Brain Science, Shanghai Key Laboratory of Brain Function and Regeneration, Institute of Neurosurgery, MOE Frontiers Center for Brain Science, Shanghai, China
| | - Jiaxin Xu
- Endoscopy Centre and Endoscopy Research Institute, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Xiangru Ye
- Department of Neurosurgery, Huashan Hospital, Shanghai Medical College, Fudan University, National Center for Neurological Disorders, National Key Laboratory for Medical Neurobiology, Institutes of Brain Science, Shanghai Key Laboratory of Brain Function and Regeneration, Institute of Neurosurgery, MOE Frontiers Center for Brain Science, Shanghai, China
| | - Kezhu Chen
- Department of Neurosurgery, Huashan Hospital, Shanghai Medical College, Fudan University, National Center for Neurological Disorders, National Key Laboratory for Medical Neurobiology, Institutes of Brain Science, Shanghai Key Laboratory of Brain Function and Regeneration, Institute of Neurosurgery, MOE Frontiers Center for Brain Science, Shanghai, China
| | - Junjie Zhong
- Department of Neurosurgery, Huashan Hospital, Shanghai Medical College, Fudan University, National Center for Neurological Disorders, National Key Laboratory for Medical Neurobiology, Institutes of Brain Science, Shanghai Key Laboratory of Brain Function and Regeneration, Institute of Neurosurgery, MOE Frontiers Center for Brain Science, Shanghai, China
| | - Jianhong Zhu
- Department of Neurosurgery, Huashan Hospital, Shanghai Medical College, Fudan University, National Center for Neurological Disorders, National Key Laboratory for Medical Neurobiology, Institutes of Brain Science, Shanghai Key Laboratory of Brain Function and Regeneration, Institute of Neurosurgery, MOE Frontiers Center for Brain Science, Shanghai, China
| | - Shijun Lu
- The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou Municipal Hospital, Gusu School, Nanjing Medical University, Suzhou, China
| | - Tongming Zhu
- Department of Neurosurgery, Huashan Hospital, Shanghai Medical College, Fudan University, National Center for Neurological Disorders, National Key Laboratory for Medical Neurobiology, Institutes of Brain Science, Shanghai Key Laboratory of Brain Function and Regeneration, Institute of Neurosurgery, MOE Frontiers Center for Brain Science, Shanghai, China
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