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McKimmie A, Keeves J, Gadowski A, Bagg MK, Antonic-Baker A, Hicks AJ, Hill R, Clarke N, Holland A, Veitch B, Fatovich D, Reeder S, Romero L, Ponsford JL, Lannin NA, O’Brien TJ, Cooper DJ, Rushworth N, Fitzgerald M, Gabbe BJ, Cameron PA. The Australian Traumatic Brain Injury Initiative: Systematic Review of Clinical Factors Associated with Outcomes in People with Moderate-Severe Traumatic Brain Injury. Neurotrauma Rep 2024; 5:0. [PMID: 39081663 PMCID: PMC11286001 DOI: 10.1089/neur.2023.0111] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/02/2024] Open
Abstract
The aim of the Australian Traumatic Brain Injury Initiative (AUS-TBI) is to design a data dictionary to inform data collection and facilitate prediction of outcomes for moderate-severe traumatic brain injury (TBI) across Australia. The process has engaged diverse stakeholders across six areas: social, health, clinical, biological, acute interventions, and long-term outcomes. Here, we report the results of the clinical review. Standardized searches were implemented across databases to April 2022. English-language reports of studies evaluating an association between a clinical factor and any clinical outcome in at least 100 patients with moderate-severe TBI were included. Abstracts, and full-text records, were independently screened by at least two reviewers in Covidence. The findings were assessed through a consensus process to determine inclusion in the AUS-TBI data resource. The searches retrieved 22,441 records, of which 1137 were screened at full text and 313 papers were included. The clinical outcomes identified were predominantly measures of survival and disability. The clinical predictors most frequently associated with these outcomes were the Glasgow Coma Scale, pupil reactivity, and blood pressure measures. Following discussion with an expert consensus group, 15 were recommended for inclusion in the data dictionary. This review identified numerous studies evaluating associations between clinical factors and outcomes in patients with moderate-severe TBI. A small number of factors were reported consistently, however, how and when these factors were assessed varied. The findings of this review and the subsequent consensus process have informed the development of an evidence-informed data dictionary for moderate-severe TBI in Australia.
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Affiliation(s)
- Ancelin McKimmie
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia
| | - Jemma Keeves
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia
- Faculty of Health Sciences, Curtin Health Innovation Research Institute, Curtin University, Bentley, Australia
- Perron Institute for Neurological and Translational Science, Nedlands, Australia
| | - Adelle Gadowski
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia
| | - Matthew K. Bagg
- Faculty of Health Sciences, Curtin Health Innovation Research Institute, Curtin University, Bentley, Australia
- Perron Institute for Neurological and Translational Science, Nedlands, Australia
- Centre for Pain IMPACT, Neuroscience Research Australia, Sydney, Australia
- School of Health Sciences, University of Notre Dame Australia, Fremantle, Australia
| | - Ana Antonic-Baker
- Department of Neuroscience, Central Clinical School, Monash University, Melbourne, Australia
| | - Amelia J. Hicks
- Monash-Epworth Rehabilitation Research Centre, Epworth Healthcare, Melbourne, Australia
- School of Psychological Sciences, Monash University, Melbourne, Australia
| | - Regina Hill
- Regina Hill Effective Consulting Pty Ltd, Melbourne, Australia
| | - Nyssa Clarke
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia
| | - Andrew Holland
- Faculty of Medicine and Health, The Children’s Hospital at Westmead Clinical School, University of Sydney School of Medicine, Westmead, Australia
| | - Bill Veitch
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia
| | - Daniel Fatovich
- Emergency Medicine, Royal Perth Hospital, University of Western Australia, Perth, Australia
- Centre for Clinical Research in Emergency Medicine, Harry Perkins Institute of Medical Research, Nedlands, Australia
| | - Sandy Reeder
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia
- Department of Neuroscience, Central Clinical School, Monash University, Melbourne, Australia
| | | | - Jennie L. Ponsford
- Monash-Epworth Rehabilitation Research Centre, Epworth Healthcare, Melbourne, Australia
- School of Psychological Sciences, Monash University, Melbourne, Australia
| | - Natasha A. Lannin
- Department of Neuroscience, Central Clinical School, Monash University, Melbourne, Australia
- Alfred Health, Melbourne, Australia
| | - Terence J. O’Brien
- Department of Neuroscience, Central Clinical School, Monash University, Melbourne, Australia
| | - D. Jamie Cooper
- School of Public Health and Preventive Medicine, Australian and New Zealand Intensive Care Research Centre, Monash University, Melbourne, Australia
- Department of Intensive Care and Hyperbaric Medicine, Melbourne, Australia
| | | | - Melinda Fitzgerald
- Faculty of Health Sciences, Curtin Health Innovation Research Institute, Curtin University, Bentley, Australia
- Perron Institute for Neurological and Translational Science, Nedlands, Australia
| | - Belinda J. Gabbe
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia
- Health Data Research UK, Swansea University Medical School, Swansea University, Singleton Park, United Kingdom
| | - Peter A. Cameron
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia
- National Trauma Research Institute, Melbourne, Australia
- Emergency and Trauma Centre, The Alfred Hospital, Melbourne, Australia
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2
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Partyka C, Alexiou A, Williams J, Bliss J, Miller M, Ferguson I. Brain Injury Associated Shock: An Under-Recognized and Challenging Prehospital Phenomenon. Prehosp Disaster Med 2024:1-6. [PMID: 38680074 DOI: 10.1017/s1049023x24000359] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/01/2024]
Abstract
OBJECTIVE Hemodynamic collapse in multi-trauma patients with severe traumatic brain injury (TBI) poses both a diagnostic and therapeutic challenge for prehospital clinicians. Brain injury associated shock (BIAS), likely resulting from catecholamine storm, can cause both ventricular dysfunction and vasoplegia but may present clinically in a manner similar to hemorrhagic shock. Despite different treatment strategies, few studies exist describing this phenomenon in the early post-injury phase. This retrospective observational study aimed to describe the frequency of shock in isolated TBI in prehospital trauma patients and to compare their clinical characteristics to those patients with hemorrhagic shock and TBI without shock. METHODS All prehospital trauma patients intubated by prehospital medical teams from New South Wales Ambulance Aeromedical Operations (NSWA-AO) with an initial Glasgow Coma Scale (GCS) of 12 or less were investigated. Shock was defined as a pre-intubation systolic blood pressure under 90mmHg and the administration of blood products or vasopressors. Injuries were classified from in-hospital computed tomography (CT) reports. From this, three study groups were derived: BIAS, hemorrhagic shock, and isolated TBI without shock. Descriptive statistics were then produced for clinical and treatment variables. RESULTS Of 1,292 intubated patients, 423 had an initial GCS of 12 or less, 24 patients (5.7% of the original cohort) had shock with an isolated TBI, and 39 patients had hemorrhagic shock. The hemodynamic parameters were similar amongst these groups, including values of tachycardia, hypotension, and elevated shock index. Prehospital clinical interventions including blood transfusion and total fluids administered were also similar, suggesting they were indistinguishable to prehospital clinicians. CONCLUSIONS Hemodynamic compromise in the setting of isolated severe TBI is a rare clinical entity. Current prehospital physiological data available to clinicians do not allow for easy delineation between these patients from those with hemorrhagic shock.
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Affiliation(s)
- Christopher Partyka
- Staff Specialist in Prehospital & Retrieval Medicine, NSW Ambulance, Aeromedical Operations, Bankstown Aerodrome, NSW, Australia
- Staff Specialist in Emergency Medicine, Royal North Shore Hospital, St Leonards, NSW, Australia
- Clinical Lecturer and PhD Candidate, Faculty of Medicine and Health, University of Sydney, Sydney, Australia
| | - Alexander Alexiou
- Consultant in Emergency Medicine, Royal London Hospital, London, England
- Consultant, Physician Response Unit, London's Air Ambulance, London, England
- Emeritus Prehospital Doctor, Essex & Herts Air Ambulance, England
| | - John Williams
- Critical Care Paramedic, NSW Ambulance, Aeromedical Operations, Bankstown Aerodrome, NSW, Australia
| | - Jimmy Bliss
- Staff Specialist in Prehospital & Retrieval Medicine, NSW Ambulance, Aeromedical Operations, Bankstown Aerodrome, NSW, Australia
- Staff Specialist in Emergency Medicine, Liverpool Hospital, Liverpool, NSW, Australia
| | - Matthew Miller
- Staff Specialist in Prehospital & Retrieval Medicine, NSW Ambulance, Aeromedical Operations, Bankstown Aerodrome, NSW, Australia
- Conjoint Lecturer, St George and Sutherland Clinical Campus, University of New South Wales, NSW, Australia
- Anesthetist, St George Hospital, Sydney, Australia
| | - Ian Ferguson
- Staff Specialist in Prehospital & Retrieval Medicine, NSW Ambulance, Aeromedical Operations, Bankstown Aerodrome, NSW, Australia
- Staff Specialist in Emergency Medicine, Liverpool Hospital, Liverpool, NSW, Australia
- Conjoint Senior Lecturer, South West Sydney Clinical School, University of New South Wales, NSW, Australia
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Marchionne G, Alcoverro E, Spinillo S, Louro LF. Anaesthetic management in a cat undergoing emergency craniotomy for meningioma excision. JFMS Open Rep 2023; 9:20551169231192287. [PMID: 37744284 PMCID: PMC10517613 DOI: 10.1177/20551169231192287] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/19/2023] [Indexed: 09/26/2023] Open
Abstract
Case summary A 15-year-old female spayed domestic shorthair cat underwent an emergency craniotomy to remove an intracranial meningioma causing marked midline shift, caudal transtentorial and foramen magnum herniation. Because intracranial structures are enclosed in the cranium, any volume-occupying lesions might raise intracranial pressure (ICP), compromising cerebral perfusion. Relevance and novel information This case report discusses the anaesthetic management of a cat that presented with marked bradycardia and concomitant hypotension. Cushing's reflex (CR) is a well-recognised cardiovascular reflex following sudden ICP increase, and it features an irregular breathing pattern and increased arterial blood pressure with reflex bradycardia. However, CR is reported to have a low sensitivity for the detection of raised ICP in humans with traumatic brain injury. In a previous study reporting seven cats undergoing surgical removal of intracranial meningioma, ICP was measured in four cases and, in these patients, CR was not observed during surgery. Because bradycardia was not secondary to hypertension, in this case, it might have been the result of direct compression of the nucleus of the vagus nerve. Based on the literature search, there is paucity of reports of cardiovascular changes in cats with increased ICP and their perianaesthetic management.
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Affiliation(s)
| | | | | | - Luis Filipe Louro
- ChesterGates Veterinary Specialists, Chester, UK
- Veterinary Anaesthesia Consultancy Services Limited, Barnsley, UK
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4
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Lulla A, Lumba-Brown A, Totten AM, Maher PJ, Badjatia N, Bell R, Donayri CTJ, Fallat ME, Hawryluk GWJ, Goldberg SA, Hennes HMA, Ignell SP, Ghajar J, Krzyzaniak BP, Lerner EB, Nishijima D, Schleien C, Shackelford S, Swartz E, Wright DW, Zhang R, Jagoda A, Bobrow BJ. Prehospital Guidelines for the Management of Traumatic Brain Injury - 3rd Edition. PREHOSP EMERG CARE 2023:1-32. [PMID: 37079803 DOI: 10.1080/10903127.2023.2187905] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/22/2023]
Affiliation(s)
- Al Lulla
- Department of Emergency Medicine, UT Southwestern Medical Center, Dallas, Texas
| | - Angela Lumba-Brown
- Department of Emergency Medicine, Stanford University, Stanford, California
| | - Annette M Totten
- Department of Medical Informatics and Clinical Epidemiology, Oregon Health & Science University, Portland, Oregon
| | - Patrick J Maher
- Department of Emergency Medicine, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Neeraj Badjatia
- Department of Neurocritical Care, Neurology, Anesthesiology, Neurosurgery, University of Maryland School of Medicine, Baltimore, Maryland
| | - Randy Bell
- Uniformed Services University, Bethesda, Maryland
| | | | - Mary E Fallat
- Hiram C. Polk Jr Department of Pediatric Surgery, University of Louisville, Norton Children's Hospital, Louisville, Kentucky
| | - Gregory W J Hawryluk
- Department of Neurosurgery, Cleveland Clinic and Akron General Hospital, Fairlawn, Ohio
| | - Scott A Goldberg
- Department of Emergency Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts
| | - Halim M A Hennes
- Department of Pediatric Emergency Medicine, UT Southwestern Medical Center, Dallas Children's Medical Center, Dallas, Texas
| | - Steven P Ignell
- Department of Emergency Medicine, Stanford University, Stanford, California
| | - Jamshid Ghajar
- Department of Neurosurgery, Stanford University, Stanford, California
| | | | - E Brooke Lerner
- Department of Emergency Medicine, Medical College of Wisconsin, Milwaukee, Wisconsin
| | - Daniel Nishijima
- Department of Emergency Medicine, UC Davis, Sacramento, California
| | - Charles Schleien
- Pediatric Critical Care, Cohen Children's Medical Center, Hofstra Northwell School of Medicine, Uniondale, New York
| | - Stacy Shackelford
- Trauma and Critical Care, USAF Center for Sustainment of Trauma Readiness Skills, Seattle, Washington
| | - Erik Swartz
- Department of Physical Therapy and Kinesiology, University of Massachusetts, Lowell, Massachusetts
| | - David W Wright
- Department of Emergency Medicine, Emory University, Atlanta, Georgia
| | - Rachel Zhang
- University of Arizona College of Medicine-Phoenix, Phoenix, Arizona
| | - Andy Jagoda
- Department of Emergency Medicine, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Bentley J Bobrow
- Department of Emergency Medicine, McGovern Medical School at The University of Texas Health Science Center at Houston (UTHealth), Houston, Texas
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5
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Kareemi H, Pratte M, English S, Hendin A. Initial Diagnosis and Management of Acutely Elevated Intracranial Pressure. J Intensive Care Med 2023:8850666231156589. [PMID: 36802976 DOI: 10.1177/08850666231156589] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/22/2023]
Abstract
Acutely elevated intracranial pressure (ICP) may have devastating effects on patient mortality and neurologic outcomes, yet its initial detection remains difficult because of the variety of manifestations that it can cause disease states it is associated with. Several treatment guidelines exist for specific disease processes such as trauma or ischemic stroke, but their recommendations may not apply to other causes. In the acute setting, management decisions must often be made before the underlying cause is known. In this review, we present an organized, evidence-based approach to the recognition and management of patients with suspected or confirmed elevated ICP in the first minutes to hours of resuscitation. We explore the utility of invasive and noninvasive methods of diagnosis, including history, physical examination, imaging, and ICP monitors. We synthesize various guidelines and expert recommendations and identify core management principles including noninvasive maneuvers, neuroprotective intubation and ventilation strategies, and pharmacologic therapies such as ketamine, lidocaine, corticosteroids, and the hyperosmolar agents mannitol and hypertonic saline. Although an in-depth discussion of the definitive management of each etiology is beyond the scope of this review, our goal is to provide an empirical approach to these time-sensitive, critical presentations in their initial stages.
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Affiliation(s)
- Hashim Kareemi
- Department of Emergency Medicine, The Ottawa Hospital, 6363University of Ottawa, Ottawa, Ontario, Canada
| | - Michael Pratte
- Department of Internal Medicine, 6363University of Ottawa, Ottawa, Ontario, Canada
| | - Shane English
- Department of Medicine (Critical Care), 6363University of Ottawa, Ottawa, Ontario, Canada.,Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, Ontario, Canada
| | - Ariel Hendin
- Department of Emergency Medicine, The Ottawa Hospital, 6363University of Ottawa, Ottawa, Ontario, Canada.,Department of Medicine (Critical Care), 6363University of Ottawa, Ottawa, Ontario, Canada
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6
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Grossman M, See AP, Mannix R, Simon EL. Complete Neurological Recovery After Emergency Burr Hole Placement Utilizing EZ-IO® for Epidural Hematoma. J Emerg Med 2022; 63:557-560. [DOI: 10.1016/j.jemermed.2022.06.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2022] [Accepted: 06/04/2022] [Indexed: 12/05/2022]
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7
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Moyer JD, Lee P, Bernard C, Henry L, Lang E, Cook F, Planquart F, Boutonnet M, Harrois A, Gauss T. Machine learning-based prediction of emergency neurosurgery within 24 h after moderate to severe traumatic brain injury. World J Emerg Surg 2022; 17:42. [PMID: 35922831 PMCID: PMC9351267 DOI: 10.1186/s13017-022-00449-5] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2022] [Accepted: 07/27/2022] [Indexed: 12/03/2022] Open
Abstract
Background Rapid referral of traumatic brain injury (TBI) patients requiring emergency neurosurgery to a specialized trauma center can significantly reduce morbidity and mortality. Currently, no model has been reported to predict the need for acute neurosurgery in severe to moderate TBI patients. This study aims to evaluate the performance of Machine Learning-based models to establish to predict the need for neurosurgery procedure within 24 h after moderate to severe TBI. Methods Retrospective multicenter cohort study using data from a national trauma registry (Traumabase®) from November 2011 to December 2020. Inclusion criteria correspond to patients over 18 years old with moderate or severe TBI (Glasgow coma score ≤ 12) during prehospital assessment. Patients who died within the first 24 h after hospital admission and secondary transfers were excluded. The population was divided into a train set (80% of patients) and a test set (20% of patients). Several approaches were used to define the best prognostic model (linear nearest neighbor or ensemble model). The Shapley Value was used to identify the most relevant pre-hospital variables for prediction. Results 2159 patients were included in the study. 914 patients (42%) required neurosurgical intervention within 24 h. The population was predominantly male (77%), young (median age 35 years [IQR 24–52]) with severe head injury (median GCS 6 [3–9]). Based on the evaluation of the predictive model on the test set, the logistic regression model had an AUC of 0.76. The best predictive model was obtained with the CatBoost technique (AUC 0.81). According to the Shapley values method, the most predictive variables in the CatBoost were a low initial Glasgow coma score, the regression of pupillary abnormality after osmotherapy, a high blood pressure and a low heart rate. Conclusion Machine learning-based models could predict the need for emergency neurosurgery within 24 h after moderate and severe head injury. Potential clinical benefits of such models as a decision-making tool deserve further assessment. The performance in real-life setting and the impact on clinical decision-making of the model requires workflow integration and prospective assessment. Supplementary Information The online version contains supplementary material available at 10.1186/s13017-022-00449-5.
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Affiliation(s)
- Jean-Denis Moyer
- Department of Anesthesiology and Critical Care, Beaujon Hospital, DMU Parabol, AP-HP. Nord, 100 Boulevard du Général Leclerc, 92110, Clichy, France.
| | - Patrick Lee
- Capgemini Invent, Insight Driven Enterprise, Focused on Data and Artificial Intelligence Services, Paris, France
| | - Charles Bernard
- Department of Anesthesiology and Critical Care, Beaujon Hospital, DMU Parabol, AP-HP. Nord, 100 Boulevard du Général Leclerc, 92110, Clichy, France
| | - Lois Henry
- Department of Anesthesiology and Critical Care, Lille, France
| | - Elodie Lang
- Department of Anesthesiology and Critical Care, Hôpital Européen Georges Pompidou, Paris, France
| | - Fabrice Cook
- Department of Anesthesiology and Critical Care, Hôpital Henri Mondor, Créteil, France
| | - Fanny Planquart
- Department of Anesthesiology and Critical Care, Strasbourg, France
| | - Mathieu Boutonnet
- Intensive Care Unit, Percy Military Teaching Hospital, 101 Avenue Henri Barbusse, 92140, Clamart, France.,Val de Grace Academy, Place Alphonse Laveran, 75005, Paris, France
| | - Anatole Harrois
- Department of Anesthesiology and Critical Care, APH-HP, Bicêtre Hôpitaux Universitaires Paris-Sud, Université Paris Saclay, Le Kremlin Bicêtre, France
| | - Tobias Gauss
- Déchocage- Bloc des urgences, Pole Anesthésie- Réanimation, CHU Grenoble Alpes, La Tronche, France
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8
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Her J, Merbl Y, Gerken K, Kim M, Hofmeister E, Bacek LM, Kuo KW, Yanke AB. Relationship between admission vitals and brain herniation in 32 cats: a retrospective study. J Feline Med Surg 2022; 24:770-778. [PMID: 34612748 PMCID: PMC10812265 DOI: 10.1177/1098612x211048639] [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: 11/16/2022]
Abstract
OBJECTIVES The aim of the study was to evaluate whether any admission vitals correlated with the presence of brain herniation diagnosed via MRI in cats presenting with neurologic signs. METHODS Medical records at two veterinary university referral centers were reviewed to identify cats that underwent brain MRI between 2010 and 2019. A control group of cats with intracranial lesions without concurrent brain herniation was analyzed for comparison. Data relating to signalment, vitals on admission, abnormalities observed on initial neurologic examination, underlying etiology, advanced imaging findings and outcome were reviewed. A Modified Glasgow Coma Scale (MGCS) score was determined retrospectively based on initial neurologic examination. Logistic regressions were performed to investigate the relationship between each risk factor and the odds of brain herniation as diagnosed on MRI. RESULTS Thirty-two cats with brain herniation and 44 cats with abnormal brain MRI without evidence of herniation (as a control group) based on MRI findings were included. Cats with intracranial neoplasia vs other diagnoses were found to be at increased risk of herniation (odds ratio [OR] 4.8, 95% confidence interval [CI] 1.8-13.8; P = 0.001). The odds of herniation increased with age (OR 1.1, 95% CI 1.01-1.2; P = 0.031). Cats with herniation had a significantly lower level of consciousness in their MGCS score (P <0.0001) than cats without herniation. There was no significant difference in either motor activity or brainstem reflexes between the groups (P >0.05). CONCLUSIONS AND RELEVANCE Admission heart rate and blood pressure were not associated with brain herniation. Cats with herniation were presented with a significantly lower level of consciousness in their MGCS score; however, this clinical feature cannot be directly attributable to and predictive of herniation. Older cats with intracranial neoplasia are more likely to have brain herniation.
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Affiliation(s)
- Jiwoong Her
- Department of Clinical Sciences, College of Veterinary Medicine, Auburn University, Auburn, AL, USA
- Department of Veterinary Clinical Sciences, College of Veterinary Medicine, The Ohio State University, Columbus, OH, USA
| | - Yael Merbl
- Department of Veterinary Clinical Sciences, College of Veterinary Medicine, Washington State University, Pullman, WA, USA
| | - Katherine Gerken
- Department of Clinical Sciences, College of Veterinary Medicine, Auburn University, Auburn, AL, USA
| | - Minjeong Kim
- Department of Clinical Sciences, College of Veterinary Medicine, Auburn University, Auburn, AL, USA
| | - Erik Hofmeister
- Department of Clinical Sciences, College of Veterinary Medicine, Auburn University, Auburn, AL, USA
| | | | - Kendon W Kuo
- Department of Clinical Sciences, College of Veterinary Medicine, Auburn University, Auburn, AL, USA
| | - Amy B Yanke
- Department of Clinical Sciences, College of Veterinary Medicine, Auburn University, Auburn, AL, USA
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9
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Abe D, Inaji M, Hase T, Takahashi S, Sakai R, Ayabe F, Tanaka Y, Otomo Y, Maehara T. A Prehospital Triage System to Detect Traumatic Intracranial Hemorrhage Using Machine Learning Algorithms. JAMA Netw Open 2022; 5:e2216393. [PMID: 35687335 PMCID: PMC9187955 DOI: 10.1001/jamanetworkopen.2022.16393] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/06/2023] Open
Abstract
IMPORTANCE An adequate system for triaging patients with head trauma in prehospital settings and choosing optimal medical institutions is essential for improving the prognosis of these patients. To our knowledge, there has been no established way to stratify these patients based on their head trauma severity that can be used by ambulance crews at an injury site. OBJECTIVES To develop a prehospital triage system to stratify patients with head trauma according to trauma severity by using several machine learning techniques and to evaluate the predictive accuracy of these techniques. DESIGN, SETTING, AND PARTICIPANTS This single-center retrospective cohort study was conducted by reviewing the electronic medical records of consecutive patients who were transported to Tokyo Medical and Dental University Hospital in Japan from April 1, 2018, to March 31, 2021. Patients younger than 16 years with cardiopulmonary arrest on arrival or with a significant amount of missing data were excluded. MAIN OUTCOMES AND MEASURES Machine learning-based prediction models to detect the presence of traumatic intracranial hemorrhage were constructed. The predictive accuracy of the models was evaluated with the area under the receiver operating curve (ROC-AUC), area under the precision recall curve (PR-AUC), sensitivity, specificity, and other representative statistics. RESULTS A total of 2123 patients (1527 male patients [71.9%]; mean [SD] age, 57.6 [19.8] years) with head trauma were enrolled in this study. Traumatic intracranial hemorrhage was detected in 258 patients (12.2%). Among several machine learning algorithms, extreme gradient boosting (XGBoost) achieved the mean (SD) highest ROC-AUC (0.78 [0.02]) and PR-AUC (0.46 [0.01]) in cross-validation studies. In the testing set, the ROC-AUC was 0.80, the sensitivity was 74.0% (95% CI, 59.7%-85.4%), and the specificity was 74.9% (95% CI, 70.2%-79.3%). The prediction model using the National Institute for Health and Care Excellence (NICE) guidelines, which was calculated after consultation with physicians, had a sensitivity of 72.0% (95% CI, 57.5%-83.8%) and a specificity of 73.3% (95% CI, 68.7%-77.7%). The McNemar test revealed no statistically significant differences between the XGBoost algorithm and the NICE guidelines for sensitivity or specificity (P = .80 and P = .55, respectively). CONCLUSIONS AND RELEVANCE In this cohort study, the prediction model achieved a comparatively accurate performance in detecting traumatic intracranial hemorrhage using only the simple pretransportation information from the patient. Further validation with a prospective multicenter data set is needed.
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Affiliation(s)
- Daisu Abe
- Department of Neurosurgery, Tokyo Medical and Dental University, Tokyo, Japan
| | - Motoki Inaji
- Department of Neurosurgery, Tokyo Medical and Dental University, Tokyo, Japan
| | - Takeshi Hase
- Institute of Education, Tokyo Medical and Dental University, Tokyo, Japan
| | - Shota Takahashi
- Department of Neurosurgery, Tokyo Medical and Dental University, Tokyo, Japan
| | - Ryosuke Sakai
- Department of Neurosurgery, Tokyo Medical and Dental University, Tokyo, Japan
| | - Fuga Ayabe
- Department of Neurosurgery, Tokyo Medical and Dental University, Tokyo, Japan
| | - Yoji Tanaka
- Department of Neurosurgery, Tokyo Medical and Dental University, Tokyo, Japan
| | - Yasuhiro Otomo
- Department of Acute Critical Care and Disaster Medicine, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University, Tokyo, Japan
| | - Taketoshi Maehara
- Department of Neurosurgery, Tokyo Medical and Dental University, Tokyo, Japan
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10
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Tommila M, Pappinen J, Raatiniemi L, Saviluoto A, Toivonen T, Björkman J, Nurmi J. Standardised data collection in prehospital critical care: a comparison of medical problem categories and discharge diagnoses. Scand J Trauma Resusc Emerg Med 2022; 30:26. [PMID: 35413859 PMCID: PMC9006464 DOI: 10.1186/s13049-022-01013-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2021] [Accepted: 03/31/2022] [Indexed: 11/23/2022] Open
Abstract
Background Prehospital medical problem reporting is essential in the management of helicopter emergency medical services (HEMS) operations. The consensus-based template for reporting and documenting in physician-staffed prehospital services exists and the classification of medical problems presented in the template is widely used in research and quality improvement. However, validation of the reported prehospital medical problem is lacking. This study aimed to describe the in-hospital diagnoses, patient characteristics and medical interventions in different categories of medical problems. Methods This retrospective, observational registry study examined the 10 most common in-hospital International Statistical Classification of Disease (ICD-10) diagnoseswithin different prehospital medical problem categories, defined by the HEMS physician/paramedic immediately after the mission was completed. Data were gathered from a national HEMS quality registry and a national hospital discharge registry. Patient characteristics and medical interventions related to different medical problem categories are also described. Results A total of 33,844 patients were included in the analyses. All the medical problem categories included a broad spectrum of ICD-10 diagnoses (the number of diagnosis classes per medical problem category ranged from 73 to 403). The most frequent diagnoses were mainly consistent with the reported medical problems. Overlapping of ICD-10 diagnoses was mostly seen in two medical problem categories: stroke and acute neurology excluding stroke. Additionally, typical patient characteristics and disturbances in vital signs were related to adequate medical problem categories. Conclusions Medical problems reported by HEMS personnel have adequate correspondence to hospital discharge diagnoses. However, the classification of cerebrovascular accidents remains challenging.
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Affiliation(s)
- Miretta Tommila
- Department of Perioperative Services, Intensive Care Medicine and Pain Management, Turku University Hospital and University of Turku, Turku, Finland
| | | | - Lasse Raatiniemi
- Centre for Prehospital Emergency Care, Oulu, Finland.,Research Group of Surgery, Anaesthesiology and Intensive Care, Division of Anesthesiology Oulu University Hospital, Medical Research Centre, University of Oulu, Oulu, Finland
| | - Anssi Saviluoto
- FinnHEMS Ltd, HEMS Operations, Vantaa, Finland.,University of Eastern Finland, Kuopio, Finland
| | | | - Johannes Björkman
- FinnHEMS Ltd, HEMS Operations, Vantaa, Finland.,Emergency Medicine and Services, Helsinki University Hospital and University of Helsinki, FinnHEMS 10, Vesikuja 9, 01530, Vantaa, Finland
| | - Jouni Nurmi
- FinnHEMS Ltd, HEMS Operations, Vantaa, Finland. .,Emergency Medicine and Services, Helsinki University Hospital and University of Helsinki, FinnHEMS 10, Vesikuja 9, 01530, Vantaa, Finland.
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11
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Johnson GGRJ, Jelic T, Derksen A, Unger B, Zeiler FA, Ziesmann MT, Gillman LM. Accuracy of Optic Nerve Sheath Diameter Measurements in Pocket-Sized Ultrasound Devices in a Simulation Model. Front Med (Lausanne) 2022; 9:831778. [PMID: 35308521 PMCID: PMC8924410 DOI: 10.3389/fmed.2022.831778] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2022] [Accepted: 01/31/2022] [Indexed: 11/13/2022] Open
Abstract
Introduction Transorbital sonographic measurement of optic nerve sheath diameter (ONSD) is an emerging non-invasive technique for the identification and monitoring of intracranial hypertension. In recent years, new pocket ultrasound devices have become available, and it is uncertain if they have the resolution to measure such small structures appropriately as compared to their predecessors. In this study, we measure the performance of three ultrasound units on a simulation model to establish their precision and accuracy. Methods ONSD was measured by three expert point-of-care sonographers using ultrasound machines three times on each of seven discrete ONS model sizes ranging from 3.5 to 7.9 mm. Two pocket ultrasounds (IVIZ, Sonosite, and Lumify, Philips) and one standard-sized portable ultrasound (M-Turbo, Sonosite) were used. Measurements were analyzed for mean error and variance and tested for significance using blocked covariance matrix regression analyses. Results The devices differed in their variances (Lumify: 0.19 mm2, M-Turbo: 0.26 mm2, IVIZ: 0.34 mm2) and their mean error (Lumify: -0.05 mm, M-Turbo: 0.10 mm, IVIZ: -0.10 mm). The difference in mean error between users is not significant (p = 0.45), but there is a significant difference in mean error between devices (p = 0.02). Conclusions Accurate ONSD measurement is possible utilizing pocket-sized ultrasound, and in some cases, may be more accurate than larger portable ultrasound units. While the differences in these devices were statistically significant, all three were highly accurate, with one pocket device (Lumify) outperforming the rest. Further study in human subjects should be conducted prior to using pocket ultrasound devices for in vivo diagnosis of intracranial hypertension.
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Affiliation(s)
| | - Tomislav Jelic
- Department of Emergency Medicine, University of Manitoba, Winnipeg, MB, Canada
| | - Angela Derksen
- Emergency Department, Seven Oaks General Hospital, Winnipeg, MB, Canada
| | - Bertram Unger
- Department of Internal Medicine, University of Manitoba, Winnipeg, MB, Canada
| | - Frederick A Zeiler
- Section of Neurosurgery, Department of Surgery, University of Manitoba, Winnipeg, MB, Canada.,Department of Anatomy and Cell Science, University of Manitoba, Winnipeg, MB, Canada.,Biomedical Engineering, Faculty of Engineering, University of Manitoba, Winnipeg, MB, Canada.,Centre on Aging, University of Manitoba, Winnipeg, MB, Canada.,Division of Anaesthesia, Department of Medicine, University of Cambridge, Cambridge, United Kingdom
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12
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Choi Y, Park JH, Hong KJ, Ro YS, Song KJ, Shin SD. Development and validation of a prehospital-stage prediction tool for traumatic brain injury: a multicentre retrospective cohort study in Korea. BMJ Open 2022; 12:e055918. [PMID: 35022177 PMCID: PMC8756263 DOI: 10.1136/bmjopen-2021-055918] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/03/2022] Open
Abstract
OBJECTIVES Predicting diagnosis and prognosis of traumatic brain injury (TBI) at the prehospital stage is challenging; however, using comprehensive prehospital information and machine learning may improve the performance of the predictive model. We developed and tested predictive models for TBI that use machine learning algorithms using information that can be obtained in the prehospital stage. DESIGN This was a multicentre retrospective study. SETTING AND PARTICIPANTS This study was conducted at three tertiary academic emergency departments (EDs) located in an urban area of South Korea. The data from adult patients with severe trauma who were assessed by emergency medical service providers and transported to three participating hospitals between 2014 to 2018 were analysed. RESULTS We developed and tested five machine learning algorithms-logistic regression analyses, extreme gradient boosting, support vector machine, random forest and elastic net (EN)-to predict TBI, TBI with intracranial haemorrhage or injury (TBI-I), TBI with ED or admission result of admission or transferred (TBI with non-discharge (TBI-ND)) and TBI with ED or admission result of death (TBI-D). A total of 1169 patients were included in the final analysis, and the proportions of TBI, TBI-I, TBI-ND and TBI-D were 24.0%, 21.5%, 21.3% and 3.7%, respectively. The EN model yielded an area under receiver-operator curve of 0.799 for TBI, 0.844 for TBI-I, 0.811 for TBI-ND and 0.871 for TBI-D. The EN model also yielded the highest specificity and significant reclassification improvement. Variables related to loss of consciousness, Glasgow Coma Scale and light reflex were the three most important variables to predict all outcomes. CONCLUSION Our results inform the diagnosis and prognosis of TBI. Machine learning models resulted in significant performance improvement over that with logistic regression analyses, and the best performing model was EN.
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Affiliation(s)
- Yeongho Choi
- Laboratory of Emergency Medical Services, Seoul National University Hospital Biomedical Research Institute, Seoul, South Korea
- Department of Emergency Medicine, Seoul National University Hospital, Seoul, South Korea
| | - Jeong Ho Park
- Laboratory of Emergency Medical Services, Seoul National University Hospital Biomedical Research Institute, Seoul, South Korea
- Department of Emergency Medicine, Seoul National University Hospital, Seoul, South Korea
| | - Ki Jeong Hong
- Laboratory of Emergency Medical Services, Seoul National University Hospital Biomedical Research Institute, Seoul, South Korea
- Department of Emergency Medicine, Seoul National University Hospital, Seoul, South Korea
| | - Young Sun Ro
- Laboratory of Emergency Medical Services, Seoul National University Hospital Biomedical Research Institute, Seoul, South Korea
- Department of Emergency Medicine, Seoul National University Hospital, Seoul, South Korea
| | - Kyoung Jun Song
- Laboratory of Emergency Medical Services, Seoul National University Hospital Biomedical Research Institute, Seoul, South Korea
- Department of Emergency Medicine, Seoul Metropolitan Government Seoul National University Boramae Medical Center, Seoul, South Korea
| | - Sang Do Shin
- Laboratory of Emergency Medical Services, Seoul National University Hospital Biomedical Research Institute, Seoul, South Korea
- Department of Emergency Medicine, Seoul National University Hospital, Seoul, South Korea
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