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Kiwanuka O, Lassarén P, Hånell A, Boström L, Thelin EP. ASA-score is associated with 90-day mortality after complicated mild traumatic brain injury - a retrospective cohort study. Acta Neurochir (Wien) 2024; 166:363. [PMID: 39259285 PMCID: PMC11390782 DOI: 10.1007/s00701-024-06247-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: 03/29/2024] [Accepted: 08/21/2024] [Indexed: 09/13/2024]
Abstract
PURPOSE This study explores the association of the American Society of Anesthesiologists (ASA) score with 90-day mortality in complicated mild traumatic brain injury (mTBI) patients, and in trauma patients without a TBI. METHODS This retrospective study was conducted using a cohort of trauma patients treated at a level III trauma center in Stockholm, Sweden from January to December 2019. The primary endpoint was 90-day mortality. The population was identified using the Swedish Trauma registry. The Trauma and Injury Severity Score (TRISS) was used to estimate the likelihood of survival. Trauma patients without TBI (NTBI) were used for comparison. Data analysis was conducted using R software, and statistical analysis included univariate and multivariate logistic regression. RESULTS A total of 244 TBI patients and 579 NTBI patients were included, with a 90-day mortality of 8.2% (n = 20) and 5.4% (n = 21), respectively. Deceased patients in both cohorts were generally older, with greater comorbidities and higher injury severity. Complicated mTBI constituted 97.5% of the TBI group. Age and an ASA score of 3 or higher were independently associated with increased mortality risk in the TBI group, with odds ratios of 1.04 (95% 1.00-1.09) and 3.44 (95% CI 1.10-13.41), respectively. Among NTBI patients, only age remained a significant mortality predictor. TRISS demonstrated limited predictive utility across both cohorts, yet a significant discrepancy was observed between the outcome groups within the NTBI cohort. CONCLUSION This retrospective cohort study highlights a significant association between ASA score and 90-day mortality in elderly patients with complicated mTBI, something that could not be observed in comparative NTBI cohort. These findings suggest the benefit of incorporating ASA score into prognostic models to enhance the accuracy of outcome prediction models in these populations, though further research is warranted.
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Affiliation(s)
- Olivia Kiwanuka
- Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden.
- Department of Clinical Science and Education, Karolinska Institutet, Södersjukhuset, Stockholm, Sweden.
| | - Philipp Lassarén
- Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
| | - Anders Hånell
- Department of Medical Sciences, Neurosurgery, Uppsala University, Uppsala, Sweden
| | - Lennart Boström
- Department of Clinical Science and Education, Karolinska Institutet, Södersjukhuset, Stockholm, Sweden
| | - Eric P Thelin
- Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
- Department of Neurology, Karolinska University Hospital, Stockholm, Sweden
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Eghzawi A, Alsabbah A, Gharaibeh S, Alwan I, Gharaibeh A, Goyal AV. Mortality Predictors for Adult Patients with Mild-to-Moderate Traumatic Brain Injury: A Literature Review. Neurol Int 2024; 16:406-418. [PMID: 38668127 PMCID: PMC11053597 DOI: 10.3390/neurolint16020030] [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: 03/06/2024] [Revised: 03/30/2024] [Accepted: 04/03/2024] [Indexed: 04/29/2024] Open
Abstract
Traumatic brain injuries (TBIs) represent a significant public health concern, with mild-to-moderate cases comprising a substantial portion of incidents. Understanding the predictors of mortality among adult patients with mild-to-moderate TBIs is crucial for optimizing clinical management and improving outcomes. This literature review examines the existing research to identify and analyze the mortality predictors in this patient population. Through a comprehensive review of peer-reviewed articles and clinical studies, key prognostic factors, such as age, Glasgow Coma Scale (GCS) score, the presence of intracranial hemorrhage, pupillary reactivity, and coexisting medical conditions, are explored. Additionally, this review investigates the role of advanced imaging modalities, biomarkers, and scoring systems in predicting mortality following a mild-to-moderate TBI. By synthesizing the findings from diverse studies, this review aims to provide clinicians and researchers with valuable insights into the factors influencing mortality outcomes in adult patients with a mild-to-moderate TBI, thus facilitating more informed decision making and targeted interventions in clinical practice.
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Affiliation(s)
- Ansam Eghzawi
- Insight Research Institute, Flint, MI 48507, USA; (A.E.); (A.A.); (S.G.); (I.A.)
- Center for Cognition and Neuroethics, University of Michigan-Flint, Flint, MI 48502, USA
- Department of Research, Insight Hospital and Medical Center, Chicago, IL 60616 USA
| | - Alameen Alsabbah
- Insight Research Institute, Flint, MI 48507, USA; (A.E.); (A.A.); (S.G.); (I.A.)
| | - Shatha Gharaibeh
- Insight Research Institute, Flint, MI 48507, USA; (A.E.); (A.A.); (S.G.); (I.A.)
- Center for Cognition and Neuroethics, University of Michigan-Flint, Flint, MI 48502, USA
| | - Iktimal Alwan
- Insight Research Institute, Flint, MI 48507, USA; (A.E.); (A.A.); (S.G.); (I.A.)
- Department of Research, Insight Hospital and Medical Center, Chicago, IL 60616 USA
| | - Abeer Gharaibeh
- Insight Research Institute, Flint, MI 48507, USA; (A.E.); (A.A.); (S.G.); (I.A.)
- Department of Research, Insight Hospital and Medical Center, Chicago, IL 60616 USA
| | - Anita V. Goyal
- Department of Emergency Medicine, Insight Hospital and Medical Center, Chicago, IL 60616, USA
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Kregel HR, Hatton GE, Harvin JA, Puzio TJ, Wade CE, Kao LS. Identifying Age-Specific Risk Factors for Poor Outcomes After Trauma With Machine Learning. J Surg Res 2024; 296:465-471. [PMID: 38320366 DOI: 10.1016/j.jss.2023.12.016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2023] [Revised: 12/04/2023] [Accepted: 12/27/2023] [Indexed: 02/08/2024]
Abstract
INTRODUCTION Risk stratification for poor outcomes is not currently age-specific. Risk stratification of older patients based on observational cohorts primarily composed of young patients may result in suboptimal clinical care and inaccurate quality benchmarking. We assessed two hypotheses. First, we hypothesized that risk factors for poor outcomes after trauma are age-dependent and, second, that the relative importance of various risk factors are also age-dependent. METHODS A cohort study of severely injured adult trauma patients admitted to the intensive care unit 2014-2018 was performed using trauma registry data. Random forest algorithms predicting poor outcomes (death or complication) were built and validated using three cohorts: (1) patients of all ages, (2) younger patients, and (3) older patients. Older patients were defined as aged 55 y or more to maintain consistency with prior trauma literature. Complications assessed included acute renal failure, acute respiratory distress syndrome, cardiac arrest, unplanned intubation, unplanned intensive care unit admission, and unplanned return to the operating room, as defined by the trauma quality improvement program. Mean decrease in model accuracy (MDA), if each variable was removed and scaled to a Z-score, was calculated. MDA change ≥4 standard deviations between age cohorts was considered significant. RESULTS Of 5489 patients, 25% were older. Poor outcomes occurred in 12% of younger and 33% of older patients. Head injury was the most important predictor of poor outcome in all cohorts. In the full cohort, age was the most important predictor of poor outcomes after head injury. Within age cohorts, the most important predictors of poor outcomes, after head injury, were surgery requirement in younger patients and arrival Glasgow Coma Scale in older patients. Compared to younger patients, head injury and arrival Glasgow Coma Scale had the greatest increase in importance for older patients, while systolic blood pressure had the greatest decrease in importance. CONCLUSIONS Supervised machine learning identified differences in risk factors and their relative associations with poor outcomes based on age. Age-specific models may improve hospital benchmarking and identify quality improvement targets for older trauma patients.
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Affiliation(s)
- Heather R Kregel
- Division of Acute Care Surgery, Department of Surgery, McGovern Medical School at UTHealth, Houston, Texas; Center for Surgical Trials and Evidence-Based Practice, McGovern Medical School at UTHealth, Houston, Texas; Center for Translational Injury, McGovern Medical School at UTHealth, Houston, Texas.
| | - Gabrielle E Hatton
- Division of Acute Care Surgery, Department of Surgery, McGovern Medical School at UTHealth, Houston, Texas; Center for Surgical Trials and Evidence-Based Practice, McGovern Medical School at UTHealth, Houston, Texas; Center for Translational Injury, McGovern Medical School at UTHealth, Houston, Texas
| | - John A Harvin
- Division of Acute Care Surgery, Department of Surgery, McGovern Medical School at UTHealth, Houston, Texas; Center for Translational Injury, McGovern Medical School at UTHealth, Houston, Texas
| | - Thaddeus J Puzio
- Division of Acute Care Surgery, Department of Surgery, McGovern Medical School at UTHealth, Houston, Texas
| | - Charles E Wade
- Division of Acute Care Surgery, Department of Surgery, McGovern Medical School at UTHealth, Houston, Texas; Center for Translational Injury, McGovern Medical School at UTHealth, Houston, Texas
| | - Lillian S Kao
- Division of Acute Care Surgery, Department of Surgery, McGovern Medical School at UTHealth, Houston, Texas; Center for Surgical Trials and Evidence-Based Practice, McGovern Medical School at UTHealth, Houston, Texas; Center for Translational Injury, McGovern Medical School at UTHealth, Houston, Texas
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Wright TJ, Elliott TR, Randolph KM, Pyles RB, Masel BE, Urban RJ, Sheffield-Moore M. Prevalence of fatigue and cognitive impairment after traumatic brain injury. PLoS One 2024; 19:e0300910. [PMID: 38517903 PMCID: PMC10959386 DOI: 10.1371/journal.pone.0300910] [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: 10/05/2023] [Accepted: 03/06/2024] [Indexed: 03/24/2024] Open
Abstract
BACKGROUND Following traumatic brain injury (TBI) some patients develop lingering comorbid symptoms of fatigue and cognitive impairment. The mild cognitive impairment self-reported by patients is often not detected with neurocognitive tests making it difficult to determine how common and severe these symptoms are in individuals with a history of TBI. This study was conducted to determine the relative prevalence of fatigue and cognitive impairment in individuals with a history of TBI. METHODS The Fatigue and Altered Cognition Scale (FACs) digital questionnaire was used to assess self-reported fatigue and cognitive impairment. Adults aged 18-70 were digitally recruited for the online anonymous study. Eligible participants provided online consent, demographic data, information about lifetime TBI history, and completed the 20 item FACs questionnaire. RESULTS A total of 519 qualifying participants completed the online digital study which included 204 participants with a history of TBI of varied cause and severity and 315 with no history of TBI. FACs Total Score was significantly higher in the TBI group (57.7 ± 22.2) compared to non-TBI (39.5 ± 23.9; p<0.0001) indicating more fatigue and cognitive impairment. When stratified by TBI severity, FACs score was significantly higher for all severity including mild (53.9 ± 21.9, p<0.0001), moderate (54.8 ± 24.4, p<0.0001), and severe (59.7 ± 20.9, p<0.0001) TBI. Correlation analysis indicated that more severe TBI was associated with greater symptom severity (p<0.0001, r = 0.3165). Ancillary analysis also suggested that FACs scores may be elevated in participants with prior COVID-19 infection but no history of TBI. CONCLUSIONS Adults with a history of even mild TBI report significantly greater fatigue and cognitive impairment than those with no history of TBI, and symptoms are more profound with greater TBI severity.
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Affiliation(s)
- Traver J. Wright
- Department of Internal Medicine, The University of Texas Medical Branch, Galveston, Texas, United States of America
| | - Timothy R. Elliott
- Department of Educational Psychology, Texas A&M University, College Station, Texas, United States of America
| | - Kathleen M. Randolph
- Department of Internal Medicine, The University of Texas Medical Branch, Galveston, Texas, United States of America
| | - Richard B. Pyles
- Department of Pediatrics, The University of Texas Medical Branch, Galveston, Texas, United States of America
| | - Brent E. Masel
- Department of Neurology, The University of Texas Medical Branch, Galveston, Texas, United States of America
- Centre for Neuro Skills, Bakersfield, California, United States of America
| | - Randall J. Urban
- Department of Internal Medicine, The University of Texas Medical Branch, Galveston, Texas, United States of America
| | - Melinda Sheffield-Moore
- Department of Internal Medicine, The University of Texas Medical Branch, Galveston, Texas, United States of America
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Caeyenberghs K, Imms P, Irimia A, Monti MM, Esopenko C, de Souza NL, Dominguez D JF, Newsome MR, Dobryakova E, Cwiek A, Mullin HAC, Kim NJ, Mayer AR, Adamson MM, Bickart K, Breedlove KM, Dennis EL, Disner SG, Haswell C, Hodges CB, Hoskinson KR, Johnson PK, Königs M, Li LM, Liebel SW, Livny A, Morey RA, Muir AM, Olsen A, Razi A, Su M, Tate DF, Velez C, Wilde EA, Zielinski BA, Thompson PM, Hillary FG. ENIGMA's simple seven: Recommendations to enhance the reproducibility of resting-state fMRI in traumatic brain injury. Neuroimage Clin 2024; 42:103585. [PMID: 38531165 PMCID: PMC10982609 DOI: 10.1016/j.nicl.2024.103585] [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: 09/21/2023] [Revised: 02/22/2024] [Accepted: 02/25/2024] [Indexed: 03/28/2024]
Abstract
Resting state functional magnetic resonance imaging (rsfMRI) provides researchers and clinicians with a powerful tool to examine functional connectivity across large-scale brain networks, with ever-increasing applications to the study of neurological disorders, such as traumatic brain injury (TBI). While rsfMRI holds unparalleled promise in systems neurosciences, its acquisition and analytical methodology across research groups is variable, resulting in a literature that is challenging to integrate and interpret. The focus of this narrative review is to address the primary methodological issues including investigator decision points in the application of rsfMRI to study the consequences of TBI. As part of the ENIGMA Brain Injury working group, we have collaborated to identify a minimum set of recommendations that are designed to produce results that are reliable, harmonizable, and reproducible for the TBI imaging research community. Part one of this review provides the results of a literature search of current rsfMRI studies of TBI, highlighting key design considerations and data processing pipelines. Part two outlines seven data acquisition, processing, and analysis recommendations with the goal of maximizing study reliability and between-site comparability, while preserving investigator autonomy. Part three summarizes new directions and opportunities for future rsfMRI studies in TBI patients. The goal is to galvanize the TBI community to gain consensus for a set of rigorous and reproducible methods, and to increase analytical transparency and data sharing to address the reproducibility crisis in the field.
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Affiliation(s)
- Karen Caeyenberghs
- Cognitive Neuroscience Unit, School of Psychology, Deakin University, Geelong, Australia.
| | - Phoebe Imms
- Ethel Percy Andrus Gerontology Center, Leonard Davis School of Gerontology, University of Southern California, Los Angeles, CA, USA.
| | - Andrei Irimia
- Ethel Percy Andrus Gerontology Center, Leonard Davis School of Gerontology, University of Southern California, Los Angeles, CA, USA; Alfred E. Mann Department of Biomedical Engineering, Andrew & Erna Viterbi School of Engineering, University of Southern California, Los Angeles, CA, USA; Department of Quantitative & Computational Biology, Dana and David Dornsife College of Arts & Sciences, University of Southern California, Los Angeles, CA, USA.
| | - Martin M Monti
- Department of Psychology, UCLA, USA; Brain Injury Research Center (BIRC), Department of Neurosurgery, UCLA, USA.
| | - Carrie Esopenko
- Department of Rehabilitation and Human Performance, Icahn School of Medicine at Mount Sinai, NY, USA.
| | - Nicola L de Souza
- Department of Rehabilitation and Human Performance, Icahn School of Medicine at Mount Sinai, NY, USA.
| | - Juan F Dominguez D
- Cognitive Neuroscience Unit, School of Psychology, Deakin University, Geelong, Australia.
| | - Mary R Newsome
- Michael E. DeBakey VA Medical Center, Houston, TX, USA; H. Ben Taub Department of Physical Medicine and Rehabilitation, Baylor College of Medicine, Houston, TX, USA; TBI and Concussion Center, Department of Neurology, University of Utah, Salt Lake City, UT, USA.
| | - Ekaterina Dobryakova
- Center for Traumatic Brain Injury, Kessler Foundation, East Hanover, NJ, USA; Rutgers New Jersey Medical School, Newark, NJ, USA.
| | - Andrew Cwiek
- Department of Psychology, Penn State University, State College, PA, USA.
| | - Hollie A C Mullin
- Department of Psychology, Penn State University, State College, PA, USA.
| | - Nicholas J Kim
- Ethel Percy Andrus Gerontology Center, Leonard Davis School of Gerontology, University of Southern California, Los Angeles, CA, USA; Alfred E. Mann Department of Biomedical Engineering, Andrew & Erna Viterbi School of Engineering, University of Southern California, Los Angeles, CA, USA.
| | - Andrew R Mayer
- Mind Research Network, Albuquerque, NM, USA; Departments of Neurology and Psychiatry, University of New Mexico School of Medicine, Albuquerque, NM, USA.
| | - Maheen M Adamson
- Women's Operational Military Exposure Network (WOMEN) & Rehabilitation Department, VA Palo Alto, Palo Alto, CA, USA; Rehabilitation Service, VA Palo Alto, Palo Alto, CA, USA; Neurosurgery, Stanford School of Medicine, Stanford, CA, USA.
| | - Kevin Bickart
- UCLA Steve Tisch BrainSPORT Program, USA; Department of Neurology, David Geffen School of Medicine at UCLA, USA.
| | - Katherine M Breedlove
- Center for Clinical Spectroscopy, Brigham and Women's Hospital, Boston, MA, USA; Department of Radiology, Harvard Medical School, Boston, MA, USA.
| | - Emily L Dennis
- TBI and Concussion Center, Department of Neurology, University of Utah, Salt Lake City, UT, USA; George E. Wahlen Veterans Affairs Medical Center, Salt Lake City, UT, USA.
| | - Seth G Disner
- Minneapolis VA Health Care System, Minneapolis, MN, USA; Department of Psychiatry and Behavioral Sciences, University of Minnesota Medical School, Minneapolis, MN, USA.
| | - Courtney Haswell
- Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC, USA.
| | - Cooper B Hodges
- TBI and Concussion Center, Department of Neurology, University of Utah, Salt Lake City, UT, USA; George E. Wahlen Veterans Affairs Medical Center, Salt Lake City, UT, USA; Department of Psychology, Brigham Young University, Provo, UT, USA.
| | - Kristen R Hoskinson
- Center for Biobehavioral Health, The Abigail Wexner Research Institute at Nationwide Children's Hospital, Columbus, OH, USA; Department of Pediatrics, The Ohio State University College of Medicine, OH, USA.
| | - Paula K Johnson
- TBI and Concussion Center, Department of Neurology, University of Utah, Salt Lake City, UT, USA; Neuroscience Center, Brigham Young University, Provo, UT, USA.
| | - Marsh Königs
- Emma Children's Hospital, Amsterdam UMC, University of Amsterdam, Emma Neuroscience Group, The Netherlands; Amsterdam Reproduction and Development, Amsterdam, The Netherlands.
| | - Lucia M Li
- C3NL, Imperial College London, United Kingdom; UK DRI Centre for Health Care and Technology, Imperial College London, United Kingdom.
| | - Spencer W Liebel
- TBI and Concussion Center, Department of Neurology, University of Utah, Salt Lake City, UT, USA; George E. Wahlen Veterans Affairs Medical Center, Salt Lake City, UT, USA.
| | - Abigail Livny
- Division of Diagnostic Imaging, Sheba Medical Center, Tel-Hashomer, Israel; Sagol School of Neuroscience, Tel-Aviv University, Tel-Aviv, Israel; Sackler Faculty of Medicine, Tel-Aviv University, Tel-Aviv, Israel.
| | - Rajendra A Morey
- Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC, USA; Duke-UNC Brain Imaging and Analysis Center, Duke University, Durham, NC, USA; VA Mid-Atlantic Mental Illness Research Education and Clinical Center, Durham, NC, USA.
| | - Alexandra M Muir
- Department of Psychology, Brigham Young University, Provo, UT, USA.
| | - Alexander Olsen
- Department of Psychology, Norwegian University of Science and Technology, Trondheim, Norway; Clinic of Rehabilitation, St. Olavs Hospital, Trondheim University Hospital, Trondheim, Norway; NorHEAD - Norwegian Centre for Headache Research, Norwegian University of Science and Technology, Trondheim, Norway.
| | - Adeel Razi
- Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, Clayton, VIC 3800, Australia; Wellcome Centre for Human Neuroimaging, University College London, WC1N 3AR London, United Kingdom; CIFAR Azrieli Global Scholars Program, CIFAR, Toronto, ON, Canada.
| | - Matthew Su
- H. Ben Taub Department of Physical Medicine and Rehabilitation, Baylor College of Medicine, Houston, TX, USA.
| | - David F Tate
- TBI and Concussion Center, Department of Neurology, University of Utah, Salt Lake City, UT, USA; George E. Wahlen Veterans Affairs Medical Center, Salt Lake City, UT, USA.
| | - Carmen Velez
- TBI and Concussion Center, Department of Neurology, University of Utah, Salt Lake City, UT, USA; George E. Wahlen Veterans Affairs Medical Center, Salt Lake City, UT, USA.
| | - Elisabeth A Wilde
- H. Ben Taub Department of Physical Medicine and Rehabilitation, Baylor College of Medicine, Houston, TX, USA; TBI and Concussion Center, Department of Neurology, University of Utah, Salt Lake City, UT, USA; George E. Wahlen Veterans Affairs Medical Center, Salt Lake City, UT, USA.
| | - Brandon A Zielinski
- Departments of Pediatrics, Neurology, and Neuroscience, University of Florida, Gainesville, FL, USA; Departments of Pediatrics, Neurology, and Radiology, University of Utah, Salt Lake City, UT, USA.
| | - Paul M Thompson
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging & Informatics Institute, University of Southern California, Marina del Rey, CA, USA.
| | - Frank G Hillary
- Department of Psychology, Penn State University, State College, PA, USA; Department of Neurology, Hershey Medical Center, PA, USA.
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Molero Y, Sharp DJ, D’Onofrio BM, Lichtenstein P, Larsson H, Fazel S, Rostami E. Medication utilization in traumatic brain injury patients-insights from a population-based matched cohort study. Front Neurol 2024; 15:1339290. [PMID: 38385038 PMCID: PMC10879380 DOI: 10.3389/fneur.2024.1339290] [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: 11/15/2023] [Accepted: 01/23/2024] [Indexed: 02/23/2024] Open
Abstract
Introduction Traumatic brain injury (TBI) is associated with health problems across multiple domains and TBI patients are reported to have high rates of medication use. However, prior evidence is thin due to methodological limitations. Our aim was thus to examine the use of a wide spectrum of medications prescribed to address pain and somatic conditions in a population-based cohort of TBI patients, and to compare this to a sex- and age-matched cohort. We also examined how patient factors such as sex, age, and TBI severity were associated with medication use. Methods We assessed Swedish nationwide registers to include all individuals treated for TBI in hospitals or specialist outpatient care between 2006 and 2012. We examined dispensed prescriptions for eight different non-psychotropic medication classes for the 12 months before, and 12 months after, the TBI. We applied a fixed-effects model to compare TBI patients with the matched population cohort. We also stratified TBI patients by sex, age, TBI severity and carried out comparisons using a generalized linear model. Results We identified 239,425 individuals with an incident TBI and 239,425 matched individuals. TBI patients were more likely to use any medication [Odds ratio (OR) = 2.03, 95% Confidence Interval (CI) = 2.00-2.05], to present with polypharmacy (OR = 1.96, 95% CI = 1.90-2.02), and to use each of the eight medication classes before their TBI, as compared to the matched population cohort. Following the TBI, TBI patients were more likely to use any medication (OR = 1.83, 95% CI = 1.80-1.86), to present with polypharmacy (OR = 1.74, 95% CI = 1.67-1.80), and to use all medication classes, although differences were attenuated. However, differences increased for antibiotics/antivirals (OR = 2.02, 95% CI = 1.99-2.05) and NSAIDs/antirheumatics (OR = 1.62, 95% CI = 1.59-1.65) post-TBI. We also found that females and older patients were more likely to use medications after their TBI than males and younger patients, respectively. Patients with more severe TBIs demonstrated increased use of antibiotics/ antivirals and NSAIDs/antirheumatics than those with less severe TBIs. Discussion Taken together, our results point to poor overall health in TBI patients, suggesting that medical follow-up should be routine, particularly in females with TBI, and include a review of medication use to address potential polypharmacy.
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Affiliation(s)
- Yasmina Molero
- Department of Clinical Neuroscience, Centre for Psychiatry Research, Karolinska Institutet, Stockholm, Sweden
- Stockholm Health Care Services, Stockholm County Council, Stockholm, Sweden
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - David J. Sharp
- Department of Brain Sciences, Imperial College London, London, United Kingdom
| | - Brian M. D’Onofrio
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN, United States
| | - Paul Lichtenstein
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Henrik Larsson
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
- School of Medical Sciences, Örebro University, Örebro, Sweden
| | - Seena Fazel
- Department of Psychiatry, University of Oxford, Oxford, United Kingdom
| | - Elham Rostami
- Department of Neuroscience, Karolinska Institutet, Stockholm, Sweden
- Department of Medical Sciences, Uppsala University, Uppsala, Sweden
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Agoston DV. Traumatic Brain Injury in the Long-COVID Era. Neurotrauma Rep 2024; 5:81-94. [PMID: 38463416 PMCID: PMC10923549 DOI: 10.1089/neur.2023.0067] [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] [Indexed: 03/12/2024] Open
Abstract
Major determinants of the biological background or reserve, such as age, biological sex, comorbidities (diabetes, hypertension, obesity, etc.), and medications (e.g., anticoagulants), are known to affect outcome after traumatic brain injury (TBI). With the unparalleled data richness of coronavirus disease 2019 (COVID-19; ∼375,000 and counting!) as well as the chronic form, long-COVID, also called post-acute sequelae SARS-CoV-2 infection (PASC), publications (∼30,000 and counting) covering virtually every aspect of the diseases, pathomechanisms, biomarkers, disease phases, symptomatology, etc., have provided a unique opportunity to better understand and appreciate the holistic nature of diseases, interconnectivity between organ systems, and importance of biological background in modifying disease trajectories and affecting outcomes. Such a holistic approach is badly needed to better understand TBI-induced conditions in their totality. Here, I briefly review what is known about long-COVID/PASC, its underlying-suspected-pathologies, the pathobiological changes induced by TBI, in other words, the TBI endophenotypes, discuss the intersection of long-COVID/PASC and TBI-induced pathobiologies, and how by considering some of the known factors affecting the person's biological background and the inclusion of mechanistic molecular biomarkers can help to improve the clinical management of TBI patients.
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Affiliation(s)
- Denes V. Agoston
- Department of Anatomy, Physiology, and Genetics, School of Medicine, Uniformed Services University, Bethesda, Maryland, USA
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Tritt A, Yue JK, Ferguson AR, Torres Espin A, Nelson LD, Yuh EL, Markowitz AJ, Manley GT, Bouchard KE. Data-driven distillation and precision prognosis in traumatic brain injury with interpretable machine learning. Sci Rep 2023; 13:21200. [PMID: 38040784 PMCID: PMC10692236 DOI: 10.1038/s41598-023-48054-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2023] [Accepted: 11/21/2023] [Indexed: 12/03/2023] Open
Abstract
Traumatic brain injury (TBI) affects how the brain functions in the short and long term. Resulting patient outcomes across physical, cognitive, and psychological domains are complex and often difficult to predict. Major challenges to developing personalized treatment for TBI include distilling large quantities of complex data and increasing the precision with which patient outcome prediction (prognoses) can be rendered. We developed and applied interpretable machine learning methods to TBI patient data. We show that complex data describing TBI patients' intake characteristics and outcome phenotypes can be distilled to smaller sets of clinically interpretable latent factors. We demonstrate that 19 clusters of TBI outcomes can be predicted from intake data, a ~ 6× improvement in precision over clinical standards. Finally, we show that 36% of the outcome variance across patients can be predicted. These results demonstrate the importance of interpretable machine learning applied to deeply characterized patients for data-driven distillation and precision prognosis.
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Affiliation(s)
- Andrew Tritt
- Applied Math and Computational Research Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - John K Yue
- Brain and Spinal Injury Center, Zuckerberg San Francisco General Hospital and Trauma Center, San Francisco, CA, USA
- Department of Neurosurgery, Weill Institute for Neurosciences, University of California San Francisco, San Francisco, CA, USA
| | - Adam R Ferguson
- Brain and Spinal Injury Center, Zuckerberg San Francisco General Hospital and Trauma Center, San Francisco, CA, USA
- Department of Neurosurgery, Weill Institute for Neurosciences, University of California San Francisco, San Francisco, CA, USA
- San Francisco Veterans Affairs Healthcare System, San Francisco, CA, USA
| | - Abel Torres Espin
- Brain and Spinal Injury Center, Zuckerberg San Francisco General Hospital and Trauma Center, San Francisco, CA, USA
- Department of Neurosurgery, Weill Institute for Neurosciences, University of California San Francisco, San Francisco, CA, USA
| | - Lindsay D Nelson
- Departments of Neurosurgery and Neurology, Medical College of Wisconsin, Milwaukee, WI, USA
| | - Esther L Yuh
- Brain and Spinal Injury Center, Zuckerberg San Francisco General Hospital and Trauma Center, San Francisco, CA, USA
- Department of Neurosurgery, Weill Institute for Neurosciences, University of California San Francisco, San Francisco, CA, USA
| | - Amy J Markowitz
- Brain and Spinal Injury Center, Zuckerberg San Francisco General Hospital and Trauma Center, San Francisco, CA, USA
- Department of Neurosurgery, Weill Institute for Neurosciences, University of California San Francisco, San Francisco, CA, USA
| | - Geoffrey T Manley
- Brain and Spinal Injury Center, Zuckerberg San Francisco General Hospital and Trauma Center, San Francisco, CA, USA
- Department of Neurosurgery, Weill Institute for Neurosciences, University of California San Francisco, San Francisco, CA, USA
- Weill Neurohub, University of California San Francisco, San Francisco, CA, USA
- Weill Neurohub, University of California Berkeley, Berkeley, CA, USA
| | - Kristofer E Bouchard
- Weill Neurohub, University of California Berkeley, Berkeley, CA, USA.
- Scientific Data Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA.
- Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA.
- Helen Wills Neuroscience Institute and Redwood Center for Theoretical Neuroscience, University of California Berkeley, Berkeley, CA, USA.
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Vervoordt SM, Hamze MK, Dell KC, Staph J, Hillary FG. Effects of preexisting stroke on acute hospital outcomes for older adults admitted with neurotrauma and orthopedic injury. Brain Inj 2022; 36:1109-1117. [PMID: 35996331 DOI: 10.1080/02699052.2022.2109742] [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: 11/02/2022]
Abstract
OBJECTIVE We aimed to examine acute trauma outcomes, specifically among those with neurotrauma (NT), in patients with preexisting cerebrovascular accident (CVA). METHODS We identified patients treated for neurotrauma or orthopedic trauma at hospitals in Pennsylvania with and without an identified history of stroke with residual deficits, aged 50-99 across four groups of N = 11,648 each. We assessed mortality, craniotomy, and total hospital, ICU, step-down, and ventilator days, functional status at discharge (FSD), and discharge destination. RESULTS Stroke history did not influence mortality but was predictive of patients undergoing craniotomy (OR = 1.25, p = 0.008). There was a moderate group effect on total ICU days, with the CVA+NT group in the ICU the longest (η2 = 0.10, p < 0.001). Patients with stroke history were less likely to be discharged to home (OR = 0.65, p < 0.001) and had poorer FSD scores across the various domains assessed. CONCLUSIONS Trauma patients with preexisting CVA were found to have poorer outcomes on a number of different metrics when compared to those without stroke history. While it is possible that functional differences pre-injury influenced FSD and discharge destination, given these results, clinicians should assess for possible comorbidities that may influence treatment.
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Affiliation(s)
- Samantha M Vervoordt
- Department of Psychology, The Pennsylvania State University, University Park, PA, USA
| | - Mohamad K Hamze
- Larner College of Medicine, The University of Vermont, Burlington, VT, USA
| | - Kristine C Dell
- Department of Psychology, The Pennsylvania State University, University Park, PA, USA
| | - Jason Staph
- Department of Psychology, The Pennsylvania State University, University Park, PA, USA
| | - Frank G Hillary
- Department of Psychology, The Pennsylvania State University, University Park, PA, USA
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