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Original Article
Trauma
Bedside ultrasonographic evaluation of optic nerve sheath diameter for monitoring of intracranial pressure in traumatic brain injury patients: a cross sectional study in level II trauma care center in India
Sujit J. Kshirsagar, Anandkumar H. Pande, Sanyogita V. Naik, Alok Yadav, Ruchira M. Sakhala, Sangharsh M. Salve, Aysath Nuhaimah, Priyanka Desai
Acute Crit Care. 2024;39(1):155-161.   Published online February 23, 2024
DOI: https://doi.org/10.4266/acc.2023.01172
  • 1,139 View
  • 128 Download
  • 1 Crossref
AbstractAbstract PDF
Background
Optic nerve sheath diameter (ONSD) is an emerging non-invasive, easily accessible, and possibly useful measurement for evaluating changes in intracranial pressure (ICP). The utilization of bedside ultrasonography (USG) to measure ONSD has garnered increased attention due to its portability, real-time capability, and lack of ionizing radiation. The primary aim of the study was to assess whether bedside USG-guided ONSD measurement can reliably predict increased ICP in traumatic brain injury (TBI) patients. Methods: A total of 95 patients admitted to the trauma intensive care unit was included in this cross sectional study. Patient brain computed tomography (CT) scans and Glasgow Coma Scale (GCS) scores were assessed at the time of admission. Bedside USG-guided binocular ONSD was measured and the mean ONSD was noted. Microsoft Excel was used for statistical analysis. Results: Patients with low GCS had higher mean ONSD values (6.4±1.0 mm). A highly significant association was found among the GCS, CT results, and ONSD measurements (P<0.001). Compared to CT scans, the bedside USG ONSD had 86.42% sensitivity and 64.29% specificity for detecting elevated ICP. The positive predictive value of ONSD to identify elevated ICP was 93.33%, and its negative predictive value was 45.00%. ONSD measurement accuracy was 83.16%. Conclusions: Increased ICP can be accurately predicted by bedside USG measurement of ONSD and can be a valuable adjunctive tool in the management of TBI patients.

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  • Measurement of Optic Nerve Sheath Diameter by Bedside Ultrasound in Patients With Traumatic Brain Injury Presenting to Emergency Department: A Review
    Preethy Koshy, Charuta Gadkari
    Cureus.2024;[Epub]     CrossRef
Review Article
Trauma
Mobilization phases in traumatic brain injury
Tommy Alfandy Nazwar, Ivan Triangto, Gutama Arya Pringga, Farhad Bal’afif, Donny Wisnu Wardana
Acute Crit Care. 2023;38(3):261-270.   Published online August 1, 2023
DOI: https://doi.org/10.4266/acc.2023.00640
  • 5,257 View
  • 292 Download
  • 1 Web of Science
  • 1 Crossref
AbstractAbstract PDF
Mobilization in traumatic brain injury (TBI) have shown the improvement of length of stay, infection, long term weakness, and disability. Primary damage as a result of trauma’s direct effect (skull fracture, hematoma, contusion, laceration, and nerve damage) and secondary damage caused by trauma’s indirect effect (microvasculature damage and pro-inflammatory cytokine) result in reduced tissue perfusion & edema. These can be facilitated through mobilization, but several precautions must be recognized as mobilization itself may further deteriorate patient’s condition. Very few studies have discussed in detail regarding mobilizing patients in TBI cases. Therefore, the scope of this review covers the detail of physiological effects, guideline, precautions, and technique of mobilization in patients with TBI.

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  • Acute orthostatic responses during early mobilisation of patients with acquired brain injury - Innowalk pro versus standing frame
    Matthijs F Wouda, Espen I Bengtson, Ellen Høyer, Alhed P Wesche, Vivien Jørgensen
    Journal of Rehabilitation and Assistive Technologies Engineering.2024;[Epub]     CrossRef
Original Article
Trauma
Comparison of admission GCS score to admission GCS-P and FOUR scores for prediction of outcomes among patients with traumatic brain injury in the intensive care unit in India
Nishant Agrawal, Shivakumar S Iyer, Vishwanath Patil, Sampada Kulkarni, Jignesh N Shah, Prashant Jedge
Acute Crit Care. 2023;38(2):226-233.   Published online May 25, 2023
DOI: https://doi.org/10.4266/acc.2023.00570
  • 3,039 View
  • 188 Download
  • 2 Web of Science
  • 2 Crossref
AbstractAbstract PDF
Background
This study aimed to determine the predictive power of the Full Outline of Unresponsiveness (FOUR) score and the Glasgow Coma Scale Pupil (GCS-P) score in determining outcomes for traumatic brain injury (TBI) patients. The Glasgow Outcome Scale (GOS) was used to evaluate patients at 1 month and 6 months after the injury. Methods: We conducted a 15-month prospective observational study. It included 50 TBI patients admitted to the ICU who met our inclusion criteria. We used Pearson’s correlation coefficient to relate coma scales and outcome measures. The predictive value of these scales was determined using the receiver operating characteristic (ROC) curve, calculating the area under the curve with a 99% confidence interval. All hypotheses were two-tailed, and significance was defined as P<0.01. Results: In the present study, the GCS-P and FOUR scores among all patients on admission as well as in the subset of patients who were mechanically ventilated were statistically significant and strongly correlated with patient outcomes. The correlation coefficient of the GCS score compared to GCS-P and FOUR scores was higher and statistically significant. The areas under the ROC curve for the GCS, GCS-P, and FOUR scores and the number of computed tomography abnormalities were 0.912, 0.905, 0.937, and 0.324, respectively. Conclusions: The GCS, GCS-P, and FOUR scores are all excellent predictors with a strong positive linear correlation with final outcome prediction. In particular, the GCS score has the best correlation with final outcome.

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  • Comparison of Glasgow Coma Scale Full Outline of UnResponsiveness and Glasgow Coma Scale: Pupils Score for Predicting Outcome in Patients with Traumatic Brain Injury
    Indu Kapoor, Hemanshu Prabhakar, Arvind Chaturvedi, Charu Mahajan, Abraham L Chawnchhim, Tej P Sinha
    Indian Journal of Critical Care Medicine.2024; 28(3): 256.     CrossRef
  • Development of a Novel Neurological Score Combining GCS and FOUR Scales for Assessment of Neurosurgical Patients with Traumatic Brain Injury: GCS-FOUR Scale
    Ali Ansari, Sina Zoghi, Amirabbas Khoshbooei, Mohammad Amin Mosayebi, Maryam Feili, Omid Yousefi, Amin Niakan, Seyed Amin Kouhpayeh, Reza Taheri, Hosseinali Khalili
    World Neurosurgery.2023;[Epub]     CrossRef
Review Article
Neurosurgery
Target temperature management in traumatic brain injury with a focus on adverse events, recognition, and prevention
Kwang Wook Jo
Acute Crit Care. 2022;37(4):483-490.   Published online November 10, 2022
DOI: https://doi.org/10.4266/acc.2022.01291
  • 3,907 View
  • 320 Download
  • 2 Web of Science
  • 3 Crossref
AbstractAbstract PDF
Traumatic brain injury (TBI) is a critical cause of disability and death worldwide. Many studies have been conducted aimed at achieving favorable neurologic outcomes by reducing secondary brain injury in TBI patients. However, ground-breaking outcomes are still insufficient so far. Because mild-to-moderate hypothermia (32°C–35°C) has been confirmed to help neurological recovery for recovered patients after circulatory arrest, it has been recognized as a major neuroprotective treatment plan for TBI patients. Thereafter, many clinical studies about the effect of therapeutic hypothermia (TH) on severe TBI have been conducted. However, efficacy and safety have not been demonstrated in many large-scale randomized controlled studies. Rather, some studies have demonstrated an increase in mortality rate due to complications such as pneumonia, so it is not highly recommended for severe TBI patients. Recently, some studies have shown results suggesting TH may help reperfusion/ischemic injury prevention after surgery in the case of mass lesions, such as acute subdural hematoma, and it has also been shown to be effective in intracranial pressure control. In conclusion, TH is still at the center of neuroprotective therapeutic studies regarding TBI. If proper measures can be taken to mitigate the many adverse events that may occur during the course of treatment, more positive efficacy can be confirmed. In this review, we look into adverse events that may occur during the process of the induction, maintenance, and rewarming of targeted temperature management and consider ways to prevent and address them.

Citations

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  • Trends and hotspots in research of traumatic brain injury from 2000 to 2022: A bibliometric study
    Yan-rui Long, Kai Zhao, Fu-chi Zhang, Yu Li, Jun-wen Wang, Hong-quan Niu, Jin Lei
    Neurochemistry International.2024; 172: 105646.     CrossRef
  • Targeted temperature control following traumatic brain injury: ESICM/NACCS best practice consensus recommendations
    Andrea Lavinio, Jonathan P. Coles, Chiara Robba, Marcel Aries, Pierre Bouzat, Dara Chean, Shirin Frisvold, Laura Galarza, Raimund Helbok, Jeroen Hermanides, Mathieu van der Jagt, David K. Menon, Geert Meyfroidt, Jean-Francois Payen, Daniele Poole, Frank R
    Critical Care.2024;[Epub]     CrossRef
  • Severe traumatic brain injury in adults: a review of critical care management
    Siobhan McLernon
    British Journal of Neuroscience Nursing.2023; 19(6): 206.     CrossRef
Original Articles
Trauma
C-reactive protein-albumin ratio and procalcitonin in predicting intensive care unit mortality in traumatic brain injury
Canan Gürsoy, Güven Gürsoy, Semra Gümüş Demirbilek
Acute Crit Care. 2022;37(3):462-467.   Published online August 5, 2022
DOI: https://doi.org/10.4266/acc.2022.00052
  • 2,351 View
  • 173 Download
  • 1 Web of Science
  • 1 Crossref
AbstractAbstract PDF
Background
Prediction of intensive care unit (ICU) mortality in traumatic brain injury (TBI), which is a common cause of death in children and young adults, is important for injury management. Neuroinflammation is responsible for both primary and secondary brain injury, and C-reactive protein-albumin ratio (CAR) has allowed use of biomarkers such as procalcitonin (PCT) in predicting mortality. Here, we compared the performance of CAR and PCT in predicting ICU mortality in TBI.
Methods
Adults with TBI were enrolled in our study. The medical records of 82 isolated TBI patients were reviewed retrospectively.
Results
The mean patient age was 49.0 ± 22.69 years; 59 of all patients (72%) were discharged, and 23 (28%) died. There was a statistically significant difference between PCT and CAR values according to mortality (P<0.05). The area under the curve (AUC) was 0.646 with 0.071 standard error for PCT and 0.642 with 0.066 standard error for CAR. The PCT showed a similar AUC of the receiver operating characteristic to CAR.
Conclusions
This study shows that CAR and PCT are usable biomarkers to predict ICU mortality in TBI. When the determined cut-off values are used to predict the course of the disease, the CAR and PCT biomarkers will provide more effective information for treatment planning and for preparation of the family for the treatment process and to manage their outcome expectations.

Citations

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  • Symptoms and Functional Outcomes Among Traumatic Brain Injury Patients 3- to 12-Months Post-Injury
    Kathryn S. Gerber, Gemayaret Alvarez, Arsham Alamian, Victoria Behar-Zusman, Charles A. Downs
    Journal of Trauma Nursing.2024; 31(2): 72.     CrossRef
Neurosurgery
Development and internal validation of a nomogram for predicting outcomes in children with traumatic subdural hematoma
Anukoon Kaewborisutsakul, Thara Tunthanathip
Acute Crit Care. 2022;37(3):429-437.   Published online June 23, 2022
DOI: https://doi.org/10.4266/acc.2021.01795
  • 2,528 View
  • 209 Download
  • 6 Web of Science
  • 5 Crossref
AbstractAbstract PDF
Background
A subdural hematoma (SDH) following a traumatic brain injury (TBI) in children can lead to unexpected death or disability. The nomogram is a clinical prediction tool used by physicians to provide prognosis advice to parents for making decisions regarding treatment. In the present study, a nomogram for predicting outcomes was developed and validated. In addition, the predictors associated with outcomes in children with traumatic SDH were determined.
Methods
In this retrospective study, 103 children with SDH after TBI were evaluated. According to the King’s Outcome Scale for Childhood Head Injury classification, the functional outcomes were assessed at hospital discharge and categorized into favorable and unfavorable. The predictors associated with the unfavorable outcomes were analyzed using binary logistic regression. Subsequently, a two-dimensional nomogram was developed for presentation of the predictive model.
Results
The predictive model with the lowest level of Akaike information criterion consisted of hypotension (odds ratio [OR], 9.4; 95% confidence interval [CI], 2.0–42.9), Glasgow coma scale scores of 3–8 (OR, 8.2; 95% CI, 1.7–38.9), fixed pupil in one eye (OR, 4.8; 95% CI, 2.6–8.8), and fixed pupils in both eyes (OR, 3.5; 95% CI, 1.6–7.1). A midline shift ≥5 mm (OR, 1.1; 95% CI, 0.62–10.73) and co-existing intraventricular hemorrhage (OR, 6.5; 95% CI, 0.003–26.1) were also included.
Conclusions
SDH in pediatric TBI can lead to mortality and disability. The predictability level of the nomogram in the present study was excellent, and external validation should be conducted to confirm the performance of the clinical prediction tool.

Citations

Citations to this article as recorded by  
  • Prognostic factors and clinical nomogram for in-hospital mortality in traumatic brain injury
    Thara Tunthanathip, Nakornchai Phuenpathom, Apisorn Jongjit
    The American Journal of Emergency Medicine.2024; 77: 194.     CrossRef
  • Development of a Clinical Nomogram for Predicting Shunt-Dependent Hydrocephalus
    Avika Trakulpanitkit, Thara Tunthanathip
    Journal of Health and Allied Sciences NU.2024;[Epub]     CrossRef
  • Prediction performance of the machine learning model in predicting mortality risk in patients with traumatic brain injuries: a systematic review and meta-analysis
    Jue Wang, Ming Jing Yin, Han Chun Wen
    BMC Medical Informatics and Decision Making.2023;[Epub]     CrossRef
  • Development and internal validation of a nomogram to predict massive blood transfusions in neurosurgical operations
    Kanisorn Sungkaro, Chin Taweesomboonyat, Anukoon Kaewborisutsakul
    Journal of Neurosciences in Rural Practice.2022; 13: 711.     CrossRef
  • Prediction of massive transfusions in neurosurgical operations using machine learning
    Chin Taweesomboonyat, Anukoon Kaewborisutsakul, Kanisorn Sungkaro
    Asian Journal of Transfusion Science.2022;[Epub]     CrossRef
Basic science and research
A machine learning model for predicting favorable outcome in severe traumatic brain injury patients after 6 months
Mehdi Nourelahi, Fardad Dadboud, Hosseinali Khalili, Amin Niakan, Hossein Parsaei
Acute Crit Care. 2022;37(1):45-52.   Published online January 21, 2022
DOI: https://doi.org/10.4266/acc.2021.00486
  • 4,235 View
  • 231 Download
  • 9 Web of Science
  • 8 Crossref
AbstractAbstract PDF
Background
Traumatic brain injury (TBI), which occurs commonly worldwide, is among the more costly of health and socioeconomic problems. Accurate prediction of favorable outcomes in severe TBI patients could assist with optimizing treatment procedures, predicting clinical outcomes, and result in substantial economic savings. Methods: In this study, we examined the capability of a machine learning-based model in predicting “favorable” or “unfavorable” outcomes after 6 months in severe TBI patients using only parameters measured on admission. Three models were developed using logistic regression, random forest, and support vector machines trained on parameters recorded from 2,381 severe TBI patients admitted to the neuro-intensive care unit of Rajaee (Emtiaz) Hospital (Shiraz, Iran) between 2015 and 2017. Model performance was evaluated using three indices: sensitivity, specificity, and accuracy. A ten-fold cross-validation method was used to estimate these indices. Results: Overall, the developed models showed excellent performance with the area under the curve around 0.81, sensitivity and specificity of around 0.78. The top-three factors important in predicting 6-month post-trauma survival status in TBI patients are “Glasgow coma scale motor response,” “pupillary reactivity,” and “age.” Conclusions: Machine learning techniques might be used to predict the 6-month outcome in TBI patients using only the parameters measured on admission when the machine learning is trained using a large data set.

Citations

Citations to this article as recorded by  
  • Enhancing hospital course and outcome prediction in patients with traumatic brain injury: A machine learning study
    Guangming Zhu, Burak B Ozkara, Hui Chen, Bo Zhou, Bin Jiang, Victoria Y Ding, Max Wintermark
    The Neuroradiology Journal.2024; 37(1): 74.     CrossRef
  • Machine Learning in Neuroimaging of Traumatic Brain Injury: Current Landscape, Research Gaps, and Future Directions
    Kevin Pierre, Jordan Turetsky, Abheek Raviprasad, Seyedeh Mehrsa Sadat Razavi, Michael Mathelier, Anjali Patel, Brandon Lucke-Wold
    Trauma Care.2024; 4(1): 31.     CrossRef
  • Science fiction or clinical reality: a review of the applications of artificial intelligence along the continuum of trauma care
    Olivia F. Hunter, Frances Perry, Mina Salehi, Hubert Bandurski, Alan Hubbard, Chad G. Ball, S. Morad Hameed
    World Journal of Emergency Surgery.2023;[Epub]     CrossRef
  • Gastrointestinal failure, big data and intensive care
    Pierre Singer, Eyal Robinson, Orit Raphaeli
    Current Opinion in Clinical Nutrition & Metabolic Care.2023; 26(5): 476.     CrossRef
  • Prediction performance of the machine learning model in predicting mortality risk in patients with traumatic brain injuries: a systematic review and meta-analysis
    Jue Wang, Ming Jing Yin, Han Chun Wen
    BMC Medical Informatics and Decision Making.2023;[Epub]     CrossRef
  • Predicting return to work after traumatic brain injury using machine learning and administrative data
    Helena Van Deynse, Wilfried Cools, Viktor-Jan De Deken, Bart Depreitere, Ives Hubloue, Eva Kimpe, Maarten Moens, Karen Pien, Ellen Tisseghem, Griet Van Belleghem, Koen Putman
    International Journal of Medical Informatics.2023; 178: 105201.     CrossRef
  • Fluid-Based Protein Biomarkers in Traumatic Brain Injury: The View from the Bedside
    Denes V. Agoston, Adel Helmy
    International Journal of Molecular Sciences.2023; 24(22): 16267.     CrossRef
  • Predicting Outcome in Patients with Brain Injury: Differences between Machine Learning versus Conventional Statistics
    Antonio Cerasa, Gennaro Tartarisco, Roberta Bruschetta, Irene Ciancarelli, Giovanni Morone, Rocco Salvatore Calabrò, Giovanni Pioggia, Paolo Tonin, Marco Iosa
    Biomedicines.2022; 10(9): 2267.     CrossRef
Trauma
The association between the initial lactate level and need for massive transfusion in severe trauma patients with and without traumatic brain injury
Young Hoon Park, Dong Hyun Ryu, Byung Kook Lee, Dong Hun Lee
Acute Crit Care. 2019;34(4):255-262.   Published online November 29, 2019
DOI: https://doi.org/10.4266/acc.2019.00640
  • 5,032 View
  • 134 Download
  • 2 Web of Science
  • 1 Crossref
AbstractAbstract PDF
Background
Exsanguination is a major cause of death in severe trauma patients. The purpose of this study was to analyze the prognostic impact of the initial lactate level for massive transfusion (MT) in severe trauma. We divided patients according to subgroups of traumatic brain injury (TBI) and non-TBI.
Methods
This single-institution retrospective study was conducted on patients who were admitted to hospital for severe trauma between January 2016 and December 2017. TBI was defined by a head Abbreviated Injury Scale ≥3. Receiver operating characteristic analysis was used to analyze the prognostic impact of the lactate level. Multivariate analyses were performed to evaluate the relationship between the MT and lactate level. The primary outcome was MT.
Results
Of the 553 patients, MT was performed in 62 patients (11.2%). The area under the curve (AUC) for the lactate level for predicting MT was 0.779 (95% confidence interval [CI], 0.742 to 0.813). The AUCs for lactate level in the TBI and non-TBI patients were 0.690 (95% CI, 0.627 to 0.747) and 0.842 (95% CI, 0.796 to 0.881), respectively. In multivariate analyses, the lactate level was independently associated with the MT (odds ratio [OR], 1.179; 95% CI, 1.070 to 1.299). The lactate level was independently associated with MT in non-TBI patients (OR, 1.469; 95% CI, 1.262 to 1.710), but not in TBI patients.
Conclusions
The initial lactate level may be a possible prognostic factor for MT in severe trauma. In TBI patients, however, the initial lactate level was not suitable for predicting MT.

Citations

Citations to this article as recorded by  
  • Association of initial lactate levels and red blood cell transfusion strategy with outcomes after severe trauma: a post hoc analysis of the RESTRIC trial
    Yoshinori Kosaki, Takashi Hongo, Mineji Hayakawa, Daisuke Kudo, Shigeki Kushimoto, Takashi Tagami, Hiromichi Naito, Atsunori Nakao, Tetsuya Yumoto
    World Journal of Emergency Surgery.2024;[Epub]     CrossRef

ACC : Acute and Critical Care