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- Use of droxidopa for blood pressure augmentation after acute spinal cord injury: case reports
- Christopher S. Hong, Muhammad K. Effendi, Abdalla A. Ammar, Kent A. Owusu, Mahmoud A. Ammar, Andrew B. Koo, Layton A. Lamsam, Aladine A. Elsamadicy, Gregory A. Kuzmik, Maxwell Laurans, Michael L. DiLuna, Mark L. Landreneau
- Acute Crit Care. 2025;40(1):138-143. Published online December 7, 2022
- DOI: https://doi.org/10.4266/acc.2021.01662
- 12,019 View
- 286 Download
- 5 Web of Science
- 6 Crossref
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Abstract
PDF - Hypotension secondary to autonomic dysfunction is a common complication of acute spinal cord injury (SCI) that may worsen neurologic outcomes. Midodrine, an enteral α-1 agonist, is often used to facilitate weaning intravenous (IV) vasopressors, but its use can be limited by reflex bradycardia. Alternative enteral agents to facilitate this wean in the acute post-SCI setting have not been described. We aim to describe novel application of droxidopa, an enteral precursor of norepinephrine that is approved to treat neurogenic orthostatic hypotension, in the acute post-SCI setting. Droxidopa may be an alternative enteral therapy for those intolerant of midodrine due to reflex bradycardia. We describe two patients suffering traumatic cervical SCI who were successfully weaned off IV vasopressors with droxidopa after failing with midodrine. The first patient was a 64-year-old male who underwent C3–6 laminectomies and fusion after a ten-foot fall resulting in quadriparesis. Post-operatively, the addition of midodrine in an attempt to wean off IV vasopressors resulted in significant reflexive bradycardia. Treatment with droxidopa facilitated rapidly weaning IV vasopressors and transfer to a lower level of care within 72 hours of treatment initiation. The second patient was a 73-year-old male who underwent C3–5 laminectomies and fusion for a traumatic hyperflexion injury causing paraplegia. The addition of midodrine resulted in severe bradycardia, prompting consideration of pacemaker placement. However, with the addition of droxidopa, this was avoided, and the patient was weaned off IV vasopressors on dual oral therapy with midodrine and droxidopa. Droxidopa may be a viable enteral therapy to treat hypotension in patients after acute SCI who are otherwise not tolerating midodrine in order to wean off IV vasopressors. This strategy may avoid pacemaker placement and facilitate shorter stays in the intensive care unit, particularly for patients who are stable but require continued intensive care unit admission for IV vasopressors, which can be cost ineffective and human resource depleting.
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Citations
Citations to this article as recorded by
- Oral Agents for the Treatment of Refractory Hypotension in the Acute Care Setting - A Narrative Review
Gabrielle A. Gibson, Annette D. Lista, Jonathon D. Pouliot
Journal of Pharmacy Practice.2026;[Epub] CrossRef - Autonomic modulation of neuroplasticity in spinal cord injury rehabilitation: insights from a narrative review
Roberto Di Palma, Luigi Falco, Armando Coccia, Federica Amitrano, Giovanni D'Addio, Maria Teresa La Rovere
Frontiers in Neurology.2026;[Epub] CrossRef - A Guide to the Use of Vasopressors and Inotropes for Patients in Shock
Anaas Moncef Mergoum, Abigail Rebecca Rhone, Nicholas James Larson, David J Dries, Benoit Blondeau, Frederick Bolles Rogers
Journal of Intensive Care Medicine.2025; 40(7): 726. CrossRef - Droxidopa for Intravenous Vasopressor Weaning in the Intensive Care Unit: A Descriptive Study
Calvin Diep, Daniella Veloria, Amy Kloosterboer
Therapeutics.2025; 2(4): 20. CrossRef - Droxidopa in Critical Care: A Systematic Review of an Emerging Off‐Label Practice
Carlos Valladares, Franklyn Vega Batista, Marc Faltas, Mareya Menteyas, Katherine Chiapaikeo-Poco, Edward A. Bittner
Critical Care Research and Practice.2025;[Epub] CrossRef - Oral blood pressure augmenting agents for intravenous vasopressor weaning
John C Robinson, Mariam ElSaban, Nathan J Smischney, Patrick M Wieruszewski
World Journal of Clinical Cases.2024; 12(36): 6892. CrossRef
- Oral Agents for the Treatment of Refractory Hypotension in the Acute Care Setting - A Narrative Review
Original Article
- 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
- 12,245 View
- 311 Download
- 17 Web of Science
- 19 Crossref
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Abstract
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- THE USE OF ARTIFICIAL INTELLIGENCE IN TRAUMATOLOGY: A SYSTEMATIC REVIEW AND RECOMMENDATIONS FOR CLINICAL PRACTICE
V. V. Savgachev, L. B. Shubin
Bulletin of Pirogov National Medical & Surgical Center.2026; 21(1): 127. CrossRef - The use of machine learning approaches for mortality prediction and classification in traumatic brain injury: a literature review
ARTHUR AFONSO SILVA-SOUSA, FERNANDA CAROLINA CAMARGO, LEOPOLDO MANDIC F. FURTADO, ANTONIO LUCIO TEIXEIRA, RAFAEL A.B. DE QUEIROZ, ALINE SILVA DE MIRANDA
Anais da Academia Brasileira de Ciências.2026;[Epub] CrossRef - Development of web- and mobile-based shared decision-making tools in the neurological intensive care unit
Winnie L. Liu, Lidan Zhang, Soussan Djamasbi, Bengisu Tulu, Susanne Muehlschlegel
Neurotherapeutics.2025; 22(1): e00503. CrossRef - Long-term survival prediction in patients with acute brain lesions using ensemble machine learning algorithms: a cohort study with combined national health insurance service and its self-run hospital database
Dougho Park, Daeyoung Hong, Suntak Jin, Jong Hun Kim, Hyoung Seop Kim
Journal of Big Data.2025;[Epub] CrossRef - Predicting outcomes after moderate and severe traumatic brain injury using artificial intelligence: a systematic review
Armaan K. Malhotra, Husain Shakil, Christopher W. Smith, Yu Qing Huang, Jethro C. C. Kwong, Kevin E. Thorpe, Christopher D. Witiw, Abhaya V. Kulkarni, Jefferson R. Wilson, Avery B. Nathens
npj Digital Medicine.2025;[Epub] CrossRef - Artificial intelligence in traumatic brain injury: Brain imaging analysis and outcome prediction: A mini review
Luca Marino, Federico Bilotta
World Journal of Critical Care Medicine.2025;[Epub] CrossRef - Prediction of Clinically Significant Improvements During the Interdisciplinary Intensive Outpatient Program for Traumatic Brain Injury Using Machine Learning
Rujirutana Srikanchana, David Samuel, Jacob Powell, Treven Pickett, Thomas DeGraba, Chandler Sours Rhodes
Annals of Biomedical Engineering.2025; 53(11): 2845. CrossRef - A practical approach to predicting long-term outcomes in traumatic brain injury: Enhancing clinical decision-making with machine learning
Amirmohammad Farrokhi, Mahtab Jalali, Mohamed Sobhi Jabal, Saeed Abdollahifard, Reza Taheri, Omid Yousefi, Amin Niakan, Hosseinali Khalili
Computers in Biology and Medicine.2025; 196: 110827. CrossRef - The effect of extended early rehabilitation on the treatment outcome of patients with moderate and severe traumatic brain injury
Nataša Keleman, Dragana Dragičević-Cvjetković, Aleksandra Mikov, Dragomir Radošević, Ðula Ðilvesi, Vladimir Mrđa, Rastislava Krasnik
Frontiers in Human Neuroscience.2025;[Epub] CrossRef - Artificial Intelligence in Traumatic Brain Injury: A Systematic Review of Prognostic, Diagnostic, and Monitoring Applications
Anas E Ahmed, Rayan M Alyami, Fatimah H Al Ghazwi, Renad H Hamzi, Nawa K Alshammari, Fawziah M Jali, Abdullah A Al Alduwayh, Thikra M Almujami, Abdullah S Alamri, Jamal A Sabban, Ghadi F Alsum
Cureus.2025;[Epub] CrossRef - 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 - A Systematic Review of the Outcomes of Utilization of Artificial Intelligence Within the Healthcare Systems of the Middle East: A Thematic Analysis of Findings
Mohsen Khosravi, Seyyed Morteza Mojtabaeian, Emine Kübra Dindar Demiray, Burak Sayar
Health Science Reports.2024;[Epub] 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
- THE USE OF ARTIFICIAL INTELLIGENCE IN TRAUMATOLOGY: A SYSTEMATIC REVIEW AND RECOMMENDATIONS FOR CLINICAL PRACTICE
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