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368 results on '"Maciel, Carolina B."'

1. Cyclooxygenase 2 Inhibitors for Headache After Elective Cranial Neurosurgery: Results from a Systematic Review of Efficacy of Cyclooxygenase 2 Inhibitors for Headache After Acute Brain Injury

Author

Comparan, Hector David Meza, Khaliq, Anum, Frota, Luciola Martins, Pomar-Forero, Daniela, Ahmad, Bakhtawar, Marnet, Erica, Teixeira, Fernanda J. P., Thomas, Anita, Patel, Priyank, Brunkal, Haley, Singireddy, Saanvi, Lucke-Wold, Brandon, Maciel, Carolina B., and Busl, Katharina M.

Published
2024
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2. Rationale and Design for the BLOCK-SAH Study (Pterygopalatine Fossa Block as an Opioid-Sparing Treatment for Acute Headache in Aneurysmal Subarachnoid Hemorrhage): A Phase II, Multicenter, Randomized, Double-Blinded, Placebo-Controlled Clinical Trial with a Sequential Parallel Comparison Design

Author

Busl, Katharina M., Smith, Cameron R., Troxel, Andrea B., Fava, Maurizio, Illenberger, Nicholas, Pop, Ralisa, Yang, Wenqing, Frota, Luciola Martins, Gao, Hanzhi, Shan, Guogen, Hoh, Brian L., and Maciel, Carolina B.

Published
2024
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4. Computable Phenotypes to Characterize Changing Patient Brain Dysfunction in the Intensive Care Unit

Author

Ren, Yuanfang, Loftus, Tyler J., Guan, Ziyuan, Uddin, Rayon, Shickel, Benjamin, Maciel, Carolina B., Busl, Katharina, Rashidi, Parisa, Bihorac, Azra, and Ozrazgat-Baslanti, Tezcan

Subjects
Quantitative Biology - Quantitative Methods, Computer Science - Machine Learning, Statistics - Machine Learning
Abstract

In the United States, more than 5 million patients are admitted annually to ICUs, with ICU mortality of 10%-29% and costs over $82 billion. Acute brain dysfunction status, delirium, is often underdiagnosed or undervalued. This study's objective was to develop automated computable phenotypes for acute brain dysfunction states and describe transitions among brain dysfunction states to illustrate the clinical trajectories of ICU patients. We created two single-center, longitudinal EHR datasets for 48,817 adult patients admitted to an ICU at UFH Gainesville (GNV) and Jacksonville (JAX). We developed algorithms to quantify acute brain dysfunction status including coma, delirium, normal, or death at 12-hour intervals of each ICU admission and to identify acute brain dysfunction phenotypes using continuous acute brain dysfunction status and k-means clustering approach. There were 49,770 admissions for 37,835 patients in UFH GNV dataset and 18,472 admissions for 10,982 patients in UFH JAX dataset. In total, 18% of patients had coma as the worst brain dysfunction status; every 12 hours, around 4%-7% would transit to delirium, 22%-25% would recover, 3%-4% would expire, and 67%-68% would remain in a coma in the ICU. Additionally, 7% of patients had delirium as the worst brain dysfunction status; around 6%-7% would transit to coma, 40%-42% would be no delirium, 1% would expire, and 51%-52% would remain delirium in the ICU. There were three phenotypes: persistent coma/delirium, persistently normal, and transition from coma/delirium to normal almost exclusively in first 48 hours after ICU admission. We developed phenotyping scoring algorithms that determined acute brain dysfunction status every 12 hours while admitted to the ICU. This approach may be useful in developing prognostic and decision-support tools to aid patients and clinicians in decision-making on resource use and escalation of care., Comment: 21 pages, 5 figures, 3 tables, 1 eTable

Published
2023

18. Which Spreading Depolarizations Are Deleterious To Brain Tissue?

Author

Shuttleworth, C William, Andrew, R David, Akbari, Yama, Ayata, Cenk, Balu, Ramani, Brennan, KC, Boutelle, Martyn, Carlson, Andrew P, Dreier, Jens P, Fabricius, Martin, Farkas, Eszter, Foreman, Brandon, Helbok, Raimund, Henninger, Nils, Jewell, Sharon L, Jones, Stephen C, Kirov, Sergei A, Lindquist, Britta E, Maciel, Carolina B, Okonkwo, David, Reinhart, Katelyn M, Robertson, R Meldrum, Rosenthal, Eric S, Watanabe, Tomas, and Hartings, Jed A

Subjects
Biomedical and Clinical Sciences, Neurosciences, Clinical Sciences, Brain Disorders, Animals, Brain, Brain Injuries, Cortical Spreading Depression, Electroencephalography, Humans, Migraine with Aura, Spreading depression, Spreading depolarization, Ischemia, Trauma, Subarachnoid hemorrhage, Neurology & Neurosurgery, Clinical sciences, Nursing
Abstract

Spreading depolarizations (SDs) are profound disruptions of cellular homeostasis that slowly propagate through gray matter and present an extraordinary metabolic challenge to brain tissue. Recent work has shown that SDs occur commonly in human patients in the neurointensive care setting and have established a compelling case for their importance in the pathophysiology of acute brain injury. The International Conference on Spreading Depolarizations (iCSD) held in Boca Raton, Florida, in September of 2018 included a discussion session focused on the question of "Which SDs are deleterious to brain tissue?" iCSD is attended by investigators studying various animal species including invertebrates, in vivo and in vitro preparations, diseases of acute brain injury and migraine, computational modeling, and clinical brain injury, among other topics. The discussion included general agreement on many key issues, but also revealed divergent views on some topics that are relevant to the design of clinical interventions targeting SDs. A draft summary of viewpoints offered was then written by a multidisciplinary writing group of iCSD members, based on a transcript of the session. Feedback of all discussants was then formally collated, reviewed and incorporated into the final document. It is hoped that this report will stimulate collection of data that are needed to develop a more nuanced understanding of SD in different pathophysiological states, as the field continues to move toward effective clinical interventions.

Published
2020

21. Pregabalin for Recurrent Seizures in Critical Illness: A Promising Adjunctive Therapy, Especially for cyclic Seizures

Author

Busl, Katharina M., Fong, Michael W. K., Newcomer, Zachary, Patel, Mitesh, Cohen, Scott A., Jadav, Rakesh, Smith, Christine N., Mitropanopoulos, Sotiris, Bruzzone, Maria, Hella, Maria, Eisenschenk, Stephan, Robinson, Christopher P., Roth, William H., Ameli, Pouya Alexander, Babi, Marc-Alain, Pizzi, Michael A., Gilmore, Emily J., Hirsch, Lawrence J., and Maciel, Carolina B.

Published
2022
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26. It’s About Time! Timing in Epilepsy Evaluation and Treatment

Author

McKee, Heather Ravvin, primary, Vidaurre, Jorge, additional, Clarke, Dave, additional, Wagner, Janelle, additional, W. Britton, Jeffrey, additional, Laux, Linda, additional, Trinka, Eugen, additional, Rubinos, Clio, additional, McDonald, Tanya J. W., additional, Lado, Fred A., additional, Bebin, Martina, additional, Papadelis, Christos, additional, Struck, Aaron F., additional, Maciel, Carolina B., additional, Velasco, Ana L., additional, Chandran, Arjun, additional, Pati, Sandipan, additional, Tandon, Nitin, additional, Vaca, Guadalupe Fernandez-Baca, additional, Berl, Madison M., additional, and Moosa, Ahsan N., additional

Published
2024
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32. Leveraging machine learning to develop a postoperative predictive model for postoperative urinary retention following lumbar spine surgery.

Author

Malnik, Samuel L., Porche, Ken, Mehkri, Yusuf, Yue, Sijia, Maciel, Carolina B., Lucke-Wold, Brandon P., Robicsek, Steven A., Decker, Matthew, and Busl, Katharina M.

Subjects
SPINAL surgery, LUMBAR vertebrae, RETENTION of urine, MACHINE learning, RECEIVER operating characteristic curves, PREDICTION models
Abstract

Introduction: Postoperative urinary retention (POUR) is the inability to urinate after a surgical procedure despite having a full bladder. It is a common complication following lumbar spine surgery which has been extensively linked to increased patient morbidity and hospital costs. This study hopes to development and validate a predictive model for POUR following lumbar spine surgery using patient demographics, surgical and anesthesia variables. Methods: This is a retrospective observational cohort study of 903 patients who underwent lumbar spine surgery over the period of June 2017 to June 2019 in a tertiary academic medical center. Four hundred and nineteen variables were collected including patient demographics, ICD-10 codes, and intraoperative factors. Least absolute shrinkage and selection operation (LASSO) regression and logistic regression models were compared. A decision tree model was fitted to the optimal model to classify each patient’s risk of developing POUR as high, intermediate, or low risk. Predictive performance of POUR was assessed by area under the receiver operating characteristic curve (AUC-ROC). Results: 903 patients were included with average age 60 ± 15 years, body mass index of 30.5 ± 6.4 kg/m², 476 (53%) male, 785 (87%) white, 446 (49%) involving fusions, with average 2.1 ± 2.0 levels. The incidence of POUR was 235 (26%) with 63 (7%) requiring indwelling catheter placement. A decision tree was constructed with an accuracy of 87.8%. Conclusion: We present a highly accurate and easy to implement decision tree model which predicts POUR following lumbar spine surgery using preoperative and intraoperative variables. [ABSTRACT FROM AUTHOR]

Published
2024
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43. American Clinical Neurophysiology Societyʼs Standardized Critical Care EEG Terminology: 2021 Version

Author

Hirsch, Lawrence J., Fong, Michael W.K., Leitinger, Markus, LaRoche, Suzette M., Beniczky, Sandor, Abend, Nicholas S., Lee, Jong Woo, Wusthoff, Courtney J., Hahn, Cecil D., Westover, M. Brandon, Gerard, Elizabeth E., Herman, Susan T., Haider, Hiba Arif, Osman, Gamaleldin, Rodriguez-Ruiz, Andres, Maciel, Carolina B., Gilmore, Emily J., Fernandez, Andres, Rosenthal, Eric S., Claassen, Jan, Husain, Aatif M., Yoo, Ji Yeoun, So, Elson L., Kaplan, Peter W., Nuwer, Marc R., van Putten, Michel, Sutter, Raoul, Drislane, Frank W., Trinka, Eugen, and Gaspard, Nicolas

Published
2021
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