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580 results on '"Patel, Mayur B."'

5. Long-term outcomes after treatment of delirium during critical illness with antipsychotics (MIND-USA): a randomised, placebo-controlled, phase 3 trial

Author

Mart, Matthew F, Boehm, Leanne M, Kiehl, Amy L, Gong, Michelle N, Malhotra, Atul, Owens, Robert L, Khan, Babar A, Pisani, Margaret A, Schmidt, Gregory A, Hite, R Duncan, Exline, Matthew C, Carson, Shannon S, Hough, Catherine L, Rock, Peter, Douglas, Ivor S, Feinstein, Daniel J, Hyzy, Robert C, Schweickert, William D, Bowton, David L, Masica, Andrew, Orun, Onur M, Raman, Rameela, Pun, Brenda T, Strength, Cayce, Rolfsen, Mark L, Pandharipande, Pratik P, Brummel, Nathan E, Hughes, Christopher G, Patel, Mayur B, Stollings, Joanna L, Ely, E Wesley, Jackson, James C, and Girard, Timothy D

Published
2024
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9. Prehospital Time Following Traumatic Injury Is Independently Associated With the Need for In-Hospital Blood and Early Mortality for Specific Injury Types

Author

Zadorozny, Eva V., Lin, Hsing-Hua S., Luther, James, Wisniewski, Stephen R., Cotton, Brian A., Fox, Erin E., Harbrecht, Brian G., Joseph, Bellal A., Moore, Ernest E., Ostenmayer, Daniel G., Patel, Mayur B., Schreiber, Martin A., Tatebe, Leah C., Todd, Samual R., Wilson, Chad, Gruen, Danielle S., Sperry, Jason L., Martin-Gill, Christian, Brown, Joshua B., and Guyette, Francis X.

Published
2024
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12. Extracting 2D weak labels from volume labels using multiple instance learning in CT hemorrhage detection

Author

Remedios, Samuel W., Wu, Zihao, Bermudez, Camilo, Kerley, Cailey I., Roy, Snehashis, Patel, Mayur B., Butman, John A., Landman, Bennett A., and Pham, Dzung L.

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Multiple instance learning (MIL) is a supervised learning methodology that aims to allow models to learn instance class labels from bag class labels, where a bag is defined to contain multiple instances. MIL is gaining traction for learning from weak labels but has not been widely applied to 3D medical imaging. MIL is well-suited to clinical CT acquisitions since (1) the highly anisotropic voxels hinder application of traditional 3D networks and (2) patch-based networks have limited ability to learn whole volume labels. In this work, we apply MIL with a deep convolutional neural network to identify whether clinical CT head image volumes possess one or more large hemorrhages (> 20cm$^3$), resulting in a learned 2D model without the need for 2D slice annotations. Individual image volumes are considered separate bags, and the slices in each volume are instances. Such a framework sets the stage for incorporating information obtained in clinical reports to help train a 2D segmentation approach. Within this context, we evaluate the data requirements to enable generalization of MIL by varying the amount of training data. Our results show that a training size of at least 400 patient image volumes was needed to achieve accurate per-slice hemorrhage detection. Over a five-fold cross-validation, the leading model, which made use of the maximum number of training volumes, had an average true positive rate of 98.10%, an average true negative rate of 99.36%, and an average precision of 0.9698. The models have been made available along with source code to enabled continued exploration and adaption of MIL in CT neuroimaging.

Published
2019

13. Distributed deep learning for robust multi-site segmentation of CT imaging after traumatic brain injury

Author

Remedios, Samuel, Roy, Snehashis, Blaber, Justin, Bermudez, Camilo, Nath, Vishwesh, Patel, Mayur B., Butman, John A., Landman, Bennett A., and Pham, Dzung L.

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Machine learning models are becoming commonplace in the domain of medical imaging, and with these methods comes an ever-increasing need for more data. However, to preserve patient anonymity it is frequently impractical or prohibited to transfer protected health information (PHI) between institutions. Additionally, due to the nature of some studies, there may not be a large public dataset available on which to train models. To address this conundrum, we analyze the efficacy of transferring the model itself in lieu of data between different sites. By doing so we accomplish two goals: 1) the model gains access to training on a larger dataset that it could not normally obtain and 2) the model better generalizes, having trained on data from separate locations. In this paper, we implement multi-site learning with disparate datasets from the National Institutes of Health (NIH) and Vanderbilt University Medical Center (VUMC) without compromising PHI. Three neural networks are trained to convergence on a computed tomography (CT) brain hematoma segmentation task: one only with NIH data,one only with VUMC data, and one multi-site model alternating between NIH and VUMC data. Resultant lesion masks with the multi-site model attain an average Dice similarity coefficient of 0.64 and the automatically segmented hematoma volumes correlate to those done manually with a Pearson correlation coefficient of 0.87,corresponding to an 8% and 5% improvement, respectively, over the single-site model counterparts.

Published
2019

16. Assessment of safety and effectiveness of non-neurosurgical management for minimal traumatic brain injury (TBI)

Author

Chen, Jeffrey W., Yengo-Kahn, Aaron, Chotai, Silky, Bhamidipati, Akshay, Smith, Candice, Davis, Philip, Reynolds, Rebecca A., Boyd, Mary Peyton, Barrett, Tyler W., Compton, Elizabeth S., Dennis, Bradley M., Norris, Michael S., Patel, Mayur B., Schwarz, Jacob P., Thomason, Nicholas R., Thompson, Reid C., and Guillamondegui, Oscar D.

Published
2023
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17. Montage based 3D Medical Image Retrieval from Traumatic Brain Injury Cohort using Deep Convolutional Neural Network

Author

Kerley, Cailey I., Huo, Yuankai, Chaganti, Shikha, Bao, Shunxing, Patel, Mayur B., and Landman, Bennett A.

Subjects
Computer Science - Information Retrieval, Computer Science - Machine Learning, Statistics - Machine Learning
Abstract

Brain imaging analysis on clinically acquired computed tomography (CT) is essential for the diagnosis, risk prediction of progression, and treatment of the structural phenotypes of traumatic brain injury (TBI). However, in real clinical imaging scenarios, entire body CT images (e.g., neck, abdomen, chest, pelvis) are typically captured along with whole brain CT scans. For instance, in a typical sample of clinical TBI imaging cohort, only ~15% of CT scans actually contain whole brain CT images suitable for volumetric brain analyses; the remaining are partial brain or non-brain images. Therefore, a manual image retrieval process is typically required to isolate the whole brain CT scans from the entire cohort. However, the manual image retrieval is time and resource consuming and even more difficult for the larger cohorts. To alleviate the manual efforts, in this paper we propose an automated 3D medical image retrieval pipeline, called deep montage-based image retrieval (dMIR), which performs classification on 2D montage images via a deep convolutional neural network. The novelty of the proposed method for image processing is to characterize the medical image retrieval task based on the montage images. In a cohort of 2000 clinically acquired TBI scans, 794 scans were used as training data, 206 scans were used as validation data, and the remaining 1000 scans were used as testing data. The proposed achieved accuracy=1.0, recall=1.0, precision=1.0, f1=1.0 for validation data, while achieved accuracy=0.988, recall=0.962, precision=0.962, f1=0.962 for testing data. Thus, the proposed dMIR is able to perform accurate CT whole brain image retrieval from large-scale clinical cohorts., Comment: Accepted for SPIE: Medical Imaging 2019

Published
2018

19. A Data-Driven Analysis of the Influence of Care Coordination on Trauma Outcome

Author

Chen, You, Patel, Mayur B., McNaughton, Candace D., and Malin, Bradley A.

Subjects
Computer Science - Computers and Society
Abstract

OBJECTIVE: To test the hypothesis that variation in care coordination is related to LOS. DESIGN We applied a spectral co-clustering methodology to simultaneously infer groups of patients and care coordination patterns, in the form of interaction networks of health care professionals, from electronic medical record (EMR) utilization data. The care coordination pattern for each patient group was represented by standard social network characteristics and its relationship with hospital LOS was assessed via a negative binomial regression with a 95% confidence interval. SETTING AND PATIENTS This study focuses on 5,588 adult patients hospitalized for trauma at the Vanderbilt University Medical Center. The EMRs were accessed by healthcare professionals from 179 operational areas during 158,467 operational actions. MAIN OUTCOME MEASURES: Hospital LOS for trauma inpatients, as an indicator of care coordination efficiency. RESULTS: Three general types of care coordination patterns were discovered, each of which was affiliated with a specific patient group. The first patient group exhibited the shortest hospital LOS and was managed by a care coordination pattern that involved the smallest number of operational areas (102 areas, as opposed to 125 and 138 for the other patient groups), but exhibited the largest number of collaborations between operational areas (e.g., an average of 27.1 connections per operational area compared to 22.5 and 23.3 for the other two groups). The hospital LOS for the second and third patient groups was 14 hours (P = 0.024) and 10 hours (P = 0.042) longer than the first patient group, respectively., Comment: 25 pages, 1 figure, 2 tables

Published
2017

20. Haloperidol and Ziprasidone for Treatment of Delirium in Critical Illness

Author

Girard, Timothy D, Exline, Matthew C, Carson, Shannon S, Hough, Catherine L, Rock, Peter, Gong, Michelle N, Douglas, Ivor S, Malhotra, Atul, Owens, Robert L, Feinstein, Daniel J, Khan, Babar, Pisani, Margaret A, Hyzy, Robert C, Schmidt, Gregory A, Schweickert, William D, Hite, R Duncan, Bowton, David L, Masica, Andrew L, Thompson, Jennifer L, Chandrasekhar, Rameela, Pun, Brenda T, Strength, Cayce, Boehm, Leanne M, Jackson, James C, Pandharipande, Pratik P, Brummel, Nathan E, Hughes, Christopher G, Patel, Mayur B, Stollings, Joanna L, Bernard, Gordon R, Dittus, Robert S, and Ely, E Wesley

Subjects
Pharmacology and Pharmaceutical Sciences, Biomedical and Clinical Sciences, Clinical Sciences, Clinical Trials and Supportive Activities, Clinical Research, Mental Health, Acquired Cognitive Impairment, Brain Disorders, Evaluation of treatments and therapeutic interventions, 6.1 Pharmaceuticals, Aged, Antipsychotic Agents, Critical Illness, Delirium, Dopamine Antagonists, Double-Blind Method, Female, Haloperidol, Humans, Kaplan-Meier Estimate, Male, Middle Aged, Piperazines, Respiratory Insufficiency, Shock, Thiazoles, Treatment Failure, MIND-USA Investigators, Medical and Health Sciences, General & Internal Medicine, Biomedical and clinical sciences, Health sciences
Abstract

BackgroundThere are conflicting data on the effects of antipsychotic medications on delirium in patients in the intensive care unit (ICU).MethodsIn a randomized, double-blind, placebo-controlled trial, we assigned patients with acute respiratory failure or shock and hypoactive or hyperactive delirium to receive intravenous boluses of haloperidol (maximum dose, 20 mg daily), ziprasidone (maximum dose, 40 mg daily), or placebo. The volume and dose of a trial drug or placebo was halved or doubled at 12-hour intervals on the basis of the presence or absence of delirium, as detected with the use of the Confusion Assessment Method for the ICU, and of side effects of the intervention. The primary end point was the number of days alive without delirium or coma during the 14-day intervention period. Secondary end points included 30-day and 90-day survival, time to freedom from mechanical ventilation, and time to ICU and hospital discharge. Safety end points included extrapyramidal symptoms and excessive sedation.ResultsWritten informed consent was obtained from 1183 patients or their authorized representatives. Delirium developed in 566 patients (48%), of whom 89% had hypoactive delirium and 11% had hyperactive delirium. Of the 566 patients, 184 were randomly assigned to receive placebo, 192 to receive haloperidol, and 190 to receive ziprasidone. The median duration of exposure to a trial drug or placebo was 4 days (interquartile range, 3 to 7). The median number of days alive without delirium or coma was 8.5 (95% confidence interval [CI], 5.6 to 9.9) in the placebo group, 7.9 (95% CI, 4.4 to 9.6) in the haloperidol group, and 8.7 (95% CI, 5.9 to 10.0) in the ziprasidone group (P=0.26 for overall effect across trial groups). The use of haloperidol or ziprasidone, as compared with placebo, had no significant effect on the primary end point (odds ratios, 0.88 [95% CI, 0.64 to 1.21] and 1.04 [95% CI, 0.73 to 1.48], respectively). There were no significant between-group differences with respect to the secondary end points or the frequency of extrapyramidal symptoms.ConclusionsThe use of haloperidol or ziprasidone, as compared with placebo, in patients with acute respiratory failure or shock and hypoactive or hyperactive delirium in the ICU did not significantly alter the duration of delirium. (Funded by the National Institutes of Health and the VA Geriatric Research Education and Clinical Center; MIND-USA ClinicalTrials.gov number, NCT01211522 .).

Published
2018

26. Developing a National Trauma Research Action Plan (NTRAP): Results from the Neurotrauma Research Panel Delphi Survey

Author

Stein, Deborah M., Braverman, Maxwell A., Phuong, Jimmy, Shipper, Edward, Price, Michelle A., Bixby, Pamela J., Adelson, P. David, Ansel, Beth M., Cifu, David X., DeVine, John G., Galvagno, Samuel M., Gelb, Daniel E., Harris, Odette, Kang, Christopher S., Kitagawa, Ryan S., McQuillan, Karen A., Patel, Mayur B., Robertson, Claudia S., Salim, Ali, Shutter, Lori, Valadka, Alex B., Bulger, Eileen M., Adelson, P. David, Ansel, Beth M., Chesnut, Randall M., Cifu, David X., DeVine, John G., Galvagno, Samuel M., Gelb, Daniel E., Giacino, Joseph T., Harris, Odette, Hawryluk, Gregory, Kang, Christopher S., Kitagawa, Ryan S., Manley, Geoffrey T., McQuillan, Karen A., Merck, Lisa H., Okonkwo, David O., Patel, Mayur B., Robertson, Claudia S., Salim, Ali, Shackelford, Col. Stacy A., Sheth, Kevin N., Shutter, Lori, Stein, Deborah M., Valadka, Alex B., Wade, Charles E., and Whyte, John

Published
2022
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29. Effect of Early High-Dose Vitamin D3 Repletion on Cognitive Outcomes in Critically Ill Adults

Author

Han, Jin H., Ginde, Adit A., Brown, Samuel M., Baughman, Adrienne, Collar, Erin M., Ely, E. Wesley, Gong, Michelle N., Hope, Aluko A., Hou, Peter C., Hough, Catherine L., Iwashyna, Theodore J., Jackson, James C., Khan, Akram, Orun, Onur M., Patel, Mayur B., Raman, Rameela, Rice, Todd W., Ringwood, Nancy, Semler, Matthew W., Shapiro, Nathan I., Talmor, Daniel S., and Self, Wesley H.

Published
2021
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31. Adverse event comparison between glucagon‐like peptide‐1 receptor agonists and other antiobesity medications following bariatric surgery.

Author

Samuels, Jason M., Niswender, Kevin D., Roumie, Christianne L., Spann, Matthew D., Flynn, C. Robb, Ye, Fei, Blankush, Joseph, Irlmeier, Rebecca, Funk, Luke M., and Patel, Mayur B.

Subjects
SLEEVE gastrectomy, BARIATRIC surgery, GASTRIC bypass, WEIGHT loss, DAPAGLIFLOZIN, EXENATIDE
Abstract

Aim: To compare the incidence of adverse events (AEs) related to antiobesity medications (AOMs; glucagon‐like peptide‐1 receptor agonists [GLP‐1RAs] vs. non‐GLP‐1RAs) after bariatric surgery. Methods: This single‐centre retrospective cohort included patients (aged 16–65 years) who had undergone laparoscopic Roux‐en‐Y gastric bypass or sleeve gastrectomy (cohort entry date) and initiated AOMs. Participants were categorized as users of US Food and Drug Administration (FDA)‐approved, off‐label, or GLP‐1RA AOMs if documented as receiving the medication on or after cohort entry date. Non‐GLP‐1RA AOMs were phentermine, orlistat, topiramate, canagliflozin, dapagliflozin, empagliflozin, naltrexone, bupropion/naltrexone and phentermine/topiramate. GLP‐1RA AOMs included: semaglutide, dulaglutide, exenatide and liraglutide. The primary outcome was AE incidence. Logistic regression was used to determine the association of AOM exposure with AEs. Results: We identified 599 patients meeting our inclusion criteria, 83% of whom were female. Their median (interquartile range [IQR]) age was 47.8 (40.9–55.4) years. The median duration of surgery to AOM exposure was 30 months. GLP‐1RAs use was not associated with higher odds of AEs: adjusted odds ratio (aOR) 1.1 (95% confidence interval [CI] 0.5–2.6) and aOR 1.1 (95% CI 0.6–2.3) for GLP‐1RA versus FDA‐approved and off‐label AOM use, respectively. AOM initiation ≥12 months after surgery was associated with lower risk of AEs compared to <12 months (aOR 0.01 [95% CI 0.0–0.01]; p < 0.001). Conclusion: Our results showed that GLP‐1RA AOMs were not associated with an increased risk of AEs compared to non‐GLP‐1RA AOMs in patients who had previously undergone bariatric surgery. Prospective studies are needed to identify the optimal timeframe for GLP‐1RA initiation. [ABSTRACT FROM AUTHOR]

Published
2024
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34. Would You Rather

Author

Maiga, Amelia W., primary, Cook, Madison R., additional, Nordness, Mina F., additional, Gao, Yue, additional, Rakhit, Shayan, additional, Rivera, Erika L., additional, Harrell, Frank E., additional, and Patel, Mayur B., additional

Published
2024
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35. Antipsychotics and the QTc Interval During Delirium in the Intensive Care Unit

Author

Stollings, Joanna L., primary, Boncyk, Christina S., additional, Birdrow, Caroline I., additional, Chen, Wencong, additional, Raman, Rameela, additional, Gupta, Deepak K., additional, Roden, Dan M., additional, Rivera, Erika L., additional, Maiga, Amelia W., additional, Rakhit, Shayan, additional, Pandharipande, Pratik P., additional, Ely, E. Wesley, additional, Girard, Timothy D., additional, and Patel, Mayur B., additional

Published
2024
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37. Impact of catastrophic brain injury guidelines on organ donation rates: Results of an EAST multicenter trial

Author

Nordham, Kristen D., Tatum, Danielle, Attia, Abdallah S., Patel, Mayur B., Paramesh, Anil, Duchesne, Juan C., Nahmias, Jeffry, Maiga, Amelia W., Peetz, Allan B., Udekwu, Pascal O., Stiles, Anquonette, Shell, Chloe, Stodghill, Joshua D., Maghsoudi, Taneen, Iacullo, Erin, McLafferty, Bryant, Coonan, Erin, Boudreau, Ryan M., Zimmerman, Steven A., Shammassian, Berje, Egodage, Tanya, Aramento, Isabella, Morris, Patrick, Metheny, Jacob, Farrell, Michael S., Painter, Matthew D., McCabe, Owen T., Spadafore, Philomene, Wong, David Tai-Wai, Serrano, Jan, Sciarretta, Jason D., Kim, Phillip, Hayton, Ryan, Gonzales, Danessa, Murry, Jason, Meadows, Katelyn, Jacobson, Lewis E., Williams, Jamie M., Bernard, Andrew C., Smith, Brennan, Morrissey, Shawna L., Patel, Nilesh, Tabello, Dina, Teicher, Erik, Chowdhury, Sharfuddin Mahmud, Ahmad, Fazal, Marcos, Basem S., West, Michaela A., Jacome, Tomas H., Davis, Greggory, Marks, Joshua A., Rattigan, Deviney, Haan, James M., Lightwine, Kelly, Matsushima, Kazuhide, Park, Stephen, Santos, Ariel, Shrestha, Kripa, Sawyer, Robert, VandenBerg, Sheri, Jean, Robert J., Hicks, R Chace, Lueckel, Stephanie, Bugaev, Nikolay, Abosena, Wael, Alvarez, Claudia, Lieser, Mark J., McDonald, Hannah, Dumas, Ryan P., Fitzgerald, Caitlin A., Terzian, William Thomas Hillman, Tian, Yuqian, Mousafeiris, Vasileios, Mulita, Francesk, Berne, John D., Mederos, Dalier R., Smith, Alison A., and Taghavi, Sharven

Abstract

Multicenter study investigated whether trauma centers with catastrophic brain injury guidelines (CBIGs) have higher organ donation rates than those without such guidelines. While CBIGs were not associated with increased odds of organ donation, hormone resuscitation was.

Published
2025
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43. Prehospital Time Following Traumatic Injury Is Independently Associated With the Need for In-Hospital Blood and Early Mortality for Specific Injury Types

Author

Zadorozny, Eva V., primary, Lin, Hsing-Hua S., additional, Luther, James, additional, Wisniewski, Stephen R., additional, Cotton, Brian A., additional, Fox, Erin E., additional, Harbrecht, Brian G., additional, Joseph, Bellal A., additional, Moore, Ernest E., additional, Ostenmayer, Daniel G., additional, Patel, Mayur B., additional, Schreiber, Martin A., additional, Tatebe, Leah C., additional, Todd, Samual R., additional, Wilson, Chad, additional, Gruen, Danielle S., additional, Sperry, Jason L., additional, Martin-Gill, Christian, additional, Brown, Joshua B., additional, and Guyette, Francis X., additional

Published
2023
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49. Multicenter International Cohort Validation of a Modified Sequential Organ Failure Assessment Score Using the Richmond Agitation-sedation Scale

Author

Rakhit, Shayan, Wang, Li, Lindsell, Christopher J., Hosay, Morgan A., Stewart, James W., Owen, Gary D., Frutos-Vivar, Fernando, Peñuelas, Oscar, Esteban, Andrés, Anzueto, Antonio R., Raymondos, Konstantinos, Rios, Fernando, Thille, Arnaud W., González, Marco, Du, Bin, Maggiore, Salvatore M., Matamis, Dimitrios, Abroug, Fekri, Amin, Pravin, Zeggwagh, Amine A., Ely, E. Wesley, Vasilevskis, Eduard E., and Patel, Mayur B.

Published
2020
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