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Lehrman B, Byerly S, Mitchell EL, Kerwin AJ, Howley IW. Trust but Verify? Utility of Intraoperative Angiography After Revascularization for Vascular Trauma. Am Surg 2024; 90:1059-1065. [PMID: 38126322 DOI: 10.1177/00031348231220593] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2023]
Abstract
BACKGROUND Trauma surgical dogma teaches that patients should have intraoperative angiography (IA) if the surgeon cannot identify a pulse in the injured extremity following a vascular repair. This study was undertaken to assess the utility of IA in trauma patients who underwent open brachial or femoral artery revascularization. METHODS Retrospective analysis of the Prospective Observational Vascular Injury Trial (PROOVIT) database from 2013 to 2021 evaluated patients >15 years with penetrating or blunt injuries requiring operative intervention of the brachial, superficial femoral, or common femoral arteries. Prospective Observational Vascular Injury Trial data evaluated included documented pulse in the injured extremity at revascularization completion, adjunctive IA, immediate revision, and vascular reintervention during the hospitalization. RESULTS Of the 5057 patients with vascular injury, 185 patients met our inclusion criteria. The majority were male (86.5%) with a median age, injury severity score, and systolic blood pressure of 29, 12, and 117, respectively. Of the study patients, 39% underwent IA, 14% had immediate revision, and 8% required vascular reoperation during their admission. Patients who underwent IA and with no documented palpable pulse after repair were significantly more likely to require immediate revision before leaving the operating room (22% vs 9%, P = .013) and were not more likely to require reoperation, than those who did not undergo IA (7% vs 9%, P = .613). CONCLUSIONS Intraoperative angiography is a valuable tool for surgeons for vascular extremity trauma and is associated with a greater rate of immediate revision. Familiarity with angiographic technique is essential for vascular trauma and should be a focal point of training.
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Affiliation(s)
- Benjamin Lehrman
- Department of Surgery, University of Tennessee Health Science Center, Memphis, TN, USA
| | - Saskya Byerly
- Department of Surgery - Division of Trauma/Critical Care, University of Tennessee Health Science Center, Memphis, TN, USA
| | - Erica L Mitchell
- Department of Surgery - Division of Vascular and Endovascular Surgery, University of Tennessee Health Science Center, Memphis, TN, USA
| | - Andrew J Kerwin
- Department of Surgery - Division of Trauma/Critical Care, University of Tennessee Health Science Center, Memphis, TN, USA
| | - Isaac W Howley
- Department of Surgery - Division of Trauma/Critical Care, University of Tennessee Health Science Center, Memphis, TN, USA
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Cao H, Guo W, Qin H, Xu M, Lehrman B, Tao Y, Shugart YY. Integrating multiple genomic data: sparse representation based biomarker selection for blood pressure. BMC Proc 2016; 10:283-288. [PMID: 27980650 PMCID: PMC5133507 DOI: 10.1186/s12919-016-0044-7] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023] Open
Abstract
BACKGROUND Although many genes have been implicated as hypertension candidates, to date, few studies have integrated different types of genomic data for the purpose of biomarker selection. METHODS Applying a newly proposed sparse representation based variable selection (SRVS) method to the Genetic Analysis Workshop19 data, we analyzed a combined data set consisting of 11522 gene expressions and 354893 single-nucleotide polymorphisms (SNPs) from 397 subjects (case/control: 151/246), with the aim to identify potential biomarkers for blood pressure using both gene expression measures and SNP data. RESULTS Among the top 1000 variables (SNPs/gene expressions = 575/425) selected, the bioinformatics analysis showed that 302 were plausibly associated with blood pressure. In addition, we identified 173 variables that were associated with body weight and 84 associated with left ventricular contractility. Together, 55.9 % of the top 1000 variables showed associations with blood pressure related phenotypes(SNP/gene expression =348/211). CONCLUSIONS Our results support the feasibility of the SRVS algorithm in integrating multiple data sets of different structure for comprehensive analysis.
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Affiliation(s)
- Hongbao Cao
- Unit on Statistical Genomics, Division of Intramural Research Programs, National Institute of Mental Health, National Institutes of Health, Building 35, Room 3A 1000, 35 Convent Drive, Bethesda, MD 20892 USA
| | - Wei Guo
- Unit on Statistical Genomics, Division of Intramural Research Programs, National Institute of Mental Health, National Institutes of Health, Building 35, Room 3A 1000, 35 Convent Drive, Bethesda, MD 20892 USA
| | - Haide Qin
- Unit on Statistical Genomics, Division of Intramural Research Programs, National Institute of Mental Health, National Institutes of Health, Building 35, Room 3A 1000, 35 Convent Drive, Bethesda, MD 20892 USA
| | - Mengyuan Xu
- Unit on Statistical Genomics, Division of Intramural Research Programs, National Institute of Mental Health, National Institutes of Health, Building 35, Room 3A 1000, 35 Convent Drive, Bethesda, MD 20892 USA
| | - Benjamin Lehrman
- Unit on Statistical Genomics, Division of Intramural Research Programs, National Institute of Mental Health, National Institutes of Health, Building 35, Room 3A 1000, 35 Convent Drive, Bethesda, MD 20892 USA
| | - Yu Tao
- Unit on Statistical Genomics, Division of Intramural Research Programs, National Institute of Mental Health, National Institutes of Health, Building 35, Room 3A 1000, 35 Convent Drive, Bethesda, MD 20892 USA
| | - Yin-Yao Shugart
- Unit on Statistical Genomics, Division of Intramural Research Programs, National Institute of Mental Health, National Institutes of Health, Building 35, Room 3A 1000, 35 Convent Drive, Bethesda, MD 20892 USA
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Qin HD, Liao XY, Chen YB, Huang SY, Xue WQ, Li FF, Ge XS, Liu DQ, Cai Q, Long J, Li XZ, Hu YZ, Zhang SD, Zhang LJ, Lehrman B, Scott AF, Lin D, Zeng YX, Shugart YY, Jia WH. Genomic Characterization of Esophageal Squamous Cell Carcinoma Reveals Critical Genes Underlying Tumorigenesis and Poor Prognosis. Am J Hum Genet 2016; 98:709-27. [PMID: 27058444 DOI: 10.1016/j.ajhg.2016.02.021] [Citation(s) in RCA: 108] [Impact Index Per Article: 13.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2015] [Accepted: 02/24/2016] [Indexed: 12/17/2022] Open
Abstract
The genetic mechanisms underlying the poor prognosis of esophageal squamous cell carcinoma (ESCC) are not well understood. Here, we report somatic mutations found in ESCC from sequencing 10 whole-genome and 57 whole-exome matched tumor-normal sample pairs. Among the identified genes, we characterized mutations in VANGL1 and showed that they accelerated cell growth in vitro. We also found that five other genes, including three coding genes (SHANK2, MYBL2, FADD) and two non-coding genes (miR-4707-5p, PCAT1), were involved in somatic copy-number alterations (SCNAs) or structural variants (SVs). A survival analysis based on the expression profiles of 321 individuals with ESCC indicated that these genes were significantly associated with poorer survival. Subsequently, we performed functional studies, which showed that miR-4707-5p and MYBL2 promoted proliferation and metastasis. Together, our results shed light on somatic mutations and genomic events that contribute to ESCC tumorigenesis and prognosis and might suggest therapeutic targets.
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Affiliation(s)
- Hai-De Qin
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou 510060, China; Unit on Statistical Genomics, Division of Intramural Research Programs, National Institute of Mental Health, NIH, Bethesda, MD 20892, USA
| | - Xiao-Yu Liao
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou 510060, China
| | - Yuan-Bin Chen
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou 510060, China
| | - Shao-Yi Huang
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou 510060, China
| | - Wen-Qiong Xue
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou 510060, China
| | - Fang-Fang Li
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou 510060, China
| | - Xiao-Song Ge
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou 510060, China; The Affiliated Hospital of Jiangnan University, Wuxi 214062, China
| | - De-Qing Liu
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou 510060, China
| | - Qiuyin Cai
- Division of Epidemiology, Department of Medicine, Vanderbilt-Ingram Cancer Center and Vanderbilt Epidemiology Center, Vanderbilt University School of Medicine, Nashville, TN 37240, USA
| | - Jirong Long
- Division of Epidemiology, Department of Medicine, Vanderbilt-Ingram Cancer Center and Vanderbilt Epidemiology Center, Vanderbilt University School of Medicine, Nashville, TN 37240, USA
| | - Xi-Zhao Li
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou 510060, China
| | - Ye-Zhu Hu
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou 510060, China
| | - Shao-Dan Zhang
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou 510060, China
| | - Lan-Jun Zhang
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou 510060, China
| | - Benjamin Lehrman
- Unit on Statistical Genomics, Division of Intramural Research Programs, National Institute of Mental Health, NIH, Bethesda, MD 20892, USA
| | - Alan F Scott
- McKusick Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA
| | - Dongxin Lin
- State Key Laboratory of Molecular Oncology, Chinese Academy of Medical Science and Peking Union Medical College, Beijing 100021, China
| | - Yi-Xin Zeng
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou 510060, China
| | - Yin Yao Shugart
- Unit on Statistical Genomics, Division of Intramural Research Programs, National Institute of Mental Health, NIH, Bethesda, MD 20892, USA.
| | - Wei-Hua Jia
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou 510060, China.
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