Literature DB >> 14501881

Assessment of severity of ovine smoke inhalation injury by analysis of computed tomographic scans.

Myung S Park1, Leopoldo C Cancio, Andriy I Batchinsky, Michael J McCarthy, Bryan S Jordan, William W Brinkley, Michael A Dubick, Cleon W Goodwin.   

Abstract

BACKGROUND: Our goal was to evaluate computed tomographic (CT) scans of the chest as a means of stratifying smoke inhalation injury (SII) severity.
METHODS: Twenty anesthetized sheep underwent graded SII: group I, no smoke; group II, 5 smoke units; group III, 10 units; and group IV, 16 units. CT scans were obtained at 6, 12, and 24 hours after injury. Each quadrant of each slice was scored subjectively: 0 = normal, 1 = interstitial markings, 2 = ground-glass appearance, and 3 = consolidation. The sum of all scores was the radiologist's score (RADS) for that scan. Computerized analysis of three-dimensional reconstructed scans was also performed, based on Hounsfield unit ranges: hyperinflated, -1,000 to -900; normal, -899 to -500; poorly aerated, -499 to -100; and nonaerated, -99 to +100. The fraction of abnormal lung tissue (FALT) was computed from poorly aerated, nonaerated, and total volumes. Mean gray-scale density (DENS) was also computed.
RESULTS: SII resulted in severity- and time-related changes in oxygenation (alveolar-arterial gradient), ventilation (respiratory rate-pressure product), DENS, FALT, and RADS. Ordinal logistic regression generated a predictive model for severity of injury (r2 = 0.623, p = 0.001), retaining RADS at 24 hours and rejecting the other variables.
CONCLUSION: At 24 hours, CT scanning enabled SII severity stratification; qualitative evaluation (RADS) outperformed current semiautomated methods (DENS, FALT).

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Year:  2003        PMID: 14501881     DOI: 10.1097/01.TA.0000083609.24440.7F

Source DB:  PubMed          Journal:  J Trauma        ISSN: 0022-5282


  12 in total

Review 1.  [Inhalation injury--epidemiology, diagnosis and therapy].

Authors:  Ulrich Thaler; Paul Kraincuk; Lars-Peter Kamolz; Manfred Frey; Philipp G H Metnitz
Journal:  Wien Klin Wochenschr       Date:  2010-01       Impact factor: 1.704

2.  In vivo detection of inhalation injury in large airway using three-dimensional long-range swept-source optical coherence tomography.

Authors:  Lidek Chou; Andriy Batchinsky; Slava Belenkiy; Joseph Jing; Tirunelveli Ramalingam; Matthew Brenner; Zhongping Chen
Journal:  J Biomed Opt       Date:  2014-03       Impact factor: 3.170

3.  Wood bark smoke induces lung and pleural plasminogen activator inhibitor 1 and stabilizes its mRNA in porcine lung cells.

Authors:  Krishna K Midde; Andriy I Batchinsky; Leopoldo C Cancio; Sreerama Shetty; Andrey A Komissarov; Galina Florova; Kerfoot P Walker; Kathy Koenig; Zissis C Chroneos; Tim Allen; Kevin Chung; Michael Dubick; Steven Idell
Journal:  Shock       Date:  2011-08       Impact factor: 3.454

4.  In vivo optical coherence tomography detection of differences in regional large airway smoke inhalation induced injury in a rabbit model.

Authors:  Matthew Brenner; Kelly Kreuter; Johnny Ju; Sari Mahon; Lillian Tseng; David Mukai; Tanya Burney; Shuguang Guo; Jianping Su; Andrew Tran; Andriy Batchinsky; Leopoldo C Cancio; Navneet Narula; Zhongping Chen
Journal:  J Biomed Opt       Date:  2008 May-Jun       Impact factor: 3.170

5.  Experimental Actinobacillus pleuropneumoniae challenge in swine: comparison of computed tomographic and radiographic findings during disease.

Authors:  Carsten Brauer; Isabel Hennig-Pauka; Doris Hoeltig; Falk F R Buettner; Martin Beyerbach; Hagen Gasse; Gerald-F Gerlach; Karl-H Waldmann
Journal:  BMC Vet Res       Date:  2012-04-30       Impact factor: 2.741

6.  Human amnion-derived mesenchymal stem cells alleviate lung injury induced by white smoke inhalation in rats.

Authors:  Pei Cui; Haiming Xin; Yongming Yao; Shichu Xiao; Feng Zhu; Zhenyu Gong; Zhiping Tang; Qiu Zhan; Wei Qin; Yanhua Lai; Xiaohui Li; Yalin Tong; Zhaofan Xia
Journal:  Stem Cell Res Ther       Date:  2018-04-12       Impact factor: 6.832

7.  Inhalation lung injury induced by smoke bombs in children: CT manifestations, dynamic evolution features and quantitative analysis.

Authors:  Yaqiong Ma; Shikui Zhang; Lianping Zhao; Xing Zhou; Zeqing Mao; Huaxin Xu; Xiaorui Ru; Gang Huang
Journal:  J Thorac Dis       Date:  2018-10       Impact factor: 2.895

8.  Multi-detector computed tomography demonstrates smoke inhalation injury at early stage.

Authors:  Virve Koljonen; Kreu Maisniemi; Kaisa Virtanen; Mika Koivikko
Journal:  Emerg Radiol       Date:  2007-02-07

9.  Chest computed tomography performed on admission helps predict the severity of smoke-inhalation injury.

Authors:  Hitoshi Yamamura; Shinichiro Kaga; Kazuhisa Kaneda; Yasumitsu Mizobata
Journal:  Crit Care       Date:  2013-05-25       Impact factor: 9.097

Review 10.  Diagnosis and management of inhalation injury: an updated review.

Authors:  Patrick F Walker; Michelle F Buehner; Leslie A Wood; Nathan L Boyer; Ian R Driscoll; Jonathan B Lundy; Leopoldo C Cancio; Kevin K Chung
Journal:  Crit Care       Date:  2015-10-28       Impact factor: 9.097

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