TY - JOUR
T1 - A random forest model using flow cytometry data identifies pulmonary infection after thoracic injury
AU - Gelbard, Rondi B.
AU - Hensman, Hannah
AU - Schobel, Seth
AU - Stempora, Linda
AU - Gann, Eric
AU - Moris, Dimitrios
AU - Dente, Christopher J.
AU - Buchman, Timothy G.
AU - Kirk, Allan D.
AU - Elster, Eric
N1 - Funding Information:
Research activities leading to the development of this manuscript were funded by the Department of Defense's Defense Health Program—USU Cooperative Agreement (HU0001-15-2-0001). DISCLOSURE
Publisher Copyright:
© Wolters Kluwer Health, Inc. All rights reserved.
Copyright © 2023 Written work prepared by employees of the Federal Government as part of their official duties is, under the U.S. Copyright Act, a "work of the United States Government" for which copyright protection under Title 17 of the United States Code is not available. As such, copyright does not extend to the contributions of employees of the Federal Government.
PY - 2023/7/1
Y1 - 2023/7/1
N2 - BACKGROUND Thoracic injury can cause impairment of lung function leading to respiratory complications such as pneumonia (PNA). There is increasing evidence that central memory T cells of the adaptive immune system play a key role in pulmonary immunity. We sought to explore whether assessment of cell phenotypes using flow cytometry (FCM) could be used to identify pulmonary infection after thoracic trauma. METHODS We prospectively studied trauma patients with thoracic injuries who survived >48 hours at a Level 1 trauma center from 2014 to 2020. Clinical and FCM data from serum samples collected within 24 hours of admission were considered as potential variables. Random forest and logistic regression models were developed to estimate the risk of hospital-acquired and ventilator-associated PNA. Variables were selected using backwards elimination, and models were internally validated with leave-one-out. RESULTS Seventy patients with thoracic injuries were included (median age, 35 years [interquartile range (IQR), 25.25-51 years]; 62.9% [44 of 70] male, 61.4% [42 of 70] blunt trauma). The most common injuries included rib fractures (52 of 70 [74.3%]) and pulmonary contusions (26 of 70 [37%]). The incidence of PNA was 14 of 70 (20%). Median Injury Severity Score was similar for patients with and without PNA (30.5 [IQR, 22.6-39.3] vs. 26.5 [IQR, 21.6-33.3]). The final random forest model selected three variables (Acute Physiology and Chronic Health Evaluation score, highest pulse rate in first 24 hours, and frequency of CD4+ central memory cells) that identified PNA with an area under the curve of 0.93, sensitivity of 0.91, and specificity of 0.88. A logistic regression with the same features had an area under the curve of 0.86, sensitivity of 0.76, and specificity of 0.85. CONCLUSION Clinical and FCM data have diagnostic utility in the early identification of patients at risk of nosocomial PNA following thoracic injury. Signs of physiologic stress and lower frequency of central memory cells appear to be associated with higher rates of PNA after thoracic trauma. LEVEL OF EVIDENCE Diagnostic Test/Criteria; Level IV.
AB - BACKGROUND Thoracic injury can cause impairment of lung function leading to respiratory complications such as pneumonia (PNA). There is increasing evidence that central memory T cells of the adaptive immune system play a key role in pulmonary immunity. We sought to explore whether assessment of cell phenotypes using flow cytometry (FCM) could be used to identify pulmonary infection after thoracic trauma. METHODS We prospectively studied trauma patients with thoracic injuries who survived >48 hours at a Level 1 trauma center from 2014 to 2020. Clinical and FCM data from serum samples collected within 24 hours of admission were considered as potential variables. Random forest and logistic regression models were developed to estimate the risk of hospital-acquired and ventilator-associated PNA. Variables were selected using backwards elimination, and models were internally validated with leave-one-out. RESULTS Seventy patients with thoracic injuries were included (median age, 35 years [interquartile range (IQR), 25.25-51 years]; 62.9% [44 of 70] male, 61.4% [42 of 70] blunt trauma). The most common injuries included rib fractures (52 of 70 [74.3%]) and pulmonary contusions (26 of 70 [37%]). The incidence of PNA was 14 of 70 (20%). Median Injury Severity Score was similar for patients with and without PNA (30.5 [IQR, 22.6-39.3] vs. 26.5 [IQR, 21.6-33.3]). The final random forest model selected three variables (Acute Physiology and Chronic Health Evaluation score, highest pulse rate in first 24 hours, and frequency of CD4+ central memory cells) that identified PNA with an area under the curve of 0.93, sensitivity of 0.91, and specificity of 0.88. A logistic regression with the same features had an area under the curve of 0.86, sensitivity of 0.76, and specificity of 0.85. CONCLUSION Clinical and FCM data have diagnostic utility in the early identification of patients at risk of nosocomial PNA following thoracic injury. Signs of physiologic stress and lower frequency of central memory cells appear to be associated with higher rates of PNA after thoracic trauma. LEVEL OF EVIDENCE Diagnostic Test/Criteria; Level IV.
KW - Flow Cytometry
KW - Humans
KW - Injury Severity Score
KW - Lung Injury/complications
KW - Male
KW - Pneumonia/complications
KW - Random Forest
KW - Retrospective Studies
KW - Thoracic Injuries/complications
KW - Wounds, Nonpenetrating/complications
UR - http://www.scopus.com/inward/record.url?scp=85163891029&partnerID=8YFLogxK
U2 - 10.1097/TA.0000000000003937
DO - 10.1097/TA.0000000000003937
M3 - Article
C2 - 37038251
AN - SCOPUS:85163891029
SN - 2163-0755
VL - 95
SP - 39
EP - 46
JO - Journal of Trauma and Acute Care Surgery
JF - Journal of Trauma and Acute Care Surgery
IS - 1
ER -