Acute and Critical Care

Original Articles

Trauma
Inclusion of lactate level measured upon emergency room arrival in trauma outcome prediction models improves mortality prediction: a retrospective, single-center study: Jonghwan Moon, Kyungjin Hwang, Dukyong Yoon, Kyoungwon Jung; Acute Crit Care. 2020;35(2):102-109. Published online May 31, 2020; DOI: https://doi.org/10.4266/acc.2019.00780

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Background
This study aimed to develop a model for predicting trauma outcomes by adding arterial lactate levels measured upon emergency room (ER) arrival to existing trauma injury severity scoring systems.
Methods
We examined blunt trauma cases that were admitted to our hospital during 2010– 2014. Eligibility criteria were cases with an Injury Severity Score of ≥9, complete Trauma and Injury Severity Score (TRISS) variable data, and lactate levels that were assessed upon ER arrival. Survivor and non-survivor groups were compared and lactate-based prediction models were generated using logistic regression. We compared the predictive performances of traditional prediction models (Revised Trauma Score [RTS] and TRISS) and lactate-based models using the area under the curve (AUC) of receiver operating characteristic curves.
Results
We included 829 patients, and the in-hospital mortality rate among these patients was 21.6%. The model that used lactate levels and age provided a significantly better AUC value than the RTS model. The model with lactate added to the TRISS variables provided the highest Youden J statistic, with 86.0% sensitivity and 70.8% specificity at a cutoff value of 0.15, as well as the highest predictive value, with a significantly higher AUC than the TRISS.
Conclusions
These findings indicate that lactate testing upon ER arrival may help supplement or replace traditional physiological parameters to predict mortality outcomes among Korean trauma patients. Adding lactate levels also appears to improve the predictive abilities of existing trauma outcome prediction models.

Citations

Citations to this article as recorded by

Plasma interleukin responses as predictors of outcome stratification in patients after major trauma: a prospective observational two centre study
Matthew Allan Jones, James Hanison, Renata Apreutesei, Basmah Allarakia, Sara Namvar, Deepa Shruthi Ramaswamy, Daniel Horner, Lucy Smyth, Richard Body, Malachy Columb, Mahesan Nirmalan, Niroshini Nirmalan
Frontiers in Immunology.2023;[Epub] CrossRef
Admission Lactate and Base Deficit in Predicting Outcomes of Pediatric Trauma
Yo Huh, Yura Ko, Kyungjin Hwang, Kyoungwon Jung, Yoon-ho Cha, Yoo Jin Choi, Jisook Lee, Jung Heon Kim
Shock.2021; 55(4): 495. CrossRef

Trauma
The Best Prediction Model for Trauma Outcomes of the Current Korean Population: a Comparative Study of Three Injury Severity Scoring Systems: Kyoungwon Jung, John Cook-Jong Lee, Rae Woong Park, Dukyong Yoon, Sungjae Jung, Younghwan Kim, Jonghwan Moon, Yo Huh, Junsik Kwon; Korean J Crit Care Med. 2016;31(3):221-228. Published online August 30, 2016; DOI: https://doi.org/10.4266/kjccm.2016.00486

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203 Download
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Abstract PDF

Background
Injury severity scoring systems that quantify and predict trauma outcomes have not been established in Korea. This study was designed to determine the best system for use in the Korean trauma population.
Methods
We collected and analyzed the data from trauma patients admitted to our institution from January 2010 to December 2014. Injury Severity Score (ISS), Revised Trauma Score (RTS), and Trauma and Injury Severity Score (TRISS) were calculated based on the data from the enrolled patients. Area under the receiver operating characteristic (ROC) curve (AUC) for the prediction ability of each scoring system was obtained, and a pairwise comparison of ROC curves was performed. Additionally, the cut-off values were estimated to predict mortality, and the corresponding accuracy, positive predictive value, and negative predictive value were obtained.
Results
A total of 7,120 trauma patients (6,668 blunt and 452 penetrating injuries) were enrolled in this study. The AUCs of ISS, RTS, and TRISS were 0.866, 0.894, and 0.942, respectively, and the prediction ability of the TRISS was significantly better than the others (p < 0.001, respectively). The cut-off value of the TRISS was 0.9082, with a sensitivity of 81.9% and specificity of 92.0%; mortality was predicted with an accuracy of 91.2%; its positive predictive value was the highest at 46.8%.
Conclusions
The results of our study were based on the data from one institution and suggest that the TRISS is the best prediction model of trauma outcomes in the current Korean population. Further study is needed with more data from multiple centers in Korea.

Citations

Citations to this article as recorded by

Outcomes in trauma patients undergoing veno-venous extracorporeal membrane oxygenation for acute respiratory distress syndrome
Seon Hee Kim, Up Huh, Seunghwan Song, Min Su Kim, Il Jae Wang, Young Jin Tak
Perfusion.2023; 38(5): 1037. CrossRef
Prehospital Trauma Scoring Systems for Evaluation of Trauma Severity and Prediction of Outcomes
Radojka Jokšić-Mazinjanin, Nikolina Marić, Aleksandar Đuričin, Zoran Gojković, Velibor Vasović, Goran Rakić, Milena Jokšić-Zelić, Siniša Saravolac
Medicina.2023; 59(5): 952. CrossRef
Correlation between trauma and injury severity score and prognosis in patients with trauma
Chusnul Chatimah, Indah D. Pratiwi, Chairul H. Al Husna
Journal of Taibah University Medical Sciences.2021; 16(6): 807. CrossRef
Trauma Volume and Performance of a regional Trauma Center in Korea: Initial 5-year analysis
Byungchul Yu, Giljae Lee, Min A Lee, Kangkook Choi, Sungyoul Hyun, Yangbin Jeon, Yong-Cheol Yoon, Jungnam Lee
Journal of Trauma and Injury.2020; 33(1): 31. CrossRef
Inclusion of lactate level measured upon emergency room arrival in trauma outcome prediction models improves mortality prediction: a retrospective, single-center study
Jonghwan Moon, Kyungjin Hwang, Dukyong Yoon, Kyoungwon Jung
Acute and Critical Care.2020; 35(2): 102. CrossRef
Trauma and Injury Severity Score modification for predicting survival of trauma in one regional emergency medical center in Korea: Construction of Trauma and Injury Severity Score coefficient model
In Hye Kang, Kang Hyun Lee, Hyun Youk, Jeong Il Lee, Hee Young Lee, Keum Seok Bae
Hong Kong Journal of Emergency Medicine.2019; 26(4): 225. CrossRef
The thorax trauma severity score and the trauma and injury severity score
Seong Ho Moon, Jong Woo Kim, Joung Hun Byun, Sung Hwan Kim, Jun Young Choi, In Seok Jang, Chung Eun Lee, Jun Ho Yang, Dong Hun Kang, Ki Nyun Kim, Hyun Oh Park
Medicine.2017; 96(42): e8317. CrossRef

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