Inclusion of lactate level measured upon emergency room arrival in trauma outcome prediction models improves mortality prediction: a retrospective, single-center study
2
조회
0
다운로드
0
인용
0
참고문헌
키워드
초록
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.
Abstract
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.
참고문헌 (30)
- Cook A, "A comparison of the Injury Severity Score and the Trauma Mortality Prediction Model", J Trauma Acute Care Surg, 76, pp.47~52 (2014)
- Cassidy LD, "Is the Trauma Mortality Prediction Model (TMPM-ICD-9) a valid predictor of mortality in pediatric trauma patients?", J Pediatr Surg, 49, pp.189~192 (2014)
- Vivien B, "The motor component does not convey all the mortality prediction capacity of the Glasgow Coma Scale in trauma patients", Am J Emerg Med, 30, pp.1032~1041 (2012)
- Haider AH, "Should the IDC-9 Trauma Mortality Prediction Model become the new paradigm for benchmarking trauma outcomes?", J Trauma Acute Care Surg, 72, pp.1695~1701 (2012)
- Moore L, "Improving trauma mortality prediction modeling for blunt trauma", J Trauma, 68, pp.698~705 (2010)
- Huber-Wagner S, "The sequential trauma score : a new instrument for the sequential mortality prediction in major trauma", Eur J Med Res, 15, pp.185~195 (2010)
- Glance LG, "TMPM-ICD9 : a trauma mortality prediction model based on ICD-9-CM codes", Ann Surg, 249, pp.1032~1039 (2009)
- Osler T, "A trauma mortality prediction model based on the anatomic injury scale", Ann Surg, 247, pp.1041~1048 (2008)
- Marcin JP, "Triage scoring systems, severity of illness measures, and mortality prediction models in pediatric trauma", Crit Care Med, 30(11 Suppl), pp.S457~S467 (2002)
- Boyd CR, "Evaluating trauma care : the TRISS method : trauma score and the injury severity score", J Trauma, 27, pp.370~378 (1987)
- Schluter PJ, "Trauma and Injury Severity Score(TRISS)coefficients 2009 revision", J Trauma, 68, pp.761~770 (2010)
- de Munter L, "Mortality prediction models in the general trauma population : a systematic review", Injury, 48, pp.221~229 (2017)
- Schluter PJ, "Trauma and Injury Severity Score (TRISS): is it time for variable re-categorisations and re-characterisations?", Injury, 42, pp.83~89 (2011)
- Champion HR, "The Major Trauma Outcome Study : establishing national norms for trauma care", J Trauma, 30, pp.1356~1365 (1990)
- Becher RD, "Trauma", pp.77~90 (2013), McGraw-Hill
- 정경원, "The Applicability of Trauma and Injury Severity Score for a Blunt Trauma Population in Korea and a Proposal of New Models Using Score Predictors", Yonsei Medical Journal, 57(3), pp.728~734 (2016), 연세대학교의과대학[DOI]
- Kyoungwon Jung, "The Best Prediction Model for Trauma Outcomes of the Current Korean Population: a Comparative Study of Three Injury Severity Scoring Systems", Acute and Critical Care, 31(3), pp.221~228 (2016), 대한중환자의학회
- 정경원, "외상환자에 대한 손상 중증도 점수체계와 국내 외상성과 연구", Journal of Acute Care Surgery, 6(1), pp.11~17 (2016), 대한외상중환자외과학회
- 박지혜, "A Statistical Analysis on Forensic Autopsies Performed in Korea in 2015", 대한법의학회지, 40(4), pp.104~118 (2016), 대한법의학회
- "Korean statistical information service" (2018)[URL]
- Baxter J, "Do lactate levels in the emergency department predict outcome in adult trauma patients? A systematic review", J Trauma Acute Care Surg, 81, pp.555~566 (2016)
- Lewis CT, "Prehospital point-of-care lactate following trauma : a systematic review", J Trauma Acute Care Surg, 81, pp.748~755 (2016)
- Parsikia A, "The predictive value of initial serum lactate in trauma patients", Shock, 42, pp.199~204 (2014)
- Ouellet JF, "Admission base deficit and lactate levels in Canadian patients with blunt trauma: are they useful markers of mortality?", J Trauma Acute Care Surg, 72, pp.1532~1535 (2012)
- Vandromme MJ, "Lactate is a better predictor than systolic blood pressure for determining blood requirement and mortality: could prehospital measures improve trauma triage?", J Am Coll Surg, 210, pp.861~869 (2010)
- Callaway DW, "Serum lactate and base deficit as predictors of mortality in normotensive elderly blunt trauma patients", J Trauma, 66, pp.1040~1044 (2009)
- Mizushima Y, "Discrepancy between heart rate and makers of hypoperfusion is a predictor of mortality in trauma patients", J Trauma, 71, pp.789~792 (2011)
- Neville AL, "Mortality risk stratification in elderly trauma patients based on initial arterial lactate and base deficit levels", Am Surg, 77, pp.1337~1341 (2011)
- Hasler RM, "Systolic blood pressure below 110 mm Hg is associated with increased mortality in blunt major trauma patients : multicentre cohort study", Resuscitation, 82, pp.1202~1207 (2011)
- Hasler RM, "Systolic blood pressure below 110 mmHg is associated with increased mortality in penetrating major trauma patients : Multicentre cohort study", Resuscitation, 83, pp.476~481 (2012)