info@biomedres.us   +1 (720) 414-3554   One Westbrook Corporate Center, Suite 300, Westchester, IL 60154, USA   Site Map
ISSN: 2574 -1241

Impact Factor : 0.548

  Submit Manuscript

Short CommunicationOpen Access

Predictive Factor of Mortality and the Scoring Systems Volume 7 - Issue 4

Ljiljana Gvozdenović*, Mirka Lukić-Šarkanović, Vladimir Dolinaj and Biljana Josipović

  • School of Medicine, University of Novi Sad, Clinical Center of Vojvodina, Novi Sad, Serbia

Received: July 30, 2018;   Published: August 09, 2018

*Corresponding author: Ljiljana Gvozdenović, Clinical Center of Vojvodina, Novi Sad, Serbia

DOI: 10.26717/BJSTR.2018.07.001546

Abstract PDF

Short Communication

Introduction

Polytrauma incidence are increasing across the world, being a major contributing factor for hospitalization of individuals under 65 years of age, and resulting in high mortality rates . Not only does it cost up to 5% of the healthcare budget in some countries, perhaps even more important to the finances/economy is the reduction of capable workers . Mortality of individuals under 65 years of age does not only impact a reduction in workforce but also leaves a burden on those that financial depended on them; especially their children who will now most likely depend on society for help. Characterization of injury severity is crucial to the scientific grading of trauma, numerous scoring systems exist, the focus will be on four Injury Severity Score (ISS), New Injury Severity Score (NISS), Revised Trauma Score (RTS) and Emergency Trauma Score (EMTRAS).

The Goal of the Study is to determine the best scoring system out of the four (ISS, NISS, RTS and EMTRAS) for predicting mortality of polytrauma patients within 30 minutes onset of injury. The research study has been done in the Emergency Center, Clinical Center of Vojvodina in Novi Sad, Serbia. Data has been collected from the Department of Anesthesia and Intensive Therapy. This was a retrospective study. The research encompassed 107 patients, ages from 20 years of age to 86 years of age. electivity was calculated for all of the four scoring systems with the following formula: True Positives/All the positives True positives are all the patients that were expected to have an unfavourable outcome (death) and did die. All the positives includes all the scores that were above a critical point and an unfavourable outcome was to be expected. Specificity was calculated for all of the four scoring system with the following formula: True Negatives/All the negatives. True negatives are all the patients that were expected to have a favourable outcome (survive) and did survive. All the negatives includes all the scores that were below a critical point and a favourable outcome was to be expected [1-17].

Results

The ratio of survivors to deaths was 78 to 29. Mortality rate being 27.1%. The average age of the survivors was 41.7 years and those that died was 57.5 years. Ratio of males to females was 84:23. Males represent 78.5% of the polytrauma patients while females represent 21.5%. Average stay in the hospital for the survivors was 16.2 days compared to 7.0 days for those that died. The main cause for polytrauma was by far traffic accidents, representing 3/4, followed by falls. All four of the scoring systems proved to be reliable for differentiating patients into the alive and dead groups, following are the average scores for the groups: ISS- Alive 21.1 and Dead 37.7 NISS-Alive 23.2 and Dead 43.7 RTS-Alive 9.9 and Dead 6.3 EMTRAS-Alive 2.3 and Dead 5.9. Selectivity and specificity values for prognosis of mortality rate are different in the four scoring systems: ISS- Selectivity 0.74 and Specificity 0.98 NISS-Selectivity 0.87 and Specificity 0.96. RTS- Selectivity 0.68 and Specificity 0.83 EMTRAS-Selectivity 0.71 and Specificity 0.88.

Conclusion

The ratio of survivors to deaths was 78 to 29. Mortality rate being 27.1%. The average age of the survivors was 41.7 years and those that died was 57.5 years. Ratio of males to females was 84:23. Males represent 78.5% of the polytrauma patients while females represent 21.5%. Average stay in the hospital for the survivors was 16.2 days compared to 7.0 days for those that died. The main cause for polytrauma was by far traffic accidents, representing 3/4, followed by falls. All four of the scoring systems proved to be reliable for differentiating patients into the alive and dead groups, following are the average scores for the groups: ISS- Alive 21.1 and Dead 37.7. NISS-Alive 23.2 and Dead 43.7 .RTS-Alive 9.9 and Dead 6.3. EMTRAS-Alive 2.3 and Dead 5.9. Selectivity and specificity values for prognosis of mortality rate are different in the four scoring systems: ISS- Selectivity 0.74 and Specificity 0.98. NISS-Selectivity 0.87 and Specificity 0.96. RTS- Selectivity 0.68 and Specificity 0.83. EMTRAS-Selectivity 0.71 and Specificity 0.88. NISS is the best scoring system to use. It has proven to be the most reliable having the best selectivity and specificity for predicting mortality rate. Applying NISS, further improvements in therapy and procedures can be achieved for polytrauma patients, can further improve the cost benefit for the healthcare.

References

  1. . M Mutschler, U Nienaber, M Münzberg (2013) The Shock Index revisited a fast guide to transfusion requirement? A retrospective analysis on 21,853 patients derived from the Trauma Register DGU. Critical Care, 17(4): R172
  2. AKB Kristensen, JG Holler, J Hallas, A Lassen, NI Shapiro (2016) Is Shock Index a Valid Predictor of Mortality in Emergency Department Patients with Hypertension, Diabetes, High Age, or Receipt of β- Or Calcium Channel Blockers. Annals of Emergency Medicine 67(1): 106-113.
  3. M Strnad, V Borovnik Lesjak, V Vujanović, T Pelcl, M Križmarić (2015) Predictors of mortality and prehospital monitoring limitations in blunt trauma patients. BioMed Research International.
  4. RW Barbee, PS Reynolds, KR Ward (2010) Assessing shock resuscitation strategies by oxygen debt repayment Shock 33(2): 113-122.
  5. AL Byrne, MH Bennett, NL Pace, P Thomas (2013) Peripheral venous blood gas analysis versus arterial blood gas analysis for the diagnosis of respiratory failure and metabolic disturbance in adults. Cochrane Database of Systematic Reviews 11: CD010841.
  6. A Kimmoun, E Novy, T Auchet, N Ducrocq, B Levy (2015) Hemodynamic consequences of severe lactic acidosis in shock states: from bench to bedside. Critical Care 19(1): 175.
  7. AJ Piper, BJ Yee (2014) Hypoventilation syndromes, Comprehensive Physiology 4(4): 1639-1676.
  8. T Richter, M Ragaller (2011) Ventilation in chest trauma, Journal of Emergencies, Trauma, and Shock, 4(2): 251-259.
  9. JT Kielstein, AM Heiden, G Beutel (2013) Renal function and survival in 200 patients undergoing ECMO therapy. Nephrology Dialysis Transplantation 28(1): 86-90.
  10. VM Ranieri, GD Rubenfeld, BT Thompson, ND Ferguson, E Caldwell, et al. (2012) Acute respiratory distress syndrome: The Berlin Definition. Journal of the American Medical Association 307(23): 2526- 2533.
  11. S Abrassart, R Stern, R Peter (2013) Unstable pelvic ring injury with hemodynamic instability: What seems the best procedure choice and sequence in the initial management?” Orthopaedics & Traumatology: Surgery & Research 99(2): 175-182.
  12. M Hravnak, MA Devita, A Clontz, L Edwards, C Valenta (2011) Cardiorespiratory instability before and after implementing an integrated monitoring system. Critical Care Medicine 39(1): 65-72.
  13. K Yousef, MR Pinsky, MA DeVita, S Sereika, M Hravnak (2012) Characteristics of patients with cardiorespiratory instability in a stepdown unit. American Journal of Critical Care 21(5): 344-350.
  14. EL Vanzant, RE Hilton, CM Lopez, J Zhang, RF Ungaro, et al. (2015) Advanced age is associated with worsened outcomes and a unique genomic response in severely injured patients with hemorrhagic shock. Critical Care 19: 77.
  15. J Cuschieri, JL Johnson, J Sperry, MA West, EE Moore, et al. (2012) Benchmarking outcomes in the critically injured trauma patient and the effect of implementing standard operating procedures. Annals of Surgery 255(5): 993-999.
  16. Z El Mestoui, H Jalalzadeh, GF Giannakopoulos, WP Zuidema (2017) Incidence and etiology of mortality in polytrauma patients in a Dutch level I trauma center. European Journal of Emergency Medicine 24(1): 49-54.
  17. IH Kim, KS Seo, MJ Lee, JB Park, JK Kim, et al. (2016) Usefulness of New Berlin Definition of polytrauma for mortality prediction in adult patients with major trauma. Journal of The Korean Society of Emergency Medicine 27(6): 497-504.