Abstract
Objective: This study aimed to evaluate the clinical characteristics, treatments administered, and mortality-related factors of adult patients treated for traffic accident–related trauma in a tertiary care intensive care unit. Materials and Methods: A total of 183 patients aged ≥18 years admitted to the Anesthesia Intensive Care Unit between 2020 and 2022 due to traffic accidents were retrospectively analyzed. Demographic data, accident type, admission GCS, APACHE II and TRISS scores, surgical intervention status, laboratory findings, length of ICU stay, and discharge outcomes were evaluated. Independent predictors of mortality were assessed using logistic regression analysis. Results: Of the 183 patients, 153 (83.6%) were male. The overall mortality rate was 11.5%. Non-survivors had significantly lower hemoglobin, GCS, and TRISS scores, and significantly higher lactate, APACHE II scores, transfusion, and inotropic support requirements compared with survivors. Logistic regression identified a low TRISS score as an independent risk factor for mortality. Conclusion: Traffic accidents represent an important cause of ICU mortality. The TRISS score is a strong and independent predictor of mortality in this patient population.
Keywords: Traffic accident; TRISS; Intensive care unit; Mortality
1. Introduction
Traffic accidents represent a major public health problem worldwide. According to WHO data, approximately 1.2 million deaths occur annually due to traffic accidents, and 20 to 50 million people sustain injuries or permanent disabilities each year. Globally, traffic accidents rank 11th among all causes of death and account for 2.1% of total mortality1.
Injuries from traffic accidents are among the leading causes of ICU hospitalizations. In Türkiye, trauma patients with poor general condition due to traffic accidents are admitted to intensive care units2–4. This study aimed to investigate the effects of various clinical and demographic factors on mortality among patients admitted to the Anesthesiology Intensive Care Unit of Kahramanmaraş Sütçü İmam University between 2020 and 2022 due to traffic accident–related trauma.
2. Materials and Methods
This study was approved by the Ethics Committee of Kahramanmaraş Sütçü İmam University Health Practice and Research Hospital (decision number 03, session 2023/06, dated June 6, 2023). Medical records of patients admitted to the Reanimation ICU between January 1, 2020, and December 31, 2022, due to traffic accident–related trauma were retrospectively reviewed. Among 278 initially identified patients, 46 younger than 18 years and 49 with incomplete data were excluded, resulting in a final population of 183 patients. Patients were divided into survivor and non-survivor groups. TRISS combines physiological parameters from the Revised Trauma Score (RTS), anatomical injury severity from the Injury Severity Score (ISS), and age to estimate survival probability.
Statistical Analysis
Normality was assessed using the Kolmogorov–Smirnov and Shapiro–Wilk tests. Non-normally distributed continuous variables were compared using the Mann–Whitney U test or Kruskal–Wallis H test with Dunn–Šidák post hoc correction. Categorical variables were compared using the Chi-square or Fisher’s exact test. Binary logistic regression assessed independent predictors of mortality. ROC analysis evaluated diagnostic performance. A p-value <0.05 was statistically significant. SPSS v22.0 and R v3.3.2 were used.
3. Results
Of 183 patients, 153 were male and 30 female, with a median age of 42 years. The most common accident type was in-vehicle (54.1%), followed by out-of-vehicle (24.0%) and motorcycle (21.9%). Fifty-seven patients underwent emergency surgery, 101 elective surgery, and 25 none. Inotrope/vasopressor support was required in 43 patients, blood transfusion in 95, and 58 had at least one chronic comorbidity (Table 1).
Table 1. Demographic and clinical characteristics of the study population (n = 183)
| Variable | n | % / Median (Q1–Q3) |
|---|---|---|
| Age (years) | 42 (28–57) | |
| Sex — Male | 153 | 83.6 |
| Sex — Female | 30 | 16.4 |
| In-vehicle traffic accident (IVTA) | 99 | 54.1 |
| Out-of-vehicle traffic accident (OVTA) | 44 | 24.0 |
| Motorcycle accident | 40 | 21.9 |
| Surgery — Yes | 158 | 86.3 |
| Surgery — No | 25 | 13.7 |
| Inotrope/vasopressor — Yes | 43 | 23.5 |
| Blood transfusion — Yes | 95 | 51.9 |
| Chronic comorbidity — Yes | 58 | 31.7 |
Data are presented as median (Q1–Q3) or n (%). IVTA: In-vehicle traffic accident; OVTA: Out-of-vehicle traffic accident.
146 patients were transferred to wards, 16 discharged directly from ICU, 17 died, and 4 had brain death. Overall mortality rate was 11.5% (21 patients) (Table 2).
Table 2. Intensive care unit discharge outcomes
| ICU Discharge Status | n | % |
|---|---|---|
| Discharged | 16 | 8.7 |
| Transferred | 146 | 79.8 |
| Death (Exitus) | 17 | 9.3 |
| Brain death | 4 | 2.2 |
ICU: Intensive Care Unit.
No significant differences were observed between survivors and non-survivors in age, sex, or accident type. Median GCS was higher in survivors (13 vs. 5), APACHE II higher in non-survivors (76 vs. 14.5), and TRISS higher in survivors (81.65 vs. 9.8). Hemoglobin was higher in survivors (12.8 vs. 10.3 g/dL) and lactate higher in non-survivors (4.0 vs. 2.3 mmol/L) (Table 3).
Table 3. Comparison of clinical scores and laboratory parameters between survivors and non-survivors
| Variable | Survivors Median (Q1–Q3) | Non-survivors Median (Q1–Q3) | p-value |
|---|---|---|---|
| GCS | 13 (8–14) | 5 (3–7) | <0.001 |
| APACHE II score | 14.5 (9–36) | 76 (63–82) | <0.001 |
| TRISS score | 81.65 (62.3–90.5) | 9.8 (5.2–20.2) | <0.001 |
| Hemoglobin (g/dL) | 12.8 (10.9–14.3) | 10.3 (8.0–11.5) | <0.001 |
| Lactate (mmol/L) | 2.3 (1.6–3.3) | 4.0 (2.6–5.2) | <0.001 |
Mann–Whitney U test. Data are presented as median (Q1–Q3).
Emergency surgery was more frequent in non-survivors (52.4%) and elective surgery in survivors (59.9%) (p = 0.002). Mechanical ventilation, inotrope/vasopressor, and transfusion requirements were significantly higher in non-survivors (p < 0.001). No significant difference in chronic comorbidity (Table 4).
Table 4. Comparison of surgical history, ICU stay, mechanical ventilation, and treatment requirements
| Variable | Survivors | Non-survivors | p |
|---|---|---|---|
| Surgery — None n (%) | 19 (11.7) | 6 (28.6) | 0.002 |
| Surgery — Emergency n (%) | 46 (28.4) | 11 (52.4) | |
| Surgery — Elective n (%) | 97 (59.9) | 4 (19.0) | |
| ICU stay (days) Median (Q1–Q3) | 6.5 (5–13) | 7 (5–15) | 0.847 |
| Mechanical ventilation (days) Median | 0 (0–2) | 7 (5–15) | <0.001 |
| Inotrope/vasopressor — No n (%) | 140 (86.4) | 0 (0) | <0.001 |
| Inotrope/vasopressor — Yes n (%) | 22 (13.6) | 21 (100) | |
| Blood transfusion — No n (%) | 86 (53.1) | 2 (9.5) | <0.001 |
| Blood transfusion — Yes n (%) | 76 (46.9) | 19 (90.5) | |
| Chronic comorbidity — No n (%) | 112 (69.1) | 13 (61.9) | 0.503 |
| Chronic comorbidity — Yes n (%) | 50 (30.9) | 8 (38.1) |
Mann–Whitney U test or Chi-square/Fisher’s exact test as appropriate.
Table 5. Binary logistic regression analysis of factors associated with mortality
| Variable | B | Wald | p-value | Odds Ratio (95% CI) |
|---|---|---|---|---|
| Age | -0.003 | 0.010 | 0.921 | 0.997 (0.947–1.050) |
| GCS | 0.565 | 2.273 | 0.132 | 1.760 (0.844–3.671) |
| APACHE II | 0.036 | 0.450 | 0.502 | 1.037 (0.933–1.151) |
| TRISS score | -0.122 | 14.330 | <0.001* | 0.885 (0.831–0.943) |
| Hemoglobin | -0.169 | 0.638 | 0.424 | 0.845 (0.558–1.278) |
| Lactate | 0.297 | 1.030 | 0.310 | 1.345 (0.759–2.385) |
*Statistically significant (p < 0.05). Nagelkerke R² = 0.737.
Binary logistic regression (Nagelkerke R² = 0.737) identified a low TRISS score as the only independent risk factor for mortality (p < 0.001; OR = 0.885, 95% CI: 0.831–0.943) (Table 5). ROC analysis demonstrated strong predictive performance (AUC = 0.949, cut-off: 26.1) (Table 6, Figure 1).
Table 6. ROC analysis of TRISS score for mortality prediction
| AUC | SE | p-value | 95% CI | Cut-off |
|---|---|---|---|---|
| 0.949 | 0.031 | <0.001* | 0.889–1.000 | 26.1 |
AUC: area under the curve; SE: standard error; CI: confidence interval. *Statistically significant.
References
- 1. World Health Organization. World report on road traffic injury prevention. Geneva: WHO; 2004.
- 2. Sewalt CA, Gravesteijn BY, Nieboer D, et al. Identifying trauma patients with benefit from direct transportation to Level-1 trauma centers. BMC Emerg Med. 2021;21(1):93.
- 3. Khari S, Zandi M, Yousefifard M. Glasgow Coma Scale versus physiologic scoring systems in predicting the outcome of ICU admitted trauma patients. Arch Acad Emerg Med. 2022;10(1):e25.
- 4. Teke C. Ülkemizde günışığı yararlanma zamanına geçilen ve geçilmeyen dönemlerdeki trafik kazası başvuru sayısının ve yaralanma ciddiyetinin geriye dönük taranması [Thesis]. Ankara: Ankara Yıldırım Beyazıt University; 2019.
- 5. General Directorate of Security. Traffic statistics 2023.
- 6. Cantürk M. Analysis of traffic accident victims admitted to intensive care unit. Kırıkkale Univ Med J. 2018;20(3):58-64.
- 7. Yavuz G, Kaan K, Zeynep NO, et al. Retrospective analysis of intensive care trauma patients. J Anest Intensive Care Med. 2018;7(5):555724.
- 8. Unlu AR, Ulger F, Dilek A, et al. Efficiency of RTS and TRISS scores on prognosis evaluation in ICU trauma patients. J Turkish Anaesthesiol Intensive Care Soc. 2012;40(3):128-135.
- 9. Taşdemir B, Tüfek D, Sıvacı R, et al. Retrospective evaluation of trauma patients followed in ICU. Turkiye Klinikleri J Anest Reanim. 2017;15(3):89-94.
- 10. Mijaljica DR, Gregoric P, Ivancevic N, et al. Predicting mortality in severe polytrauma with limited resources. Ulus Travma Acil Cerrahi Derg. 2022;28:1404-1411.
- 11. Yazar MA, Sarıkuş Z, Horasalı E. Thirty-day mortality outcomes of trauma patients in the ICU. Turkish J Intensive Care. 2019;17(1):18-24.
- 12. Papadimitriou-Olivgeris M, Panteli E, Koutsileou K, et al. Predictors of mortality of trauma patients admitted to the ICU. Braz J Anesthesiol. 2021;71:23-30.
- 13. Kara İ, Altınsoy S, Gök U, et al. Mortality analysis of trauma patients in ICU. Turk J Intensive Care. 2015;13(2):68-74.
- 14. Bayissa BB, Alemu S. Pattern of trauma admission and outcome among patients. Trauma Surg Acute Care Open. 2021;6:e000609.
- 15. Manikis P, Jankowski S, Zhang H, et al. Correlation of serial blood lactate levels to organ failure and mortality after trauma. Am J Emerg Med. 1995;13:619-622.
- 16. Odom SR, Howell MD, Silva GS, et al. Lactate clearance as a predictor of mortality in trauma patients. J Trauma Acute Care Surg. 2013;74:999-1004.
- 17. Atlas A, Büyükfırat E, Ethemoğlu KB, et al. Factors affecting mortality in trauma patients hospitalized in ICU. J Surg Med. 2020;4:930-933.
- 18. Kara İ, Altınsoy S, Gök U. Mortality analysis of trauma patients in general ICU. Turk J Intensive Care. 2015;13:68-74.
- 19. Adıyaman E, Tokur ME, Mermi Bal Z, et al. Retrospective analysis of trauma patients treated in anesthesia ICU. Turkish J Intensive Care. 2019;17(3):146-153.
- 20. Chegondi M, Sasaki J, Raszynski A, Totapally B. Hemoglobin threshold for blood transfusion in pediatric intensive care unit. Transfus Med Hemother. 2016;43:297-301.
Cite this article
Muammer Kesen, Mahmut Arslan. Clinical Characteristics and Outcomes of Adult Traffic Accident Patients in a Tertiary Care Intensive Care Unit. Journal of Cukurova Anesthesia and Surgical Sciences. 9(2):396-401. https://doi.org/10.36516/jocass.1895544