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Abstract
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OBJECTIVE: To explore the risk factors of acute respiratory distress syndrome (ARDS) secondary to severe multiple trauma and the role of clinical guidance.
METHODS: The clinical data of 115 patients with severe multiple trauma admitted to the trauma center of Zhenjiang First People's Hospital from December 2017 to September 2020 were retrospectively analyzed. According to whether ARDS occurred within 1 week of the disease course, the patients were divided into ARDS group and non-ARDS group. The basic post-traumatic data, initial treatment measures (within 24 hours), pathophysiology, stress metabolism, and post-traumatic complications of the two groups of patients were selected for univariate analysis, the statistically different indicators of univariate analysis were incorporated into the multivariate Logistic regression analysis to screen out independent high-risk factors that affect the occurrence of ARDS in patients with severe multiple trauma, and a receiver operating characteristic curve (ROC curve) was drawn to analyze the effects of each risk factor on the occurrence of ARDS.
RESULTS: Among 115 patients, there were 45 cases in the ARDS group and 70 cases in the non-ARDS group. Compared with the non-ARDS group, the patients in the ARDS group were older (years: 57.45±15.37 vs. 45.68±12.70), and the proportion of patients combined with moderate-severe chest trauma, traumatic brain injury (TBI), shock, and massive blood transfusion were higher (71.11% vs. 31.43%, 44.44% vs. 28.57%, 80.00% vs. 67.14%, 46.67% vs. 27.14%). In the ARDS group, procalcitonin [PCT (μg/L): 29.73±6.08 vs. 12.45±2.12], thrombomodulin [TM (ng/L): 83.43±16.34 vs. 37.66±14.64], blood glucose (mmol/L: 17.2±5.0 vs. 10.3±2.4), triacylglycerol [TG (mmol/L): 3.77±0.57 vs. 2.22±0.63], interleukin-6 [IL-6 (ng/L): 38.97±10.79 vs. 25.98±5.40], tumor necrosis factor-α [TNF-α (ng/L): 48.78±13.99 vs. 35.30±13.03], intra-abdominal pressure [mmHg (1 mmHg = 0.133 kPa): 25.21±3.59 vs. 11.98±4.91], serum creatinine [SCr (μmol/L): 180.45±42.35 vs. 132.17±49.36] and blood urea nitrogen [BUN (mmol/L): 13.83±4.97 vs. 8.80±4.32] were significantly higher than those in the non-ARDS group; the proportion of patients with crystal infusion volume ≥ 3 000 mL (26.67% vs. 34.29%) and platelet count [PLT (×10(9)/L): 72.67±7.96 vs. 127.99±17.65] and the levels of plasma glutathione peroxidase [GSH-Px (kU/L): 87.15±27.81 vs. 161.15±17.94], plasma superoxide dismutase [SOD (kU/L): 92.65±32.67 vs. 125.58±38.96] were significantly lower than those in the non-ARDS group, the differences were statistically significant (all P < 0.05). Multivariate Logistic regression analysis showed that 11 indicators such as age, combined moderate-severe chest trauma, combined TBI, massive blood transfusion, PCT, TM, blood glucose, TNF-α, plasma GSH-Px, intra-abdominal pressure and SCr were independent risk factors that could predict ARDS secondary to severe multiple trauma, the odds ratio (OR) and 95% confidence interval (95%CI) were 1.201 (1.035-1.165), 3.414 (1.217-8.876), 2.889 (1.124-8.109), 3.134 (1.322-9.261), 1.467 (1.096-2.307), 2.428 (0.024-0.973), 5.787 (1.246-9.642), 1.106 (0.949-5.108), 7.450 (1.587-10.261), 3.144 (1.217-8.876), 1.051 (1.002-1.542) respectively, the P values were 0.008, 0.024, 0.044, 0.017, 0.018, 0.045, 0.026, 0.037, 0.005, 0.029, 0.033 respectively. ROC curve analysis showed that plasma GSH-Px had a higher predictive value for ARDS secondary to severe multiple trauma, the area under ROC curve (AUC) = 0.873, 95%CI was 0.798-0.928, P = 0.000, when the best cut-off value at 72.22 kU/L, its sensitivity was 86.7%, specificity was 75.7%, positive predictive value was 69.6%, and negative predictive value was 89.8%. The Logistic regression model established by 11 independent high-risk factors had an accuracy rate of 81.74% in predicting ARDS secondary to severe multiple trauma, which had a good guiding significance for predicting ARDS.
CONCLUSIONS: Our study showed that there are many risk factors for ARDS secondary to severe multiple trauma, involving basic post-traumatic data, initial treatment measures, pathophysiology, stress metabolism, post-traumatic complications, etc. Early identification and intervention may be beneficial to improve the success rate of treatment for such patients.
Language: zh |