Prediction model of early risk of limb infection secondary to trimersurus mucrosquamatus snakebite
10.3760/cma.j.issn.1671-0282.2022.11.014
- VernacularTitle:原矛头腹蛇咬伤继发患肢感染早期风险预测模型研究
- Author:
Jie LUO
1
;
Yisi ZHAO
;
Xin ZHONG
;
Haojie WU
;
Wanshu QUE
;
Fang XU
Author Information
1. 重庆医科大学附属第一医院重症医学科，重庆 400016
- Keywords:
Snake bite;
Infection risk;
Prediction model;
Trimersurus mucrosquamatus
- From:
Chinese Journal of Emergency Medicine
2022;31(11):1515-1520
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To explore the method of early prediction of the risk of limb infection in patients bitten by trimersurus mucrosquamatus snake. Methods:Totally 108 inpatients with limbs bitten by trimersurus mucrosquamatus snake in Chongqing Emergency Medical Center from January 2019 to October 2020 were respectively collected. They were divided into the infection group (23 cases) and non infection group (85 cases) according to whether they had secondary infection in the course of the disease. The clinical characteristics and serum indexes before admission were compared between the two groups to screen out the risk factors of infection. By combining all the above methods, the risk factor score was screened out; and the prediction model was constructed according to the snake bite severity score (SSS) and appearance score. The differences of the three prediction models between the two groups of patients were compared, and the predictive value of the three prediction models for the risk of limb infection in patients bitten by trimersurus mucrosquamatus snake was evaluated through the receiver operating characteristic (ROC) curve. Results:There were significant differences in clinical characteristics and serum indexes before admission, injury time, hand and foot finger injury, edema score, tension blister, subcutaneous hemorrhage and admission platelet count between the two groups ( P<0.05). The scores of the three predictive models differed between the two groups ( P<0.05). The ROC curve was used to evaluate the predictive value of the three models for the risk of infection in the course of the disease. The predictive AUC value of the risk factors score was 0.830 (95% CI: 0.635-0.850), the cutoff value was 2.5, the sensitivity was 0.870 and the specificity was 0.671, which was the best in the three prediction models. Conclusions:The prediction model based on the risk factors can effectively predict the infection risk of snake bite patients. It indicates that the infection risk is high when the score of risk factors ≥3 points, which can be used as the basis for guiding clinical treatment plan and is worthy of promotion.
