Objective:To explore the feasibility of emergency video call system in remote guidance of non-medical volunteers to implement single person cardiopulmonary resuscitation (CPR).Methods:A scenario of sudden cardiac arrest with a bystander in a public place was created at Clinical Skill Training Center. 60 non-medical volunteers were randomly (ramdom number) divided into video group ( n = 40) and audio group ( n = 20). Volunteers in video group were remote instructed with the smart phone application software (APP) of Emergency Video Call System to implement CPR; the audio group receives remote voice guidance for CPR with a smart phone. The pressing depth, pressing frequency, volume of ventilation and the time of the first compression were compared between the two groups. The video group was divided into 5 subgroups to compare the cardiopulmonary resuscitation effect of 5 different models of smart phones. Ten CPR cycles were observed in each group. Results:the accuracy rate of pressing position in the video group was significantly higher than that in the audio group (91.5% vs 71.35%, P < 0.05); the proportion of pressing depth in the range of 5-6 cm was significantly higher than that in the audio group (62.79% vs. 44.73%, P < 0.05); the average pressing frequency was 100-120 times / min (70% vs. 52%, P < 0.05); the ventilation volume was 500-600 mL / time (18.25% vs. 10.75%, P < 0.05); The proportion of ventilation volume greater than 500ml / min was higher than that of audio group (64.88% vs. 43%, P < 0.05). The first pressing time was longer in the video group than in the audio group (131 s vs. 106 s, P < 0.05). There was no significant difference in the first ventilation time between the two groups (148 s vs. 144 s, P > 0.05). The total pressing pause time in video group was less than that in audio group (122.4 s vs. 164.2 s, P < 0.05). There was no significant difference in the above indicators among the five different models of smart phones in the video group ( P > 0.05). Conclusions:compared with audio remote guidance, video emergency system has obvious advantages in the accuracy of pressing position, pressing depth, pressing frequency, ventilation volume and pressing pause time, but the first pressing time is slightly longer than that of audio group. The popularization and application of the video system is supposed to improve the CPR quality and recovery success rate of non-medical personnel, and facilitated to encourage the first witness to implement CPR.

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.