Objective:To construct a risk identification model based on the Dynamic Bayesian Network(DBN)and explore its safety management effect in the precision medical research equipment of the central laboratory.Methods:Based on the DBN model,a risk identification model was constructed after various risk factors in the management for precision medical research equipment of central laboratory were identified,and a risk index evaluation set was constructed to provide early warning and effective prevention of potential risks during the operation stage of the equipment.A total of using 51 precision medical research equipment in central laboratory of Shuguang Hospital of Shanghai University of Traditional Chinese Medicine from February 2023 to February 2024 were selected.According to different management modes,26 of them were managed by the conventional management mode,and 25 were managed by the DBN risk identification model management mode(DBN management mode).A self-made questionnaire on equipment management recognition was used to conduct a survey among 15 engineers,15 equipment operators and 10 doctors involved in equipment use.The equipment target achievement degree,clinical service quality,equipment risk incidence rate and the recognition scores of personnel related to equipment use for equipment management under the two management modes were compared.Results:The scores of equipment operation standardization,disinfection and cleaning completion and emergency management timeliness under the DBN management mode were(91.58±3.36),(90.58±3.69)and(93.69±4.25)points respectively,all of which were higher than those under the conventional management mode,and the differences were statistically significant(t=19.466,15.704,15.549,P<0.05).The average values of equipment execution rate,quality inspection qualification rate and fault handling rate were(92.69±3.69)%,(93.27±3.01)%and(94.57±3.65)%respectively,all of which were higher than those under the conventional management mode,and the differences were statistically significant(t=13.811,18.401,20.374,P<0.05).The equipment failure rate,component damage rate and unqualified rate of cleaning and disinfection were 4%(1/25),8%(2/25)and 4%(1/25)respectively,all of which were lower than those under the conventional management mode,and the differences were statistically significant(x2=10.395,12.829 and 13.542,P<0.05).The average recognition scores of engineers,operators and doctors involved in equipment use for the DBN management mode were(90.26±4.65),(91.54±4.62)and(93.25±3.65)points respectively,all of which were higher than those under the conventional management mode,and the differences were statistically significant(t=12.910,12.379,18.328,P<0.05).Conclusion:The application of the DBN-based risk identification model in the management for precision medical research equipment of central laboratory of hospitals can enhance the operation quality and efficiency of equipment,reduce the risk of equipment use,and improve the equipment service level.

Objective:To construct a risk identification model based on the Dynamic Bayesian Network(DBN)and explore its safety management effect in the precision medical research equipment of the central laboratory.Methods:Based on the DBN model,a risk identification model was constructed after various risk factors in the management for precision medical research equipment of central laboratory were identified,and a risk index evaluation set was constructed to provide early warning and effective prevention of potential risks during the operation stage of the equipment.A total of using 51 precision medical research equipment in central laboratory of Shuguang Hospital of Shanghai University of Traditional Chinese Medicine from February 2023 to February 2024 were selected.According to different management modes,26 of them were managed by the conventional management mode,and 25 were managed by the DBN risk identification model management mode(DBN management mode).A self-made questionnaire on equipment management recognition was used to conduct a survey among 15 engineers,15 equipment operators and 10 doctors involved in equipment use.The equipment target achievement degree,clinical service quality,equipment risk incidence rate and the recognition scores of personnel related to equipment use for equipment management under the two management modes were compared.Results:The scores of equipment operation standardization,disinfection and cleaning completion and emergency management timeliness under the DBN management mode were(91.58±3.36),(90.58±3.69)and(93.69±4.25)points respectively,all of which were higher than those under the conventional management mode,and the differences were statistically significant(t=19.466,15.704,15.549,P<0.05).The average values of equipment execution rate,quality inspection qualification rate and fault handling rate were(92.69±3.69)%,(93.27±3.01)%and(94.57±3.65)%respectively,all of which were higher than those under the conventional management mode,and the differences were statistically significant(t=13.811,18.401,20.374,P<0.05).The equipment failure rate,component damage rate and unqualified rate of cleaning and disinfection were 4%(1/25),8%(2/25)and 4%(1/25)respectively,all of which were lower than those under the conventional management mode,and the differences were statistically significant(x2=10.395,12.829 and 13.542,P<0.05).The average recognition scores of engineers,operators and doctors involved in equipment use for the DBN management mode were(90.26±4.65),(91.54±4.62)and(93.25±3.65)points respectively,all of which were higher than those under the conventional management mode,and the differences were statistically significant(t=12.910,12.379,18.328,P<0.05).Conclusion:The application of the DBN-based risk identification model in the management for precision medical research equipment of central laboratory of hospitals can enhance the operation quality and efficiency of equipment,reduce the risk of equipment use,and improve the equipment service level.

Background::Ciprofol (HSK3486; Haisco Pharmaceutical Group Co., Ltd., Chengdu, China), developed as a novel 2,6-disubstituted phenol derivative showed similar tolerability and efficacy characteristics as propofol when applicated as continuous intravenous infusion for 12 h maintenance sedation in a previous phase 1 trial. The phase 2 trial was designed to investigate the safety, efficacy, and pharmacokinetic characteristics of ciprofol for sedation of patients undergoing mechanical ventilation.Methods::In this multicenter, open label, randomized, propofol positive-controlled, phase 2 trial, 39 Chinese intensive care unit patients receiving mechanical ventilation were enrolled and randomly assigned to a ciprofol or propofol group in a 2:1 ratio. The ciprofol infusion was started with a loading infusion of 0.1-0.2 mg/kg for 0.5-5.0 min, followed by an initial maintenance infusion rate of 0.30 mg·kg -1·h -1, which could be adjusted to an infusion rate of 0.06 to 0.80 mg·kg -1·h -1, whereas for propofol the loading infusion dose was 0.5-1.0 mg/kg for 0.5-5.0 min, followed by an initial maintenance infusion rate of 1.50 mg·kg -1·h -1, which could be adjusted to 0.30-4.00 mg·kg -1·h -1 to achieve -2 to +1 Richmond Agitation-Sedation Scale sedation within 6-24 h of drug administration. Results::Of the 39 enrolled patients, 36 completed the trial. The median (min, max) of the average time to sedation compliance values for ciprofol and propofol were 60.0 (52.6, 60.0) min and 60.0 (55.2, 60.0) min, with median difference of 0.00 (95% confidence interval: 0.00, 0.00). In total, 29 (74.4%) patients comprising 18 (69.2%) in the ciprofol and 11 (84.6%) in the propofol group experienced 86 treatment emergent adverse events (TEAEs), the majority being of severity grade 1 or 2. Drug- and sedation-related TEAEs were hypotension (7.7% vs. 23.1%, P = 0.310) and sinus bradycardia (3.8% vs. 7.7%, P = 1.000) in the ciprofol and propofol groups, respectively. The plasma concentration-time curves for ciprofol and propofol were similar. Conclusions::ciprofol is comparable to propofol with good tolerance and efficacy for sedation of Chinese intensive care unit patients undergoing mechanical ventilation in the present study setting.Trial registration::ClinicalTrials.gov, NCT04147416.