Objective To discuss the factors influencing the occurrence of frailty in patients with acute myocardial infarction(AMI)after receiving interventional treatment,and to construct a prediction model,to draw a nomogram,and to make the validation of the model.Methods Using convenient sampling method,a total of 462 patients with AMI,who were admitted to the Department of Cardiovascular Medicine of three Grade Ⅲ-A hospitals in Shandong Province of China from July 2023 to January 2024,were selected as the study subjects.Among them,324 AMI patients encountered from July 2023 to November 2023 were selected as modeling group,and logistic regression was used to construct a risk prediction model and draw a nomogram to visualize the model.The remaining 138 AMI patients encountered from December 2023 to January 2024 were used as the validation group.The receiver operating characteristic(ROC)curve and Hosmer-Lemeshow testing were adopted to verify the predictive effect of the model.Results Of 324 patients in the modeling group,170(52.47％)developed frailty.Univariate analysis showed that significant differences in age,education level,body mass index(BMI),Charlson comorbidity index,grip strength,walking speed,brain natriuretic peptide precursor level,physical exercise,multiple medication,and kinesophobia existed between the two groups(all P＜0.05).Multivariate logistic regression analysis revealed that age,BMI,Charlson comorbidity index,grip strength,walking speed,NT-ProBNP precursor level,physical exercise,multiple medication,and kinesophobia were the influencing factors of frailty in patients with AMI after receiving interventional treatment,with an OR value of 1.061,0.630,1.529,0.931,0.005,0.358,1.783,2.929,and 0.497 respectively.The above nine factors were used as independent variables to draw the nomogram,the area under ROC curve of the model was 0.851(95％CI:0.809-0.892),the optimal critical value was 0.562,the sensitivity was 84.1％,and the specificity was 72.1％.Hosmer-Lemeshow goodness of fit testing showed that the model had anx2=12.957 and P=0.113.Conclusion The frailty condition of AMI patients after receiving interventional treatment is at a low to medium levels.The frailty risk prediction model constructed in this study has good prediction effect,which can provide guidance for clinical nurses to timely identify high-risk patients and to promptly adopt interventional measures.

Objective:To explore the application effect of global trigger tool (GTT) in detecting adverse events in ICU patients.Methods:According to contents of triggers in the GTT white paper, combined with the scope of domestic adverse event reporting and some ICU indexes, 16 triggers were established. A total of 1 683 medical records were collected from ICU of the Second People's Hospital of Liaocheng in Shandong Province from July 2018 to June 2019. According to the inclusion/exclusion criteria, 421 medical records of discharged patients were randomly selected according to the number of sampling intervals and 420 medical records were reviewed after eliminating repeated medical records. GTT method was used for retrospective analysis.Results:Of the 420 medical records actually used for review, 14 of the 16 triggers were positive, and the positive frequency of triggers was 128 cases, involving 62 patients. Adverse events were identified 51 times, involving 43 patients, with a detection rate of 10.24% (43/420) . Among the 51 cases of adverse events, 37 cases (72.55%) were Grade E, 13 cases (25.49%) were Grade F, and 1 case (1.96%) was Grade H. No Grade G or I was found. In the same year, 18 cases of adverse events were reported voluntarily in ICU, the reporting rate was 1.07% (18/1 683) .Conclusions:GTT can be effectively applied to the detection of adverse events in ICU patients, and a properly designed trigger can improve the detection rate.