Prediction Model and Validation of Acquired Weakness in Critically Ill Patients in ICU
Objective To explore the independent influencing factors of acquired weakness in critically ill patients in the ICU,construct a predictive model,and validate the predictive effect.Methods A retrospective analysis was conducted on the data of 368 patients treated in Sui County Traditional Chinese Medicine Hospital ICU from January 2017 to December 2022.The data of patients with acquired frailty in ICU were collected for univariate analysis,multi-factor analysis,prediction model construction and prediction efficacy analysis.Results Among the 368 patients included in this survey,189 patients were diagnosed with ICU acquired weakness,with an incidence rate of 51.36%.The results of univariate analysis showed that there were statistically significant differences in the length of ICU admission time,The Acute Physical and Chronic Health score(APACHE Ⅱ)score,use of neuromuscular blockers,and highest blood lactate levels between patients with and without ICU acquired weakness(P＜0.05).The results of multivariate analysis showed that the length of time spent in the ICU,APACHE Ⅱ score,use of neuromuscular blockers,and highest blood lactate levels were independent influencing factors for the occurrence of ICU acquired weakness in patients(P＜0.05).Construct a column chart risk model based on the variables selected through multiple factor analysis.The C-index is 0.713.Using Bootstrap self sampling method for internal validation,repeat self sampling 1000 times to obtain a calibration curve with an average absolute error of 0.043.The independent influencing factors and P-value prediction probability of logistic regression model were used to predict the ROC curve of patients with ICU acquired weakness.The youden indices were 19.97%,32.92%,15.11%,37.30%,and 47.20%,respectively.Conclusion ICU patients have a high risk of acquired weakness,and several factors are influencing the occurrence of this disease.The prediction model constructed using these influencing factors has good predictive performance,and it is also necessary to monitor these influencing factors in work for early detection and intervention.
intensive care unitacquired weaknessprediction modelneuromuscular systemrespiratory failure
国家科技期刊平台
NSTL
万方数据
维普
