Construction of a risk prediction model for adverse events in intra-hospital transfer of critically ill patients
Objective To explore the risk factors for adverse events in intra-hospital transport of critically ill patients and construct a risk prediction model.Methods A total of 342 patients with acute and critical illnesses in the emergency department of a tertiary hospital in Urumqi City from January to June 2023 were selected for the study by purposive sampling,and they were divided into the occurrence group and the non-occurrence group according to whether they had an adverse event of intra-hospital transport.The Logistic regression model was used to analyze the related risk factors,construct the prediction model,and draw a visual nomogram.The predictive efficacy of the model was verified by using the area under the ROC curve and the calibration curve.Results Seventy-five out of 239 patients with acute and critical ill-nesses in the modeling group experienced intra-hospital transport adverse events,with an incidence rate of 31.38%.Logistic regression analy-sis showed that systolic blood pressure,Modified Early Warning Score(MEWS),oxygen supply device,monitor,transfer shift,and total trans-port duration were independent risk factors for adverse events in intra-hospital transport of critically ill patients(P＜0.05).The results showed that the area under the ROC curve in the modeling group was 0.943,with a sensitivity of 0.968 and specificity of 0.875,and the area under the ROC curve in the validation group was 0.922,with a sensitivity of 0.903 and a specificity of 0.875.Hosmer-Lemeshow test showed?2=7.348,P=0.403,and the calibration curve showed good agreement between the predicted and actual probabilities of this nomogram model.Conclusion The incidence of adverse events of intra-hospital transport is higher in critical patients.Systolic blood pressure,MEWS score,oxygen supply device,monitor,transfer shift,and total transport duration are the influencing factors of adverse events in intra-hospital trans-port,and the risk prediction model constructed on the basis of the above influencing factors has good risk identification ability,and the nomo-gram model has a better degree of differentiation and calibration.
万方数据
维普
