Construction and validation of a risk prediction model for nursing adverse events in hospitalized patients

QING Qiuqiong; WANG Jing; HUANG Youhong; HUANG Yanling; HE Jinyang

doi:10.12102/j.issn.1009-6493.2024.22.008

Abstract：Objective:To construct a risk prediction model for nursing adverse events in hospitalized patients and test its effectiveness.Methods:A total of 150 patients who experienced nursing adverse events and 160 patients who did not experience nursing adverse events in a tertiary grade A hospital in Jiangxi province were selected as research subjects by convenient sampling method from January to December 2023.Risk factors that may lead to nursing adverse events in hospitalized patients based on Hospital Information System were collected.Logistic regression analysis was used to establish the risk prediction model,and the receiver operator characteristic curve and Hosmer-Lemeshow goodness-of-fit test were used to evaluate the constructed model.Results:Logistic regression analysis showed that nursing professional title,"three basics and three stricts"entry training performance,clinical specialty training performance,first level nursing quality supervision,patient safety culture,nursing human resources,and hospital hardware facilities and equipment were the influencing factors of nursing adverse events(P<0.05).A risk prediction model for nursing adverse events in hospitalized patients was constructed based on the results of Logistic regression analysis,area under the curve of receiver operator characteristic of the model was 0.850(95%CI 0.808-0.891).The Hosmer-Lemeshow test results showed that χ2=9.816,P=0.278,the maximum Youden index of the model was 0.584,the sensitivity was 0.747,and the specificity was 0.838.Conclusion:Nursing professional title,"three basics and three stricts"entry training performance,clinical specialty training performance,first level nursing quality supervision,patient safety culture,nursing human resources,and hospital hardware facilities and equipment have impact on the occurrence of nursing adverse events in hospitalized patients.The risk prediction model for adverse nursing events in hospitalized patients has good predictive performance and can provide reference for clinical prevention and control of adverse nursing events.