Establishment of a short-term death warning model for elderly COPD patients with severe multidrug-resistant Acinetobacter baumannii pneumonia
Objective To construct a model based on the LASSO(Least Absolute Shrinkage and Selection Operator)-logistic regression method,and to predict the short-term mortality risk in elderly patients with chronic obstructive pulmonary disease(COPD)and severe multidrug-resistant Acinetobacter baumannii pneumonia.Methods In this study,74 elderly COPD patients with severe multidrug-resistant Acinetobacter Baumannii pneumonia from the Department of Respiratory and Critical Care Medicine of Xi'an Gaoxin Hospital from January 2022 to December 2023 were selected as the study objects,including 55 males and 19 females,41 patients＜70 years old and 33 patients ≥70 years old.The LASSO-logistic regression algorithm was used to identify clinical variables significantly associated with short-term death risk,and these variables were used to construct a risk prediction model.The 10-fold cross-validation and Bootstrap method were used to validate the model internally,and the performance of the model was evaluated from the aspects of discrimination[area under the curve(AUC)]and calibration(calibration curve).x2 test and independent sample t test were used.Results The survival of the patients within 30 days was observed.According to the final outcomes,the patients were divided into a death group(21 cases)and a survival group(53 cases),with a mortality rate of 28.37％(21/74).After LASSO cross-validation to determine the optimal parameters,the model selected five variables that were closely related to short-term death from general data,clinicopathological features,and previous treatment information:mechanical ventilation,fiberoptic bronchoscopy,sedation,septic shock,and use of antifungal medications.These variables were included in the logistic regression model,and the regression analysis showed that they were independent influencing factors for death in elderly COPD patients with severe multidrug-resistant Acinetobacter baumannii pneumonia(all P＜0.05).The forest plot model constructed based on these predictors demonstrated excellent predictive performance for predicting short-term death in elderly COPD patients with severe multidrug-resistant Acinetobacter baumannii pneumonia,with an AUC of 0.927.In the analysis of the training set,the 1 000 bootstrap resamples and calibration curve analysis showed that the model's prediction results were highly consistent with the actual situation,with a mean absolute error(MAE)of 0.027.The Hosmer-Lemeshow goodness of fit test also confirmed the model's good calibration.Conclusions The LASSO-logistic regression model constructed in this study can effectively predict the short-term death risk in elderly COPD patients with severe multidrug-resistant Acinetobacter Baumannii pneumonia.This model helps clinicians to better identify high-risk patients during the treatment decision-making process,so that appropriate treatment measures can be taken in time.
Chronic obstructive pulmonary diseaseMultidrug-resistant Acinetobacter Baumannii pneumoniaElderlyShort term death warning modelLASSO logistic regression method
万方数据
维普
