Healthcare-associated infection status and construction of a risk prediction model for coronary heart disease patients after percutaneous coronary in-tervention
Objective To evaluate healthcare-associated infection(HAI)status and influencing factors in coronary heart disease(CHD)patients after percutaneous coronary intervention(PCI)treatment,and construct a risk predic-tion model.Methods CHD patients who underwent PCI in a hospital from May 2019 to October 2023 were retro-spectively selected as the research subjects.Infection status of the CHD patients was analyzed.Patients were ran-domly divided into a modeling set and a testing set in a 7:3 ratio.Univariate and multivariate logistic regression ana-lyses were performed to analyze the data in the modeling set and determine the influencing factors for HAI in pa-tients.R software was used to construct and validate a nomogram model.Results A total of 858 CHD patients were included in the analysis,601 in the modeling set and 257 in the testing set.In the modeling set,41 cases were in the infected group and 560 cases in the non-infected group.The incidence of HAI in CHD patients after PCI treat-ment was 6.88％(59/858).Infection site were mainly upper respiratory tract and urinary tract.A total of 74 pathogens were isolated,including Gram-positive bacteria,Gram-negative bacteria,and fungi being 39,31,and 4 strains,respectively.Multivariate analysis showed that old age,combined diabetes,high grade of New York Heart Association(NYHA)classification,and invasive procedures were all risk factors for HAI in CHD patients after PCI treatment(all P＜0.05),while high mini-nutritional assessment short-form(MNA-SF)score was a protective fac-tor(P＜0.05).The area under the receiver operating characteristic(ROC)curve(AUC)of the nomogram predic-tion model constructed based on the above five indicators was 0.894(95％CI:0.815-0.931),with a sensitivity of 89.0％and a specificity of 82.5％.The testing set data validation showed an AUC value of 0.879(95％CI:0.801-0.923),with a sensitivity of 87.5％and a specificity of 81.3％,which were comparable to the modeling set and presented the stability of the model.The H-L goodness of fit test showed no statistical significance(P＞0.05),in-dicating that the model didn't exhibit overfitting.Calibration curve analysis showed that the model had good consis-tency.Decision curve analysis confirmed that the model had practical value in clinical practice.Conclusion The no-mogram model has a good predictive ability for HAI in CHD patients after PCI treatment,and can provide a simple and effective evaluation tool for medical staff to identify HAI high-risk individuals.
万方数据
维普
