A novel multivariate logistic model for predicting risk factors of failed treatment with carbapenem-resistant Acinetobacter baumannii ventilator-associated pneumonia

一种用于预测耐碳青霉烯类鲍曼不动杆菌呼吸机相关性肺炎治疗失败风险因素的新型多元逻辑回归模型

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Abstract

BACKGROUND: This study aimed to explore the risk factors for failed treatment of carbapenem-resistant Acinetobacter baumannii ventilator-associated pneumonia (CRAB-VAP) with tigecycline and to establish a predictive model to predict the incidence of failed treatment and the prognosis of CRAB-VAP. METHODS: A total of 189 CRAB-VAP patients were included in the safety analysis set from two Grade 3 A national-level hospitals between 1 January 2022 and 31 December 2022. The risk factors for failed treatment with CRAB-VAP were identified using univariate analysis, multivariate logistic analysis, and an independent nomogram to show the results. RESULTS: Of the 189 patients, 106 (56.1%) patients were in the successful treatment group, and 83 (43.9%) patients were in the failed treatment group. The multivariate logistic model analysis showed that age (OR = 1.04, 95% CI: 1.02, 1.07, p = 0.001), yes. of hypoproteinemia (OR = 2.43, 95% CI: 1.20, 4.90, p = 0.013), the daily dose of 200 mg (OR = 2.31, 95% CI: 1.07, 5.00, p = 0.034), yes. of medication within 14 days prior to surgical intervention (OR = 2.98, 95% CI: 1.19, 7.44, p = 0.019), and no. of microbial clearance (OR = 0.31, 95% CI: 0.14, 0.70, p = 0.005) were risk factors for the failure of tigecycline treatment. Receiver operating characteristic (ROC) analysis showed that the AUC area of the prediction model was 0.745 (0.675-0.815), and the decision curve analysis (DCA) showed that the model was effective in clinical practice. CONCLUSION: Age, hypoproteinemia, daily dose, medication within 14 days prior to surgical intervention, and microbial clearance are all significant risk factors for failed treatment with CRAB-VAP, with the nomogram model indicating that high age was the most important factor. Because the failure rate of CRAB-VAP treatment with tigecycline was high, this prediction model can help doctors correct or avoid risk factors during clinical treatment.

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