老年患者呼吸机相关性肺炎风险预测模型构建

摘要〓目的〓构建老年患者呼吸机相关性肺炎(VAP)风险预测模型。方法〓选取2014年1月-2019年6月于某医院住院期间曾使用呼吸机的老年患者为研究对象,应用Logistic回归构建VAP发生风险预测模型,并对模型进行拟合优度检验|使用受试者工作特征曲线(ROC)曲线下面积(AUC)评价模型的预测准确度。结果〓574例使用呼吸机的老年患者,呼吸机使用总天数为9 968 d,呼吸机使用率为57.31%,发生VAP的患者为52例,VAP的感染发生率为9.06%,千日感染率为5.22‰|多因素Logistic回归分析结果显示,昏迷、平卧位、插管天数＞10 d、插管次数＞2次是老年患者发生VAP的独立危险因素(P<0.05)|Logistic回归模型的灵敏度为77.6%,特异度为80.8%,ROC曲线下面积(AUC)为0.851(95% CI:0.801~0.902)。结论〓本研究建立的Logistic回归模型对老年患者VAP的发生风险预测拟合优度较好,可为制订及时有效的防控措施提供依据。

Construction of risk prediction model for ventilator-associated pneumonia in elderly patients

Objective To construct a risk prediction model for ventilator-associated pneumonia( VAP) in elderly patients. Methods The elderly patients who used ventilator during hospitalization in our hospital from January 2014 to June2019 were selected as the research objects. Logistic regression was used to construct the risk prediction model for the occurrence of VAP. The model was tested for the goodness of fit,and the prediction accuracy of the model was evaluated using the area under curve( AUC) of the receiver operating characteristic( ROC) curve. Results In 574 elderly patients using ventilator,the total days of using ventilator were 9 968 d,and the use rate of ventilator was 57. 31%;52 patients had VAP,and the incidence of VAP infection was 9. 06%. The infection rate per thousand days was 5. 22‰. Multivariate Logistic regression analysis showed that coma,supine position,intubation days > 10 d,and intubation times > 2 were independent risk factors for VAP in elderly patients( P < 0. 05). The sensitivity of the Logistic regression model was 77. 6%,the specificity was 80. 8%,and AUC was 0. 851( 95% CI: 0. 801 ~ 0. 902). Conclusion The Logistic regression model established in this study has a good fit for predicting the risk of VAP in elderly patients,and it can provide a basis for timely and effective prevention and control measures.