心力衰竭院内并发肺部感染的预测模型构建-现代医学

心力衰竭院内并发肺部感染的预测模型构建

单位：1. 青岛市第八人民医院呼吸与危重症医学科, 山东青岛 266000;
2. 青岛市第八人民医院心血管内科, 山东青岛 266000

分类号：R541.6

出版年·卷·期（页码）：2026·54·第三期（497-503）

摘要：

目的：探究心力衰竭院内并发肺部感染的影响因素，并构建预测模型。方法：选取2022年6月至2025年6月本院收治的心力衰竭患者155例，根据住院期间是否并发肺部感染分为感染组和未感染组。采集并对比两组临床资料，最小绝对值收敛和选择算子(LASSO)回归进行变量筛选，Logistic回归模型确定独立影响因素，并构建列线图预测模型，受试者工作特征(ROC)曲线、校准曲线、决策曲线、临床影响曲线对模型效能进行评价。结果：155例心力衰竭患者院内并发肺部感染50例。LASSO回归变量筛选获得6个非零系数变量，分别为年龄、慢性阻塞性肺病、侵入性操作、纽约心脏协会(NYHA)心功能分级、中性粒细胞百分比、降钙素原与白蛋白比值(PCT/ALB)。多因素Logistic回归分析结果显示，NYHA心功能分级、年龄、慢性阻塞性肺疾病、侵入性操作、PCT/ALB均为心力衰竭院内并发肺部感染的独立影响因素(P＜0.05)。列线图模型预测心力衰竭患者院内并发肺部感染的ROC曲线下面积为0.923(95%CI 0.881～0.965)，Hosmer-Lemeshow拟合优度检验 χ²=11.954，P=0.153。Bootstrap法获得模型内部验证后的一致性指数为0.931。校准曲线显示预测值与实际值接近，决策曲线和临床影响曲线显示该模型具有一定的参考价值。结论：心力衰竭患者院内并发肺部感染受NYHA心功能分级、年龄、慢性阻塞性肺疾病、侵入性操作及PCT/ALB的影响，据此构建的列线图预测模型具有较好的预测价值。

Objective: To explore the influencing factors of in-hospital pulmonary infection in patients with heart failure and construct a prediction model.Methods: A total of 155 patients with heart failure admitted to our hospital from June 2022 to June 2025 were selected and divided into infection group and non-infection group according to whether pulmonary infection occurred during hospitalization. Clinical data of the two groups were collected and compared. Least absolute shrinkage and selection operator(LASSO) regression was used for variable screening. Logistic regression model was used to determine independent influencing factors, and a nomogram prediction model was constructed. Receiver operating characteristic(ROC) curve, calibration curve, decision curve, and clinical impact curve were used to evaluate the model performance.Results: Among 155 patients with heart failure, 50 cases developed in-hospital pulmonary infection. LASSO regression variable screening obtained 6 non-zero coefficient variables: age, chronic obstructive pulmonary disease, invasive procedures, New York Heart Association(NYHA) functional classification, neutrophil percentage, and procalcitonin to albumin ratio(PCT/ALB). Multivariate Logistic regression analysis showed that NYHA functional classification, age, chronic obstructive pulmonary disease, invasive procedures, and PCT/ALB were all independent influencing factors for in-hospital pulmonary infection in patients with heart failure(P<0.05). The area under the ROC curve of the nomogram model for predicting in-hospital pulmonary infection in patients with heart failure was 0.923(95%CI 0.881-0.965). Hosmer-Lemeshow goodness-of-fit test showed χ²=11.954, P=0.153. The concordance index after internal validation of the model obtained by Bootstrap method was 0.931. Calibration curve showed that the predicted values were close to the actual values. Clinical decision curve and clinical impact curve showed that the model had certain reference value. Conclusion: In-hospital pulmonary infection in patients with heart failure is influenced by NYHA functional classification, age, chronic obstructive pulmonary disease, invasive procedures, and PCT/ALB. The nomogram prediction model constructed accordingly has good predictive value.

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