血管性痴呆患者并发肺部感染的危险因素及nomogram模型构建与验证-东南大学学报医学版

血管性痴呆患者并发肺部感染的危险因素及nomogram模型构建与验证

单位：上海市黄浦区肿瘤防治院内科, 上海 200126

分类号：R749.13

出版年·卷·期（页码）：2026·45·第一期（97-104）

摘要：

目的:构建并验证血管性痴呆(VaD)患者并发肺部感染的nomogram模型。方法:回顾性从医院病历系统筛查符合条件的2019年1月至2021年9月在本院进行治疗的300例VaD患者的临床资料,再以同样的标准收集2022年11月至2024年12月129例VaD患者的临床资料用来进行外部验证。依据VaD患者是否存在肺部感染分为并发组和N-并发组。多因素Logistic回归模型分析影响因素,R 3.6.3软件及rms程序包构建预测VaD患者肺部感染的nomogram模型及决策曲线分析(DCA)图,受试者工作特征(ROC)曲线、校准曲线评估模型的实用性。结果:并发组年龄以及吸烟、基础疾病≥2种、低蛋白血症、吞咽障碍、长期卧床、侵入性操作的比例均大或高于N-并发组,护理质量好的比例低于N-并发组(P<0.05)。多因素Logistic回归分析结果显示,年龄、基础疾病种类、低蛋白血症、侵入性操作均是肺部感染的影响因素(P＜0.05)。将多因素Logistic回归分析筛选出的年龄、基础疾病种类、低蛋白血症、侵入性操作纳入模型,并运用R 3.6.3软件中的rms程序包,将其转化为可视化风险预测模型,建立预测肺部感染的nomogram模型。内部及外部验证中Hosmer-Lemeshow(H-L)及ROC曲线均表明模型一致性、区分度较好。DCA图表明,阈值概率在10%~84%范围内时,使用本nomogram模型来识别高危患者并进行针对性干预,能够获得最大的临床净收益。结论:年龄、基础疾病种类、低蛋白血症、侵入性操作均是VaD患者肺部感染的危险因素,基于此4项指标建立的nomogram模型具有较高的临床实用性。

Objective: To construct and validate a nomogram model for pulmonary infection in patients with vascular dementia(VaD). Methods: The clinical data of 300 VaD patients treated in our hospital from January 2019 to September 2021 were retrospectively collected, the data of 129 patients from November 2022 to December 2024 were collected using the same standards for external validation. According to whether VaD patients had pulmonary infections, they were assigned into the infected group(with pulmonary infection) and non-infected group(without pulmonary infection). Eligible patients' data were extracted from the hospital's case system. The multiple Logistic regression model was used to analyze the influencing factors. A nomogram model and decision curve analysis(DCA) chart for predicting pulmonary infection in VaD patients were constructed using R software(version 3.6.3) and the rms package. The discriminative ability and calibration of the model were evaluated by receiver operating characteristic(ROC) curves and calibration curves, respectively. Results: Compared with the non-infected group, the infected group had a higher median age, and higher proportions of smoking history, ≥2 underlying diseases, hypoalbuminemia, dysphagia, long-term bed rest, and invasive procedures, while the proportion of good nursing quality was significantly lower(all P<0.05). Multivariate Logistic regression analysis showed that age, number of underlying diseases, hypoalbuminemia, and invasive procedures were independent risk factors for pulmonary infection in VaD patients(all P<0.05). These four factors were included in the model, and a visual nomogram model was established using the rms package in R 3.6.3 software. Both Hosmer-Lemeshow(H-L) test and ROC curve analysis in internal and external validations demonstrated good calibration and discriminative ability of the model. The DCA chart indicated that when the threshold probability ranged from 10%to 84%, using this nomogram to identify high-risk patients and implement targeted interventions could yield the maximum clinical net benefit. Conclusion: Age, number of underlying diseases, hypoalbuminemia, and invasive procedures are independent risk factors for pulmonary infection in VaD patients. The nomogram model established in this study based on these four indicators exhibits high clinical utility and can be used to predict the risk of pulmonary infection in VaD patients.

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