Risk Prediction Model for Pulmonary Fungal Infections in Patients with Lung Cancer

DU Wei-wei; JI Wen-tao; LUO Tian; LIANG Jian-ping; LV Yan-hua

doi:10.13471/j.cnki.j.sun.yat-sen.univ(med.sci).2023.0617

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Risk Prediction Model for Pulmonary Fungal Infections in Patients with Lung Cancer

Clinical Research | 更新时间：2024-02-19

- Risk Prediction Model for Pulmonary Fungal Infections in Patients with Lung Cancer
- Journal of Sun Yat-sen University(Medical Sciences) Vol. 44, Issue 6, Pages: 1022-1029(2023)
- 作者机构：
  
  1.遵义医科大学珠海校区研究生院，广东珠海519041
  2.中山市人民医院呼吸与危重症医学科，广东中山528499
- 作者简介：
  
  LV Yan-hua； E-mail：lyhzhongshan@163.com
- 基金信息：
- DOI：10.13471/j.cnki.j.sun.yat-sen.univ(med.sci).2023.0617
  CLC： R56
- Published：20 November 2023，
  
  Received：17 July 2023，
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杜伟伟,季文涛,罗甜等.肺癌患者合并肺部真菌感染的风险预测模型[J].中山大学学报（医学科学版）,2023,44(06):1022-1029.

DU Wei-wei,JI Wen-tao,LUO Tian,et al.Risk Prediction Model for Pulmonary Fungal Infections in Patients with Lung Cancer[J].Journal of Sun Yat-sen University(Medical Sciences),2023,44(06):1022-1029.
杜伟伟,季文涛,罗甜等.肺癌患者合并肺部真菌感染的风险预测模型[J].中山大学学报（医学科学版）,2023,44(06):1022-1029. DOI： 10.13471/j.cnki.j.sun.yat-sen.univ(med.sci).2023.0617.

DU Wei-wei,JI Wen-tao,LUO Tian,et al.Risk Prediction Model for Pulmonary Fungal Infections in Patients with Lung Cancer[J].Journal of Sun Yat-sen University(Medical Sciences),2023,44(06):1022-1029. DOI： 10.13471/j.cnki.j.sun.yat-sen.univ(med.sci).2023.0617.

摘要

目的

探究肺癌患者合并肺部感染的风险因素，构建和验证一个风险预测模型，使用现有的临床数据来预测肺癌患者的肺部真菌感染风险。

方法

这是一项回顾性研究，收集了2021年1月至2023年3月在中山市人民医院接受治疗的390例肺癌患者的信息，利用合并和不合并肺部真菌感染的肺癌患者人口统计学和临床特征来构建预测发生肺部真菌感染的列线图。所有患者按7：3的比例随机分为训练集和内部验证集两组，应用LASSO回归方法筛选变量和选择预测因子，并使用训练集的多元logistic回归方法构建列线图模型。通过计算受试者工作特征曲线下面积（AUC）确定模型的判断能力，此外，还对模型进行了校正分析和决策曲线分析（DCA）评价预测效果。

结果

LASSO回归筛选出14个潜在的预测因素，进一步的Logistic回归分析结果显示肝损伤、手术、贫血、低蛋白血症、疾病历程、侵入性操作、住院时间大于2周、全身糖皮质激素应用大于2周是肺癌患者发生肺部真菌感染的独立预测因素。根据这些变量建立了一个预测模型，该模型对训练集的AUC95%CI

0.980（0.973，0.896）和内部验证的AUC95%CI

0.956 （0.795，1.000），显示具有很高的区分度。训练集和验证集的校准曲线均基本沿45°线分布，DCA 曲线显示在阈概率为大于0.03时存在净获益。

结论

肺癌患者合并肺部真菌感染风险预测模型的构建和验证有助于临床确定高危人群，及早进行干预或调整治疗决策。

Abstract

Objective

To investigate the risk factors for pulmonary fungal infection in lung cancer patients， construct and validate a risk prediction model using available clinical data to predict the risk of pulmonary fungal infections in patients with lung cancer.

Methods

We conducted a retrospective study and collected information of 390 lung cancer patients treated at Zhongshan People's Hospital from January 2021 to March 2023. Demographic and clinical characteristics of the patients with and without pulmonary fungal infections were used to construct column line graphs to predict the occurrence of pulmonary fungal infections. All enrolled patients were randomly assigned to training set and internal validation set in the ratio of 7：3. For the modelling group， LASSO regression was applied to screen variables and select predictors， and multivariate logistic regression with a training set was used to construct the Noe column line graph model. The judgment ability of the model was determined by calculating the area under the curve （AUC）， and in addition， calibration analysis and decision curve analysis （DCA） were performed on the model.

Results

LASSO regression identified 14 potential predictive factors， and further logistic regression analysis showed that hepatic injury， surgery， anemia， hypoalbuminemia， illness course， invasive operation， hospital stay at least 2 weeks and glucocorticoid used for at least 2 weeks were independent predictors for the occurrence of pulmonary fungal infection in lung cancer patients. A predictive model was established based on these variables， with an AUC95%CI of 0.980 （0.973， 0.896） for the training set and an AUC95%CI of 0.956 （0.795， 1.000） for internal validation， indicating high discriminative ability. The calibration curves for both the training set and validation set were distributed along the 45°line， and the decision curve analysis （DCA） showed net benefit for threshold probabilities greater than 0.03.

Conclusions

The construction and validation of a predictive model for the risk of lung fungal infections in lung cancer patients will help clinical practitioners to identify high-risk groups and give timely intervention or adjust treatment decisions.

关键词

肺癌肺部真菌感染危险因素列线图模型

Keywords

lung cancerfungal infection of the lungrisk factorsnomogrammodel

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