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已发表论文
肺癌住院患者肺部真菌感染的独立风险因素及基于列线图的预测：一项单中心回顾性研究
Received 10 September 2025
Accepted for publication 29 January 2026
Published 11 February 2026 Volume 2026:18 562884
DOI https://doi.org/10.2147/CMAR.S562884
Checked for plagiarism Yes
Review by Single anonymous peer review
Peer reviewer comments 4
Editor who approved publication: Professor Bilikere Dwarakanath
Yanran Xu,1,2 Yan Chen1,2
1Department of Respiratory and Critical Care Medicine, The Second Clinical Medical College of North Sichuan Medical College, Nanchong, Sichuan, 637000, People’s Republic of China; 2Department of Respiratory and Critical Care Medicine, Beijing Anzhen Nanchong Hospital of Capital Medical University & Nanchong Central Hospital, Nanchong, Sichuan, 637000, People’s Republic of China
Correspondence: Yan Chen, Email 13990829946@163.com
Purpose: To investigate independent risk factors and construct an internally validated risk prediction model for invasive pulmonary fungal infection (IPFI) in patients with lung cancer.
Patients and Methods: Clinical data from 250 consecutive lung cancer inpatients admitted to Nanchong Central Hospital between February 2022 and March 2025 were retrospectively analyzed; 41 patients developed IPFI and 209 did not. Patients were randomly assigned to a training set (n=175) and a validation set (n=75) at a 7:3 ratio. Candidate predictors were screened by univariate logistic regression, reduced using least absolute shrinkage and selection operator (LASSO) regression with 10-fold cross-validation, and entered into multivariable logistic regression to construct a nomogram. Model performance was evaluated using bootstrap internal validation (1000 resamples), calibration curves and goodness-of-fit testing, receiver operating characteristic analysis, and decision curve analysis.
Results: Diabetes mellitus, invasive procedures, systemic glucocorticoid use, lower CD4+ T-cell count, and length of hospital stay > 14 days were associated with IPFI and were retained as independent predictors in the final model. The model showed good discrimination, with an area under the curve of 0.876 (95% CI: 0.809– 0.944) in the training set and 0.861 (95% CI: 0.750– 0.973) in the validation set, and demonstrated clinical net benefit across threshold probability ranges of 0.03– 0.90 (training) and 0.04– 0.78 (validation).
Conclusion: This nomogram may support early risk stratification for IPFI among lung cancer inpatients, while confirmation in external, multi-center cohorts is needed before broader clinical application.
Keywords: lung cancer, invasive pulmonary fungal infection, risk factors, nomogram, prediction model
1Department of Respiratory and Critical Care Medicine, The Second Clinical Medical College of North Sichuan Medical College, Nanchong, Sichuan, 637000, People’s Republic of China; 2Department of Respiratory and Critical Care Medicine, Beijing Anzhen Nanchong Hospital of Capital Medical University & Nanchong Central Hospital, Nanchong, Sichuan, 637000, People’s Republic of China
Correspondence: Yan Chen, Email 13990829946@163.com
Purpose: To investigate independent risk factors and construct an internally validated risk prediction model for invasive pulmonary fungal infection (IPFI) in patients with lung cancer.
Patients and Methods: Clinical data from 250 consecutive lung cancer inpatients admitted to Nanchong Central Hospital between February 2022 and March 2025 were retrospectively analyzed; 41 patients developed IPFI and 209 did not. Patients were randomly assigned to a training set (n=175) and a validation set (n=75) at a 7:3 ratio. Candidate predictors were screened by univariate logistic regression, reduced using least absolute shrinkage and selection operator (LASSO) regression with 10-fold cross-validation, and entered into multivariable logistic regression to construct a nomogram. Model performance was evaluated using bootstrap internal validation (1000 resamples), calibration curves and goodness-of-fit testing, receiver operating characteristic analysis, and decision curve analysis.
Results: Diabetes mellitus, invasive procedures, systemic glucocorticoid use, lower CD4+ T-cell count, and length of hospital stay > 14 days were associated with IPFI and were retained as independent predictors in the final model. The model showed good discrimination, with an area under the curve of 0.876 (95% CI: 0.809– 0.944) in the training set and 0.861 (95% CI: 0.750– 0.973) in the validation set, and demonstrated clinical net benefit across threshold probability ranges of 0.03– 0.90 (training) and 0.04– 0.78 (validation).
Conclusion: This nomogram may support early risk stratification for IPFI among lung cancer inpatients, while confirmation in external, multi-center cohorts is needed before broader clinical application.
Keywords: lung cancer, invasive pulmonary fungal infection, risk factors, nomogram, prediction model