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Peer-reviewedCC BYFrontiers in Cellular and Infection Microbiology

A novel predictive tool for pulmonary aspergillosis in critically ill patients with viral pneumonia: a multi-center, ambispective cohort study

  • Jing Zhang1
  • Shengnan Xu1
  • Leilei Guo2
  • Bo Qiao3
  • Yuanyuan Li4
  • Yansheng Zhou5
  • Xiufeng Hu6
  • Yanyan Guo7
  • Bin Hu8
  • Sangang Zheng9
  • Chao Chen10
  • Yajun Li1
3 more authors
  • Junping Liu11
  • Jiangfeng Zhang12
  • Peng Li13
13 affiliations
  1. 1Respiratory Intensive Care Unit, Henan Provincial People’s Hospital, People’s Hospital of Zhengzhou University, Zhengzhou, China
  2. 2Infection Prevention and Control Department, Zhengzhou Central Hospital Affiliated to Zhengzhou University, Zhengzhou, China
  3. 3Infection Prevention and Control Department, Henan Provincial Chest Hospital, Zhengzhou, China
  4. 4Infection Prevention and Control Department, The First Affiliated Hospital of Nanyang Medical College, Nanyang, China
  5. 5Infection Prevention and Control Department, The First Affiliated Hospital of Henan Medical University, Xinxiang, China
  6. 6Infection Prevention and Control Department, The First Affiliated Hospital of Henan University of Science and Technology, Luoyang, China
  7. 7Infection Prevention and Control Department, Huaihe Hospital of Henan University, Kaifeng, China
  8. 8Infection Prevention and Control Department, Zhumadian Central Hospital, Zhumadian, China
  9. 9Infection Prevention and Control Department, Jiyuan People’s Hospital, Jiyuan, China
  10. 100Central Intensive Care Unit, Henan Provincial People’s Hospital, People’s Hospital of Zhengzhou University, Zhengzhou, China
  11. 111Department of Infectious Disease, Henan Provincial People’s Hospital, People’s Hospital of Zhengzhou University, Zhengzhou, China
  12. 122Department of Clinical Microbiology, Henan Provincial People’s Hospital, People’s Hospital of Zhengzhou University, Zhengzhou, China
  13. 133Department of Infection Control, Henan Provincial People’s Hospital, People’s Hospital of Zhengzhou University, Zhengzhou, China
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Abstract

BackgroundViral pneumonia-associated pulmonary aspergillosis (VAPA) is a lethal complication in critically ill patients. We aimed to develop a generalizable and methodologically robust prediction tool for early VAPA risk, overcoming key limitations of existing models.MethodsIn a multi-center, ambispective cohort of 4,966 patients with influenza or COVID-19 admitted to ICU (Jan 2023–Jun 2025), we developed and validated two prediction models for VAPA using LASSO-selected variables: a standard Cox proportional hazards model and a Fine-Gray subdistribution hazards model accounting for non-VAPA death as a competing risk. For both models, restricted cubic splines were used to address nonlinear associations between continuous predictors and VAPA. Performance was assessed at 7 and 14 days post-ICU admission. Subgroup analysis was employed to examine the virus-specific models’ performance.ResultsThe incidence of VAPA was 8.8%. The Fine-Gray model demonstrated excellent discrimination and calibration in both derivation and validation cohorts. In contrast, the standard Cox model substantially overestimated VAPA risk, particularly among high-risk patients (by 20-30%). Key predictors included haematological malignancy, halo sign, use of priority antibiotic combinations, SOFA score, neutrophil-to-lymphocyte ratio (NLR), and pre-ICU corticosteroid dose; the latter three exhibited J-shaped associations with VAPA risk. A significant interaction was observed between viral subtype and pre-ICU corticosteroid dose (P = 0.006), with a stronger association in COVID-19 patients (sHR=1.17, 95% CI: 1.09-1.26). Overall model performance remained consistent across viral subtypes. An interactive web-based tool was then deployed, which generates both individualized VAPA risk estimates and corresponding clinical management recommendations.ConclusionsIn critically ill patients with viral pneumonia, a competing-risk model accounting for non-VAPA death provides more accurate estimates of VAPA risk than traditional approaches. The model demonstrates robust generalizability across influenza and COVID-19 pneumonia and offers a practical tool for early, personalized VAPA prediction.

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Cite

BibTeXRIS
BibTeX
@article{zhang2026novel,
  title = {{A novel predictive tool for pulmonary aspergillosis in critically ill patients with viral pneumonia: a multi-center, ambispective cohort study}},
  author = {Jing Zhang and Shengnan Xu and Leilei Guo and Bo Qiao and Yuanyuan Li and Yansheng Zhou and Xiufeng Hu and Yanyan Guo and Bin Hu and Sangang Zheng and Chao Chen and Yajun Li and Junping Liu and Jiangfeng Zhang and Peng Li},
  journal = {Frontiers in Cellular and Infection Microbiology},
  year = {2026},
  month = oct,
  doi = {10.3389/fcimb.2026.1956822},
  url = {https://doi.org/10.3389/fcimb.2026.1956822},
  note = {CC BY},
}
RIS
TY  - JOUR
TI  - A novel predictive tool for pulmonary aspergillosis in critically ill patients with viral pneumonia: a multi-center, ambispective cohort study
AU  - Jing Zhang
AU  - Shengnan Xu
AU  - Leilei Guo
AU  - Bo Qiao
AU  - Yuanyuan Li
AU  - Yansheng Zhou
AU  - Xiufeng Hu
AU  - Yanyan Guo
AU  - Bin Hu
AU  - Sangang Zheng
AU  - Chao Chen
AU  - Yajun Li
AU  - Junping Liu
AU  - Jiangfeng Zhang
AU  - Peng Li
PY  - 2026
DA  - 2026/10/27
JO  - Frontiers in Cellular and Infection Microbiology
DO  - 10.3389/fcimb.2026.1956822
UR  - https://doi.org/10.3389/fcimb.2026.1956822
AB  - BackgroundViral pneumonia-associated pulmonary aspergillosis (VAPA) is a lethal complication in critically ill patients. We aimed to develop a generalizable and methodologically robust prediction tool for early VAPA risk, overcoming key limitations of existing models.MethodsIn a multi-center, ambispective cohort of 4,966 patients with influenza or COVID-19 admitted to ICU (Jan 2023–Jun 2025), we developed and validated two prediction models for VAPA using LASSO-selected variables: a standard Cox proportional hazards model and a Fine-Gray subdistribution hazards model accounting for non-VAPA death as a competing risk. For both models, restricted cubic splines were used to address nonlinear associations between continuous predictors and VAPA. Performance was assessed at 7 and 14 days post-ICU admission. Subgroup analysis was employed to examine the virus-specific models’ performance.ResultsThe incidence of VAPA was 8.8%. The Fine-Gray model demonstrated excellent discrimination and calibration in both derivation and validation cohorts. In contrast, the standard Cox model substantially overestimated VAPA risk, particularly among high-risk patients (by 20-30%). Key predictors included haematological malignancy, halo sign, use of priority antibiotic combinations, SOFA score, neutrophil-to-lymphocyte ratio (NLR), and pre-ICU corticosteroid dose; the latter three exhibited J-shaped associations with VAPA risk. A significant interaction was observed between viral subtype and pre-ICU corticosteroid dose (P = 0.006), with a stronger association in COVID-19 patients (sHR=1.17, 95% CI: 1.09-1.26). Overall model performance remained consistent across viral subtypes. An interactive web-based tool was then deployed, which generates both individualized VAPA risk estimates and corresponding clinical management recommendations.ConclusionsIn critically ill patients with viral pneumonia, a competing-risk model accounting for non-VAPA death provides more accurate estimates of VAPA risk than traditional approaches. The model demonstrates robust generalizability across influenza and COVID-19 pneumonia and offers a practical tool for early, personalized VAPA prediction.
SN  - 2235-2988
ER  -