Yujing Li,1,2,* Xindie Ren,3,* Qianqian Wang,4,* Songying Shen,2,* Yihao Li,1,2 Xinling Qian,2 Yufei Tang,2 Jinguang Jia,2 Hao Zhang,2 Junjie Ding,2 Yinsen Song,1 Sisen Zhang,1 Shengfeng Wang,5 Yinghe Xu,6 Yongpo Jiang,6 Xuwei He,7 Muhua Dai,8 Lin Zhong,9 Yonghui Xiong,10 Yujie Pan,11 Mingqiang Wang,12 Huanzhang Shao,12 Hongliu Cai,3 Lingtong Huang,3 Hongyu Wang1,2 

1Department of Critical Care Medicine, The Fifth Clinical Medical College of Henan University of Chinese Medicine, Zhengzhou, Henan Province, People’s Republic of China; 2Department of Critical Care Medicine, People’s Hospital of Henan University of Chinese Medicine/People’s Hospital of Zhengzhou, Zhengzhou, Henan Province, People’s Republic of China; 3Department of Critical Care Medicine, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang Province, People’s Republic of China; 4Department of Critical Care Medicine, The First Hospital of Jiaxing, Jiaxing, Zhejiang Province, People’s Republic of China; 5Department of Critical Care Medicine, The Second Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan Province, People’s Republic of China; 6Department of Critical Care Medicine, Taizhou Hospital of Zhejiang Province affiliated with Wenzhou Medical University, Taizhou, Zhejiang Province, People’s Republic of China; 7Department of Critical Care Medicine, Lishui People’s Hospital, Lishui, Zhejiang Province, People’s Republic of China; 8Department of Critical Care Medicine, Tongde Hospital of Zhejiang Province, Hangzhou, Zhejiang Province, People’s Republic of China; 9Department of Critical Care Medicine, The First People’s Hospital of Pinghu, Pinghu, Zhejiang Province, People’s Republic of China; 10Department of Critical Care Medicine, Lanxi Hospital of Traditional Chinese Medicine, Lanxi, Zhejiang Province, People’s Republic of China; 11Department of Critical Care Medicine, Wenzhou Central Hospital, Wenzhou, Zhejiang Province, People’s Republic of China; 12Department of Critical Care Medicine, Henan Key Laboratory for Critical Care Medicine, Zhengzhou Key Laboratory for Critical Care Medicine, Henan Provincial People’s Hospital; Zhengzhou University People’s Hospital, Henan University People’s Hospital, Zhengzhou, Henan Province, People’s Republic of China

*These authors contributed equally to this work

Correspondence: Lingtong Huang, Department of Critical Care Medicine, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, People’s Republic of China, Email lingtonghuang@zju.edu.cn Hongyu Wang, Department of Critical Care Medicine, People’s Hospital of Henan University of Chinese Medicine/People’s Hospital of Zhengzhou, Zhengzhou, Henan Province, People’s Republic of China, Email hongyu260@163.com

Background: Several predictive models for invasive pulmonary aspergillosis (IPA) based on clinical characteristics have been reported. Nevertheless, the significance of other concurrently detected microorganisms in IPA patients is equally noteworthy. This study aimed to develop a risk prediction model for IPA by integrating clinical and microbiological characteristics.
Methods: This retrospective study was conducted in adult intensive care units (ICUs) of 17 medical centers in China. Clinical data were collected from patients with severe pneumonia who underwent clinical metagenomics of bronchoalveolar lavage fluid between January 1, 2019, and June 30, 2023. Subsequently, patients were randomly assigned to training and validation cohorts in a 7:3 ratio. In the training cohort, potential influencing factors were identified through univariate analysis, clinical practice, and existing literature, and a risk prediction model was constructed using multivariate logistic regression analysis. The performance of this model was then assessed and validated in the validation cohort.
Results: Out of 1737 patients initially included in the study, 898 were ultimately analyzed, of which 100 (11%) were diagnosed with IPA. The risk prediction model for IPA, incorporating microbiological characteristics, identified six independent risk factors, namely age, immunosuppression, chronic kidney disease, connective tissue disease, liver failure, and cytomegalovirus positivity. The model demonstrated a superior discriminative ability, with area under the curve (AUC) values of 0.791 and 0.792 in the training and validation cohorts, respectively. Sensitivity and specificity reached 73.1% and 74.9%, respectively, and the model demonstrated good calibration.
Conclusion: This study developed a novel risk prediction model for IPA incorporating microbiological characteristics based on clinical metagenomics. The model exhibited good discriminative ability and calibration.

Keywords: CAP, community-acquired pneumonia, IPA, invasive pulmonary aspergillosis, prediction model, microbiological characteristics, clinical metagenomics