Yamin Fan,1,* Zhuanyun Li,2,* Nanchuan Jiang,3,* Yaya Zhou,1,* Jianping Song,1 Fan Yu,1 Jianchu Zhang,1,* Xiaorong Wang1,* 

1Department of Respiratory and Critical Care Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, People’s Republic of China; 2Department of Emergency Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, People’s Republic of China; 3Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, People’s Republic of China

*These authors contributed equally to this work

Correspondence: Jianchu Zhang, Department of Respiratory and Critical Care Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, 430022, People’s Republic of China, Email zsn0928@163.com Xiaorong Wang, Department of Respiratory and Critical Care Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, 430022, People’s Republic of China, Email rong-100@163.com

Background: Under persistent inflammation, asymptomatic radiological bronchiectasis (ARB) may develop into clinical bronchiectasis (CB). Although CB has been extensively studied, the potential for ARB to evolve into CB remains largely unexplored. Whether the ARB could progress to CB and the risk factors to speed up the process are poorly understood.
Methods: This was an observational cohort study. 370 patients with radiological bronchiectasis were included in Wuhan Union Hospital in 2018. 296 ARB patients were followed up in 2022 to verify if they progressed to CB and divided the development and validation of clinical prediction models into a training set (n=207) and a validation set (n=89) by the ratio of 7:3. LASSO algorithm and multivariable logistic regression analysis were performed to construct a new nomogram model. ROC, a calibration and decision curve were used to assess the predictive performance of our new prediction model.
Results: 370 patients (74, 20% with CB) were finally included. Compared with ARB, CB had lower BMI, Bhalla score, FEV1% predicted, greater extent and degree of bronchodilation, more lobes with mucus plugs, greater thickness of bronchodilation, greater likelihood of pulmonary heart disease and chronic obstructive pulmonary disease (COPD), and lower likelihood of hypertension and coronary artery disease (P< 0.05). In 2022, 60 out of 296 ARB patients progressed to CB. Age, FEV1% predicted, COPD, heart failure (HF), degree of bronchiectasis, number of lobes with bronchiectasis and number of lung segments with mucus plugs were risk factors. The AUCs of the prediction model were 0.866 (95% CI, 0.802– 0.931) in the training set and 0.860 (95% CI, 0.770– 0.949) in the validation set.
Conclusion: ARB may progress to CB under the risk factors, including age, FEV1% predicted, COPD, HF and CT images including degree of bronchiectasis, number of lobes with bronchiectasis and number of lung segments with mucus plugs), based on which the nomogram model is a convenient and efficient tool for follow-up management and preventing CB in patients with ARB.

Keywords: asymptomatic radiological bronchiectasis, clinical bronchiectasis, nomogram, predictive model