Acute exacerbation of idiopathic pulmonary fibrosis disease: a diagnosis model in China.

OBJECTIVE: To develop and validate a diagnosis model to inform risk stratified decisions for idiopathic pulmonary fibrosis patients experiencing acute exacerbations (AE-IPF).

METHODS: In this retrospective cohort study performed from 1 January 2016 to 31 December 2022, we used data from the West China Hospital of Sichuan University for model development and validation. Blood test results and the underlying diseases of patients were collected through the HIS system and LIS system. An algorithm for filtering candidate variables based on least absolute shrinkage and selection operator (LASSO) regression. Logistic regression was performed to develop the risk model. Multiple imputation handled missing predictor data. Model performance was assessed through calibration and diagnostic odds ratio.

RESULTS: 311 and 133 participants were included in the development and validation cohorts, respectively. 3 candidate predictors (29 parameters) were included. A logistic regression analysis revealed that dyspnea, percentage of CD4+   T-lymphocytes, and percentage of monocytes are independent risk factors for AE-IPF. Nomographic model was constructed using these independent risk factors, and the C-index was 0.69. For internal validation, the C-index was 0.69, and that indicated good accuracy. Diagnostic odds ratio was 5.40. Meanwhile, in mild, moderate, and severe subgroups, AE positivity rates were 0.37, 0.47, and 0.81, respectively. The diagnostic model can classify patients with AE-IPF into different risk classes based on dyspnea, percentage of CD4+   T-lymphocytes, and percentage of monocytes.

CONCLUSION: A diagnosis model was developed and validated that used information collected from HIS system and LIS system and may be used to risk stratify idiopathic pulmonary fibrosis patients experiencing acute exacerbations.