Identifying Risk Factors for Progressive Pulmonary Fibrosis in Interstitial Lung Disease: A Prognostic Nomogram Approach

Reliable prognostic tools to help shed light on the critical clinical characteristics associated with progressive pulmonary fibrosis in interstitial lung disease patients would help guide treatment

A recent study aimed to identify these clinical characteristics, with the goal of developing a prognostic nomogram model for clinical application.

A group of China-based researchers conducted the study, which was published in Frontiers in Medicine earlier this year. Jia-Jia Fin of the Department of Pulmonary Disease at Sunsimiao Hospital in Shanxi, China, is listed as the first author.

The researchers enrolled interstitial lung disease patients with relatively comprehensive clinical data in the retrospective study and assessed the incidence of progressive pulmonary fibrosis over the course of a year by utilizing the collected demographics, laboratory data, high-resolution computed tomography and pulmonary function test results.

In all, 307 patients from January 2015 to December 2022 were included in the study.

“We used a training cohort of interstitial lung disease patients to identify early predictors of progressive pulmonary fibrosis and then validated them in an internal validation cohort and subsets of interstitial lung disease patients using a multivariable logistic regression analysis,” one of study authors said. “A prognostic nomogram was formulated based on these predictors, and the accuracy and efficiency were evaluated using the area under the receiver operating characteristic curve, calibration plot and decision curve analysis.”

Through the data, the authors developed and validated a prognostic nomogram model for predicting the risk of progressive pulmonary fibrosis in interstitial lung disease patients based on four factors: diffusing capacity of the lungs for carbon monoxide, complicated pneumonia, the Medical Research Council dyspnea score and high-resolution computed tomography score. They found that combined pneumonia, low baseline diffusing capacity of the lungs for carbon monoxide, a high Medical Research Council dyspnea score and a high-resolution computed tomography score were significant predictors of progressive pulmonary fibrosis.

Almost 40% of the patients in their study experienced progression of pulmonary fibrosis despite existing therapies.

“The nomogram showed good discrimination and calibration in both the training and validation cohorts, and it outperformed every single predictor in terms of accuracy and efficiency,” the authors wrote in their paper. “We believe the nomogram could help clinicians stratify lung disease patients into different risk groups and tailor their management accordingly.”

The study also identified certain features of lung disease patients who may develop a progressive fibrosis phenotype, including those with connective tissue disease-related interstitial lung disease and non-IPF idiopathic interstitial pneumonia.

The authors cautioned that there are some limitations to their findings, including a retrospective design, a single-center setting and the potential selection bias. With that in mind, they suggest longitudinal studies should be run to confirm and optimize the predictive model in different populations and settings.

“The nomogram is a useful tool for risk assessment and decision-making, but it is not a substitute for clinical judgment or individualized care,” Fin and the co-authors authors wrote.