A proposed prognostic prediction score for pleuroparenchymal fibroelastosis

BackgroundClinical course of pleuroparenchymal fibroelastosis (PPFE) shows considerable variation among patients, but there is no established prognostic prediction model for PPFE.MethodsThe prediction model was developed using retrospective data from two cohorts: our single-center cohort and a nationwide multicenter cohort involving 21 institutions. Cox regression analyses were used to identify prognostic factors. The total score was defined as the weighted sum of values for the selected variables. The performance of the prediction models was evaluated by Harrell's concordance index (C-index). We also examined the usefulness of the gender-age-physiology (GAP) model for predicting the prognosis of PPFE patients.ResultsWe examined 104 patients with PPFE (52 cases from each cohort). In a multivariate Cox analysis, a lower forced vital capacity (FVC [defined as FVC<65%]; hazard ratio [HR], 2.23), a history of pneumothorax (HR, 3.27), the presence of a lower lobe interstitial lung disease (ILD) (HR, 2.31), and higher serum Krebs von den Lungen-6 (KL-6) levels (>550 U/mL, HR, 2.56) were significantly associated with a poor prognosis. The total score was calculated as 1x(FVC, <65%)+1x(history of pneumothorax)+1x(presence of lower lobe ILD)+1x(KL-6, >550 U/mL). PPFE patients were divided into three groups based on the prognostic score: stage I (0-1 points), stage II (2 points), and stage III (3-4 points). The survival rates were significantly different in each stage. The GAP stage was significantly associated with the prognosis of PPFE, but no difference was found between moderate (stage II) and severe (stage III) disease. Our new model for PPFE patients (PPFE Prognosis Score) showed better performance in the prediction of mortality in comparison to the GAP model (C-index of 0.713 vs. 0.649).ConclusionsOur new model for PPFE patients could be useful for predicting their prognosis.

Automated summary

A prognostic prediction model for PPFE patients was developed using retrospective data, which identified lower forced vital capacity, history of pneumothorax, lower lobe interstitial lung disease, and higher serum KL-6 levels as factors significantly associated with poor prognosis. Patients were divided into three prognostic stages based on a total score, with the new model showing better predictive performance compared to the traditional GAP model.