A risk-predictive model for invasive pulmonary aspergillosis in patients with acute exacerbation of chronic obstructive pulmonary disease

Abstract

Objectives: Invasive pulmonary aspergillosis (IPA) is increasingly reported in chronic obstructive pulmonary disease (COPD) patients. These patients often have poor clinical outcomes. Early recognition of IPA in COPD is always challenging. We aimed to develop and validate a risk model using readily available clinical parameters to predict IPA for acute exacerbation of COPD (AECOPD) patients. Methods: We performed a retrospective cohort study. AECOPD patients who were admitted to Jinling Hospital between January 2012 and December 2017 were included. 880 AECOPD patients were randomly divided into the training set (70%, n = 616) and validation set (30%, n = 264). A nomogram model was developed using multivariate logistic regression from training set. The discrimination and calibration of model were validated internally. Decision curve analyses assessed the clinical utility of the nomogram. Results: The incidence of IPA in hospitalized AECOPD patients was 9.6% in the training set (59 cases of IPA) and 9.1% in the validation set (24 cases of IPA), respectively. The nomogram model consisted of independent factors associated with IPA included lung function GOLD III–IV, use of broad-spectrum antibiotic over 10 days in the last month, oral or intravenous corticosteroids (prednisone) over 265 mg in the last 3 months and serum albumin < 30 g/L. The model performed good discrimination and calibration in validation set (c-statistic, 0.79 [95%CI 0.68–0.90]). The 95%CI region of calibration belt did not cross the 45-degree diagonal bisector line (P = 0.887). Conclusion: The simple risk predictive model for earlier recognition of IPA is useful in hospitalized AECOPD patients.