Machine Learning Model Predictors of Intrapleural tPA and DNase Failure in Pleural Infection: A Multicenter Study.

Abstract

Rationale: Intrapleural enzyme therapy (IET) with tissue plasminogen activator (tPA) and deoxyribonuclease (DNase) has been shown to reduce the need for surgical intervention for complicated parapneumonic effusion/empyema (CPPE/empyema). Failure of IET may lead to delayed care, and increased length of stay.

Objective: The goal of this study was to identify risk factors for failure of IET.

Methods: We performed a multicenter, retrospective study of patients who received IET for the treatment of CPPE/empyema. Clinical and radiological variables at the time of diagnosis were included. We compared four different machine learning classifiers (L1-penalized logistic regression, support vector machine (SVM), XGBoost and LightGBM) by multiple bootstrap-validated metrics, including F-beta to demonstrate model performances.

Results: 466 participants who received IET for pleural infection were included from five institutions across the United States. Resolution of CPPE/empyema with IET was achieved in 78% (n=365). SVM performed superior with median F-beta of 56%, followed by L1-penalized logistic regression, LGBM and XGBoost. Clinical and radiological variables were graded based on their ranked variable importance. The top two significant predictors of IET failure using SVM were the presence of an abscess/necrotizing pneumonia (17%) and pleural thickening (13%). Similarly, LightGBM identified abscess/necrotizing pneumonia (35%) and pleural thickening (26%) and XGBoost indicated pleural thickening (36%) and abscess/necrotizing pneumonia (17%) as the most significant predictors of treatment failure. Predictors identified by L1-penalized logistic regression model were pleural thickening (18%) and pleural fluid LDH (9%).

Conclusions: The presence of abscess/necrotizing pneumonia and pleural thickening consistently ranked among the strongest predictors of IET failure in all machine learning models. The difference in rankings between models may be a consequence of the different algorithms used by each model. These results indicate that the presence of abscess/necrotizing pneumonia, and pleural thickening may predict IET failure. These results should be confirmed in larger studies.