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

Autor:	Khemasuwan D; Virginia Commonwealth University, Richmond, Virginia, United States; danai.khemasuwan@vcuhealth.org., Wilshire C; Swedish Medical Center and Cancer Institute, Thoracic Surgery and Interventional Pulmonology, Seattle, Washington, United States., Reddy C; University of Utah Health, Salt Lake City, Utah, United States., Gilbert C; Medical University of South Carolina, Division of Pulmonary and Critical Care Medicine, Charleston, South Carolina, United States., Gordon J; Swedish Cancer Institute, Seattle, Washington, United States., Balwan A; University of Utah, Salt Lake City, Utah, United States., Sanchez TM; virginia commonwealth university, richmond, Virginia, United States., Bixby B; University of Arizona Arizona Health Sciences Center, Pulmonary and Critical Care Medicine, Tucson, Arizona, United States., Sorensen JS; Nublu, Salt Lake City, United States., Shojaee S; Vanderbilt University Medical Center, Division of Allergy, Pulmonary, and Critical Care Medicine, Department of Medicine, , Nashville, Tennessee, United States.
Jazyk:	angličtina
Zdroj:	Annals of the American Thoracic Society [Ann Am Thorac Soc] 2024 Oct 29. Date of Electronic Publication: 2024 Oct 29.
DOI:	10.1513/AnnalsATS.202402-151OC
Abstrakt:	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.
Databáze:	MEDLINE
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