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Related Experiment Video

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Experimental Model to Evaluate Resolution of Pneumonia

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Published on: February 17, 2023

Machine Learning Model Predictors of Intrapleural Tissue Plasminogen Activator and DNase Failure in Pleural

Danai Khemasuwan¹, Candice Wilshire², Chakravarthy Reddy³

¹Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, Virginia Commonwealth University Health System, Richmond, Virginia.

Annals of the American Thoracic Society

|October 29, 2024

Summary

Intrapleural enzyme therapy (IET) failure in complicated parapneumonic effusion/empyema is predicted by abscess/necrotizing pneumonia and pleural thickening. These findings can help optimize treatment strategies for pleural infections.

Keywords:

complex parapneumonic effusion empyema intrapleural enzyme therapy intrapleural tissue plasminogen activator and DNase pleural infection

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Area of Science:

Pulmonology
Thoracic Surgery
Medical Informatics

Background:

Intrapleural enzyme therapy (IET) using tissue plasminogen activator (tPA) and DNase can reduce surgical intervention for complicated parapneumonic effusion/empyema (CPPE/empyema).
Failure of IET may result in delayed treatment and prolonged hospital stays.

Purpose of the Study:

To identify clinical and radiological risk factors predicting failure of IET in patients with CPPE/empyema.
To compare the performance of different machine learning models in predicting IET failure.

Main Methods:

A multicenter, retrospective study involving 466 patients treated with IET for pleural infection.
Comparison of four machine learning classifiers: L1-penalized logistic regression, support vector machine (SVM), extreme gradient boosting (XGBoost), and light gradient-boosting machine (LightGBM).
Evaluation of model performance using bootstrap-validated metrics, including F-β, and assessment of variable importance.

Main Results:

IET successfully resolved CPPE/empyema in 78% of patients.
The support vector machine (SVM) model demonstrated superior performance.
Abscess/necrotizing pneumonia and pleural thickening were consistently identified as the strongest predictors of IET failure across multiple machine learning models.

Conclusions:

Abscess/necrotizing pneumonia and pleural thickening are significant predictors of IET failure in CPPE/empyema.
Machine learning models can effectively identify risk factors for IET failure.
Further validation in larger studies is recommended to confirm these findings.