Khemasuwan Danai, Wilshire Candice, Reddy Chakravarthy, Gilbert Christopher, Gorden Jed, Balwan Akshu, Sanchez Trinidad M, Bixby Billie, Sorensen Jeffrey S, Shojaee Samira
Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, Virginia Commonwealth University Health System, Richmond, Virginia.
Department of Thoracic Surgery and Interventional Pulmonology, Center for Lung Research in Honor of Wayne Gittinger, Swedish Cancer Institute, Seattle, Washington.
Ann Am Thorac Soc. 2025 Feb;22(2):187-192. doi: 10.1513/AnnalsATS.202402-151OC.
Intrapleural enzyme therapy (IET) with tissue plasminogen activator (tPA) and 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. The goal of this study was to identify risk factors for failure of IET. 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], extreme gradient boosting [XGBoost], and light gradient-boosting machine [LightGBM]) by multiple bootstrap-validated metrics, including F-β, to demonstrate model performances. A total of 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% ( = 365). SVM performed superiorly, with median F-β of 56%, followed by L1-penalized logistic regression, LightGBM, 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 the L1-penalized logistic regression model were pleural thickening (18%) and pleural fluid lactate dehydrogenase (LDH) (9%). 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.
组织型纤溶酶原激活剂（tPA）和脱氧核糖核酸酶进行的胸膜内酶疗法（IET）已被证明可减少复杂类肺炎性胸腔积液/脓胸（CPPE/脓胸）的手术干预需求。IET失败可能导致治疗延迟和住院时间延长。本研究的目的是确定IET失败的风险因素。我们对接受IET治疗CPPE/脓胸的患者进行了一项多中心回顾性研究。纳入了诊断时的临床和放射学变量。我们通过包括F-β在内的多个自助验证指标比较了四种不同的机器学习分类器（L1惩罚逻辑回归、支持向量机[SVM]、极端梯度提升[XGBoost]和轻梯度提升机[LightGBM]），以展示模型性能。来自美国五个机构的总共466名接受IET治疗胸膜感染的参与者被纳入研究。78%（n = 365）的患者通过IET实现了CPPE/脓胸的消退。SVM表现最佳，中位F-β为56%，其次是L1惩罚逻辑回归、LightGBM和XGBoost。临床和放射学变量根据其排序后的变量重要性进行分级。使用SVM时，IET失败的前两个重要预测因素是脓肿/坏死性肺炎的存在（17%）和胸膜增厚（13%）。同样，LightGBM确定脓肿/坏死性肺炎（35%）和胸膜增厚（26%），XGBoost指出胸膜增厚（36%）和脓肿/坏死性肺炎（17%）是治疗失败的最重要预测因素。L1惩罚逻辑回归模型确定的预测因素是胸膜增厚（18%）和胸腔积液乳酸脱氢酶（LDH）（9%）。在所有机器学习模型中，脓肿/坏死性肺炎和胸膜增厚的存在一直是IET失败的最强预测因素之一。模型之间排名的差异可能是由于每个模型使用的算法不同。这些结果表明，脓肿/坏死性肺炎和胸膜增厚的存在可能预测IET失败。这些结果应在更大规模的研究中得到证实。
