Article Abstract

Forecasting pulmonary air leak duration following lung surgery using transpleural airflow data from a digital pleural drainage device

Authors: Ching Yeung, Mohsen Ghazel, Daniel French, Nathalie Japkowicz, Bram Gottlieb, Donna Maziak, Andrew J. E. Seely, Farid Shamji, Sudhir Sundaresan, Patrick James Villeneuve, Sebastien Gilbert


Background: Prolonged air leak (PAL) is often the limiting factor for hospital discharge after lung surgery. Our goal was to develop a statistical model that reliably predicts pulmonary air leak resolution by applying statistical time series modeling and forecasting techniques to digital drainage data.
Methods: Autoregressive Integrated Moving Average (ARIMA) modeling was used to forecast air leak flow from transplural air flow data. The results from ARIMA were retrospectively internally validated with a group of 100 patients who underwent lung resection between December 2012 and March 2017, for whom digital pleural drainage data was available for analysis and a persistent air leak was the limiting factor for chest tube removal.
Results: The ARIMA model correctly identified 82% (82/100) of patients as to whether or not the last chest tube removal was appropriate. The performance characteristics of the model in properly identifying patients whose air leak would resolve and who would therefore be candidates for safe chest tube removal were: sensitivity 80% (95% CI, 69–88%), specificity 88% (95% CI, 68–97%), positive predictive value 95% (95% CI, 86–99%), and negative predictive value 59% (95% CI, 42–79%). The false positive and false negative rate was 12% (95% CI, 12–31%) and 20% (95% CI, 12–31%).
Conclusions: We were able to validate a statistical model that that reliably predicted resolution of pulmonary air leak resolution over a 24-hour period. This information may improve the care of patients with chest tube by optimizing duration of pleural drainage.