Chest Tube Management After Lung Resection Surgery using a Classifier

Abstract

After lung surgery, a chest tube and a pump are used to manage air leaks and fluid drainage from the chest. The decision to remove or maintain the chest tube is based on drainage data collected from a digital pump that continuously monitors the patient. We construct a classifier to support this clinical decision-making process by identifying patients who may suffer adverse, extended air leaks early on. Intuitively, this problem can be modelled as a time-series fitted to monitoring data. However, we present a solution using a simple classifier constructed from data collected in a specific time frame (36- 48 hours) after surgery. We hypothesize that after surgery, patients struggle to attain a stable (favourable or adverse) status which prevails after a period of discrepancies and inconsistencies in the data. A solutions, we propose, is to identify this time frame when the majority of patients achieve their states of stability. Advantages of this approach include better classification performance with a lower burden of data collection during patient treatment. The paper presents the chest tube management as a classification task performed in a sliding window over time during patient monitoring. Our results show that reliable predictions can be achieved in the time window we identify, and that our classifier reduces unsafe chest tube removals at the expense of potentially maintaining a few that can be removed, i.e., we ensure that chest tubes that need to be maintained are not removed with potentially maintaining a few unnecessarily.