Optimising perioperative care and understanding the role of chest drains

Digital chest drainage data provides confidence for quality improvement

Leveraging Longitudinal Data Analysis in Postoperative Care

In the first study, Dr. Gilbert and colleagues constructed, tested, and validated a random forest classifier to identify patients who could have chest tubes removed safely and timely during follow-up, based on longitudinal fluid drainage data collected by the Thopaz⁺ system.¹ Their results showed that reliable predictions could be achieved early in the process and that the classifier ensured that chest tubes that needed to be maintained were not removed at the cost of potentially maintaining a few unnecessarily.

Case Study: Digital Chest Drainage Data to Determine Optimal Air Leak Criteria

The goal of the second study was to use digital data from the Thopaz⁺ system to derive optimal parenchymal air-leak resolution criteria that minimize duration of chest tube drainage without increasing complications.² The investigators collected airflow data prospectively from 400 patients and averaged it in 10-minute intervals, and analysed air leak duration and air leak recurrence, frequency, and volume. The analysis was used to identify the optimal criteria based on patient safety (low frequency and volume of air leak recurrences), and efficiency (shortest initial air leak duration). Authors concluded that a postoperative air leak can be deemed resolved if it remains at < 50 mL/minute for 8 consecutive hours. Chest tube removal when air leak reaches this level was optimised for both safety and efficiency.

Together, these studies exemplify how digitally collected data can be combined with advanced analytical techniques to derive information for decision-support.