Chest Tube Learning Synthesis and Evaluation Assistant (CheLSEA): A Prospective Observational Trial of an Intelligent Decision Support System

OBJECTIVE: This study evaluates the performance of an artificial intelligence predictive clinical decision support system (CheLSEA) in generating chest tube management recommendations. METHODS: From October 2020 to May 2021, 50 adult elective pulmonary resection patients with at least 24 h of chest tube drainage were enrolled in a single-arm, double-anonymized, observational study to evaluate CheLSEA's performance compared with standard chest tube care. Clinical status, digital pleural drainage data, and chest X-ray data were collected prospectively. For each query, CheLSEA generated a recommendation for chest tube removal or maintenance. If maintenance was recommended, CheLSEA generated a removal time prediction. RESULTS: Most patients were female (29 of 47, 62%), smokers (39 of 47, 83%), with a median age of 73 (interquartile range [IQR]: 66 to 77) years, who underwent minimally invasive (44 of 47, 94%) lobectomy (41 of 47, 87%) for primary non-small cell lung cancer (35 of 47, 75%). CheLSEA was queried 174 times, 21% (36 of 174) of which triggered the CheLSEA safeguard system, mostly due to grade 3 or increasing subcutaneous emphysema (20 of 36, 56%). CheLSEA recommended chest tube removal in 9% of remaining requests (12 of 138), 83% of which were safe (10 of 12) and 17% of which were premature by ≤6 h (2 of 12). The remaining 126 queries were answered with chest tube maintenance recommendations up to the optimal removal time (97 of 126, 77%) or shortly thereafter (29 of 126, 23%; median = 17 h, IQR: 17 to 22). When predicting chest tube removal time, 93% of responses (82 of 88) were accurate. CONCLUSIONS: CheLSEA provides safe chest tube management recommendations and can potentially enhance care by reliably emulating expert-level clinical guidance.