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.