Behaviour artificial intelligence technology to support the decision-making process of continuation or withdrawal of life-sustaining therapy

BACKGROUND: Withholding and withdrawing life-sustaining therapy (LST) is common in European ICUs but significant variations exist. Behaviour artificial intelligence technology (BAIT) may help standardize the ethical dilemma to continue or withdraw LST for patients already admitted to the ICU. METHODS: Several sessions with intensivists of an academic medical centre and a large urban teaching hospital were held to determine the criteria influencing the process. A discrete choice experiment was conducted during which 25 hypothetical cases were presented to the participants. For each case the participants had to decide whether they would continue, continue with a time limited trial of one week, or withdraw LST. The results of the experiment were used to develop a multinomial logistic regression model that was incorporated in a web-based decision-support system. RESULTS: 0.25). The most important covariates were age, patient values, expected cardiovascular and pulmonary impairment after ICU discharge and frailty at admission. The BAIT system lets intensivists view expected decisions based on documented criteria and uses color-coding to show the magnitude of the effect and its direction (i.e. to continue or withdraw LST). CONCLUSIONS: We developed a BAIT system that may support clinicians facing the dilemma of continuing or withdrawing LST by elucidating the key criteria involved in assessing medical futility.