How is remifentanil dosed without dedicated indicator?

TL;DR

This study analyzes how anesthesiologists adjust remifentanil dosing based on arterial pressure features, aiming to inform future closed-loop control algorithms for anesthesia management.

Contribution

It identifies arterial pressure features as key indicators for remifentanil dose adjustments and introduces a data-driven approach applicable to other decision-making domains.

Findings

Remifentanil dose changes correlate with arterial pressure trends.

High or rising arterial pressure triggers increased remifentanil targets.

Stable or declining arterial pressure is linked to dose reductions.

Abstract

This study investigates the paradigm of intraoperative analgesic dosage using a data-driven approach based on retrospective clinical data. Remifentanil, an analgesic widely used during anesthesia, presents a dosing challenge due to the absence of an universally accepted indicator of analgesia. To examine how changes in patient state correlate with adjustments in remifentanil target concentration triggered by the practitioner, we analyzed data from two sources: VitalDB (Seoul, Korea) and PREDIMED (Grenoble, France). Results show that only features derived from arterial pressure are consistently associated with changes in remifentanil targets. This finding is robust across both datasets despite variations in specific thresholds. In particular, increases in remifentanil targets are associated with high or rising arterial pressure over short periods (1--2 minutes), whereas decreases are linked to low, stable, or declining arterial pressure over longer periods (5--7 minutes). By capturing anesthesiologists' dosing strategies we provide a foundation for the future development of closed-loop control algorithms. Beyond the specific example of remifentanil's change prediction, the proposed feature generation and associated sparse fitting approach can be applied to other domain where human decision can be viewed as sensors interpretation.