Unsupervised machine learning identifies opioid taper reversal patterns in a longitudinal cohort (2008-2018)

Area of Science:

• Pain Management
• Pharmacology
• Artificial Intelligence in Healthcare

Background:

• Long-term opioid therapy is common for chronic pain, but dose reduction strategies are not well understood.
• Rapid opioid dose tapering is linked to adverse events, yet patient heterogeneity requires further investigation.
• Understanding patient subgroups is crucial for safe opioid dose management and tapering protocols.

Purpose of the Study:

• To analyze opioid dose management and patient characteristics in a diverse national cohort undergoing tapering.
• To identify distinct patient subpopulations based on opioid tapering patterns using artificial intelligence.
• To compare adverse events and outcomes across different opioid tapering profiles.

Main Methods:

• Utilized spectral clustering, an unsupervised artificial intelligence (AI) approach, on national claims data (2008-2018).
• Analyzed 30,932 patients with stable high opioid doses (≥50 MME) undergoing a tapering period.
• Identified 10 distinct patient clusters based on tapering magnitude, velocity, duration, and endpoint.

Main Results:

• Ten patient clusters were identified, similar at baseline but differing in tapering characteristics.
• A cluster (42%) with moderately rapid tapering to zero dose showed higher drug-related events, mental health issues, and deaths compared to slow tapering (55%).
• Clusters with taper reversals had adverse event rates similar to slow tapering groups.

Conclusions:

• Unsupervised AI effectively identifies clinically meaningful patterns and subpopulations in opioid tapering data.
• Tapering velocity, duration, and final dose are critical factors influencing patient outcomes.
• Findings emphasize the need for patient-centered care and tailored tapering protocols, especially considering taper reversals.