Self-reported preoperative patient characteristics can with high accuracy predict daily opioid use at six months after surgery.
That’s according to new research reported in The Clinical Journal of Pain by Daniel Larach, MD, MSTR, Stephen Bruehl, PhD, both in the Department of Anesthesiology at Vanderbilt University Medical Center, and colleagues. The report, involving 108 total knee replacement patients, is based on a secondary analysis of a prospective observational study.
The team tested individual factors and models of varying complexity for predictive power at six weeks and six months following surgery. Going beyond what is routinely gathered from preoperative patients, the characteristics at issue include a range of pain, psychosocial and opioid-related factors.
The area under the receiver operating characteristic curve, or AUC, gauges a predictive model’s overall clinical usefulness and can be interpreted as measuring accuracy in ranking patients according to risk. With 90% or greater considered outstanding, the team’s combined model had an AUC of 97% (for daily opioid use at six months), suggesting opportunities for development of electronic risk-stratification algorithms.
Others on the study include Miklos Kertai, MD, PhD, Frederic Billings IV, MD, MSCI, Sara Anderson, BA, Gregory Polkowski, MD, MSCR, Andrew Shinar, MD, Ginger Milne, PhD, and Puneet Mishra, MD. The National Institutes of Health supported the research (grant DA050334).