Developing A Risk Stratification Tool For Predicting Opioid-Related Respiratory Depression After Noncardiac Surgery

Abstract Background: Accurately assessing the probability of significant respiratory depression following opioid administration can potentially enhance perioperative risk assessment and pain management. We developed and validated a risk prediction tool to estimate the probability of significant respiratory depression (indexed by naloxone administration) in patients undergoing noncardiac surgery. Methods: We studied n=63,084 patients (mean age 47.1±18.2 years; 50% male) who underwent emergency or elective noncardiac surgery between January 1st, 2007 and October 30th, 2017 at Vanderbilt University Medical Center. A derivation subsample reflecting two-thirds of available patients (n=42,082) was randomly selected for model development, and associations were identified between predictor variables and naloxone administration occurring within 5 days following surgery. The resulting probability model for predicting naloxone administration was then cross-validated in a separate validation cohort reflecting the remaining one-third of patients (n=21,002).Results: The rate of naloxone administration was identical in the derivation (n = 2,720 [6.5%]) and validation (n = 1,360 [6.5%]) cohorts. The risk prediction model identified female sex (Odds Ratio [OR]: 3.01; 95% confidence interval [CI]: 2.73-3.32), high-risk surgical procedures (OR: 4.16; 95% CI: 3.78-4.58), history of drug abuse (OR: 1.81; 95% CI: 1.52-2.16), and any opioids being administered on a scheduled rather than as-needed basis (OR: 8.31; 95% CI: 7.26-9.51) as risk factors for naloxone administration. Advanced age (OR: 0.40; 95% CI: 0.36-0.43), opioids administered via patient-controlled analgesia pump (OR: 0.55; 95% CI:0.49-0.62), and any scheduled non-opioids (OR: 0.63; 95% CI: 0.58-0.69) were associated with decreased risk of naloxone administration. An overall risk-prediction model incorporating the common clinically-available variables above displayed excellent discriminative ability both the derivation and validation cohorts (c-index = 0.820 and 0.814, respectively).Conclusion: Our cross-validated clinical predictive model accurately estimates the risk of serious opioid-related respiratory depression requiring naloxone administration in postoperative patients. In the precision medicine context, it may prove useful in facilitating selection of opioid-sparing pain management strategies for high-risk surgical patients.