Developing a risk stratification tool for predicting opioid-related respiratory depression after non-cardiac surgery: a retrospective study

Abstract

ObjectivesAccurately 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.DesignRetrospective cohort study.SettingSingle academic centre.ParticipantsWe studied n=63 084 patients (mean age 47.1±18.2 years; 50% men) who underwent emergency or elective non-cardiac surgery between 1 January 2007 and 30 October 2017.InterventionsA 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).ResultsThe rate of naloxone administration was identical in the derivation (n=2720 (6.5%)) and validation (n=1360 (6.5%)) cohorts. The risk prediction model identified female sex (OR: 3.01; 95% CI: 2.73 to 3.32), high-risk surgical procedures (OR: 4.16; 95% CI: 3.78 to 4.58), history of drug abuse (OR: 1.81; 95% CI: 1.52 to 2.16) and any opioids being administered on a scheduled rather than as-needed basis (OR: 8.31; 95% CI: 7.26 to 9.51) as risk factors for naloxone administration. Advanced age (OR: 0.971; 95% CI: 0.968 to 0.973), opioids administered via patient-controlled analgesia pump (OR: 0.55; 95% CI: 0.49 to 0.62) and any scheduled non-opioids (OR: 0.63; 95% CI: 0.58 to 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 in both the derivation and validation cohorts (c-index=0.820 and 0.814, respectively).ConclusionOur cross-validated clinical predictive model accurately estimates the risk of serious opioid-related respiratory depression requiring naloxone administration in postoperative patients.

Overdose mortality rates for opioids or stimulants are higher in males than females, controlling for rates of drug misuse: State-level data

“…Ascribed in part to greater rapidity of onset or chest rigidity, recent studies suggest that fentanyl may pose greater overdose risk than heroin or morphine derivatives [28][29][30]. It is unclear if there is sexually dimorphic vulnerability or resilience to the respiratory depressant effects of MOP-r agonists in humans [31][32][33][34].…”

“…[28][29][30] It is not clear whether there are generalizable sex differences in sensitivity to respiratory depressant effects of mu-opioid agonists in humans. [31][32][33][34] Epidemiological data do not directly address this question, but could provide evidence against it (or in favor of additional explanations) if the sex difference in overdose mortality is not consistent across state-level jurisdictions.…”