Predictors of oversedation in hospitalized patients

Abstract

Purpose. Results of a study to determine demographic and clinical characteristics predictive of oversedation and potential opioid-induced respiratory depression (OIRD) in hospitalized patients are reported. Methods. In a retrospective case-controlled study, an incident reporting database was searched to identify cases of in-hospital oversedation; to form the control group, patients who did not experience an oversedation event while hospitalized were sampled in reverse chronological order until the desired total sample size (n = 225) was obtained. An allocation ratio of 2:1 was specified to adjust for case variability. Binary logistic regression was employed to identify factors predictive of oversedation. Results. Female sex (odds ratio [OR], 2.41; 95% confidence interval [CI], 1.05–5.50), comorbid renal disease (OR, 4.22; 95% CI, 1.66–10.70), untreated sleep apnea (OR, 32.32; 95% CI, 2.72–384.72), receipt of long-acting oxycodone (OR, 4.76; 95% CI, 1.70–13.33), and as-needed use of hydromorphone (OR, 2.73; 95% CI, 1.19–6.27) were significant predictors of oversedation; as-needed analgesia administered by the oral route (OR, 0.16; 95% CI, 0.07–0.36) or i.v. route (OR, 0.33; 95% CI, 0.14–0.80) had a significant protective effect. The final prediction model explained 47.8% of variance in oversedation risk and was found to have strong discriminatory performance. Conclusion. The identified risk factors for oversedation and potential OIRD in hospitalized patients can form the basis of quality-improvement initiatives to prevent oversedation through improved prescribing and patient monitoring.