Development of an algorithm to identify inpatient opioid‐related overdoses and oversedation using electronic data. Issue 8 (16th May 2019)

• Record Type: Journal Article
• Authors: Green, Carla A.; Hazlehurst, Brian; Brandes, John; Sapp, Daniel S.; Janoff, Shannon L.; Coplan, Paul M.; DeVeaugh‐Geiss, Angela
• Is Part Of: Pharmacoepidemiology and drug safety. Volume 28:Issue 8(2019)
• Page Start: 1138
• Page End: 1142
• Publication Date: 2019-05-16
• Subjects: algorithm -- inpatient -- methods -- opioid -- overdose -- oversedation -- pharmacoepidemiology; Pharmacoepidemiology -- Periodicals; Chemotherapy -- Periodicals; Epidemiology -- Periodicals; 615.705
• DOI: 10.1002/pds.4797
• Languages: English
• ISSNs: 1053-8569

Abstract

Abstract: Purpose: To facilitate surveillance and evaluate interventions addressing opioid‐related overdoses, algorithms are needed for use in large health care databases to identify and differentiate community‐occurring opioid‐related overdoses from inpatient‐occurring opioid‐related overdose/oversedation. Methods: Data were from Kaiser Permanente Northwest (KPNW), a large integrated health plan. We iteratively developed and evaluated an algorithm for electronically identifying inpatient overdose/oversedation in KPNW hospitals from 1 January 2008 to 31 December 2014. Chart audits assessed accuracy; data sources included administrative and clinical records. Results: The best‐performing algorithm used these rules: (1) Include events with opioids administered in an inpatient setting (including emergency department/urgent care) followed by naloxone administration within 275 hours of continuous inpatient stay; (2) exclude events with electroconvulsive therapy procedure codes; and (3) exclude events in which an opioid was administered prior to hospital discharge and followed by readmission with subsequent naloxone administration. Using this algorithm, we identified 870 suspect inpatient overdose/oversedation events and chart audited a random sample of 235. Of the random sample, 185 (78.7%) were deemed overdoses/oversedation, 37 (15.5%) were not, and 13 (5.5%) were possible cases. The number of hours between time of opioid and naloxone administration did not affect algorithm accuracy. When "possible" overdoses/oversedations were included with confirmed events, overall positive predictive value (PPV) was very good (PPV = 84.0%). Additionally, PPV was reasonable when evaluated specifically for hospital stays with emergency/urgent care admissions (PPV = 77.0%) and excellent for elective surgery admissions (PPV = 97.0%). Conclusions: Algorithm performance was reasonable for identifying inpatient overdose/oversedation with best performance among elective surgery patients.