Development of an algorithm to identify inpatient opioid-related overdoses and oversedation using electronic data

Carla A Green, Brian Hazlehurst, John Brandes, Daniel S Sapp, Shannon L Janoff, Paul M Coplan, Angela DeVeaugh-Geiss, Carla A Green, Brian Hazlehurst, John Brandes, Daniel S Sapp, Shannon L Janoff, Paul M Coplan, Angela DeVeaugh-Geiss

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.

Trial registration: ClinicalTrials.gov NCT02667197.

Keywords: algorithm; inpatient; methods; opioid; overdose; oversedation; pharmacoepidemiology.

Conflict of interest statement

All authors have received research funding from the Opioid Postmarketing Requirement Consortium (OPC), which funded this project. This project was conducted as part of a Food and Drug Administration (FDA)–required postmarketing study of extended‐release and long‐acting opioid analgesics (https://www.fda.gov/downloads/Drugs/DrugSafety/InformationbyDrugClass/UCM484415.pdf). The OPC is composed of companies that hold NDAs of extended‐release and long‐acting opioid analgesics and, at the time of submission, the included the following companies: Allergan; Assertio Therapeutics, Inc; BioDelivery Sciences, Inc; Collegium Pharmaceutical, Inc; Daiichi Sankyo, Inc; Egalet Corporation; Endo Pharmaceuticals, Inc; Hikma Pharmaceuticals USA Inc; Janssen Pharmaceuticals, Inc; SpecGX, LLC; Pernix Therapeutics Holdings, Inc; Pfizer, Inc; and Purdue Pharma, LP. The study was designed in collaboration between OPC members and independent investigators with input from FDA. Investigators maintained intellectual freedom in terms of publishing final results. This study was registered with ClinicalTrials.gov as study NCT02667197 on 28 January 2016.

Dr Green, Mr Sapp, and Ms Janoff received funding from Purdue Pharma, LP for related research, prior to the present study. Dr Green has provided research consulting to the OPC. Kaiser Permanente Center for Health Research (KPCHR) staff were responsible for study design, analysis, publication decisions, publication content, and manuscript preparation, though OPC members provided comments on the manuscript. KPCHR authors made all final decisions about manuscript content. Drs DeVeaugh‐Geiss and Coplan were employees of Purdue Pharma, LP at the time this research was completed.

© 2019 The Authors. Pharmacoepidemiology and Drug Safety Published by John Wiley & Sons Ltd.

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Source: PubMed

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