Development of an algorithm to identify inpatient opioid-related overdoses and oversedation using electronic data
| Autor: | Paul Coplan, Daniel S. Sapp, Brian Hazlehurst, Carla A. Green, John Brandes, Shannon L. Janoff, Angela DeVeaugh-Geiss |
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| Rok vydání: | 2018 |
| Předmět: |
pharmacoepidemiology
Databases Factual Epidemiology medicine.medical_treatment Narcotic Antagonists Psychological intervention 030226 pharmacology & pharmacy inpatient methods 03 medical and health sciences 0302 clinical medicine Electroconvulsive therapy Predictive Value of Tests Naloxone Health care Original Reports Medicine Electronic Health Records Humans Original Report Pharmacology (medical) 030212 general & internal medicine Elective surgery Inpatients algorithm business.industry oversedation Emergency department Pharmacoepidemiology Analgesics Opioid Hospitalization opioid Electronic data Drug Overdose overdose business Emergency Service Hospital Algorithm Algorithms medicine.drug |
| Zdroj: | Pharmacoepidemiology and Drug Safety |
| ISSN: | 1099-1557 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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