Development of an algorithm to identify serious opioid toxicity in children

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Cecilia P Chung1, S Todd Callahan2, William O Cooper3,4

  • 1Division of Rheumatology, Department of Medicine, Vanderbilt University School of Medicine, T-3113 MCN, 1161 21st Ave. S., Nashville, TN, 37232, USA. c.chung@vanderbilt.edu.

BMC Research Notes
|July 5, 2015
PubMed

Insights

Developing an algorithm to identify pediatric opioid toxicity using administrative data is crucial. This study created a method combining ICD-9 codes and medical record review to accurately detect cases in children.

Area of Science:

  • Pediatric Health
  • Public Health Surveillance
  • Pharmacovigilance

Background:

  • Opioid use is rising in children, posing a significant public health risk.
  • Existing administrative databases lack a validated algorithm for identifying pediatric opioid toxicity.
  • This study addresses the need for a reliable method to detect opioid toxicity in pediatric populations.

Purpose of the Study:

  • To develop and validate an algorithm for identifying pediatric opioid toxicity using administrative healthcare databases.
  • To improve the accuracy of case ascertainment for opioid toxicity in children.

Main Methods:

  • Literature review and analysis of de-identified computer profiles to select International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9 CM) codes indicative of serious opioid toxicity.
  • Identification of potential cases in children enrolled in Tennessee Medicaid based on selected codes.
  • Independent physician review of medical records, with adjudication for equivocal cases, to confirm or exclude opioid toxicity.

Main Results:

  • Out of 195 potential cases, 85 were confirmed as opioid toxicity (50.6% overall positive predictive value).
  • The algorithm demonstrated higher positive predictive values for unintentional overdose (80.7%) and intentional overdose (50.0%).
  • A significant proportion of confirmed cases (65.8%) were linked to therapeutic opioid use.

Conclusions:

  • The developed algorithm, utilizing a broad set of screening codes and medical record review, is effective for identifying pediatric opioid toxicity.
  • This approach enhances the completeness and accuracy of case ascertainment in administrative claims data.
  • Findings are crucial for future research on the incidence and impact of opioid toxicity in children.
Abstract

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