Development of an algorithm to identify serious opioid toxicity in children.

Chung CP, Callahan ST, Cooper WO, Murray KT, Hall K, Dudley JA, Stein CM, Ray WA
BMC Res Notes. 2015 8: 293

PMID: 26141980 · PMCID: PMC4491206 · DOI:10.1186/s13104-015-1185-x

BACKGROUND - The use of opioids is increasing in children; therefore, opioid toxicity could be a public health problem in this vulnerable population. However, we are not aware of a published algorithm to identify cases of opioid toxicity in children using administrative databases. We sought to develop an algorithm to identify them. After review of literature and de-identified computer profiles, a broad set of ICD-9 CM codes consistent with serious opioid toxicity was selected. Based on these codes, we identified 195 potential cases of opioid toxicity in children enrolled in Tennessee Medicaid. Medical records were independently reviewed by two physicians; episodes considered equivocal were reviewed by an adjudication committee. Cases were adjudicated as Group 1 (definite/probable), Group 2 (possible), or Group 3 (excluded).

RESULTS - Of the 195 potential cases, 168 (86.2%) had complete records for review and 85 were confirmed cases. The overall positive predictive value (PPV) for all codes was 50.6%. The PPV for codes indicating: unintentional opioid overdose (25/31) was 80.7%; intentional opioid overdose (15/30) was 50.0%, adverse events (33/58) was 56.9%, the presence of signs or symptoms compatible with opioid toxicity (12/47) was 25.5%, and no cases were confirmed in records from the two deaths. Of the confirmed cases, 65.8% were related to therapeutic opioid use.

CONCLUSION - For studies utilizing administrative claims to quantify incidence of opioid toxicity in children, our findings suggest that use of a broad set of screening codes coupled with medical record review is important to increase the completeness of case ascertainment.

MeSH Terms (16)

Adolescent Algorithms Analgesics, Opioid Child Child, Preschool Clinical Coding Drug-Related Side Effects and Adverse Reactions Female Humans Male Medicaid Predictive Value of Tests Reproducibility of Results Severity of Illness Index Tennessee United States

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