Article Text

SAT0336 Development of an Algorithm to Identify Serious Opioid Toxicity in Children
  1. C.P. Chung,
  2. S.T. Callahan,
  3. W.O. Cooper,
  4. K.T. Murray,
  5. K. Hall,
  6. J.A. Dudley,
  7. C.M. Stein,
  8. W.A. Ray
  1. Vanderbilt University, Nashville, United States


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.

Objectives We sought to develop an algorithm to identify cases of opioid toxicity in children using administrative databases.

Methods 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 definite/probable, possible, or were 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.

Conclusions The collective and individual PPV for many ICD-9 CM codes consistent with opioid toxicity is low. For studies utilizing administrative claims, medical record review is to be important to accurately identify episodes of opioid toxicity and optimize case ascertainment.

Disclosure of Interest None declared

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