title: Development of an algorithm to identify serious opioid toxicity in children.
abstract: 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.
  
  
  CONCLUSIONS
  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.
authors.affiliation: Division 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.
authors.forename: Cecilia P
authors.initials: CP
authors.lastname: Chung
authors.affiliation: Department of Pediatrics, Vanderbilt University School of Medicine, Nashville, TN, 37232, USA. todd.callahan@vanderbilt.edu.
authors.forename: S Todd
authors.initials: ST
authors.lastname: Callahan
authors.affiliation: Department of Health Policy, Vanderbilt University School of Medicine, Nashville, TN, 37232, USA. william.cooper@vanderbilt.edu.|Department of Pediatrics, Vanderbilt University School of Medicine, Nashville, TN, 37232, USA. william.cooper@vanderbilt.edu.
authors.forename: William O
authors.initials: WO
authors.lastname: Cooper
authors.affiliation: Division of Rheumatology, Department of Medicine, Vanderbilt University School of Medicine, T-3113 MCN, 1161 21st Ave. S., Nashville, TN, 37232, USA. kathy.murray@vanderbilt.edu.
authors.forename: Katherine T
authors.initials: KT
authors.lastname: Murray
authors.affiliation: Department of Health Policy, Vanderbilt University School of Medicine, Nashville, TN, 37232, USA. kathi.hall@vanderbilt.edu.
authors.forename: Kathi
authors.initials: K
authors.lastname: Hall
authors.affiliation: Department of Health Policy, Vanderbilt University School of Medicine, Nashville, TN, 37232, USA. judy.dudley@vanderbilt.edu.
authors.forename: Judith A
authors.initials: JA
authors.lastname: Dudley
authors.affiliation: Division of Rheumatology, Department of Medicine, Vanderbilt University School of Medicine, T-3113 MCN, 1161 21st Ave. S., Nashville, TN, 37232, USA. michael.stein@vanderbilt.edu.
authors.forename: C Michael
authors.initials: CM
authors.lastname: Stein
authors.affiliation: Department of Health Policy, Vanderbilt University School of Medicine, Nashville, TN, 37232, USA. wayne.ray@vanderbilt.edu.
authors.forename: Wayne A
authors.initials: WA
authors.lastname: Ray
mesh_terms: D000293:Adolescent; D000465:Algorithms; D000701:Analgesics, Opioid / Q000633:toxicity*; D002648:Child; D002675:Child, Preschool; D059019:Clinical Coding; D064420:Drug-Related Side Effects and Adverse Reactions / Q000453:epidemiology*; D005260:Female; D006801:Humans; D008297:Male; D008484:Medicaid / Q000706:statistics & numerical data; D011237:Predictive Value of Tests; D015203:Reproducibility of Results; D012720:Severity of Illness Index; D013714:Tennessee / Q000453:epidemiology; D014481:United States
chemical_list: D000701:Analgesics, Opioid