SAT0336 Development of an Algorithm to Identify Serious Opioid Toxicity in Children

BackgroundThe 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.ObjectivesWe sought to...

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Veröffentlicht in:Annals of the rheumatic diseases 2015-06, Vol.74 (Suppl 2), p.780-780
Hauptverfasser: Chung, C.P., Callahan, S.T., Cooper, W.O., Murray, K.T., Hall, K., Dudley, J.A., Stein, C.M., Ray, W.A.
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container_end_page 780
container_issue Suppl 2
container_start_page 780
container_title Annals of the rheumatic diseases
container_volume 74
creator Chung, C.P.
Callahan, S.T.
Cooper, W.O.
Murray, K.T.
Hall, K.
Dudley, J.A.
Stein, C.M.
Ray, W.A.
description BackgroundThe 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.ObjectivesWe sought to develop an algorithm to identify cases of opioid toxicity in children using administrative databases.MethodsAfter 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.ResultsOf 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.ConclusionsThe 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 InterestNone declared
doi_str_mv 10.1136/annrheumdis-2015-eular.1028
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However, we are not aware of a published algorithm to identify cases of opioid toxicity in children using administrative databases.ObjectivesWe sought to develop an algorithm to identify cases of opioid toxicity in children using administrative databases.MethodsAfter 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.ResultsOf 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.ConclusionsThe 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 InterestNone declared</description><identifier>ISSN: 0003-4967</identifier><identifier>EISSN: 1468-2060</identifier><identifier>DOI: 10.1136/annrheumdis-2015-eular.1028</identifier><identifier>CODEN: ARDIAO</identifier><language>eng</language><publisher>London: BMJ Publishing Group LTD</publisher><ispartof>Annals of the rheumatic diseases, 2015-06, Vol.74 (Suppl 2), p.780-780</ispartof><rights>2015, Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions</rights><rights>Copyright: 2015 (c) 2015, Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-b1859-304fcd0dfe4a188dcf05da8b46fedf108ec3817fbbacf84058360f086db0ef7a3</citedby></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttp://ard.bmj.com/content/74/Suppl_2/780.3.full.pdf$$EPDF$$P50$$Gbmj$$H</linktopdf><linktohtml>$$Uhttp://ard.bmj.com/content/74/Suppl_2/780.3.full$$EHTML$$P50$$Gbmj$$H</linktohtml><link.rule.ids>114,115,314,780,784,3196,23571,27924,27925,77600,77631</link.rule.ids></links><search><creatorcontrib>Chung, C.P.</creatorcontrib><creatorcontrib>Callahan, S.T.</creatorcontrib><creatorcontrib>Cooper, W.O.</creatorcontrib><creatorcontrib>Murray, K.T.</creatorcontrib><creatorcontrib>Hall, K.</creatorcontrib><creatorcontrib>Dudley, J.A.</creatorcontrib><creatorcontrib>Stein, C.M.</creatorcontrib><creatorcontrib>Ray, W.A.</creatorcontrib><title>SAT0336 Development of an Algorithm to Identify Serious Opioid Toxicity in Children</title><title>Annals of the rheumatic diseases</title><description>BackgroundThe 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.ObjectivesWe sought to develop an algorithm to identify cases of opioid toxicity in children using administrative databases.MethodsAfter 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.ResultsOf 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.ConclusionsThe collective and individual PPV for many ICD-9 CM codes consistent with opioid toxicity is low. 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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.ObjectivesWe sought to develop an algorithm to identify cases of opioid toxicity in children using administrative databases.MethodsAfter 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.ResultsOf 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.ConclusionsThe 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 InterestNone declared</abstract><cop>London</cop><pub>BMJ Publishing Group LTD</pub><doi>10.1136/annrheumdis-2015-eular.1028</doi><tpages>1</tpages></addata></record>
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title SAT0336 Development of an Algorithm to Identify Serious Opioid Toxicity in Children
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