Evaluation of an Opioid Overdose Composite Risk Score Cutoff in Active Duty Military Service Members
Abstract Objective To evaluate the current cutoff score and a recalibrated adaptation of the Veterans Health Administration (VHA) Risk Index for Serious Prescription Opioid-Induced Respiratory Depression or Overdose (RIOSORD) in active duty service members. Design Retrospective case-control. Setting...
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Veröffentlicht in: | Pain medicine (Malden, Mass.) Mass.), 2022-11, Vol.23 (11), p.1902-1907 |
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container_title | Pain medicine (Malden, Mass.) |
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creator | Dunham, Jacob R Highland, Krista B Costantino, Ryan C Cliff Rutter, W Rittel, Alexander G Kazanis, William H Palmrose, Gregory H |
description | Abstract
Objective
To evaluate the current cutoff score and a recalibrated adaptation of the Veterans Health Administration (VHA) Risk Index for Serious Prescription Opioid-Induced Respiratory Depression or Overdose (RIOSORD) in active duty service members.
Design
Retrospective case-control.
Setting
Military Health System.
Subjects
Active duty service members dispensed ≥ 1 opioid prescription between January 1, 2018, and December 31, 2019.
Methods
Service members with a documented opioid overdose were matched 1:10 to controls. An active duty-specific (AD) RIOSORD was constructed using the VHA RIOSORD components. Analyses examined the risk stratification and predictive characteristics of two RIOSORD versions (VHA and AD).
Results
Cases (n = 95) were matched with 950 controls. Only 6 of the original 17 elements were retained in the AD RIOSORD. Long-acting or extended-release opioid prescriptions, antidepressant prescriptions, hospitalization, and emergency department visits were associated with overdose events. The VHA RIOSORD had fair performance (C-statistic 0.77, 95% CI 0.75, 0.79), while the AD RIOSORD did not demonstrate statistically significant performance improvement (C-statistic 0.78, 95% CI, 0.77, 0.80). The DoD selected cut point (VHA RIOSORD > 32) only identified 22 of 95 ORD outcomes (Sensitivity 0.23), while an AD-specific cut point (AD RIOSORD > 16) correctly identified 53 of 95 adverse events (Sensitivity 0.56).
Conclusions
Results highlight the need to continually recalibrate predictive models and to consider multiple measures of performance. Although both models had similar overall performance with respect to the C-statistic, an AD-specific index threshold improves sensitivity. The calibrated AD RIOSORD does not represent an end-state, but a bridge to a future model developed on a wider range of patient variables, taking into consideration features that capture both care received, and care that was not received. |
doi_str_mv | 10.1093/pm/pnac064 |
format | Article |
fullrecord | <record><control><sourceid>gale_proqu</sourceid><recordid>TN_cdi_proquest_miscellaneous_2654295381</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><galeid>A773797400</galeid><oup_id>10.1093/pm/pnac064</oup_id><sourcerecordid>A773797400</sourcerecordid><originalsourceid>FETCH-LOGICAL-c371t-2ef268f3e22b07f8b73b27048fb2308338f2346fba7ef9fd6375ccfd9145d23e3</originalsourceid><addsrcrecordid>eNp9kduKFDEQhoMo7kFvfAAJiCDC7ObYSV8O464Kuwy4eh3S6Ypk7e60SXpg394MMyqKSF1UUXz1U1U_Qi8ouaCk5ZfzeDlP1pFGPEKnVLJmJRquHh9rxpU8QWc53xNCG6H5U3TCpZC0lqeov9rZYbElxAlHj-2Et3OIocfbHaQ-ZsCbOM4xhwL4U8jf8J2LqTaXEr3HYcJrV8IO8LulPODbMIRi0wO-g7QLDvAtjB2k_Aw98XbI8PyYz9GX66vPmw-rm-37j5v1zcpxRcuKgWeN9hwY64jyulO8Y4oI7TvGieZce8ZF4zurwLe-r0dK53zfUiF7xoGfozcH3TnF7wvkYsaQHQyDnSAu2bBGCtZKrmlFX_2F3sclTXU7wzTjWrasJb-pr3YAEyYfS7JuL2rWSnHVKkH21MU_qBo9jMHFCXyo_T8G3h4GXIo5J_BmTmGsjzOUmL2lZh7N0dIKvzxuunQj9L_Qnx5W4PUBiMv8P6EfmhqnYA</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2823859290</pqid></control><display><type>article</type><title>Evaluation of an Opioid Overdose Composite Risk Score Cutoff in Active Duty Military Service Members</title><source>Oxford University Press Journals</source><source>Alma/SFX Local Collection</source><creator>Dunham, Jacob R ; Highland, Krista B ; Costantino, Ryan C ; Cliff Rutter, W ; Rittel, Alexander G ; Kazanis, William H ; Palmrose, Gregory H</creator><creatorcontrib>Dunham, Jacob R ; Highland, Krista B ; Costantino, Ryan C ; Cliff Rutter, W ; Rittel, Alexander G ; Kazanis, William H ; Palmrose, Gregory H</creatorcontrib><description>Abstract
Objective
To evaluate the current cutoff score and a recalibrated adaptation of the Veterans Health Administration (VHA) Risk Index for Serious Prescription Opioid-Induced Respiratory Depression or Overdose (RIOSORD) in active duty service members.
Design
Retrospective case-control.
Setting
Military Health System.
Subjects
Active duty service members dispensed ≥ 1 opioid prescription between January 1, 2018, and December 31, 2019.
Methods
Service members with a documented opioid overdose were matched 1:10 to controls. An active duty-specific (AD) RIOSORD was constructed using the VHA RIOSORD components. Analyses examined the risk stratification and predictive characteristics of two RIOSORD versions (VHA and AD).
Results
Cases (n = 95) were matched with 950 controls. Only 6 of the original 17 elements were retained in the AD RIOSORD. Long-acting or extended-release opioid prescriptions, antidepressant prescriptions, hospitalization, and emergency department visits were associated with overdose events. The VHA RIOSORD had fair performance (C-statistic 0.77, 95% CI 0.75, 0.79), while the AD RIOSORD did not demonstrate statistically significant performance improvement (C-statistic 0.78, 95% CI, 0.77, 0.80). The DoD selected cut point (VHA RIOSORD > 32) only identified 22 of 95 ORD outcomes (Sensitivity 0.23), while an AD-specific cut point (AD RIOSORD > 16) correctly identified 53 of 95 adverse events (Sensitivity 0.56).
Conclusions
Results highlight the need to continually recalibrate predictive models and to consider multiple measures of performance. Although both models had similar overall performance with respect to the C-statistic, an AD-specific index threshold improves sensitivity. The calibrated AD RIOSORD does not represent an end-state, but a bridge to a future model developed on a wider range of patient variables, taking into consideration features that capture both care received, and care that was not received.</description><identifier>ISSN: 1526-2375</identifier><identifier>EISSN: 1526-4637</identifier><identifier>DOI: 10.1093/pm/pnac064</identifier><identifier>PMID: 35451483</identifier><language>eng</language><publisher>England: Oxford University Press</publisher><subject>Advertising executives ; Antidepressants ; Complications and side effects ; Drug overdose ; Drug therapy ; Drugs ; Emergency medical care ; Medical colleges ; Military personnel ; Narcotics ; Opioids ; Overdose ; Pharmaceutical research ; Pharmacy ; Prediction models ; Reference values (Medicine) ; Statistical analysis</subject><ispartof>Pain medicine (Malden, Mass.), 2022-11, Vol.23 (11), p.1902-1907</ispartof><rights>Published by Oxford University Press on behalf of the American Academy of Pain Medicine. This work is written by US Government employees and is in the public domain in the US. 2022</rights><rights>Published by Oxford University Press on behalf of the American Academy of Pain Medicine. This work is written by US Government employees and is in the public domain in the US.</rights><rights>COPYRIGHT 2022 Oxford University Press</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c371t-2ef268f3e22b07f8b73b27048fb2308338f2346fba7ef9fd6375ccfd9145d23e3</cites><orcidid>0000-0003-4554-3802 ; 0000-0003-3815-2571</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784,1584,27924,27925</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/35451483$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Dunham, Jacob R</creatorcontrib><creatorcontrib>Highland, Krista B</creatorcontrib><creatorcontrib>Costantino, Ryan C</creatorcontrib><creatorcontrib>Cliff Rutter, W</creatorcontrib><creatorcontrib>Rittel, Alexander G</creatorcontrib><creatorcontrib>Kazanis, William H</creatorcontrib><creatorcontrib>Palmrose, Gregory H</creatorcontrib><title>Evaluation of an Opioid Overdose Composite Risk Score Cutoff in Active Duty Military Service Members</title><title>Pain medicine (Malden, Mass.)</title><addtitle>Pain Med</addtitle><description>Abstract
Objective
To evaluate the current cutoff score and a recalibrated adaptation of the Veterans Health Administration (VHA) Risk Index for Serious Prescription Opioid-Induced Respiratory Depression or Overdose (RIOSORD) in active duty service members.
Design
Retrospective case-control.
Setting
Military Health System.
Subjects
Active duty service members dispensed ≥ 1 opioid prescription between January 1, 2018, and December 31, 2019.
Methods
Service members with a documented opioid overdose were matched 1:10 to controls. An active duty-specific (AD) RIOSORD was constructed using the VHA RIOSORD components. Analyses examined the risk stratification and predictive characteristics of two RIOSORD versions (VHA and AD).
Results
Cases (n = 95) were matched with 950 controls. Only 6 of the original 17 elements were retained in the AD RIOSORD. Long-acting or extended-release opioid prescriptions, antidepressant prescriptions, hospitalization, and emergency department visits were associated with overdose events. The VHA RIOSORD had fair performance (C-statistic 0.77, 95% CI 0.75, 0.79), while the AD RIOSORD did not demonstrate statistically significant performance improvement (C-statistic 0.78, 95% CI, 0.77, 0.80). The DoD selected cut point (VHA RIOSORD > 32) only identified 22 of 95 ORD outcomes (Sensitivity 0.23), while an AD-specific cut point (AD RIOSORD > 16) correctly identified 53 of 95 adverse events (Sensitivity 0.56).
Conclusions
Results highlight the need to continually recalibrate predictive models and to consider multiple measures of performance. Although both models had similar overall performance with respect to the C-statistic, an AD-specific index threshold improves sensitivity. The calibrated AD RIOSORD does not represent an end-state, but a bridge to a future model developed on a wider range of patient variables, taking into consideration features that capture both care received, and care that was not received.</description><subject>Advertising executives</subject><subject>Antidepressants</subject><subject>Complications and side effects</subject><subject>Drug overdose</subject><subject>Drug therapy</subject><subject>Drugs</subject><subject>Emergency medical care</subject><subject>Medical colleges</subject><subject>Military personnel</subject><subject>Narcotics</subject><subject>Opioids</subject><subject>Overdose</subject><subject>Pharmaceutical research</subject><subject>Pharmacy</subject><subject>Prediction models</subject><subject>Reference values (Medicine)</subject><subject>Statistical analysis</subject><issn>1526-2375</issn><issn>1526-4637</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><recordid>eNp9kduKFDEQhoMo7kFvfAAJiCDC7ObYSV8O464Kuwy4eh3S6Ypk7e60SXpg394MMyqKSF1UUXz1U1U_Qi8ouaCk5ZfzeDlP1pFGPEKnVLJmJRquHh9rxpU8QWc53xNCG6H5U3TCpZC0lqeov9rZYbElxAlHj-2Et3OIocfbHaQ-ZsCbOM4xhwL4U8jf8J2LqTaXEr3HYcJrV8IO8LulPODbMIRi0wO-g7QLDvAtjB2k_Aw98XbI8PyYz9GX66vPmw-rm-37j5v1zcpxRcuKgWeN9hwY64jyulO8Y4oI7TvGieZce8ZF4zurwLe-r0dK53zfUiF7xoGfozcH3TnF7wvkYsaQHQyDnSAu2bBGCtZKrmlFX_2F3sclTXU7wzTjWrasJb-pr3YAEyYfS7JuL2rWSnHVKkH21MU_qBo9jMHFCXyo_T8G3h4GXIo5J_BmTmGsjzOUmL2lZh7N0dIKvzxuunQj9L_Qnx5W4PUBiMv8P6EfmhqnYA</recordid><startdate>20221101</startdate><enddate>20221101</enddate><creator>Dunham, Jacob R</creator><creator>Highland, Krista B</creator><creator>Costantino, Ryan C</creator><creator>Cliff Rutter, W</creator><creator>Rittel, Alexander G</creator><creator>Kazanis, William H</creator><creator>Palmrose, Gregory H</creator><general>Oxford University Press</general><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7RV</scope><scope>7TK</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>88G</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>K9.</scope><scope>KB0</scope><scope>M0S</scope><scope>M1P</scope><scope>M2M</scope><scope>NAPCQ</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>PSYQQ</scope><scope>Q9U</scope><scope>7X8</scope><orcidid>https://orcid.org/0000-0003-4554-3802</orcidid><orcidid>https://orcid.org/0000-0003-3815-2571</orcidid></search><sort><creationdate>20221101</creationdate><title>Evaluation of an Opioid Overdose Composite Risk Score Cutoff in Active Duty Military Service Members</title><author>Dunham, Jacob R ; Highland, Krista B ; Costantino, Ryan C ; Cliff Rutter, W ; Rittel, Alexander G ; Kazanis, William H ; Palmrose, Gregory H</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c371t-2ef268f3e22b07f8b73b27048fb2308338f2346fba7ef9fd6375ccfd9145d23e3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Advertising executives</topic><topic>Antidepressants</topic><topic>Complications and side effects</topic><topic>Drug overdose</topic><topic>Drug therapy</topic><topic>Drugs</topic><topic>Emergency medical care</topic><topic>Medical colleges</topic><topic>Military personnel</topic><topic>Narcotics</topic><topic>Opioids</topic><topic>Overdose</topic><topic>Pharmaceutical research</topic><topic>Pharmacy</topic><topic>Prediction models</topic><topic>Reference values (Medicine)</topic><topic>Statistical analysis</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Dunham, Jacob R</creatorcontrib><creatorcontrib>Highland, Krista B</creatorcontrib><creatorcontrib>Costantino, Ryan C</creatorcontrib><creatorcontrib>Cliff Rutter, W</creatorcontrib><creatorcontrib>Rittel, Alexander G</creatorcontrib><creatorcontrib>Kazanis, William H</creatorcontrib><creatorcontrib>Palmrose, Gregory H</creatorcontrib><collection>PubMed</collection><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Nursing & Allied Health Database (ProQuest)</collection><collection>Neurosciences Abstracts</collection><collection>Health & Medical Collection (Proquest)</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Medical Database (Alumni Edition)</collection><collection>Psychology Database (Alumni)</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ProQuest Central (Alumni)</collection><collection>ProQuest Central</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Central Student</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Nursing & Allied Health Database (Alumni Edition)</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>PML(ProQuest Medical Library)</collection><collection>Psychology Database (ProQuest)</collection><collection>Nursing & Allied Health Premium</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>ProQuest One Psychology</collection><collection>ProQuest Central Basic</collection><collection>MEDLINE - Academic</collection><jtitle>Pain medicine (Malden, Mass.)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Dunham, Jacob R</au><au>Highland, Krista B</au><au>Costantino, Ryan C</au><au>Cliff Rutter, W</au><au>Rittel, Alexander G</au><au>Kazanis, William H</au><au>Palmrose, Gregory H</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Evaluation of an Opioid Overdose Composite Risk Score Cutoff in Active Duty Military Service Members</atitle><jtitle>Pain medicine (Malden, Mass.)</jtitle><addtitle>Pain Med</addtitle><date>2022-11-01</date><risdate>2022</risdate><volume>23</volume><issue>11</issue><spage>1902</spage><epage>1907</epage><pages>1902-1907</pages><issn>1526-2375</issn><eissn>1526-4637</eissn><abstract>Abstract
Objective
To evaluate the current cutoff score and a recalibrated adaptation of the Veterans Health Administration (VHA) Risk Index for Serious Prescription Opioid-Induced Respiratory Depression or Overdose (RIOSORD) in active duty service members.
Design
Retrospective case-control.
Setting
Military Health System.
Subjects
Active duty service members dispensed ≥ 1 opioid prescription between January 1, 2018, and December 31, 2019.
Methods
Service members with a documented opioid overdose were matched 1:10 to controls. An active duty-specific (AD) RIOSORD was constructed using the VHA RIOSORD components. Analyses examined the risk stratification and predictive characteristics of two RIOSORD versions (VHA and AD).
Results
Cases (n = 95) were matched with 950 controls. Only 6 of the original 17 elements were retained in the AD RIOSORD. Long-acting or extended-release opioid prescriptions, antidepressant prescriptions, hospitalization, and emergency department visits were associated with overdose events. The VHA RIOSORD had fair performance (C-statistic 0.77, 95% CI 0.75, 0.79), while the AD RIOSORD did not demonstrate statistically significant performance improvement (C-statistic 0.78, 95% CI, 0.77, 0.80). The DoD selected cut point (VHA RIOSORD > 32) only identified 22 of 95 ORD outcomes (Sensitivity 0.23), while an AD-specific cut point (AD RIOSORD > 16) correctly identified 53 of 95 adverse events (Sensitivity 0.56).
Conclusions
Results highlight the need to continually recalibrate predictive models and to consider multiple measures of performance. Although both models had similar overall performance with respect to the C-statistic, an AD-specific index threshold improves sensitivity. The calibrated AD RIOSORD does not represent an end-state, but a bridge to a future model developed on a wider range of patient variables, taking into consideration features that capture both care received, and care that was not received.</abstract><cop>England</cop><pub>Oxford University Press</pub><pmid>35451483</pmid><doi>10.1093/pm/pnac064</doi><tpages>6</tpages><orcidid>https://orcid.org/0000-0003-4554-3802</orcidid><orcidid>https://orcid.org/0000-0003-3815-2571</orcidid></addata></record> |
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source | Oxford University Press Journals; Alma/SFX Local Collection |
subjects | Advertising executives Antidepressants Complications and side effects Drug overdose Drug therapy Drugs Emergency medical care Medical colleges Military personnel Narcotics Opioids Overdose Pharmaceutical research Pharmacy Prediction models Reference values (Medicine) Statistical analysis |
title | Evaluation of an Opioid Overdose Composite Risk Score Cutoff in Active Duty Military Service Members |
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