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
Hauptverfasser: Dunham, Jacob R, Highland, Krista B, Costantino, Ryan C, Cliff Rutter, W, Rittel, Alexander G, Kazanis, William H, Palmrose, Gregory H
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container_end_page 1907
container_issue 11
container_start_page 1902
container_title Pain medicine (Malden, Mass.)
container_volume 23
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
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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 &gt; 32) only identified 22 of 95 ORD outcomes (Sensitivity 0.23), while an AD-specific cut point (AD RIOSORD &gt; 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 &gt; 32) only identified 22 of 95 ORD outcomes (Sensitivity 0.23), while an AD-specific cut point (AD RIOSORD &gt; 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. 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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 &gt; 32) only identified 22 of 95 ORD outcomes (Sensitivity 0.23), while an AD-specific cut point (AD RIOSORD &gt; 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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