Diagnostic Algorithms for Cardiovascular Death in Administrative Claims Databases: A Systematic Review

Introduction Valid algorithms for identification of cardiovascular (CV) deaths allow researchers to reliably assess the CV safety of medications, which is of importance to regulatory science, patient safety, and public health. Objective The aim was to conduct a systematic review of algorithms to ide...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Drug safety 2019-04, Vol.42 (4), p.515-527
Hauptverfasser: Singh, Sonal, Fouayzi, Hassan, Anzuoni, Kathryn, Goldman, Leah, Min, Jea Young, Griffin, Marie, Grijalva, Carlos G., Morrow, James A., Whitmore, Christine C., Leonard, Charles E., Selvan, Mano, Nair, Vinit, Zhou, Yunping, Toh, Sengwee, Petrone, Andrew, Williams, James, Fazio-Eynullayeva, Elnara, Swain, Richard, Tyler Coyle, D., Andrade, Susan
Format: Artikel
Sprache:eng
Schlagworte:
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
container_end_page 527
container_issue 4
container_start_page 515
container_title Drug safety
container_volume 42
creator Singh, Sonal
Fouayzi, Hassan
Anzuoni, Kathryn
Goldman, Leah
Min, Jea Young
Griffin, Marie
Grijalva, Carlos G.
Morrow, James A.
Whitmore, Christine C.
Leonard, Charles E.
Selvan, Mano
Nair, Vinit
Zhou, Yunping
Toh, Sengwee
Petrone, Andrew
Williams, James
Fazio-Eynullayeva, Elnara
Swain, Richard
Tyler Coyle, D.
Andrade, Susan
description Introduction Valid algorithms for identification of cardiovascular (CV) deaths allow researchers to reliably assess the CV safety of medications, which is of importance to regulatory science, patient safety, and public health. Objective The aim was to conduct a systematic review of algorithms to identify CV death in administrative health plan claims databases. Methods We searched MEDLINE, EMBASE, and Cochrane Library for English-language studies published between January 1, 2012 and October 17, 2017. We examined references in systematic reviews to identify earlier studies. Selection included any observational study using electronic health care data to evaluate the sensitivity, specificity, positive predictive value (PPV), or negative predictive value (NPV) of algorithms for CV death (sudden cardiac death [SCD], myocardial infarction [MI]-related death, or stroke-related death) among adults aged ≥ 18 years in the United States. Data were extracted by two independent reviewers, with disagreements resolved through further discussion and consensus. The Quality Assessment of Diagnostic Accuracy Studies-2 instrument was used to assess the risk of bias. Results Five studies ( n  = 4 on SCD, n  = 1 on MI- and stroke-related death) were included after a review of 2053 citations. All studies reported algorithm PPVs, with incomplete reporting on other accuracy parameters. One study was at low risk of bias, three studies were at moderate risk of bias, and one study was at unclear risk of bias. Two studies identified community-occurring SCD: one identified events using International Classification of Disease, Ninth Revision (ICD-9) codes on death certificates and other criteria from medical claims (PPV = 86.8%) and the other identified events resulting in hospital presentation using first-listed ICD-9 codes on emergency department or inpatient medical claims (PPV = 92.3%). Two studies used death certificates alone to identify SCD (PPV = 27% and 32%, respectively). One study used medical claims to identify CV death (PPV = 36.4%), coronary heart disease mortality (PPV = 28.3%), and stroke mortality (PPV = 34.5%). Conclusion Two existing algorithms based on medical claims diagnoses with or without death certificates can accurately identify SCD to support pharmacoepidemiologic studies. Developing valid algorithms identifying MI- and stroke-related death should be a research priority. PROSPERO 2017 CRD42017078745.
doi_str_mv 10.1007/s40264-018-0754-z
format Article
fullrecord <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_journals_2260391299</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2260391299</sourcerecordid><originalsourceid>FETCH-LOGICAL-c372t-40715cfa20ac4bc6baa4316f2f9ee03c0d678e44ac3dec0ec7acb62cf21facdd3</originalsourceid><addsrcrecordid>eNp1kEtPAjEUhRujEUR_gBvTxPXobad0GHcEfCUkJj7WzZ1OCyXMDLYFA7_eIaCuXN3FPec7yUfIJYMbBpDdBgFcigTYIIGsL5LtEekyluUJywU_Jl1gTCT9nMkOOQthDgADLgenpJOCyBgI2SV27HBaNyE6TYeLaeNdnFWB2sbTEfrSNWsMerVAT8cG44y6mg7LytUuRI_RrQ0dLdC1jTFGLDCYcEeH9G0ToqlwB301a2e-zsmJxUUwF4fbIx8P9--jp2Ty8vg8Gk4SnWY8JgIy1tcWOaAWhZYFokiZtNzmxkCqoZTZwAiBOi2NBqMz1IXk2nJmUZdl2iPXe-7SN58rE6KaNytft5OKcwlpznietym2T2nfhOCNVUvvKvQbxUDtzKq9WdWaVTuzatt2rg7kVVGZ8rfxo7IN8H0gtK96avzf9P_Ub79nhmA</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2260391299</pqid></control><display><type>article</type><title>Diagnostic Algorithms for Cardiovascular Death in Administrative Claims Databases: A Systematic Review</title><source>MEDLINE</source><source>Springer Nature - Complete Springer Journals</source><creator>Singh, Sonal ; Fouayzi, Hassan ; Anzuoni, Kathryn ; Goldman, Leah ; Min, Jea Young ; Griffin, Marie ; Grijalva, Carlos G. ; Morrow, James A. ; Whitmore, Christine C. ; Leonard, Charles E. ; Selvan, Mano ; Nair, Vinit ; Zhou, Yunping ; Toh, Sengwee ; Petrone, Andrew ; Williams, James ; Fazio-Eynullayeva, Elnara ; Swain, Richard ; Tyler Coyle, D. ; Andrade, Susan</creator><creatorcontrib>Singh, Sonal ; Fouayzi, Hassan ; Anzuoni, Kathryn ; Goldman, Leah ; Min, Jea Young ; Griffin, Marie ; Grijalva, Carlos G. ; Morrow, James A. ; Whitmore, Christine C. ; Leonard, Charles E. ; Selvan, Mano ; Nair, Vinit ; Zhou, Yunping ; Toh, Sengwee ; Petrone, Andrew ; Williams, James ; Fazio-Eynullayeva, Elnara ; Swain, Richard ; Tyler Coyle, D. ; Andrade, Susan</creatorcontrib><description>Introduction Valid algorithms for identification of cardiovascular (CV) deaths allow researchers to reliably assess the CV safety of medications, which is of importance to regulatory science, patient safety, and public health. Objective The aim was to conduct a systematic review of algorithms to identify CV death in administrative health plan claims databases. Methods We searched MEDLINE, EMBASE, and Cochrane Library for English-language studies published between January 1, 2012 and October 17, 2017. We examined references in systematic reviews to identify earlier studies. Selection included any observational study using electronic health care data to evaluate the sensitivity, specificity, positive predictive value (PPV), or negative predictive value (NPV) of algorithms for CV death (sudden cardiac death [SCD], myocardial infarction [MI]-related death, or stroke-related death) among adults aged ≥ 18 years in the United States. Data were extracted by two independent reviewers, with disagreements resolved through further discussion and consensus. The Quality Assessment of Diagnostic Accuracy Studies-2 instrument was used to assess the risk of bias. Results Five studies ( n  = 4 on SCD, n  = 1 on MI- and stroke-related death) were included after a review of 2053 citations. All studies reported algorithm PPVs, with incomplete reporting on other accuracy parameters. One study was at low risk of bias, three studies were at moderate risk of bias, and one study was at unclear risk of bias. Two studies identified community-occurring SCD: one identified events using International Classification of Disease, Ninth Revision (ICD-9) codes on death certificates and other criteria from medical claims (PPV = 86.8%) and the other identified events resulting in hospital presentation using first-listed ICD-9 codes on emergency department or inpatient medical claims (PPV = 92.3%). Two studies used death certificates alone to identify SCD (PPV = 27% and 32%, respectively). One study used medical claims to identify CV death (PPV = 36.4%), coronary heart disease mortality (PPV = 28.3%), and stroke mortality (PPV = 34.5%). Conclusion Two existing algorithms based on medical claims diagnoses with or without death certificates can accurately identify SCD to support pharmacoepidemiologic studies. Developing valid algorithms identifying MI- and stroke-related death should be a research priority. PROSPERO 2017 CRD42017078745.</description><identifier>ISSN: 0114-5916</identifier><identifier>EISSN: 1179-1942</identifier><identifier>DOI: 10.1007/s40264-018-0754-z</identifier><identifier>PMID: 30471046</identifier><language>eng</language><publisher>Cham: Springer International Publishing</publisher><subject>Algorithms ; Bias ; Cardiovascular disease ; Cardiovascular diseases ; Cardiovascular System - pathology ; Cerebral infarction ; Certificates ; Coronary artery disease ; Data Collection - methods ; Databases, Factual ; Death ; Death, Sudden, Cardiac - epidemiology ; Diagnostic systems ; Drug Safety and Pharmacovigilance ; Emergency medical services ; Health risk assessment ; Heart diseases ; Humans ; Identification methods ; International Classification of Diseases ; Literature reviews ; Medical diagnosis ; Medicine ; Medicine &amp; Public Health ; Mortality ; Myocardial infarction ; Observational Studies as Topic ; Pharmacology ; Pharmacology/Toxicology ; Public health ; Quality assessment ; Quality control ; Risk assessment ; Safety ; Sensitivity analysis ; Studies ; Systematic Review</subject><ispartof>Drug safety, 2019-04, Vol.42 (4), p.515-527</ispartof><rights>Springer Nature Switzerland AG 2018</rights><rights>Copyright Springer Nature B.V. Apr 2019</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c372t-40715cfa20ac4bc6baa4316f2f9ee03c0d678e44ac3dec0ec7acb62cf21facdd3</citedby><cites>FETCH-LOGICAL-c372t-40715cfa20ac4bc6baa4316f2f9ee03c0d678e44ac3dec0ec7acb62cf21facdd3</cites><orcidid>0000-0003-0912-941X ; 0000-0002-7578-2620</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s40264-018-0754-z$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s40264-018-0754-z$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,776,780,27901,27902,41464,42533,51294</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/30471046$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Singh, Sonal</creatorcontrib><creatorcontrib>Fouayzi, Hassan</creatorcontrib><creatorcontrib>Anzuoni, Kathryn</creatorcontrib><creatorcontrib>Goldman, Leah</creatorcontrib><creatorcontrib>Min, Jea Young</creatorcontrib><creatorcontrib>Griffin, Marie</creatorcontrib><creatorcontrib>Grijalva, Carlos G.</creatorcontrib><creatorcontrib>Morrow, James A.</creatorcontrib><creatorcontrib>Whitmore, Christine C.</creatorcontrib><creatorcontrib>Leonard, Charles E.</creatorcontrib><creatorcontrib>Selvan, Mano</creatorcontrib><creatorcontrib>Nair, Vinit</creatorcontrib><creatorcontrib>Zhou, Yunping</creatorcontrib><creatorcontrib>Toh, Sengwee</creatorcontrib><creatorcontrib>Petrone, Andrew</creatorcontrib><creatorcontrib>Williams, James</creatorcontrib><creatorcontrib>Fazio-Eynullayeva, Elnara</creatorcontrib><creatorcontrib>Swain, Richard</creatorcontrib><creatorcontrib>Tyler Coyle, D.</creatorcontrib><creatorcontrib>Andrade, Susan</creatorcontrib><title>Diagnostic Algorithms for Cardiovascular Death in Administrative Claims Databases: A Systematic Review</title><title>Drug safety</title><addtitle>Drug Saf</addtitle><addtitle>Drug Saf</addtitle><description>Introduction Valid algorithms for identification of cardiovascular (CV) deaths allow researchers to reliably assess the CV safety of medications, which is of importance to regulatory science, patient safety, and public health. Objective The aim was to conduct a systematic review of algorithms to identify CV death in administrative health plan claims databases. Methods We searched MEDLINE, EMBASE, and Cochrane Library for English-language studies published between January 1, 2012 and October 17, 2017. We examined references in systematic reviews to identify earlier studies. Selection included any observational study using electronic health care data to evaluate the sensitivity, specificity, positive predictive value (PPV), or negative predictive value (NPV) of algorithms for CV death (sudden cardiac death [SCD], myocardial infarction [MI]-related death, or stroke-related death) among adults aged ≥ 18 years in the United States. Data were extracted by two independent reviewers, with disagreements resolved through further discussion and consensus. The Quality Assessment of Diagnostic Accuracy Studies-2 instrument was used to assess the risk of bias. Results Five studies ( n  = 4 on SCD, n  = 1 on MI- and stroke-related death) were included after a review of 2053 citations. All studies reported algorithm PPVs, with incomplete reporting on other accuracy parameters. One study was at low risk of bias, three studies were at moderate risk of bias, and one study was at unclear risk of bias. Two studies identified community-occurring SCD: one identified events using International Classification of Disease, Ninth Revision (ICD-9) codes on death certificates and other criteria from medical claims (PPV = 86.8%) and the other identified events resulting in hospital presentation using first-listed ICD-9 codes on emergency department or inpatient medical claims (PPV = 92.3%). Two studies used death certificates alone to identify SCD (PPV = 27% and 32%, respectively). One study used medical claims to identify CV death (PPV = 36.4%), coronary heart disease mortality (PPV = 28.3%), and stroke mortality (PPV = 34.5%). Conclusion Two existing algorithms based on medical claims diagnoses with or without death certificates can accurately identify SCD to support pharmacoepidemiologic studies. Developing valid algorithms identifying MI- and stroke-related death should be a research priority. PROSPERO 2017 CRD42017078745.</description><subject>Algorithms</subject><subject>Bias</subject><subject>Cardiovascular disease</subject><subject>Cardiovascular diseases</subject><subject>Cardiovascular System - pathology</subject><subject>Cerebral infarction</subject><subject>Certificates</subject><subject>Coronary artery disease</subject><subject>Data Collection - methods</subject><subject>Databases, Factual</subject><subject>Death</subject><subject>Death, Sudden, Cardiac - epidemiology</subject><subject>Diagnostic systems</subject><subject>Drug Safety and Pharmacovigilance</subject><subject>Emergency medical services</subject><subject>Health risk assessment</subject><subject>Heart diseases</subject><subject>Humans</subject><subject>Identification methods</subject><subject>International Classification of Diseases</subject><subject>Literature reviews</subject><subject>Medical diagnosis</subject><subject>Medicine</subject><subject>Medicine &amp; Public Health</subject><subject>Mortality</subject><subject>Myocardial infarction</subject><subject>Observational Studies as Topic</subject><subject>Pharmacology</subject><subject>Pharmacology/Toxicology</subject><subject>Public health</subject><subject>Quality assessment</subject><subject>Quality control</subject><subject>Risk assessment</subject><subject>Safety</subject><subject>Sensitivity analysis</subject><subject>Studies</subject><subject>Systematic Review</subject><issn>0114-5916</issn><issn>1179-1942</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2019</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><sourceid>BENPR</sourceid><recordid>eNp1kEtPAjEUhRujEUR_gBvTxPXobad0GHcEfCUkJj7WzZ1OCyXMDLYFA7_eIaCuXN3FPec7yUfIJYMbBpDdBgFcigTYIIGsL5LtEekyluUJywU_Jl1gTCT9nMkOOQthDgADLgenpJOCyBgI2SV27HBaNyE6TYeLaeNdnFWB2sbTEfrSNWsMerVAT8cG44y6mg7LytUuRI_RrQ0dLdC1jTFGLDCYcEeH9G0ToqlwB301a2e-zsmJxUUwF4fbIx8P9--jp2Ty8vg8Gk4SnWY8JgIy1tcWOaAWhZYFokiZtNzmxkCqoZTZwAiBOi2NBqMz1IXk2nJmUZdl2iPXe-7SN58rE6KaNytft5OKcwlpznietym2T2nfhOCNVUvvKvQbxUDtzKq9WdWaVTuzatt2rg7kVVGZ8rfxo7IN8H0gtK96avzf9P_Ub79nhmA</recordid><startdate>20190401</startdate><enddate>20190401</enddate><creator>Singh, Sonal</creator><creator>Fouayzi, Hassan</creator><creator>Anzuoni, Kathryn</creator><creator>Goldman, Leah</creator><creator>Min, Jea Young</creator><creator>Griffin, Marie</creator><creator>Grijalva, Carlos G.</creator><creator>Morrow, James A.</creator><creator>Whitmore, Christine C.</creator><creator>Leonard, Charles E.</creator><creator>Selvan, Mano</creator><creator>Nair, Vinit</creator><creator>Zhou, Yunping</creator><creator>Toh, Sengwee</creator><creator>Petrone, Andrew</creator><creator>Williams, James</creator><creator>Fazio-Eynullayeva, Elnara</creator><creator>Swain, Richard</creator><creator>Tyler Coyle, D.</creator><creator>Andrade, Susan</creator><general>Springer International Publishing</general><general>Springer Nature B.V</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>4T-</scope><scope>7RV</scope><scope>7T2</scope><scope>7TK</scope><scope>7U7</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>BENPR</scope><scope>C1K</scope><scope>CCPQU</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>K9.</scope><scope>KB0</scope><scope>M0S</scope><scope>M1P</scope><scope>NAPCQ</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><orcidid>https://orcid.org/0000-0003-0912-941X</orcidid><orcidid>https://orcid.org/0000-0002-7578-2620</orcidid></search><sort><creationdate>20190401</creationdate><title>Diagnostic Algorithms for Cardiovascular Death in Administrative Claims Databases: A Systematic Review</title><author>Singh, Sonal ; Fouayzi, Hassan ; Anzuoni, Kathryn ; Goldman, Leah ; Min, Jea Young ; Griffin, Marie ; Grijalva, Carlos G. ; Morrow, James A. ; Whitmore, Christine C. ; Leonard, Charles E. ; Selvan, Mano ; Nair, Vinit ; Zhou, Yunping ; Toh, Sengwee ; Petrone, Andrew ; Williams, James ; Fazio-Eynullayeva, Elnara ; Swain, Richard ; Tyler Coyle, D. ; Andrade, Susan</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c372t-40715cfa20ac4bc6baa4316f2f9ee03c0d678e44ac3dec0ec7acb62cf21facdd3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2019</creationdate><topic>Algorithms</topic><topic>Bias</topic><topic>Cardiovascular disease</topic><topic>Cardiovascular diseases</topic><topic>Cardiovascular System - pathology</topic><topic>Cerebral infarction</topic><topic>Certificates</topic><topic>Coronary artery disease</topic><topic>Data Collection - methods</topic><topic>Databases, Factual</topic><topic>Death</topic><topic>Death, Sudden, Cardiac - epidemiology</topic><topic>Diagnostic systems</topic><topic>Drug Safety and Pharmacovigilance</topic><topic>Emergency medical services</topic><topic>Health risk assessment</topic><topic>Heart diseases</topic><topic>Humans</topic><topic>Identification methods</topic><topic>International Classification of Diseases</topic><topic>Literature reviews</topic><topic>Medical diagnosis</topic><topic>Medicine</topic><topic>Medicine &amp; Public Health</topic><topic>Mortality</topic><topic>Myocardial infarction</topic><topic>Observational Studies as Topic</topic><topic>Pharmacology</topic><topic>Pharmacology/Toxicology</topic><topic>Public health</topic><topic>Quality assessment</topic><topic>Quality control</topic><topic>Risk assessment</topic><topic>Safety</topic><topic>Sensitivity analysis</topic><topic>Studies</topic><topic>Systematic Review</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Singh, Sonal</creatorcontrib><creatorcontrib>Fouayzi, Hassan</creatorcontrib><creatorcontrib>Anzuoni, Kathryn</creatorcontrib><creatorcontrib>Goldman, Leah</creatorcontrib><creatorcontrib>Min, Jea Young</creatorcontrib><creatorcontrib>Griffin, Marie</creatorcontrib><creatorcontrib>Grijalva, Carlos G.</creatorcontrib><creatorcontrib>Morrow, James A.</creatorcontrib><creatorcontrib>Whitmore, Christine C.</creatorcontrib><creatorcontrib>Leonard, Charles E.</creatorcontrib><creatorcontrib>Selvan, Mano</creatorcontrib><creatorcontrib>Nair, Vinit</creatorcontrib><creatorcontrib>Zhou, Yunping</creatorcontrib><creatorcontrib>Toh, Sengwee</creatorcontrib><creatorcontrib>Petrone, Andrew</creatorcontrib><creatorcontrib>Williams, James</creatorcontrib><creatorcontrib>Fazio-Eynullayeva, Elnara</creatorcontrib><creatorcontrib>Swain, Richard</creatorcontrib><creatorcontrib>Tyler Coyle, D.</creatorcontrib><creatorcontrib>Andrade, Susan</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Docstoc</collection><collection>Proquest Nursing &amp; Allied Health Source</collection><collection>Health and Safety Science Abstracts (Full archive)</collection><collection>Neurosciences Abstracts</collection><collection>Toxicology Abstracts</collection><collection>Health &amp; Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Medical Database (Alumni Edition)</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 Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>ProQuest Central</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ProQuest One Community College</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Health &amp; Medical Complete (Alumni)</collection><collection>Nursing &amp; Allied Health Database (Alumni Edition)</collection><collection>Health &amp; Medical Collection (Alumni Edition)</collection><collection>Medical Database</collection><collection>Nursing &amp; 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><jtitle>Drug safety</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Singh, Sonal</au><au>Fouayzi, Hassan</au><au>Anzuoni, Kathryn</au><au>Goldman, Leah</au><au>Min, Jea Young</au><au>Griffin, Marie</au><au>Grijalva, Carlos G.</au><au>Morrow, James A.</au><au>Whitmore, Christine C.</au><au>Leonard, Charles E.</au><au>Selvan, Mano</au><au>Nair, Vinit</au><au>Zhou, Yunping</au><au>Toh, Sengwee</au><au>Petrone, Andrew</au><au>Williams, James</au><au>Fazio-Eynullayeva, Elnara</au><au>Swain, Richard</au><au>Tyler Coyle, D.</au><au>Andrade, Susan</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Diagnostic Algorithms for Cardiovascular Death in Administrative Claims Databases: A Systematic Review</atitle><jtitle>Drug safety</jtitle><stitle>Drug Saf</stitle><addtitle>Drug Saf</addtitle><date>2019-04-01</date><risdate>2019</risdate><volume>42</volume><issue>4</issue><spage>515</spage><epage>527</epage><pages>515-527</pages><issn>0114-5916</issn><eissn>1179-1942</eissn><abstract>Introduction Valid algorithms for identification of cardiovascular (CV) deaths allow researchers to reliably assess the CV safety of medications, which is of importance to regulatory science, patient safety, and public health. Objective The aim was to conduct a systematic review of algorithms to identify CV death in administrative health plan claims databases. Methods We searched MEDLINE, EMBASE, and Cochrane Library for English-language studies published between January 1, 2012 and October 17, 2017. We examined references in systematic reviews to identify earlier studies. Selection included any observational study using electronic health care data to evaluate the sensitivity, specificity, positive predictive value (PPV), or negative predictive value (NPV) of algorithms for CV death (sudden cardiac death [SCD], myocardial infarction [MI]-related death, or stroke-related death) among adults aged ≥ 18 years in the United States. Data were extracted by two independent reviewers, with disagreements resolved through further discussion and consensus. The Quality Assessment of Diagnostic Accuracy Studies-2 instrument was used to assess the risk of bias. Results Five studies ( n  = 4 on SCD, n  = 1 on MI- and stroke-related death) were included after a review of 2053 citations. All studies reported algorithm PPVs, with incomplete reporting on other accuracy parameters. One study was at low risk of bias, three studies were at moderate risk of bias, and one study was at unclear risk of bias. Two studies identified community-occurring SCD: one identified events using International Classification of Disease, Ninth Revision (ICD-9) codes on death certificates and other criteria from medical claims (PPV = 86.8%) and the other identified events resulting in hospital presentation using first-listed ICD-9 codes on emergency department or inpatient medical claims (PPV = 92.3%). Two studies used death certificates alone to identify SCD (PPV = 27% and 32%, respectively). One study used medical claims to identify CV death (PPV = 36.4%), coronary heart disease mortality (PPV = 28.3%), and stroke mortality (PPV = 34.5%). Conclusion Two existing algorithms based on medical claims diagnoses with or without death certificates can accurately identify SCD to support pharmacoepidemiologic studies. Developing valid algorithms identifying MI- and stroke-related death should be a research priority. PROSPERO 2017 CRD42017078745.</abstract><cop>Cham</cop><pub>Springer International Publishing</pub><pmid>30471046</pmid><doi>10.1007/s40264-018-0754-z</doi><tpages>13</tpages><orcidid>https://orcid.org/0000-0003-0912-941X</orcidid><orcidid>https://orcid.org/0000-0002-7578-2620</orcidid></addata></record>
fulltext fulltext
identifier ISSN: 0114-5916
ispartof Drug safety, 2019-04, Vol.42 (4), p.515-527
issn 0114-5916
1179-1942
language eng
recordid cdi_proquest_journals_2260391299
source MEDLINE; Springer Nature - Complete Springer Journals
subjects Algorithms
Bias
Cardiovascular disease
Cardiovascular diseases
Cardiovascular System - pathology
Cerebral infarction
Certificates
Coronary artery disease
Data Collection - methods
Databases, Factual
Death
Death, Sudden, Cardiac - epidemiology
Diagnostic systems
Drug Safety and Pharmacovigilance
Emergency medical services
Health risk assessment
Heart diseases
Humans
Identification methods
International Classification of Diseases
Literature reviews
Medical diagnosis
Medicine
Medicine & Public Health
Mortality
Myocardial infarction
Observational Studies as Topic
Pharmacology
Pharmacology/Toxicology
Public health
Quality assessment
Quality control
Risk assessment
Safety
Sensitivity analysis
Studies
Systematic Review
title Diagnostic Algorithms for Cardiovascular Death in Administrative Claims Databases: A Systematic Review
url https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-30T07%3A44%3A18IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Diagnostic%20Algorithms%20for%20Cardiovascular%20Death%20in%20Administrative%20Claims%20Databases:%20A%20Systematic%20Review&rft.jtitle=Drug%20safety&rft.au=Singh,%20Sonal&rft.date=2019-04-01&rft.volume=42&rft.issue=4&rft.spage=515&rft.epage=527&rft.pages=515-527&rft.issn=0114-5916&rft.eissn=1179-1942&rft_id=info:doi/10.1007/s40264-018-0754-z&rft_dat=%3Cproquest_cross%3E2260391299%3C/proquest_cross%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2260391299&rft_id=info:pmid/30471046&rfr_iscdi=true