Detection of Diabetes Status and Type in Youth Using Electronic Health Records: The SEARCH for Diabetes in Youth Study
Diabetes surveillance often requires manual medical chart reviews to confirm status and type. This project aimed to create an electronic health record (EHR)-based procedure for improving surveillance efficiency through automation of case identification. Youth (
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| Veröffentlicht in: | Diabetes care 2020-10, Vol.43 (10), p.2418-2425 |
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| creator | Wells, Brian J Lenoir, Kristin M Wagenknecht, Lynne E Mayer-Davis, Elizabeth J Lawrence, Jean M Dabelea, Dana Pihoker, Catherine Saydah, Sharon Casanova, Ramon Turley, Christine Liese, Angela D Standiford, Debra Kahn, Michael G Hamman, Richard Divers, Jasmin |
| description | Diabetes surveillance often requires manual medical chart reviews to confirm status and type. This project aimed to create an electronic health record (EHR)-based procedure for improving surveillance efficiency through automation of case identification.
Youth ( |
| doi_str_mv | 10.2337/dc20-0063 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_pubme</sourceid><recordid>TN_cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_7510036</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2429781717</sourcerecordid><originalsourceid>FETCH-LOGICAL-c403t-7dbd4022bc439707ea0de27682e7eaae2ce1ac9b20246dbd64d1831ab883ed643</originalsourceid><addsrcrecordid>eNpdkU1vEzEQhi0EoqFw4A8gS1zgsDD-2PUuB6QqDQSpElKTHjhZXnvSuNrYwd6tlH-Po5bwcfKM5_GjsV5CXjP4wIVQH53lUAE04gmZsU7UVV3L9imZAZNdVXcdPyMvcr4DACnb9jk5E1wJxSTMyP0ljmhHHwONG3rpTV_6TFejGadMTXB0fdgj9YH-iNO4pTfZh1u6GMqbFIO3dIlmKPfXaGNy-RNdb5GuFhfX8yXdxPTHeDKsxskdXpJnGzNkfPV4npObL4v1fFldff_6bX5xVVkJYqyU650EznsrRadAoQGHXDUtx1Ib5BaZsV3PgcumsI10rBXM9G0rsHTinHx-8O6nfofOYhiTGfQ--Z1JBx2N1_9Ogt_q23ivVc0ARFME7x4FKf6cMI9657PFYTAB45Q1l7xTLVNMFfTtf-hdnFIo3ytUDaoTXB6p9w-UTTHnhJvTMgz0MU19TFMf0yzsm7-3P5G_4xO_AIJxml4</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2450793247</pqid></control><display><type>article</type><title>Detection of Diabetes Status and Type in Youth Using Electronic Health Records: The SEARCH for Diabetes in Youth Study</title><source>MEDLINE</source><source>EZB Electronic Journals Library</source><creator>Wells, Brian J ; Lenoir, Kristin M ; Wagenknecht, Lynne E ; Mayer-Davis, Elizabeth J ; Lawrence, Jean M ; Dabelea, Dana ; Pihoker, Catherine ; Saydah, Sharon ; Casanova, Ramon ; Turley, Christine ; Liese, Angela D ; Standiford, Debra ; Kahn, Michael G ; Hamman, Richard ; Divers, Jasmin</creator><creatorcontrib>Wells, Brian J ; Lenoir, Kristin M ; Wagenknecht, Lynne E ; Mayer-Davis, Elizabeth J ; Lawrence, Jean M ; Dabelea, Dana ; Pihoker, Catherine ; Saydah, Sharon ; Casanova, Ramon ; Turley, Christine ; Liese, Angela D ; Standiford, Debra ; Kahn, Michael G ; Hamman, Richard ; Divers, Jasmin</creatorcontrib><description>Diabetes surveillance often requires manual medical chart reviews to confirm status and type. This project aimed to create an electronic health record (EHR)-based procedure for improving surveillance efficiency through automation of case identification.
Youth (<20 years old) with potential evidence of diabetes (
= 8,682) were identified from EHRs at three children's hospitals participating in the SEARCH for Diabetes in Youth Study. True diabetes status/type was determined by manual chart reviews. Multinomial regression was compared with an ICD-10 rule-based algorithm in the ability to correctly identify diabetes status and type. Subsequently, the investigators evaluated a scenario of combining the rule-based algorithm with targeted chart reviews where the algorithm performed poorly.
The sample included 5,308 true cases (89.2% type 1 diabetes). The rule-based algorithm outperformed regression for overall accuracy (0.955 vs. 0.936). Type 1 diabetes was classified well by both methods: sensitivity (
) (>0.95), specificity (
) (>0.96), and positive predictive value (PPV) (>0.97). In contrast, the PPVs for type 2 diabetes were 0.642 and 0.778 for the rule-based algorithm and the multinomial regression, respectively. Combination of the rule-based method with chart reviews (
= 695, 7.9%) of persons predicted to have non-type 1 diabetes resulted in perfect PPV for the cases reviewed while increasing overall accuracy (0.983). The
,
, and PPV for type 2 diabetes using the combined method were ≥0.91.
An ICD-10 algorithm combined with targeted chart reviews accurately identified diabetes status/type and could be an attractive option for diabetes surveillance in youth.</description><identifier>ISSN: 0149-5992</identifier><identifier>ISSN: 1935-5548</identifier><identifier>EISSN: 1935-5548</identifier><identifier>DOI: 10.2337/dc20-0063</identifier><identifier>PMID: 32737140</identifier><language>eng</language><publisher>United States: American Diabetes Association</publisher><subject>Adolescent ; Adult ; Age of Onset ; Algorithms ; Automation ; Children ; Diabetes ; Diabetes mellitus ; Diabetes mellitus (insulin dependent) ; Diabetes mellitus (non-insulin dependent) ; Diabetes Mellitus - diagnosis ; Diabetes Mellitus - epidemiology ; Diabetes Mellitus, Type 1 - diagnosis ; Diabetes Mellitus, Type 1 - epidemiology ; Diabetes Mellitus, Type 2 - diagnosis ; Diabetes Mellitus, Type 2 - epidemiology ; Electronic health records ; Electronic Health Records - statistics & numerical data ; Electronic medical records ; Epidemiology/Health Services Research ; Female ; Humans ; Male ; Mass Screening - methods ; Predictive Value of Tests ; Regression ; Research design ; Surveillance ; United States - epidemiology ; Young Adult</subject><ispartof>Diabetes care, 2020-10, Vol.43 (10), p.2418-2425</ispartof><rights>2020 by the American Diabetes Association.</rights><rights>Copyright American Diabetes Association Oct 1, 2020</rights><rights>2020 by the American Diabetes Association 2020</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c403t-7dbd4022bc439707ea0de27682e7eaae2ce1ac9b20246dbd64d1831ab883ed643</citedby><cites>FETCH-LOGICAL-c403t-7dbd4022bc439707ea0de27682e7eaae2ce1ac9b20246dbd64d1831ab883ed643</cites><orcidid>0000-0002-7022-0178 ; 0000-0001-9074-7770 ; 0000-0002-2480-8480 ; 0000-0001-7310-6525</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>230,314,780,784,885,27924,27925</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/32737140$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Wells, Brian J</creatorcontrib><creatorcontrib>Lenoir, Kristin M</creatorcontrib><creatorcontrib>Wagenknecht, Lynne E</creatorcontrib><creatorcontrib>Mayer-Davis, Elizabeth J</creatorcontrib><creatorcontrib>Lawrence, Jean M</creatorcontrib><creatorcontrib>Dabelea, Dana</creatorcontrib><creatorcontrib>Pihoker, Catherine</creatorcontrib><creatorcontrib>Saydah, Sharon</creatorcontrib><creatorcontrib>Casanova, Ramon</creatorcontrib><creatorcontrib>Turley, Christine</creatorcontrib><creatorcontrib>Liese, Angela D</creatorcontrib><creatorcontrib>Standiford, Debra</creatorcontrib><creatorcontrib>Kahn, Michael G</creatorcontrib><creatorcontrib>Hamman, Richard</creatorcontrib><creatorcontrib>Divers, Jasmin</creatorcontrib><title>Detection of Diabetes Status and Type in Youth Using Electronic Health Records: The SEARCH for Diabetes in Youth Study</title><title>Diabetes care</title><addtitle>Diabetes Care</addtitle><description>Diabetes surveillance often requires manual medical chart reviews to confirm status and type. This project aimed to create an electronic health record (EHR)-based procedure for improving surveillance efficiency through automation of case identification.
Youth (<20 years old) with potential evidence of diabetes (
= 8,682) were identified from EHRs at three children's hospitals participating in the SEARCH for Diabetes in Youth Study. True diabetes status/type was determined by manual chart reviews. Multinomial regression was compared with an ICD-10 rule-based algorithm in the ability to correctly identify diabetes status and type. Subsequently, the investigators evaluated a scenario of combining the rule-based algorithm with targeted chart reviews where the algorithm performed poorly.
The sample included 5,308 true cases (89.2% type 1 diabetes). The rule-based algorithm outperformed regression for overall accuracy (0.955 vs. 0.936). Type 1 diabetes was classified well by both methods: sensitivity (
) (>0.95), specificity (
) (>0.96), and positive predictive value (PPV) (>0.97). In contrast, the PPVs for type 2 diabetes were 0.642 and 0.778 for the rule-based algorithm and the multinomial regression, respectively. Combination of the rule-based method with chart reviews (
= 695, 7.9%) of persons predicted to have non-type 1 diabetes resulted in perfect PPV for the cases reviewed while increasing overall accuracy (0.983). The
,
, and PPV for type 2 diabetes using the combined method were ≥0.91.
An ICD-10 algorithm combined with targeted chart reviews accurately identified diabetes status/type and could be an attractive option for diabetes surveillance in youth.</description><subject>Adolescent</subject><subject>Adult</subject><subject>Age of Onset</subject><subject>Algorithms</subject><subject>Automation</subject><subject>Children</subject><subject>Diabetes</subject><subject>Diabetes mellitus</subject><subject>Diabetes mellitus (insulin dependent)</subject><subject>Diabetes mellitus (non-insulin dependent)</subject><subject>Diabetes Mellitus - diagnosis</subject><subject>Diabetes Mellitus - epidemiology</subject><subject>Diabetes Mellitus, Type 1 - diagnosis</subject><subject>Diabetes Mellitus, Type 1 - epidemiology</subject><subject>Diabetes Mellitus, Type 2 - diagnosis</subject><subject>Diabetes Mellitus, Type 2 - epidemiology</subject><subject>Electronic health records</subject><subject>Electronic Health Records - statistics & numerical data</subject><subject>Electronic medical records</subject><subject>Epidemiology/Health Services Research</subject><subject>Female</subject><subject>Humans</subject><subject>Male</subject><subject>Mass Screening - methods</subject><subject>Predictive Value of Tests</subject><subject>Regression</subject><subject>Research design</subject><subject>Surveillance</subject><subject>United States - epidemiology</subject><subject>Young Adult</subject><issn>0149-5992</issn><issn>1935-5548</issn><issn>1935-5548</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNpdkU1vEzEQhi0EoqFw4A8gS1zgsDD-2PUuB6QqDQSpElKTHjhZXnvSuNrYwd6tlH-Po5bwcfKM5_GjsV5CXjP4wIVQH53lUAE04gmZsU7UVV3L9imZAZNdVXcdPyMvcr4DACnb9jk5E1wJxSTMyP0ljmhHHwONG3rpTV_6TFejGadMTXB0fdgj9YH-iNO4pTfZh1u6GMqbFIO3dIlmKPfXaGNy-RNdb5GuFhfX8yXdxPTHeDKsxskdXpJnGzNkfPV4npObL4v1fFldff_6bX5xVVkJYqyU650EznsrRadAoQGHXDUtx1Ib5BaZsV3PgcumsI10rBXM9G0rsHTinHx-8O6nfofOYhiTGfQ--Z1JBx2N1_9Ogt_q23ivVc0ARFME7x4FKf6cMI9657PFYTAB45Q1l7xTLVNMFfTtf-hdnFIo3ytUDaoTXB6p9w-UTTHnhJvTMgz0MU19TFMf0yzsm7-3P5G_4xO_AIJxml4</recordid><startdate>20201001</startdate><enddate>20201001</enddate><creator>Wells, Brian J</creator><creator>Lenoir, Kristin M</creator><creator>Wagenknecht, Lynne E</creator><creator>Mayer-Davis, Elizabeth J</creator><creator>Lawrence, Jean M</creator><creator>Dabelea, Dana</creator><creator>Pihoker, Catherine</creator><creator>Saydah, Sharon</creator><creator>Casanova, Ramon</creator><creator>Turley, Christine</creator><creator>Liese, Angela D</creator><creator>Standiford, Debra</creator><creator>Kahn, Michael G</creator><creator>Hamman, Richard</creator><creator>Divers, Jasmin</creator><general>American Diabetes Association</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>K9.</scope><scope>NAPCQ</scope><scope>7X8</scope><scope>5PM</scope><orcidid>https://orcid.org/0000-0002-7022-0178</orcidid><orcidid>https://orcid.org/0000-0001-9074-7770</orcidid><orcidid>https://orcid.org/0000-0002-2480-8480</orcidid><orcidid>https://orcid.org/0000-0001-7310-6525</orcidid></search><sort><creationdate>20201001</creationdate><title>Detection of Diabetes Status and Type in Youth Using Electronic Health Records: The SEARCH for Diabetes in Youth Study</title><author>Wells, Brian J ; 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This project aimed to create an electronic health record (EHR)-based procedure for improving surveillance efficiency through automation of case identification.
Youth (<20 years old) with potential evidence of diabetes (
= 8,682) were identified from EHRs at three children's hospitals participating in the SEARCH for Diabetes in Youth Study. True diabetes status/type was determined by manual chart reviews. Multinomial regression was compared with an ICD-10 rule-based algorithm in the ability to correctly identify diabetes status and type. Subsequently, the investigators evaluated a scenario of combining the rule-based algorithm with targeted chart reviews where the algorithm performed poorly.
The sample included 5,308 true cases (89.2% type 1 diabetes). The rule-based algorithm outperformed regression for overall accuracy (0.955 vs. 0.936). Type 1 diabetes was classified well by both methods: sensitivity (
) (>0.95), specificity (
) (>0.96), and positive predictive value (PPV) (>0.97). In contrast, the PPVs for type 2 diabetes were 0.642 and 0.778 for the rule-based algorithm and the multinomial regression, respectively. Combination of the rule-based method with chart reviews (
= 695, 7.9%) of persons predicted to have non-type 1 diabetes resulted in perfect PPV for the cases reviewed while increasing overall accuracy (0.983). The
,
, and PPV for type 2 diabetes using the combined method were ≥0.91.
An ICD-10 algorithm combined with targeted chart reviews accurately identified diabetes status/type and could be an attractive option for diabetes surveillance in youth.</abstract><cop>United States</cop><pub>American Diabetes Association</pub><pmid>32737140</pmid><doi>10.2337/dc20-0063</doi><tpages>8</tpages><orcidid>https://orcid.org/0000-0002-7022-0178</orcidid><orcidid>https://orcid.org/0000-0001-9074-7770</orcidid><orcidid>https://orcid.org/0000-0002-2480-8480</orcidid><orcidid>https://orcid.org/0000-0001-7310-6525</orcidid><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 0149-5992 |
| ispartof | Diabetes care, 2020-10, Vol.43 (10), p.2418-2425 |
| issn | 0149-5992 1935-5548 1935-5548 |
| language | eng |
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| source | MEDLINE; EZB Electronic Journals Library |
| subjects | Adolescent Adult Age of Onset Algorithms Automation Children Diabetes Diabetes mellitus Diabetes mellitus (insulin dependent) Diabetes mellitus (non-insulin dependent) Diabetes Mellitus - diagnosis Diabetes Mellitus - epidemiology Diabetes Mellitus, Type 1 - diagnosis Diabetes Mellitus, Type 1 - epidemiology Diabetes Mellitus, Type 2 - diagnosis Diabetes Mellitus, Type 2 - epidemiology Electronic health records Electronic Health Records - statistics & numerical data Electronic medical records Epidemiology/Health Services Research Female Humans Male Mass Screening - methods Predictive Value of Tests Regression Research design Surveillance United States - epidemiology Young Adult |
| title | Detection of Diabetes Status and Type in Youth Using Electronic Health Records: The SEARCH for Diabetes in Youth Study |
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