1294-P: A Novel Electronic Medical Record (EMR) Search Method to Identify Atypical Diabetes Patients
Introduction & Objectives: Efficient automated EMR review methods to identify people with atypical diabetes (DM) for research are lacking. We aimed to develop a novel Python-based Expeditious Program for EMR Review (PEPPER) and assess its efficiency. Methods: We extracted the list of 1660 youth...
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| Veröffentlicht in: | Diabetes (New York, N.Y.) N.Y.), 2024-06, Vol.73 (Supplement_1), p.1 |
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| container_title | Diabetes (New York, N.Y.) |
| container_volume | 73 |
| creator | AHMED, MAAZ KUBOTA-MISHRA, ELIZABETH A. SILLER, ALEJANDRO F. DAVIS, ANSLEY E. MIGACZ, ILIANA SISLEY, STEPHANIE FARUQI, JORDANA SAEED, ZEB I. AHMED, SARAH PHILIPSON, LOUIS H. REDONDO, MARIA J. BALASUBRAMANYAM, ASHOK TOSUR, MUSTAFA STUDY GROUP, RADIANT |
| description | Introduction & Objectives: Efficient automated EMR review methods to identify people with atypical diabetes (DM) for research are lacking. We aimed to develop a novel Python-based Expeditious Program for EMR Review (PEPPER) and assess its efficiency.
Methods: We extracted the list of 1660 youth ( |
| doi_str_mv | 10.2337/db24-1294-P |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_journals_3111275656</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>3111275656</sourcerecordid><originalsourceid>FETCH-LOGICAL-c646-e6ee5223e50b7cb344f6acc58c8cbb1a2b210f0a51588ba1899630ce347f3ead3</originalsourceid><addsrcrecordid>eNotkE1LAzEURYMoWKsr_0DAjSLRfEwyM-5KW7XQaqlduAtJ5g2dMjY1SYX-e6dW7uIt7uFdOAhdM_rAhcgfK8szwniZkfkJ6rFSlETw_PMU9ShlnLC8zM_RRYxrSqnq0kPVkX7CA_zmf6DF4xZcCn7TODyDqnGmxQtwPlT4djxb3OEPMMGtui6tfIWTx5MKNqmp93iQ9ts_ftQYCwkinpvUdGW8RGe1aSNc_d8-Wj6Pl8NXMn1_mQwHU-JUpggoAMm5AElt7qzIsloZ52ThCmctM9xyRmtqJJNFYQ0rylIJ6kBkeS3AVKKPbo5vt8F_7yAmvfa7sOkWtWCM8VwqqTrq_ki54GMMUOttaL5M2GtG9cGiPljUBy96Ln4BR01i_g</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>3111275656</pqid></control><display><type>article</type><title>1294-P: A Novel Electronic Medical Record (EMR) Search Method to Identify Atypical Diabetes Patients</title><source>EZB-FREE-00999 freely available EZB journals</source><creator>AHMED, MAAZ ; KUBOTA-MISHRA, ELIZABETH A. ; SILLER, ALEJANDRO F. ; DAVIS, ANSLEY E. ; MIGACZ, ILIANA ; SISLEY, STEPHANIE ; FARUQI, JORDANA ; SAEED, ZEB I. ; AHMED, SARAH ; PHILIPSON, LOUIS H. ; REDONDO, MARIA J. ; BALASUBRAMANYAM, ASHOK ; TOSUR, MUSTAFA ; STUDY GROUP, RADIANT</creator><creatorcontrib>AHMED, MAAZ ; KUBOTA-MISHRA, ELIZABETH A. ; SILLER, ALEJANDRO F. ; DAVIS, ANSLEY E. ; MIGACZ, ILIANA ; SISLEY, STEPHANIE ; FARUQI, JORDANA ; SAEED, ZEB I. ; AHMED, SARAH ; PHILIPSON, LOUIS H. ; REDONDO, MARIA J. ; BALASUBRAMANYAM, ASHOK ; TOSUR, MUSTAFA ; STUDY GROUP, RADIANT</creatorcontrib><description>Introduction & Objectives: Efficient automated EMR review methods to identify people with atypical diabetes (DM) for research are lacking. We aimed to develop a novel Python-based Expeditious Program for EMR Review (PEPPER) and assess its efficiency.
Methods: We extracted the list of 1660 youth (<19 yo) with type 2 DM (T2D) seen between 2019-2022 from EMR to identify candidates with A-β+ (autoantibody negative, preserved β-cell function) Ketosis-prone DM (KPD) for enrollment into Rare and Atypical Diabetes Network (RADIANT). We developed PEPPER to identify diabetic ketoacidosis (DKA) occurrence within 6 months (mo) of diagnosis to prioritize individuals for detailed manual chart review for RADIANT eligibility. We also manually reviewed EMR of 100 youth with T2D to identify DKA occurrence within 6 mo of diagnosis without PEPPER for comparison.
Results: PEPPER identified 110 youth with T2D who had DKA within 6 mo of diagnosis. Twenty-one met the RADIANT A-β+ KPD criteria. The time spent to identify those with T2D and DKA was significantly shorter with PEPPER compared to manual review (13.4 ± 3.9 vs. 26.6 ± 9.4 seconds, p<0.001), translating to 6.2 vs. 12.3 hours to review 1660 charts with and without PEPPER. Both methods yielded identical results, confirming PEPPER’s accuracy.
Conclusion: We developed a novel, efficient and reliable EMR review method that could be used on large cohorts to identify research candidates.</description><identifier>ISSN: 0012-1797</identifier><identifier>EISSN: 1939-327X</identifier><identifier>DOI: 10.2337/db24-1294-P</identifier><language>eng</language><publisher>New York: American Diabetes Association</publisher><subject>Autoantibodies ; Beta cells ; Diabetes ; Diabetes mellitus ; Diagnosis ; Electronic medical records ; Ketoacidosis</subject><ispartof>Diabetes (New York, N.Y.), 2024-06, Vol.73 (Supplement_1), p.1</ispartof><rights>Copyright American Diabetes Association Jun 2024</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,776,780,27901,27902</link.rule.ids></links><search><creatorcontrib>AHMED, MAAZ</creatorcontrib><creatorcontrib>KUBOTA-MISHRA, ELIZABETH A.</creatorcontrib><creatorcontrib>SILLER, ALEJANDRO F.</creatorcontrib><creatorcontrib>DAVIS, ANSLEY E.</creatorcontrib><creatorcontrib>MIGACZ, ILIANA</creatorcontrib><creatorcontrib>SISLEY, STEPHANIE</creatorcontrib><creatorcontrib>FARUQI, JORDANA</creatorcontrib><creatorcontrib>SAEED, ZEB I.</creatorcontrib><creatorcontrib>AHMED, SARAH</creatorcontrib><creatorcontrib>PHILIPSON, LOUIS H.</creatorcontrib><creatorcontrib>REDONDO, MARIA J.</creatorcontrib><creatorcontrib>BALASUBRAMANYAM, ASHOK</creatorcontrib><creatorcontrib>TOSUR, MUSTAFA</creatorcontrib><creatorcontrib>STUDY GROUP, RADIANT</creatorcontrib><title>1294-P: A Novel Electronic Medical Record (EMR) Search Method to Identify Atypical Diabetes Patients</title><title>Diabetes (New York, N.Y.)</title><description>Introduction & Objectives: Efficient automated EMR review methods to identify people with atypical diabetes (DM) for research are lacking. We aimed to develop a novel Python-based Expeditious Program for EMR Review (PEPPER) and assess its efficiency.
Methods: We extracted the list of 1660 youth (<19 yo) with type 2 DM (T2D) seen between 2019-2022 from EMR to identify candidates with A-β+ (autoantibody negative, preserved β-cell function) Ketosis-prone DM (KPD) for enrollment into Rare and Atypical Diabetes Network (RADIANT). We developed PEPPER to identify diabetic ketoacidosis (DKA) occurrence within 6 months (mo) of diagnosis to prioritize individuals for detailed manual chart review for RADIANT eligibility. We also manually reviewed EMR of 100 youth with T2D to identify DKA occurrence within 6 mo of diagnosis without PEPPER for comparison.
Results: PEPPER identified 110 youth with T2D who had DKA within 6 mo of diagnosis. Twenty-one met the RADIANT A-β+ KPD criteria. The time spent to identify those with T2D and DKA was significantly shorter with PEPPER compared to manual review (13.4 ± 3.9 vs. 26.6 ± 9.4 seconds, p<0.001), translating to 6.2 vs. 12.3 hours to review 1660 charts with and without PEPPER. Both methods yielded identical results, confirming PEPPER’s accuracy.
Conclusion: We developed a novel, efficient and reliable EMR review method that could be used on large cohorts to identify research candidates.</description><subject>Autoantibodies</subject><subject>Beta cells</subject><subject>Diabetes</subject><subject>Diabetes mellitus</subject><subject>Diagnosis</subject><subject>Electronic medical records</subject><subject>Ketoacidosis</subject><issn>0012-1797</issn><issn>1939-327X</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><recordid>eNotkE1LAzEURYMoWKsr_0DAjSLRfEwyM-5KW7XQaqlduAtJ5g2dMjY1SYX-e6dW7uIt7uFdOAhdM_rAhcgfK8szwniZkfkJ6rFSlETw_PMU9ShlnLC8zM_RRYxrSqnq0kPVkX7CA_zmf6DF4xZcCn7TODyDqnGmxQtwPlT4djxb3OEPMMGtui6tfIWTx5MKNqmp93iQ9ts_ftQYCwkinpvUdGW8RGe1aSNc_d8-Wj6Pl8NXMn1_mQwHU-JUpggoAMm5AElt7qzIsloZ52ThCmctM9xyRmtqJJNFYQ0rylIJ6kBkeS3AVKKPbo5vt8F_7yAmvfa7sOkWtWCM8VwqqTrq_ki54GMMUOttaL5M2GtG9cGiPljUBy96Ln4BR01i_g</recordid><startdate>20240614</startdate><enddate>20240614</enddate><creator>AHMED, MAAZ</creator><creator>KUBOTA-MISHRA, ELIZABETH A.</creator><creator>SILLER, ALEJANDRO F.</creator><creator>DAVIS, ANSLEY E.</creator><creator>MIGACZ, ILIANA</creator><creator>SISLEY, STEPHANIE</creator><creator>FARUQI, JORDANA</creator><creator>SAEED, ZEB I.</creator><creator>AHMED, SARAH</creator><creator>PHILIPSON, LOUIS H.</creator><creator>REDONDO, MARIA J.</creator><creator>BALASUBRAMANYAM, ASHOK</creator><creator>TOSUR, MUSTAFA</creator><creator>STUDY GROUP, RADIANT</creator><general>American Diabetes Association</general><scope>AAYXX</scope><scope>CITATION</scope><scope>K9.</scope><scope>NAPCQ</scope></search><sort><creationdate>20240614</creationdate><title>1294-P: A Novel Electronic Medical Record (EMR) Search Method to Identify Atypical Diabetes Patients</title><author>AHMED, MAAZ ; KUBOTA-MISHRA, ELIZABETH A. ; SILLER, ALEJANDRO F. ; DAVIS, ANSLEY E. ; MIGACZ, ILIANA ; SISLEY, STEPHANIE ; FARUQI, JORDANA ; SAEED, ZEB I. ; AHMED, SARAH ; PHILIPSON, LOUIS H. ; REDONDO, MARIA J. ; BALASUBRAMANYAM, ASHOK ; TOSUR, MUSTAFA ; STUDY GROUP, RADIANT</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c646-e6ee5223e50b7cb344f6acc58c8cbb1a2b210f0a51588ba1899630ce347f3ead3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2024</creationdate><topic>Autoantibodies</topic><topic>Beta cells</topic><topic>Diabetes</topic><topic>Diabetes mellitus</topic><topic>Diagnosis</topic><topic>Electronic medical records</topic><topic>Ketoacidosis</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>AHMED, MAAZ</creatorcontrib><creatorcontrib>KUBOTA-MISHRA, ELIZABETH A.</creatorcontrib><creatorcontrib>SILLER, ALEJANDRO F.</creatorcontrib><creatorcontrib>DAVIS, ANSLEY E.</creatorcontrib><creatorcontrib>MIGACZ, ILIANA</creatorcontrib><creatorcontrib>SISLEY, STEPHANIE</creatorcontrib><creatorcontrib>FARUQI, JORDANA</creatorcontrib><creatorcontrib>SAEED, ZEB I.</creatorcontrib><creatorcontrib>AHMED, SARAH</creatorcontrib><creatorcontrib>PHILIPSON, LOUIS H.</creatorcontrib><creatorcontrib>REDONDO, MARIA J.</creatorcontrib><creatorcontrib>BALASUBRAMANYAM, ASHOK</creatorcontrib><creatorcontrib>TOSUR, MUSTAFA</creatorcontrib><creatorcontrib>STUDY GROUP, RADIANT</creatorcontrib><collection>CrossRef</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Nursing & Allied Health Premium</collection><jtitle>Diabetes (New York, N.Y.)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>AHMED, MAAZ</au><au>KUBOTA-MISHRA, ELIZABETH A.</au><au>SILLER, ALEJANDRO F.</au><au>DAVIS, ANSLEY E.</au><au>MIGACZ, ILIANA</au><au>SISLEY, STEPHANIE</au><au>FARUQI, JORDANA</au><au>SAEED, ZEB I.</au><au>AHMED, SARAH</au><au>PHILIPSON, LOUIS H.</au><au>REDONDO, MARIA J.</au><au>BALASUBRAMANYAM, ASHOK</au><au>TOSUR, MUSTAFA</au><au>STUDY GROUP, RADIANT</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>1294-P: A Novel Electronic Medical Record (EMR) Search Method to Identify Atypical Diabetes Patients</atitle><jtitle>Diabetes (New York, N.Y.)</jtitle><date>2024-06-14</date><risdate>2024</risdate><volume>73</volume><issue>Supplement_1</issue><spage>1</spage><pages>1-</pages><issn>0012-1797</issn><eissn>1939-327X</eissn><abstract>Introduction & Objectives: Efficient automated EMR review methods to identify people with atypical diabetes (DM) for research are lacking. We aimed to develop a novel Python-based Expeditious Program for EMR Review (PEPPER) and assess its efficiency.
Methods: We extracted the list of 1660 youth (<19 yo) with type 2 DM (T2D) seen between 2019-2022 from EMR to identify candidates with A-β+ (autoantibody negative, preserved β-cell function) Ketosis-prone DM (KPD) for enrollment into Rare and Atypical Diabetes Network (RADIANT). We developed PEPPER to identify diabetic ketoacidosis (DKA) occurrence within 6 months (mo) of diagnosis to prioritize individuals for detailed manual chart review for RADIANT eligibility. We also manually reviewed EMR of 100 youth with T2D to identify DKA occurrence within 6 mo of diagnosis without PEPPER for comparison.
Results: PEPPER identified 110 youth with T2D who had DKA within 6 mo of diagnosis. Twenty-one met the RADIANT A-β+ KPD criteria. The time spent to identify those with T2D and DKA was significantly shorter with PEPPER compared to manual review (13.4 ± 3.9 vs. 26.6 ± 9.4 seconds, p<0.001), translating to 6.2 vs. 12.3 hours to review 1660 charts with and without PEPPER. Both methods yielded identical results, confirming PEPPER’s accuracy.
Conclusion: We developed a novel, efficient and reliable EMR review method that could be used on large cohorts to identify research candidates.</abstract><cop>New York</cop><pub>American Diabetes Association</pub><doi>10.2337/db24-1294-P</doi></addata></record> |
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| ispartof | Diabetes (New York, N.Y.), 2024-06, Vol.73 (Supplement_1), p.1 |
| issn | 0012-1797 1939-327X |
| language | eng |
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| source | EZB-FREE-00999 freely available EZB journals |
| subjects | Autoantibodies Beta cells Diabetes Diabetes mellitus Diagnosis Electronic medical records Ketoacidosis |
| title | 1294-P: A Novel Electronic Medical Record (EMR) Search Method to Identify Atypical Diabetes Patients |
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