1046-P: Automated Early Flagging of Undiagnosed CKD Using Real-World Data
Aims: Early diagnosis and treatment of chronic kidney disease (CKD) may delay disease progression since 45% of patients are not diagnosed until stage 3. We examine the Kidney Disease Improving Global Outcomes (KDIGO) clinical guidelines that define rules based on the longitudinal progression of urin...
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creator | MALAGARRIGA, DANIEL CHITTAJALLU, SIVA GALLEY, PAUL J. SINGH, INDERJIT ODEGARD, JOHN T. |
description | Aims: Early diagnosis and treatment of chronic kidney disease (CKD) may delay disease progression since 45% of patients are not diagnosed until stage 3. We examine the Kidney Disease Improving Global Outcomes (KDIGO) clinical guidelines that define rules based on the longitudinal progression of urine albumin creatinine ratio (UACR) and estimated glomerular filtration rate (eGFR) for flagging CKD in people with type 2 diabetes (T2-PwD).
Methods: EHR data from over 400,000 T2-PwDs in the Indiana Network for Patient Care (INPC) database were used for this analysis. The KDIGO clinical practice guidelines defines CKD as persistently elevated urine albumin excretion (>=30 mg/g), or persistently reduced eGFR (< 60 ml/min per 1.73m2), or both, for more than 3 months. Our automated system scanned each patient’s longitudinal record and identified those patients with abnormal UACR and eGFR values in the cases where sufficient values were available. A flag associated with either UACR or eGFR or both and the date of flagging is indicated by the system if abnormal marker values persist for more than 3 months. CKD is indicated if one or both flags are triggered.
Results: Out of 422,089 patients 35,573 (8%) were identified as diagnosed with CKD. Only 3% of these patients had at least 2 UACR readings and, therefore, the automated system considered primarily eGFR as the marker for flagging CKD. Out of 35,573 patients with CKD diagnosis, 16,342 (46%) had an eGFR warning flag that predated the CKD diagnosis date. The system flagged a potential identification as much as 599 days (median value) in advance of the diagnosis of CKD in the dataset.
Conclusion: Our results show that earlier identification of CKD onset can be automated to flag patients wherein a positive diagnosis of CKD is likely. This flag can then prompt a confirmatory diagnosis of CKD, allowing better treatment interventions and management of CKD, reducing the burden of disease, and potentially decreasing costs associated with treatments in later CKD stages. |
doi_str_mv | 10.2337/db21-1046-P |
format | Article |
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Methods: EHR data from over 400,000 T2-PwDs in the Indiana Network for Patient Care (INPC) database were used for this analysis. The KDIGO clinical practice guidelines defines CKD as persistently elevated urine albumin excretion (>=30 mg/g), or persistently reduced eGFR (< 60 ml/min per 1.73m2), or both, for more than 3 months. Our automated system scanned each patient’s longitudinal record and identified those patients with abnormal UACR and eGFR values in the cases where sufficient values were available. A flag associated with either UACR or eGFR or both and the date of flagging is indicated by the system if abnormal marker values persist for more than 3 months. CKD is indicated if one or both flags are triggered.
Results: Out of 422,089 patients 35,573 (8%) were identified as diagnosed with CKD. Only 3% of these patients had at least 2 UACR readings and, therefore, the automated system considered primarily eGFR as the marker for flagging CKD. Out of 35,573 patients with CKD diagnosis, 16,342 (46%) had an eGFR warning flag that predated the CKD diagnosis date. The system flagged a potential identification as much as 599 days (median value) in advance of the diagnosis of CKD in the dataset.
Conclusion: Our results show that earlier identification of CKD onset can be automated to flag patients wherein a positive diagnosis of CKD is likely. This flag can then prompt a confirmatory diagnosis of CKD, allowing better treatment interventions and management of CKD, reducing the burden of disease, and potentially decreasing costs associated with treatments in later CKD stages.</description><identifier>ISSN: 0012-1797</identifier><identifier>EISSN: 1939-327X</identifier><identifier>DOI: 10.2337/db21-1046-P</identifier><language>eng</language><publisher>New York: American Diabetes Association</publisher><subject>Albumin ; Automation ; Clinical practice guidelines ; Creatinine ; Diabetes ; Diabetes mellitus (non-insulin dependent) ; Diagnosis ; Epidermal growth factor receptors ; Glomerular filtration rate ; Kidney diseases ; Patients</subject><ispartof>Diabetes (New York, N.Y.), 2021-06, Vol.70 (Supplement_1)</ispartof><rights>Copyright American Diabetes Association Jun 1, 2021</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>314,777,781,27905,27906</link.rule.ids></links><search><creatorcontrib>MALAGARRIGA, DANIEL</creatorcontrib><creatorcontrib>CHITTAJALLU, SIVA</creatorcontrib><creatorcontrib>GALLEY, PAUL J.</creatorcontrib><creatorcontrib>SINGH, INDERJIT</creatorcontrib><creatorcontrib>ODEGARD, JOHN T.</creatorcontrib><title>1046-P: Automated Early Flagging of Undiagnosed CKD Using Real-World Data</title><title>Diabetes (New York, N.Y.)</title><description>Aims: Early diagnosis and treatment of chronic kidney disease (CKD) may delay disease progression since 45% of patients are not diagnosed until stage 3. We examine the Kidney Disease Improving Global Outcomes (KDIGO) clinical guidelines that define rules based on the longitudinal progression of urine albumin creatinine ratio (UACR) and estimated glomerular filtration rate (eGFR) for flagging CKD in people with type 2 diabetes (T2-PwD).
Methods: EHR data from over 400,000 T2-PwDs in the Indiana Network for Patient Care (INPC) database were used for this analysis. The KDIGO clinical practice guidelines defines CKD as persistently elevated urine albumin excretion (>=30 mg/g), or persistently reduced eGFR (< 60 ml/min per 1.73m2), or both, for more than 3 months. Our automated system scanned each patient’s longitudinal record and identified those patients with abnormal UACR and eGFR values in the cases where sufficient values were available. A flag associated with either UACR or eGFR or both and the date of flagging is indicated by the system if abnormal marker values persist for more than 3 months. CKD is indicated if one or both flags are triggered.
Results: Out of 422,089 patients 35,573 (8%) were identified as diagnosed with CKD. Only 3% of these patients had at least 2 UACR readings and, therefore, the automated system considered primarily eGFR as the marker for flagging CKD. Out of 35,573 patients with CKD diagnosis, 16,342 (46%) had an eGFR warning flag that predated the CKD diagnosis date. The system flagged a potential identification as much as 599 days (median value) in advance of the diagnosis of CKD in the dataset.
Conclusion: Our results show that earlier identification of CKD onset can be automated to flag patients wherein a positive diagnosis of CKD is likely. This flag can then prompt a confirmatory diagnosis of CKD, allowing better treatment interventions and management of CKD, reducing the burden of disease, and potentially decreasing costs associated with treatments in later CKD stages.</description><subject>Albumin</subject><subject>Automation</subject><subject>Clinical practice guidelines</subject><subject>Creatinine</subject><subject>Diabetes</subject><subject>Diabetes mellitus (non-insulin dependent)</subject><subject>Diagnosis</subject><subject>Epidermal growth factor receptors</subject><subject>Glomerular filtration rate</subject><subject>Kidney diseases</subject><subject>Patients</subject><issn>0012-1797</issn><issn>1939-327X</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><recordid>eNotkE9LAzEUxIMoWKsnv0DAo0Rf_jTZ9VZqq8WCRVr0Ft52k6Vlu6nJ9tBv7y6VObzD_HjDDCH3HJ6ElOa5LARnHJRmywsy4LnMmRTm55IMALhg3OTmmtyktAMA3WlA5mf6hY6Pbdhj60o6xVif6KzGqto2FQ2erptyi1UTUudOPl7pOvXGl8OafYdYl_QVW7wlVx7r5O7-75CsZtPV5J0tPt_mk_GCbbSSzCgAKRBKoZ1WXsp85EBwOcq898qYErhHrQospM69ERykFjnHDWa8UJDJIXk4vz3E8Ht0qbW7cIxNl2jFSAuhsy6mox7P1CaGlKLz9hC3e4wny8H2U9l-KtuXt0v5B1vBV9w</recordid><startdate>20210601</startdate><enddate>20210601</enddate><creator>MALAGARRIGA, DANIEL</creator><creator>CHITTAJALLU, SIVA</creator><creator>GALLEY, PAUL J.</creator><creator>SINGH, INDERJIT</creator><creator>ODEGARD, JOHN T.</creator><general>American Diabetes Association</general><scope>AAYXX</scope><scope>CITATION</scope><scope>K9.</scope><scope>NAPCQ</scope></search><sort><creationdate>20210601</creationdate><title>1046-P: Automated Early Flagging of Undiagnosed CKD Using Real-World Data</title><author>MALAGARRIGA, DANIEL ; CHITTAJALLU, SIVA ; GALLEY, PAUL J. ; SINGH, INDERJIT ; ODEGARD, JOHN T.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c643-740032a0d26e64f3395e021358fff477d01fa64bab369f721036291aca81b4083</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Albumin</topic><topic>Automation</topic><topic>Clinical practice guidelines</topic><topic>Creatinine</topic><topic>Diabetes</topic><topic>Diabetes mellitus (non-insulin dependent)</topic><topic>Diagnosis</topic><topic>Epidermal growth factor receptors</topic><topic>Glomerular filtration rate</topic><topic>Kidney diseases</topic><topic>Patients</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>MALAGARRIGA, DANIEL</creatorcontrib><creatorcontrib>CHITTAJALLU, SIVA</creatorcontrib><creatorcontrib>GALLEY, PAUL J.</creatorcontrib><creatorcontrib>SINGH, INDERJIT</creatorcontrib><creatorcontrib>ODEGARD, JOHN T.</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>MALAGARRIGA, DANIEL</au><au>CHITTAJALLU, SIVA</au><au>GALLEY, PAUL J.</au><au>SINGH, INDERJIT</au><au>ODEGARD, JOHN T.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>1046-P: Automated Early Flagging of Undiagnosed CKD Using Real-World Data</atitle><jtitle>Diabetes (New York, N.Y.)</jtitle><date>2021-06-01</date><risdate>2021</risdate><volume>70</volume><issue>Supplement_1</issue><issn>0012-1797</issn><eissn>1939-327X</eissn><abstract>Aims: Early diagnosis and treatment of chronic kidney disease (CKD) may delay disease progression since 45% of patients are not diagnosed until stage 3. We examine the Kidney Disease Improving Global Outcomes (KDIGO) clinical guidelines that define rules based on the longitudinal progression of urine albumin creatinine ratio (UACR) and estimated glomerular filtration rate (eGFR) for flagging CKD in people with type 2 diabetes (T2-PwD).
Methods: EHR data from over 400,000 T2-PwDs in the Indiana Network for Patient Care (INPC) database were used for this analysis. The KDIGO clinical practice guidelines defines CKD as persistently elevated urine albumin excretion (>=30 mg/g), or persistently reduced eGFR (< 60 ml/min per 1.73m2), or both, for more than 3 months. Our automated system scanned each patient’s longitudinal record and identified those patients with abnormal UACR and eGFR values in the cases where sufficient values were available. A flag associated with either UACR or eGFR or both and the date of flagging is indicated by the system if abnormal marker values persist for more than 3 months. CKD is indicated if one or both flags are triggered.
Results: Out of 422,089 patients 35,573 (8%) were identified as diagnosed with CKD. Only 3% of these patients had at least 2 UACR readings and, therefore, the automated system considered primarily eGFR as the marker for flagging CKD. Out of 35,573 patients with CKD diagnosis, 16,342 (46%) had an eGFR warning flag that predated the CKD diagnosis date. The system flagged a potential identification as much as 599 days (median value) in advance of the diagnosis of CKD in the dataset.
Conclusion: Our results show that earlier identification of CKD onset can be automated to flag patients wherein a positive diagnosis of CKD is likely. This flag can then prompt a confirmatory diagnosis of CKD, allowing better treatment interventions and management of CKD, reducing the burden of disease, and potentially decreasing costs associated with treatments in later CKD stages.</abstract><cop>New York</cop><pub>American Diabetes Association</pub><doi>10.2337/db21-1046-P</doi></addata></record> |
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source | Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals; PubMed Central |
subjects | Albumin Automation Clinical practice guidelines Creatinine Diabetes Diabetes mellitus (non-insulin dependent) Diagnosis Epidermal growth factor receptors Glomerular filtration rate Kidney diseases Patients |
title | 1046-P: Automated Early Flagging of Undiagnosed CKD Using Real-World Data |
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