EMR-Based Tool for Identifying Type 2 Diabetic Patients at High Risk for Hypoglycemia
Objective. To develop and validate a risk stratification tool to categorize 12-month risk of hypoglycemia-related emergency department (ED) or hospital use among patients with type 2 diabetes (T2D). Design. Prospective cohort study. Setting and participants. Patients with T2D from Kaiser Permanente...
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description | Objective. To develop and validate a risk stratification tool to categorize 12-month risk of hypoglycemia-related emergency department (ED) or hospital use among patients with type 2 diabetes (T2D). Design. Prospective cohort study. Setting and participants. Patients with T2D from Kaiser Permanente Northern California were identified using electronic medical records (EMR). Patients had to be 21 years of age or older as of the baseline date of 1 January 2014, with continuous health plan membership for 24 months prebaseline and pharmacy benefits for 12 months prebaseline. Of the 233,330 adults identified, 24,719 were excluded for unknown diabetes type, and 3614 were excluded for type 1 diabetes. The remaining 206,435 eligible patients with T2D were randomly split into an 80% derivation sample (n = 165,148) for tool development and 20% internal validation sample (n = 41,287). Using similar eligibility criteria, 2 external validation samples were derived from the Veterans Administration Diabetes Epidemiology Cohort (VA) (n = 1,335,966 adults) as well as from Group Health Cooperative (GH) (n = 14,972). Main outcome measure. The primary outcome was the occurrence of any hypoglycemia-related ED visit or hospital use during the 12 months postbaseline. A primary diagnosis of hypoglycemia was ascertained using the following International Classification of Diseases, Ninth Revision (ICD-9) codes: 251.0, 251.1, 251.2, 962.3, or 250.8, without concurrent 259.3, 272.7, 681.xx, 686.9x, 707.a-707.9, 709.3, 730.0-730.2, or 731.8 codes [1]. Secondary discharge diagnoses for hypoglycemia were not used because they are often attributable to events that occurred during the ED or hospital encounter. Main results. Beginning with 156 (122 categorical and 34 continuous) candidate clinical, demographic, and behavioral predictor variables for model development, the final classification tree was based on 6 patient-specific variables: total number of prior episodes of hypoglycemia-related ED or hospital utilization (0, 1-2, ≥ 3 times), number of ED encounters for any reason in the prior 12 months (< 2, ≥ 2 times), insulin use (yes/no), sulfonylurea use (yes/no), presence of severe or end-stage kidney disease (dialysis or chronic kidney disease stage 4 or 5 determined by estimated glomerular filtration rate of ≤ 29 mL/min/1.73 m2 (yes/no), and age younger than 77 years (yes/no). This classification tree resulted in 10 mutually exclusive leaf nodes, each yielding an estimated annual risk |
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fullrecord | <record><control><sourceid>proquest</sourceid><recordid>TN_cdi_proquest_journals_2007938534</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2007938534</sourcerecordid><originalsourceid>FETCH-LOGICAL-p98t-a7e1c69ba50c9c65f463404c20989562c02df7a8e00a563756a2e439b9a8a7753</originalsourceid><addsrcrecordid>eNotj01Lw0AYhBdRsFb_w4LnwNvd7NdRazWFilLiubzZbOLWmI3Z7SH_3lA9zcAwMzwXZLEyXGcrzuXl7EGZTArOr8lNjEcAUKBhQT42r_vsEaOraRlCR5sw0m3t-uSbyfctLafBUUafPFYueUvfMfk5jRQTLXz7Sfc-fp1bxTSEtpus-_Z4S64a7KK7-9clKZ835brIdm8v2_XDLhuMThkqt7LSVCjAGitFk0ueQ24ZGG2EZBZY3SjUDgCF5EpIZC7npjKoUSnBl-T-b3YYw8_JxXQ4htPYz48HNgPO-ILn_BdmM0uA</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2007938534</pqid></control><display><type>article</type><title>EMR-Based Tool for Identifying Type 2 Diabetic Patients at High Risk for Hypoglycemia</title><source>Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals</source><description>Objective. To develop and validate a risk stratification tool to categorize 12-month risk of hypoglycemia-related emergency department (ED) or hospital use among patients with type 2 diabetes (T2D). Design. Prospective cohort study. Setting and participants. Patients with T2D from Kaiser Permanente Northern California were identified using electronic medical records (EMR). Patients had to be 21 years of age or older as of the baseline date of 1 January 2014, with continuous health plan membership for 24 months prebaseline and pharmacy benefits for 12 months prebaseline. Of the 233,330 adults identified, 24,719 were excluded for unknown diabetes type, and 3614 were excluded for type 1 diabetes. The remaining 206,435 eligible patients with T2D were randomly split into an 80% derivation sample (n = 165,148) for tool development and 20% internal validation sample (n = 41,287). Using similar eligibility criteria, 2 external validation samples were derived from the Veterans Administration Diabetes Epidemiology Cohort (VA) (n = 1,335,966 adults) as well as from Group Health Cooperative (GH) (n = 14,972). Main outcome measure. The primary outcome was the occurrence of any hypoglycemia-related ED visit or hospital use during the 12 months postbaseline. A primary diagnosis of hypoglycemia was ascertained using the following International Classification of Diseases, Ninth Revision (ICD-9) codes: 251.0, 251.1, 251.2, 962.3, or 250.8, without concurrent 259.3, 272.7, 681.xx, 686.9x, 707.a-707.9, 709.3, 730.0-730.2, or 731.8 codes [1]. Secondary discharge diagnoses for hypoglycemia were not used because they are often attributable to events that occurred during the ED or hospital encounter. Main results. Beginning with 156 (122 categorical and 34 continuous) candidate clinical, demographic, and behavioral predictor variables for model development, the final classification tree was based on 6 patient-specific variables: total number of prior episodes of hypoglycemia-related ED or hospital utilization (0, 1-2, ≥ 3 times), number of ED encounters for any reason in the prior 12 months (< 2, ≥ 2 times), insulin use (yes/no), sulfonylurea use (yes/no), presence of severe or end-stage kidney disease (dialysis or chronic kidney disease stage 4 or 5 determined by estimated glomerular filtration rate of ≤ 29 mL/min/1.73 m2 (yes/no), and age younger than 77 years (yes/no). This classification tree resulted in 10 mutually exclusive leaf nodes, each yielding an estimated annual risk of hypoglycemia-related utilization, which were categorized as high (> 5%), intermediate (1%-5%), or low (< 1%). The above classification model was then transcribed into a checklist-style hypoglycemia risk stratification tool by mapping the combination of risk factors to high, intermediate, or low risk of having any hypoglycemia-related utilization in the following 12 months. Regarding patient characteristics, there were no significant differences in the distribution of the 6 predictors between the Kaiser derivation vs. validation samples, but there were significant differences across external validation samples. For example, the VA sample was predominantly men, with a higher proportion of patients older than 77 years, and had the highest proportion of patients with severe or end-stage kidney disease. Regarding model validation, the tool performed well in both internal validation (C statistic = 0.83) and external validation samples (VA C statistic = 0.81; GH C statistic = 0.79). Conclusion. This hypoglycemia risk stratification tool categorizes the 12-month risk of hypoglycemia-related utilization in patients with T2D using 6 easily obtained inputs. This tool can facilitate efficient targeting of population management interventions to reduce hypoglycemia risk and improve patient safety.</description><identifier>ISSN: 1079-6533</identifier><identifier>EISSN: 1938-1336</identifier><language>eng</language><publisher>Wayne: Turner White Communications Inc</publisher><subject>Diabetes ; Electronic health records ; Emergency medical care ; Hypoglycemia ; Risk factors</subject><ispartof>Journal of clinical outcomes management, 2017-10, Vol.24 (10)</ispartof><rights>Copyright Turner White Communications Inc. Oct 2017</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>315,782,786</link.rule.ids></links><search><title>EMR-Based Tool for Identifying Type 2 Diabetic Patients at High Risk for Hypoglycemia</title><title>Journal of clinical outcomes management</title><description>Objective. To develop and validate a risk stratification tool to categorize 12-month risk of hypoglycemia-related emergency department (ED) or hospital use among patients with type 2 diabetes (T2D). Design. Prospective cohort study. Setting and participants. Patients with T2D from Kaiser Permanente Northern California were identified using electronic medical records (EMR). Patients had to be 21 years of age or older as of the baseline date of 1 January 2014, with continuous health plan membership for 24 months prebaseline and pharmacy benefits for 12 months prebaseline. Of the 233,330 adults identified, 24,719 were excluded for unknown diabetes type, and 3614 were excluded for type 1 diabetes. The remaining 206,435 eligible patients with T2D were randomly split into an 80% derivation sample (n = 165,148) for tool development and 20% internal validation sample (n = 41,287). Using similar eligibility criteria, 2 external validation samples were derived from the Veterans Administration Diabetes Epidemiology Cohort (VA) (n = 1,335,966 adults) as well as from Group Health Cooperative (GH) (n = 14,972). Main outcome measure. The primary outcome was the occurrence of any hypoglycemia-related ED visit or hospital use during the 12 months postbaseline. A primary diagnosis of hypoglycemia was ascertained using the following International Classification of Diseases, Ninth Revision (ICD-9) codes: 251.0, 251.1, 251.2, 962.3, or 250.8, without concurrent 259.3, 272.7, 681.xx, 686.9x, 707.a-707.9, 709.3, 730.0-730.2, or 731.8 codes [1]. Secondary discharge diagnoses for hypoglycemia were not used because they are often attributable to events that occurred during the ED or hospital encounter. Main results. Beginning with 156 (122 categorical and 34 continuous) candidate clinical, demographic, and behavioral predictor variables for model development, the final classification tree was based on 6 patient-specific variables: total number of prior episodes of hypoglycemia-related ED or hospital utilization (0, 1-2, ≥ 3 times), number of ED encounters for any reason in the prior 12 months (< 2, ≥ 2 times), insulin use (yes/no), sulfonylurea use (yes/no), presence of severe or end-stage kidney disease (dialysis or chronic kidney disease stage 4 or 5 determined by estimated glomerular filtration rate of ≤ 29 mL/min/1.73 m2 (yes/no), and age younger than 77 years (yes/no). This classification tree resulted in 10 mutually exclusive leaf nodes, each yielding an estimated annual risk of hypoglycemia-related utilization, which were categorized as high (> 5%), intermediate (1%-5%), or low (< 1%). The above classification model was then transcribed into a checklist-style hypoglycemia risk stratification tool by mapping the combination of risk factors to high, intermediate, or low risk of having any hypoglycemia-related utilization in the following 12 months. Regarding patient characteristics, there were no significant differences in the distribution of the 6 predictors between the Kaiser derivation vs. validation samples, but there were significant differences across external validation samples. For example, the VA sample was predominantly men, with a higher proportion of patients older than 77 years, and had the highest proportion of patients with severe or end-stage kidney disease. Regarding model validation, the tool performed well in both internal validation (C statistic = 0.83) and external validation samples (VA C statistic = 0.81; GH C statistic = 0.79). Conclusion. This hypoglycemia risk stratification tool categorizes the 12-month risk of hypoglycemia-related utilization in patients with T2D using 6 easily obtained inputs. This tool can facilitate efficient targeting of population management interventions to reduce hypoglycemia risk and improve patient safety.</description><subject>Diabetes</subject><subject>Electronic health records</subject><subject>Emergency medical care</subject><subject>Hypoglycemia</subject><subject>Risk factors</subject><issn>1079-6533</issn><issn>1938-1336</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2017</creationdate><recordtype>article</recordtype><recordid>eNotj01Lw0AYhBdRsFb_w4LnwNvd7NdRazWFilLiubzZbOLWmI3Z7SH_3lA9zcAwMzwXZLEyXGcrzuXl7EGZTArOr8lNjEcAUKBhQT42r_vsEaOraRlCR5sw0m3t-uSbyfctLafBUUafPFYueUvfMfk5jRQTLXz7Sfc-fp1bxTSEtpus-_Z4S64a7KK7-9clKZ835brIdm8v2_XDLhuMThkqt7LSVCjAGitFk0ueQ24ZGG2EZBZY3SjUDgCF5EpIZC7npjKoUSnBl-T-b3YYw8_JxXQ4htPYz48HNgPO-ILn_BdmM0uA</recordid><startdate>20171001</startdate><enddate>20171001</enddate><general>Turner White Communications Inc</general><scope>K9.</scope><scope>NAPCQ</scope></search><sort><creationdate>20171001</creationdate><title>EMR-Based Tool for Identifying Type 2 Diabetic Patients at High Risk for Hypoglycemia</title></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-p98t-a7e1c69ba50c9c65f463404c20989562c02df7a8e00a563756a2e439b9a8a7753</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2017</creationdate><topic>Diabetes</topic><topic>Electronic health records</topic><topic>Emergency medical care</topic><topic>Hypoglycemia</topic><topic>Risk factors</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Nursing & Allied Health Premium</collection><jtitle>Journal of clinical outcomes management</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>EMR-Based Tool for Identifying Type 2 Diabetic Patients at High Risk for Hypoglycemia</atitle><jtitle>Journal of clinical outcomes management</jtitle><date>2017-10-01</date><risdate>2017</risdate><volume>24</volume><issue>10</issue><issn>1079-6533</issn><eissn>1938-1336</eissn><abstract>Objective. To develop and validate a risk stratification tool to categorize 12-month risk of hypoglycemia-related emergency department (ED) or hospital use among patients with type 2 diabetes (T2D). Design. Prospective cohort study. Setting and participants. Patients with T2D from Kaiser Permanente Northern California were identified using electronic medical records (EMR). Patients had to be 21 years of age or older as of the baseline date of 1 January 2014, with continuous health plan membership for 24 months prebaseline and pharmacy benefits for 12 months prebaseline. Of the 233,330 adults identified, 24,719 were excluded for unknown diabetes type, and 3614 were excluded for type 1 diabetes. The remaining 206,435 eligible patients with T2D were randomly split into an 80% derivation sample (n = 165,148) for tool development and 20% internal validation sample (n = 41,287). Using similar eligibility criteria, 2 external validation samples were derived from the Veterans Administration Diabetes Epidemiology Cohort (VA) (n = 1,335,966 adults) as well as from Group Health Cooperative (GH) (n = 14,972). Main outcome measure. The primary outcome was the occurrence of any hypoglycemia-related ED visit or hospital use during the 12 months postbaseline. A primary diagnosis of hypoglycemia was ascertained using the following International Classification of Diseases, Ninth Revision (ICD-9) codes: 251.0, 251.1, 251.2, 962.3, or 250.8, without concurrent 259.3, 272.7, 681.xx, 686.9x, 707.a-707.9, 709.3, 730.0-730.2, or 731.8 codes [1]. Secondary discharge diagnoses for hypoglycemia were not used because they are often attributable to events that occurred during the ED or hospital encounter. Main results. Beginning with 156 (122 categorical and 34 continuous) candidate clinical, demographic, and behavioral predictor variables for model development, the final classification tree was based on 6 patient-specific variables: total number of prior episodes of hypoglycemia-related ED or hospital utilization (0, 1-2, ≥ 3 times), number of ED encounters for any reason in the prior 12 months (< 2, ≥ 2 times), insulin use (yes/no), sulfonylurea use (yes/no), presence of severe or end-stage kidney disease (dialysis or chronic kidney disease stage 4 or 5 determined by estimated glomerular filtration rate of ≤ 29 mL/min/1.73 m2 (yes/no), and age younger than 77 years (yes/no). This classification tree resulted in 10 mutually exclusive leaf nodes, each yielding an estimated annual risk of hypoglycemia-related utilization, which were categorized as high (> 5%), intermediate (1%-5%), or low (< 1%). The above classification model was then transcribed into a checklist-style hypoglycemia risk stratification tool by mapping the combination of risk factors to high, intermediate, or low risk of having any hypoglycemia-related utilization in the following 12 months. Regarding patient characteristics, there were no significant differences in the distribution of the 6 predictors between the Kaiser derivation vs. validation samples, but there were significant differences across external validation samples. For example, the VA sample was predominantly men, with a higher proportion of patients older than 77 years, and had the highest proportion of patients with severe or end-stage kidney disease. Regarding model validation, the tool performed well in both internal validation (C statistic = 0.83) and external validation samples (VA C statistic = 0.81; GH C statistic = 0.79). Conclusion. This hypoglycemia risk stratification tool categorizes the 12-month risk of hypoglycemia-related utilization in patients with T2D using 6 easily obtained inputs. This tool can facilitate efficient targeting of population management interventions to reduce hypoglycemia risk and improve patient safety.</abstract><cop>Wayne</cop><pub>Turner White Communications Inc</pub></addata></record> |
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title | EMR-Based Tool for Identifying Type 2 Diabetic Patients at High Risk for Hypoglycemia |
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