Trends in Glycemic among Youth with Diabetes: The SEARCH for Diabetes in Youth Study

10.2337/figshare.17052590 ◽

2022 ◽

Author(s):

Faisal S. Malik ◽

Katherine A. Sauder ◽

Scott Isom ◽

Beth A. Reboussin ◽

Dana Dabelea ◽

...

Keyword(s):

Type 2 Diabetes ◽

Type 1 Diabetes ◽

Glycemic Control ◽

Household Income ◽

Age Groups ◽

Race Ethnicity ◽

Youth Study ◽

Diabetes In Youth

OBJECTIVES: To describe temporal trends and correlates of glycemic control in youth and young adults (YYA) with youth-onset diabetes. RESEARCH DESIGN AND METHODS: The study included 6,492 participants with type 1 or type 2 diabetes from the SEARCH for Diabetes in Youth study. Participant visit data were categorized into time periods 2002-2007, 2008-2013 and 2014-2019, diabetes durations of 1-4, 5-9, and 10+ years, and age groups 1-9, 10-14, 15-19, 20-24, 25+ years. Participants contributed one randomly selected data point to each duration and age group per time period. Multivariable regression models were used to test differences in hemoglobin A1c (HbA1c) over time by diabetes type. Models were adjusted for site, age, sex, race/ethnicity, household income, health insurance status, insulin regimen and diabetes duration, overall and stratified for each duration and age group. RESULTS: Adjusted mean HbA1c for the 2014-2019 cohort of YYA with type 1 diabetes was 8.8%±0.04%. YYA with type 1 diabetes in the 10-14, 15-19, and 20-24 age groups from the 2014-2019 cohort had worse glycemic control than the 2002-2007 cohort. Race/ethnicity, household income and treatment regimen predicted differences in glycemic control in 2014-2019 type 1 diabetes participants. Adjusted mean HbA1c was 8.6%±0.12% for 2014-2019 YYA with type 2 diabetes. Participants age 25+ with type 2 diabetes had worse glycemic control relative to the 2008-2013 cohort. Only treatment regimen was associated with differences in glycemic control in type 2 diabetes participants. CONCLUSIONS: Despite advances in diabetes technologies, medications, and dissemination of more aggressive glycemic targets, many current YYA are less likely to achieve desired glycemic control relative to earlier cohorts.

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Trends in Glycemic Control Among Youth and Young Adults With Diabetes: The SEARCH for Diabetes in Youth Study

Diabetes Care ◽

10.2337/dc21-0507 ◽

2022 ◽

Author(s):

Faisal S. Malik ◽

Katherine A. Sauder ◽

Scott Isom ◽

Beth A. Reboussin ◽

Dana Dabelea ◽

...

Keyword(s):

Type 2 Diabetes ◽

Type 1 Diabetes ◽

Glycemic Control ◽

Age Groups ◽

Youth And Young Adults ◽

Race Ethnicity ◽

Youth Study ◽

Diabetes In Youth

OBJECTIVES To describe temporal trends and correlates of glycemic control in youth and young adults (YYA) with youth-onset diabetes. RESEARCH DESIGN AND METHODS The study included 6,369 participants with type 1 or type 2 diabetes from the SEARCH for Diabetes in Youth study. Participant visit data were categorized into time periods of 2002–2007, 2008–2013, and 2014–2019, diabetes durations of 1–4, 5–9, and ≥10 years, and age groups of 1–9, 10–14, 15–19, 20–24, and ≥25 years. Participants contributed one randomly selected data point to each duration and age group per time period. Multivariable regression models were used to test differences in hemoglobin A1c (HbA1c) over time by diabetes type. Models were adjusted for site, age, sex, race/ethnicity, household income, health insurance status, insulin regimen, and diabetes duration, overall and stratified for each diabetes duration and age group. RESULTS Adjusted mean HbA1c for the 2014–2019 cohort of YYA with type 1 diabetes was 8.8 ± 0.04%. YYA with type 1 diabetes in the 10–14-, 15–19-, and 20–24-year-old age groups from the 2014–2019 cohort had worse glycemic control than the 2002–2007 cohort. Race/ethnicity, household income, and treatment regimen predicted differences in glycemic control in participants with type 1 diabetes from the 2014–2019 cohort. Adjusted mean HbA1c was 8.6 ± 0.12% for 2014–2019 YYA with type 2 diabetes. Participants aged ≥25 years with type 2 diabetes had worse glycemic control relative to the 2008–2013 cohort. Only treatment regimen was associated with differences in glycemic control in participants with type 2 diabetes. CONCLUSIONS Despite advances in diabetes technologies, medications, and dissemination of more aggressive glycemic targets, many current YYA are less likely to achieve desired glycemic control relative to earlier cohorts.

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Detection of Diabetes Status and Type in Youth using Electronic Health Records (EHRs): The SEARCH for Diabetes in Youth Study

10.2337/figshare.12558875.v1 ◽

2020 ◽

Author(s):

Brian J. Wells ◽

Kristin M. Lenoir ◽

Lynne E. Wagenknecht ◽

Elizabeth J. Mayer-Davis ◽

Jean M. Lawrence ◽

...

Keyword(s):

Type 2 Diabetes ◽

Type 1 Diabetes ◽

Multinomial Regression ◽

Rule Based ◽

Diabetes Status ◽

Icd 10 ◽

Youth Study ◽

Diabetes In Youth

Objective: 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. Research Design and Methods: Youth (< 20 years) with potential evidence of diabetes (N=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. Results: The sample included 5308 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 (Se) (>0.95), specificity (Sp) (>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. Combining the rule-based method with chart reviews (n=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 sensitivity, specificity, and PPV for type 2 diabetes using the combined method were >=0.91. Conclusions: 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.

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Review for "Dietary strategies to manage diabetes and glycemic control in youth and young adults with youth‐onset type 1 and type 2 diabetes: The SEARCH for Diabetes in Youth Study"

10.1111/pedi.13091/v1/review2 ◽

2020 ◽

Keyword(s):

Type 2 Diabetes ◽

Young Adults ◽

Glycemic Control ◽

Onset Type ◽

Youth And Young Adults ◽

Youth Study ◽

Dietary Strategies ◽

Diabetes In Youth

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Decision letter for "Dietary strategies to manage diabetes and glycemic control in youth and young adults with youth‐onset type 1 and type 2 diabetes: The SEARCH for Diabetes in Youth Study"

10.1111/pedi.13091/v1/decision1 ◽

2020 ◽

Keyword(s):

Type 2 Diabetes ◽

Young Adults ◽

Glycemic Control ◽

Onset Type ◽

Youth And Young Adults ◽

Youth Study ◽

Dietary Strategies ◽

Diabetes In Youth

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Dietary strategies to manage diabetes and glycemic control in youth and young adults with youth‐onset type 1 and type 2 diabetes: The SEARCH for diabetes in youth study

Pediatric Diabetes ◽

10.1111/pedi.13091 ◽

2020 ◽

Vol 21 (7) ◽

pp. 1093-1101

Author(s):

Katherine A. Sauder ◽

Jeanette M. Stafford ◽

Natalie S. The ◽

Elizabeth J. Mayer‐Davis ◽

Joan Thomas ◽

...

Keyword(s):

Type 2 Diabetes ◽

Young Adults ◽

Glycemic Control ◽

Onset Type ◽

Youth And Young Adults ◽

Youth Study ◽

Dietary Strategies ◽

Diabetes In Youth

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Detection of Diabetes Status and Type in Youth using Electronic Health Records: The SEARCH for Diabetes in Youth Study

10.2337/figshare.12558875.v2 ◽

2020 ◽

Author(s):

Brian J. Wells ◽

Kristin M. Lenoir ◽

Lynne E. Wagenknecht ◽

Elizabeth J. Mayer-Davis ◽

Jean M. Lawrence ◽

...

Keyword(s):

Type 2 Diabetes ◽

Type 1 Diabetes ◽

Multinomial Regression ◽

Rule Based ◽

Diabetes Status ◽

Icd 10 ◽

Youth Study ◽

Diabetes In Youth

Objective: 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. Research Design and Methods: Youth (< 20 years) with potential evidence of diabetes (N=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. Results: The sample included 5308 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 (Se) (>0.95), specificity (Sp) (>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. Combining the rule-based method with chart reviews (n=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 sensitivity, specificity, and PPV for type 2 diabetes using the combined method were >=0.91. Conclusions: 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.

Download Full-text

Review for "Dietary strategies to manage diabetes and glycemic control in youth and young adults with youth‐onset type 1 and type 2 diabetes: The SEARCH for Diabetes in Youth Study"

10.1111/pedi.13091/v1/review1 ◽

2020 ◽

Keyword(s):

Type 2 Diabetes ◽

Young Adults ◽

Glycemic Control ◽

Onset Type ◽

Youth And Young Adults ◽

Youth Study ◽

Dietary Strategies ◽

Diabetes In Youth

Download Full-text

Detection of Diabetes Status and Type in Youth using Electronic Health Records: The SEARCH for Diabetes in Youth Study

10.2337/figshare.12558875 ◽

2020 ◽

Author(s):

Brian J. Wells ◽

Kristin M. Lenoir ◽

Lynne E. Wagenknecht ◽

Elizabeth J. Mayer-Davis ◽

Jean M. Lawrence ◽

...

Keyword(s):

Type 2 Diabetes ◽

Type 1 Diabetes ◽

Multinomial Regression ◽

Rule Based ◽

Diabetes Status ◽

Icd 10 ◽

Youth Study ◽

Diabetes In Youth

Objective: 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. Research Design and Methods: Youth (< 20 years) with potential evidence of diabetes (N=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. Results: The sample included 5308 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 (Se) (>0.95), specificity (Sp) (>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. Combining the rule-based method with chart reviews (n=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 sensitivity, specificity, and PPV for type 2 diabetes using the combined method were >=0.91. Conclusions: 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.

Download Full-text