scholarly journals Increase in Prevalence of Diabetic Ketoacidosis at Diagnosis Among Youth With Type 1 Diabetes: The SEARCH for Diabetes in Youth Study

Diabetes Care ◽  
2021 ◽  
pp. dc200389
Author(s):  
Elizabeth T. Jensen ◽  
Jeanette M. Stafford ◽  
Sharon Saydah ◽  
Ralph B. D'Agostino ◽  
Lawrence M. Dolan ◽  
...  
Keyword(s):  
Type 1 Diabetes ◽  
Diabetic Ketoacidosis ◽  
Youth Study ◽  
Diabetes In Youth

scholarly journals Diabetic ketoacidosis at diagnosis of type 1 diabetes and glycemic control over time: The SEARCH for diabetes in youth study

2018 ◽  
Vol 20 (2) ◽  
pp. 172-179 ◽  
Author(s):  
Lindsey M. Duca ◽  
Beth A. Reboussin ◽  
Catherine Pihoker ◽  
Giuseppina Imperatore ◽  
Sharon Saydah ◽  
...  
Keyword(s):  
Type 1 Diabetes ◽  
Glycemic Control ◽  
Diabetic Ketoacidosis ◽  
Youth Study ◽  
Diabetes In Youth ◽  
Over Time

scholarly journals Insulin Regimens and Clinical Outcomes in a Type 1 Diabetes Cohort: The SEARCH for Diabetes in Youth study

Diabetes Care ◽  
2012 ◽  
Vol 36 (1) ◽  
pp. 27-33 ◽  
Author(s):  
C. Pihoker ◽  
A. Badaru ◽  
A. Anderson ◽  
T. Morgan ◽  
L. Dolan ◽  
...  
Keyword(s):  
Type 1 Diabetes ◽  
Clinical Outcomes ◽  
Youth Study ◽  
Diabetes In Youth

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

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

<u>Objective:</u> 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. <p><u> </u></p> <p><u>Research Design and Methods:</u> 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.</p> <p> </p> <p><u>Results:</u> 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 (<i>Se</i>) (>0.95), specificity (<i>Sp</i>) (>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. </p> <p> </p> <p><u>Conclusions</u>: 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. </p> <br>

scholarly journals Association of metformin and statin medications with surrogate measures of cardiovascular disease in youth with type 1 diabetes: the SEARCH for diabetes in youth study

2019 ◽  
Vol 24 (3) ◽  
pp. 187-194 ◽  
Author(s):  
Evgenia Gourgari ◽  
Jeanette M. Stafford ◽  
Ralph D'Agostino Jr ◽  
Lawrence M. Dolan ◽  
Jean M. Lawrence ◽  
...  
Keyword(s):  
Cardiovascular Disease ◽  
Type 1 Diabetes ◽  
Youth Study ◽  
Surrogate Measures ◽  
Diabetes In Youth

161-OR: Trends in Glycemic Control between 2002 and 2016 among Youth Recently Diagnosed with Type 1 Diabetes: The SEARCH for Diabetes in Youth Study

Diabetes ◽  
2021 ◽  
Vol 70 (Supplement 1) ◽  
pp. 161-OR
Author(s):  
DARIA IGUDESMAN ◽  
BETH A. REBOUSSIN ◽  
KATHERINE J. SOURIS ◽  
CATHERINE PIHOKER ◽  
LAWRENCE M. DOLAN ◽  
...  
Keyword(s):  
Type 1 Diabetes ◽  
Glycemic Control ◽  
Youth Study ◽  
Diabetes In Youth

scholarly journals Adiponectin and arterial stiffness in youth with type 1 diabetes: the SEARCH for Diabetes in Youth Study

2012 ◽  
Vol 25 (7-8) ◽  
Author(s):  
Amy S. Shah ◽  
Lawrence M. Dolan ◽  
Abigail Lauer ◽  
Cralen Davis ◽  
Dana Dabelea ◽  
...  
Keyword(s):  
Type 1 Diabetes ◽  
Arterial Stiffness ◽  
Youth Study ◽  
Diabetes In Youth

scholarly journals Predictors of Dyslipidemia Over Time in Youth With Type 1 Diabetes: For the SEARCH for Diabetes in Youth Study

Diabetes Care ◽  
2017 ◽  
Vol 40 (4) ◽  
pp. 607-613 ◽  
Author(s):  
Amy S. Shah ◽  
David M. Maahs ◽  
Jeanette M. Stafford ◽  
Lawrence M. Dolan ◽  
Wei Lang ◽  
...  
Keyword(s):  
Type 1 Diabetes ◽  
Youth Study ◽  
Diabetes In Youth ◽  
Over Time

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

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

<b>OBJECTIVES: </b>To describe temporal trends and correlates of glycemic control in youth and young adults (YYA) with youth-onset diabetes. <p><b>RESEARCH DESIGN AND METHODS: </b>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 A<sub>1c</sub> (HbA<sub>1c</sub>) 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.</p> <p><b>RESULTS: </b>Adjusted mean HbA<sub>1c</sub> 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.</p> <p><b>CONCLUSIONS: </b>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.</p> <br>

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