Aim: Providers may avoid offering pump therapy to teens with EF challenges (planning, organization). We assessed whether the association of pump use and A1c is modified by EF status, adjusting for relevant factors. Methods: EF was assessed by parent proxy-report using Behavior Rating Inventory of Executive Function (BRIEF). T-score ≥60 defined risk of EF problems for Global Executive Composite (GEC), Behavioral Regulation Index (BRI), and Metacognition Index (MI). Parents also completed the Diabetes Family Conflict Scale (DFCS). T-tests stratified by EF status compared A1c by pump use and non-use. Generalized linear models were adjusted (Tukey) for variables with p<.1 in univariate analysis. Results: Teens (N=169, 54% male) had mean age 14.9±1.3 yrs, diagnosis age 7.5±3.6 yrs, A1c 8.5±1%, 69% pump use, 9% CGM use, and 31% with GEC ≥60. With GEC <60, A1c was similar in pump users and non-users (8.3 vs 8.6%, p=.25); with GEC ≥60, A1c was lower in pump users vs non-users (8.5 vs 9.2%, p=.009). Similar A1c patterns were seen when stratified by EF problems in BRI or MI. In multivariate analysis, with GEC <60, A1c did not differ in pump users vs non-users (8.4 vs 8.6%, p=.36), adjusting for DFCS; a similar pattern was seen with BRI <60 (p=.26) and MI <60 (p=.25). With GEC ≥60, there was a trend towards lower A1c in pump users vs non-users (8.1% vs 8.7%, p=.07), adjusting for income and CGM use. With BRI ≥60, A1c was lower in pump users vs non-users (7.8 vs 8.4%, p=.03), adjusting for CGM use and DFCS score. With MI ≥60, A1c was not significantly different in pump users vs non-users (8.0% vs 8.4%, p=.25), adjusting for income, CGM use, and DFCS score. With elevated GEC, BRI, or MI scores in above multivariate analyses, A1c was lower in CGM users vs non-users (all p<.05). Conclusion: The association of diabetes technology use with lower A1c in those with elevated BRIEF scores suggests a need to re-evaluate provider reluctance to recommend use of diabetes technologies in teens presenting with potential EF problems. Disclosure R.J.Vitale: None. L.K.Volkening: None. L.J.Tinsley: None. L.M.Laffel: Advisory Panel; Medtronic, Lilly Diabetes, Novo Nordisk, Vertex Pharmaceuticals Incorporated, Roche Diagnostics, Provention Bio, Inc., Consultant; Dexcom, Inc., Janssen Pharmaceuticals, Inc., Medscape. Funding National Institutes of Health (P30DK036836, R01DK095273); JDRF (2-SRA-2014-253-M-B); Iacocca Family Foundation
Aim: Despite disruptions caused by the COVID-pandemic, prior studies suggest some improvements in glycemic control. We investigated whether this improvement was equitable and seen across socioeconomic status (SES) groups in youth with T1D. Method: Using EHR-extracted visit and CGM data, we geocoded patient addresses linked with census-tract derived education from the 20American Community Survey and a composite measure of SES, the Neighborhood Deprivation Index (NDI) . Analyses included youth ≤18 years old using CGM with T1D duration ≥6 months (age <6 yrs) or ≥1 yr (age ≥6 yrs) . We performed t-tests and regressions comparing SES and CGM metrics during the pandemic (4/1/20-3/15/21) with pre-pandemic (4/1/19-3/15/20) . Results: The pre-pandemic sample had 641 youth (52% female, age 12.5±3.5, T1D 6.2±3.5 yrs) and the pandemic sample had 650 youth (52% female, age 13.5±3.6, T1D 6.8±3.8 yrs) ; 86% were common to both samples. Addresses allowed for geocoding of 98%; 44% of youth lived in low education census tracts where ≥30% of adults in the census tract had no more than a high school education. Mean CGM-derived glucose management indicator (GMI) improved during the pandemic for both those living in lower (8.07±0.05% pre vs. 7.91±0.% during, p<0.05) and higher SES education tracts (7.82±0.% pre vs. 7.69±0.% during) . There was similar improvement in GMI in lower vs. higher SES education tracts (0.16±.vs. 0.13±.06) . Other CGM metrics similarly improved during the pandemic, mean CGM glucose decreased by 6.7 mg/dL and 5.4 mg/dL in low and high SES education tract patients respectively (both p<.05) . Those living in the most deprived NDI areas had the highest GMI both pre and during the pandemic (p<0.05) and demonstrated similar or greater improvements than those from Iess deprived areas. Conclusion: Equitable improvements in CGM metrics during the pandemic was evident in youth with T1D. Future studies can assess how changes in healthcare delivery during the pandemic can reduce disparities and sustain benefits to all patients. Disclosure S.Ojukwu: None. A.Adam: None. T.Kaushal: None. L.J.Tinsley: None. L.K.Volkening: None. C.Chen: None. L.M.Laffel: Advisory Panel; Medtronic, Roche Diabetes Care, Consultant; Boehringer Ingelheim International GmbH, Dexcom, Inc., Dompé, Insulet Corporation, Janssen Pharmaceuticals, Inc., Lilly Diabetes, Novo Nordisk, Provention Bio, Inc. Funding National Institutes of Health (K12DK094721, P30DK036836)
The pandemic required altering care delivery to a hybrid model of telehealth and in-person visits. We evaluated if SES disparities during the pandemic impacted clinical visit adherence and CGM device use in youth with T1D. Retrospective EHR captured data on youth with T1D, ≤18 years old, with ≥1 clinical visit both pre-pandemic (9/15/18-3/15/20) and during the pandemic (4/1/20-12/22/21) . Patient addresses were geocoded to link with census tract SES measures from the 20American Community Survey. Low education status defined as ≥30% of residents having HS or lower education and low income as median household income ≤$40,000. Poisson and logistic regression models assessed appointment adherence and CGM device use, respectively, during the pandemic vs. pre-pandemic. Models were adjusted for age, gender, baseline A1c, and T1D duration. In both periods, 689 youth (age 13.8±3.3 years; male 49%; T1D duration 7.4 ±3.6 years) had clinical encounters. There were more visits in the pandemic vs. pre-pandemic (8 [IQR 5-11] vs. 6 [IQR 5-9], p<.001) . In adjusted models, youth living in tracts with low education or low income had 1.16x (95% CI 1.06-1.27, p-.002) and 1.20x (95% CI 1.09-1.32, p<.001) ,respectively, greater rates of increased visits during the later time period vs. those not living in lower education or income tracts. W/R to CGM use, 57% (n=392) used CGM pre-pandemic and 21% (n=83) stopped using CGM during the pandemic. In adjusted models, youth in tracts with low vs. high education had 2.81-fold increased odds (95% CI 1.56-5.07, p<.001) of stopping CGM during the pandemic. Similarly, youth from low vs. high income tracts had 2.49-fold increased odds (95% CI 1.39-4.46, p<.001) of stopping CGM. These data indicate variability in appointment adherence and CGM use during the pandemic based on census SES status. Vulnerable youth by SES factors appear to benefit from hybrid care model W/R to maintaining visits, although such youth appear more likely to stop CGM. Further research is needed further reduce health disparities. Disclosure A.Adam: None. C.Chen: None. S.Ojukwu: None. T.Kaushal: None. L.J.Tinsley: None. L.K.Volkening: None. L.M.Laffel: Advisory Panel; Medtronic, Roche Diabetes Care, Consultant; Boehringer Ingelheim International GmbH, Dexcom, Inc., Dompé, Insulet Corporation, Janssen Pharmaceuticals, Inc., Lilly Diabetes, Novo Nordisk, Provention Bio, Inc.
Aim: Providers may resist offering pump therapy to teens with T1D who have challenges with EF (planning, organization) due to concerns for DKA. We evaluated rates of DKA and severe hypoglycemia according to pump use in teens with and without EF problems. Methods: Parents of teens aged 13-17 years with T1D provided proxy reports of teen EF using the Behavior Rating Inventory of Executive Function (BRIEF). Global Executive Composite (GEC) t-score ≥60 defined risk of executive dysfunction. Families reported severe hypoglycemic episodes and ER visits/hospitalizations for hyperglycemia (including DKA) every 3 months for 18 months. Incidence rates (IR) were calculated as events/100 person-years. DKA IR includes ER visits/hospitalizations for either hyperglycemia or DKA. Poisson regressions evaluated differences in IR by pump use, CGM use, and BRIEF score. Results: 169 teens (54% male) had baseline age 14.9±1.3 years, T1D duration 7.4±3.7 years, A1c 8.5±1.0%; 69% were pump users, 41% used CGM some time during follow-up, and 31% had GEC ≥60. During 297 person-years, severe hypoglycemia IR was 38.3/100 person-years and DKA IR was 7.7/100 person-years. There were no differences in hypoglycemia IR by pump or CGM use or GEC score. There was no difference in DKA IR by CGM use while DKA IR was lower in pump users vs non-users (4.9 vs 14.0, p=.01) and in those with GEC <60 vs ≥60 (4.9 vs 14.0, p=.01). Among those with GEC <60, DKA IR was lower in pump users vs non-users (2.0 vs 13.7, p=.003); in those with GEC ≥60, DKA IR was similar in pump users and non-users (13.7 vs 14.4, p=.94). Conclusion: In this sample of teens with T1D, hypoglycemia IR did not differ by pump or CGM use or EF status; in the teens with EF challenges, DKA IR was not higher in pump users vs non-users. These observations suggest that hesitation by clinicians to offer insulin pump therapy to teens with clinical suspicion of executive dysfunction due to concerns for increased DKA risk may not be warranted. Disclosure R.J.Vitale: None. L.K.Volkening: None. L.M.Laffel: Advisory Panel; Medtronic, Lilly Diabetes, Novo Nordisk, Vertex Pharmaceuticals Incorporated, Roche Diagnostics, Provention Bio, Inc., Consultant; Dexcom, Inc., Janssen Pharmaceuticals, Inc., Medscape. Funding National Institutes of Health (P30DK036836, R01DK095273); JDRF (2-SRA-2014-253-M-B); Iacocca Family Foundation
Aim: Maintaining in-range glucose on days with PA is challenging for youth with T1D, often requiring fine-tuning of diet and insulin. We examined how PA and macronutrient intake impact glycemic outcomes in youth with T1D. Methods: Youth and parents completed 3-day PA and diet records and youth wore 3-day masked CGM (iPro) every 3 months for 18 months. Days were classified as active (≥60 min PA) or inactive. Diet data were analyzed using Nutrition Data System for Research (NDSR) . Daytime (6 AM-11:59 PM) CGM glucometrics were: % time (T) in range (TIR) 70-180 mg/dL, %T<70, %T>180, and glucose CV. Analyses included complete days for PA, diet, and CGM data. Separate longitudinal mixed models for daily carb, fat, and protein intake (adjusted for age, T1D duration, sex, pump vs. MDI) , assessed changes in glucometrics on active vs. inactive days. Results: Youth (N=136, 49% male, 73% pump users) were ages 8-17 yrs (12.9±2.6) with T1D duration 6.0±3.1 yrs and daily insulin 0.9±0.3 U/kg. At baseline, A1c was 8.0±0.9%, daytime %TIR 50±22% (12.0 hrs) , %T<70 5±8% (1.2 hrs) , %T>180 44±25% (10.6 hrs) , CV 37±11%; macronutrient intake was 49±9% carb, 35±8% fat, 16±4% protein. In all models of macronutrient intakes, %TIR increased by ∼5.3% (76 min; p=.01) with pump use; by 2.8% (40 min; p=.02) on active days; by 1.4% (20 min; p=.03) with 10% increase in carb intake; and by 1.7% (24 min; p=.02) with 10% decrease in fat intake. PA and carb intake did not impact %T<70; %T<70 decreased by 0.6% (9 min; p=.03) with 10% increase in fat intake. PA did not impact %T>180; %T>180 decreased by ∼5.6% (81 min; p=.02) with pump use; by 1.7% (24 min; p=.02) with 10% increase in CHO intake; and by 2.2% (32 min; p=.01) with 10% decrease in fat intake. CV decreased by 1.3% on inactive days (p=.04) ; by 1.1% with 10% decrease in CHO intake (p<.01) ; and by 1.3% with 10% increase in fat intake (p<.01) . Conclusion: PA and macronutrient intake have varying effects on glycemic outcomes. The findings reinforce the need to tailor insulin dosing algorithms for diet and PA. Disclosure R.O.La banca: None. L.K.Volkening: None. E.Dassau: Employee; Eli Lilly and Company, Research Support; Dexcom, Inc., JDRF, National Institute of Diabetes and Digestive and Kidney Diseases, Stock/Shareholder; Eli Lilly and Company. S.N.Mehta: None. L.M.Laffel: Advisory Panel; Medtronic, Roche Diabetes Care, Consultant; Boehringer Ingelheim International GmbH, Dexcom, Inc., Dompé, Insulet Corporation, Janssen Pharmaceuticals, Inc., Lilly Diabetes, Novo Nordisk, Provention Bio, Inc. Funding National Institutes of Health (K12DK094721, P30DK036836) ; Iacocca Foundation
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