Predictors of Lost to Follow-Up among Children with Type 2 Diabetes

Background/Aims: Youth with type 2 diabetes (T2D) have poor compliance with medical care. This study aimed to determine which demographic and clinical factors differ between youth with T2D who receive care in a pediatric diabetes center versus youth lost to follow-up for >18 months. Methods: Data were analyzed from 496 subjects in the Pe­diatric Diabetes Consortium registry. Enrollment variables were selected a priori and analyzed with univariable and multivariable logistic regression models. Results: After a median of 1.3 years from enrollment, 55% of patients were lost to follow-up. The final model included age, race/ethnicity, parent education, and estimated distance to study site. The odds ratio (99% confidence interval) of loss to follow-up was 2.87 (1.34, 6.16) for those aged 15 to <18 years versus those aged 10 to <13 years and 6.57 (2.67, 16.15) for those aged ≥18 years versus those aged 10 to <13 years. Among patients living more than 50 miles from the clinic, the odds ra tio of loss to follow-up was 3.11 (1.14, 8.49) versus those living within 5 miles of the site. Conclusion: Older adolescents with T2D are more likely to be lost to follow-up, but other socioeconomic factors were not significant predictors of clinic follow-up

Feasibility of Ideal Cardiovascular Health Evaluation in a Pediatric Clinic Setting

The feasibility of “point-of-care” screening for ideal cardiovascular health was explored in a pediatric specialty clinic setting. Children and adolescents aged 9–18 years (n=91) with treated and stabilized diseases were recruited at a pediatric endocrinology clinic. A table-top device was used to assay fingerstick samples for non-HDL cholesterol (non-HDL-C), which was used to divide participants into two groups based on the non-HDL-C threshold for comparison of the remaining metrics between groups. A significant number of children had low scores, and score frequency distribution was similar to larger retrospective studies, with few participants achieving none or all of the health metrics. Healthy diet was the metric least often achieved. Those with a non-HDL-C above the ideal threshold of 3.1 mmol/L (120 mg/dl) had a higher BMI percentile (p<0.01) and diastolic blood pressure percentile (p<0.05). We conclude that pediatric risk factor screening and scoring can be performed in a specialty clinic with meaningful cardiovascular health scores for patients and providers. Association of abnormal “point-of care” non-HDL-C levels with elevated BMI and blood pressure supports evidence for risk factor clustering and use of the ideal health construct in pediatric clinic settings