Multisite Quality Improvement Program Within the Project ECHO Diabetes Remote Network.

BACKGROUND: The telementoring Project ECHO (Extension for Community Healthcare Outcomes) model has been shown to improve disease management in diabetes in many underserved communities. The authors aim to evaluate if ECHO could also be an effective tool for quality improvement (QI) of diabetes care in these communities. METHODS: Thirteen clinics in underserved communities in California and Florida participating in Project ECHO Diabetes were recruited for a 12-month QI program. The program provided weekly tele-education sessions, including a didactic presentation and case-based discussion. In addition, clinics chose their own set of quality measures to improve and met remotely to discuss their efforts, successes, and setbacks every quarter with mentorship from QI experts. RESULTS: Of the 31 QI initiatives attempted by different clinics, all had either made improvements (25 initiatives, 80.6%) or were in the process of making improvements (6 initiatives, 19.4%) in structural, process, and outcome measures. Examples of these measures include whether clinics have protocols to identify high-risk patients (structure), numbers of continuous glucose monitor prescriptions submitted by the clinics (process), and percentage of patients with diabetes whose most recent HbA1c are > 9% (outcome). For one measure, 40.0% of the clinics had achieved a higher percentage of cumulative HbA1c measurement in the third quarter of the year, compared to the fourth quarter in the previous year. The cost of QI implementation varied widely due to different number of personnel involved across sites. CONCLUSION: A QI program delivered via Project ECHO Diabetes can facilitate quality improvements in underserved communities.

Assessment of the Risk Evaluation and Mitigation Strategy (REMS) for Phentermine-Topiramate to Prevent Exposure During Pregnancy.

BACKGROUND: The U.S. Food and Drug Administration approved phentermine-topiramate for obesity in 2012 and required a Risk Evaluation and Mitigation Strategy (REMS) to prevent prenatal exposure. No such requirement was introduced for topiramate. OBJECTIVE: To evaluate the rate of prenatal exposure, contraceptive use, and pregnancy testing among patients with phentermine-topiramate compared with topiramate or other antiobesity medications (AOMs). DESIGN: Retrospective cohort study. SETTING: Nationwide health insurance claims database. PARTICIPANTS: Females aged 12 to 55 years with no infertility diagnosis or sterilization procedure. Patients with other indications for topiramate were excluded to identify a cohort that was likely treated for obesity. MEASUREMENTS: Patients initiated use of phentermine-topiramate, topiramate, or an AOM (liraglutide, lorcaserin, or bupropion-naltrexone). Pregnancy at treatment initiation, conception during treatment, contraceptive use, and pregnancy testing outcomes were ascertained. Measurable confounders were adjusted for, and extensive sensitivity analyses were done. RESULTS: A total of 156 280 treatment episodes were observed. Adjusted prevalence of pregnancy at treatment initiation was 0.9 versus 1.6 per 1000 episodes (prevalence ratio, 0.54 [95% CI, 0.31 to 0.95]) for phentermine-topiramate versus topiramate. The incidence rate of conception during treatment was 9.1 versus 15.0 per 1000 person-years (rate ratio, 0.61 [CI, 0.40 to 0.91]) for phentermine-topiramate versus topiramate. Both outcomes were similarly lower for phentermine-topiramate compared with AOM. Prenatal exposure was marginally lower in topiramate users compared with AOM users. Approximately 20% of patients in all cohorts had at least 50% of treatment days covered by contraceptives. Few patients had pregnancy tests before treatment (≤5%), but this was more common among phentermine-topiramate users. LIMITATIONS: Outcome misclassification; unmeasured confounding due to lack of prescriber data to account for possible clustering and spillover effects. CONCLUSION: Prenatal exposure seemed to be significantly lower among phentermine-topiramate users under the REMS. Pregnancy testing and contraceptive use appeared to be inadequate for all groups, which deserves attention to prevent the remaining potential exposures. PRIMARY FUNDING SOURCE: None.

Impact of Contextual-Level Social Determinants of Health on Newer Antidiabetic Drug Adoption in Patients with Type 2 Diabetes.

BACKGROUND: We aimed to investigate the association between contextual-level social determinants of health (SDoH) and the use of novel antidiabetic drugs (ADD), including sodium-glucose cotransporter-2 inhibitors (SGLT2i) and glucagon-like peptide-1 receptor agonists (GLP1a) for patients with type 2 diabetes (T2D), and whether the association varies across racial and ethnic groups. METHODS: Using electronic health records from the OneFlorida+ network, we assembled a cohort of T2D patients who initiated a second-line ADD in 2015-2020. A set of 81 contextual-level SDoH documenting social and built environment were spatiotemporally linked to individuals based on their residential histories. We assessed the association between the contextual-level SDoH and initiation of SGTL2i/GLP1a and determined their effects across racial groups, adjusting for clinical factors. RESULTS: Of 28,874 individuals, 61% were women, and the mean age was 58 (±15) years. Two contextual-level SDoH factors identified as significantly associated with SGLT2i/GLP1a use were neighborhood deprivation index (odds ratio [OR] 0.87, 95% confidence interval [CI] 0.81-0.94) and the percent of vacant addresses in the neighborhood (OR 0.91, 95% CI 0.85-0.98). Patients living in such neighborhoods are less likely to be prescribed with newer ADD. There was no interaction between race-ethnicity and SDoH on the use of newer ADD. However, in the overall cohort, the non-Hispanic Black individuals were less likely to use newer ADD than the non-Hispanic White individuals (OR 0.82, 95% CI 0.76-0.88). CONCLUSION: Using a data-driven approach, we identified the key contextual-level SDoH factors associated with not following evidence-based treatment of T2D. Further investigations are needed to examine the mechanisms underlying these associations.

Topiramate Utilization After Phentermine/Topiramate Approval for Obesity Management: Risk Minimization in the Era of Drug Repurposing.

INTRODUCTION: The US FDA required a Risk Evaluation and Mitigation Strategy (REMS) for phentermine/topiramate, an anti-obesity medication, to prevent congenital malformations. No REMS is required for single-ingredient topiramate, which may be used off-label for the same purpose. OBJECTIVE: The aim of this study was to evaluate the impact of phentermine/topiramate approval in 2012 on subsequent topiramate use among patients with obesity. METHODS: We used a national insurance claims database to conduct an interrupted time-series study (2009-2015). Enrollees aged 18-65 years in each examined calendar quarter had full insurance benefits during that quarter and the preceding 6 months. We required patients to have an obesity diagnosis and no other conditions warranting topiramate use. We calculated topiramate or comparator drug (atorvastatin, metformin) initiation rates and evaluated changes in trends before and after 2012 (transition period). RESULTS: Among topiramate users, 80% were female, and demographic characteristics remained consistent during the study period. Between 2009 and 2011, the topiramate initiation rate (95% confidence interval) among patients with obesity was 0.85 (0.73-0.98) per 1000 patients, with no significant upward or downward trend. In the first quarter of 2013, this rate had increased more than 2.5-fold (change: + 1.36 [1.19-1.52]). Metformin and atorvastatin initiation rates did not change. Topiramate initiation rates were threefold higher than phentermine/topiramate rates during the post-approval period. CONCLUSION: Phentermine/topiramate approval was associated with increased topiramate use among patients with obesity. Prescribers are encouraged to enhance patient education and monitoring in such clinical use since topiramate prescribing information, compared with REMS for phentermine/topiramate, has less emphasis on preventing prenatal exposure.

An Iterative Process for Identifying Pediatric Patients With Type 1 Diabetes: Retrospective Observational Study.

BACKGROUND: The incidence of both type 1 diabetes (T1DM) and type 2 diabetes (T2DM) in children and youth is increasing. However, the current approach for identifying pediatric diabetes and separating by type is costly, because it requires substantial manual efforts. OBJECTIVE: The purpose of this study was to develop a computable phenotype for accurately and efficiently identifying diabetes and separating T1DM from T2DM in pediatric patients. METHODS: This retrospective study utilized a data set from the University of Florida Health Integrated Data Repository to identify 300 patients aged 18 or younger with T1DM, T2DM, or that were healthy based on a developed computable phenotype. Three endocrinology residents/fellows manually reviewed medical records of all probable cases to validate diabetes status and type. This refined computable phenotype was then used to identify all cases of T1DM and T2DM in the OneFlorida Clinical Research Consortium. RESULTS: A total of 295 electronic health records were manually reviewed; of these, 128 cases were found to have T1DM, 35 T2DM, and 132 no diagnosis. The positive predictive value was 94.7%, the sensitivity was 96.9%, specificity was 95.8%, and the negative predictive value was 97.6%. Overall, the computable phenotype was found to be an accurate and sensitive method to pinpoint pediatric patients with T1DM. CONCLUSIONS: We developed a computable phenotype for identifying T1DM correctly and efficiently. The computable phenotype that was developed will enable researchers to identify a population accurately and cost-effectively. As such, this will vastly improve the ease of identifying patients for future intervention studies.

Chronic opioid use emerging after bariatric surgery.

PURPOSE: Little is known about opioid use after bariatric surgery among patients who did not use opioids chronically before surgery. Our purpose was to determine opioid use the year after bariatric surgery among patients who did not use opioids chronically pre-surgery and to identify pre-surgery characteristics associated with chronic opioid use after surgery. METHODS: This retrospective cohort study across nine US health systems included 10 643 patients aged 21 years or older who underwent bariatric surgery and who were not chronic opioid users pre-surgery. The main outcome was chronic opioid use the post-surgery year (excluding 30 post-operative days) defined as ≥10 dispensings over ≥90 days or ≥120 total days' supply. RESULTS: Overall, 4.0% (n = 421) of patients became chronic opioid users the post-surgery year. Pre-surgery opioid total days' supply was strongly associated with chronic use post-surgery (1-29 days adjusted odds ratio [OR] 1.89 [95%CI, 1.24-2.88]; 90-119 days OR, 14.29 [95%CI, 6.94-29.42] compared with no days). Other factors associated with increased likelihood of post-surgery chronic use included pre-surgery use of non-narcotic analgesics (OR, 2.22 [95%CI, 1.39-3.54]), antianxiety agents (OR, 1.67 [95%CI, 1.12-2.50]), and tobacco (OR, 1.44 [95%CI, 1.03-2.02]). Older age (OR, 0.84 [95%CI, 0.73-0.97] each decade) and a laparoscopic band procedure (OR, 0.42 [95%CI, 0.25-0.70] vs. laparoscopic bypass) were associated with decreased likelihood of chronic opioid use post-surgery. CONCLUSIONS: Most patients who became chronic opioid users the year after bariatric surgery used opioids intermittently before surgery.

Resting and ambulatory heart rate variability in chronic anorexia nervosa.

Heart rate variability was measured at rest and during ambulation in 6 women with anorexia nervosa. Compared with 10 nonanorexic women controls, resting and ambulatory measures of heart rate variability tended to be lower in patients, despite no differences in resting heart rate.