Katrina Ramsey

Factors Influencing Internal Medicine Resident Choice of Infectious Diseases or Other Specialties: A National Cross-sectional Study

BACKGROUND Only 49% of infectious diseases (ID) fellowship programs were filled in 2015 through the national match, but little is known about internal medicine (IM) resident perceptions of ID and factors related to IM resident career choice. METHODS We conducted 25 interviews and disseminated a Web-based survey to graduating IM residents in the United States utilizing a 2-stage sampling strategy. Participants were categorized into 3 groups based on interest in ID: (1) applied/intended to apply to ID; (2) interested in ID but did not apply; (3) never interested in ID. We conducted all analysis using poststratification adjustment weights with survey data analysis procedures. RESULTS Of the 590 participants, 42 (7%) selected category 1, 188 (32%) category 2, and 360 (61%) category 3. Most (65%) developed an interest in their ultimate career before residency. Of those interested in ID, >52% rated their ID medical school curriculum as very good and influential on their interest in ID. Ninety-one percent of category 2 participants felt mentorship was influential on career choice, although 43% identified an ID mentor. Category 2 chose salary as the most dissuading factor and the most likely intervention to increase ID interest. CONCLUSIONS In this nationally representative sample of graduating IM residents, most develop an interest in their ultimate career before residency. Factors influencing this decision reside in both medical school and residency, which is consistent with career decision-making constructs. By identifying career determining factors and understanding how they fit into medical training frameworks, we can develop targeted initiatives to reinvigorate interest in ID.

Randomized trial of a dual‐hormone artificial pancreas with dosing adjustment during exercise compared with no adjustment and sensor‐augmented pump therapy

To test whether adjusting insulin and glucagon in response to exercise within a dual‐hormone artificial pancreas (AP) reduces exercise‐related hypoglycaemia.

Effect of Repeated Glucagon Doses on Hepatic Glycogen in Type 1 Diabetes: Implications for a Bihormonal Closed-Loop System.

OBJECTIVE To evaluate subjects with type 1 diabetes for hepatic glycogen depletion after repeated doses of glucagon, simulating delivery in a bihormonal closed-loop system. RESEARCH DESIGN AND METHODS Eleven adult subjects with type 1 diabetes participated. Subjects underwent estimation of hepatic glycogen using 13C MRS. MRS was performed at the following four time points: fasting and after a meal at baseline, and fasting and after a meal after eight doses of subcutaneously administered glucagon at a dose of 2 µg/kg, for a total mean dose of 1,126 µg over 16 h. The primary and secondary end points were, respectively, estimated hepatic glycogen by MRS and incremental area under the glucose curve for a 90-min interval after glucagon administration. RESULTS In the eight subjects with complete data sets, estimated glycogen stores were similar at baseline and after repeated glucagon doses. In the fasting state, glycogen averaged 21 ± 3 g/L before glucagon administration and 25 ± 4 g/L after glucagon administration (mean ± SEM) (P = NS). In the fed state, glycogen averaged 40 ± 2 g/L before glucagon administration and 34 ± 4 g/L after glucagon administration (P = NS). With the use of an insulin action model, the rise in glucose after the last dose of glucagon was comparable to the rise after the first dose, as measured by the 90-min incremental area under the glucose curve. CONCLUSIONS In adult subjects with well-controlled type 1 diabetes (mean A1C 7.2%), glycogen stores and the hyperglycemic response to glucagon administration are maintained even after receiving multiple doses of glucagon. This finding supports the safety of repeated glucagon delivery in the setting of a bihormonal closed-loop system.

Engaging the Underserved: A Process Model to Mobilize Rural Community Health Coalitions as Partners in Translational Research†

Community engagement (CE) and community‐engaged research (CEnR) are increasingly recognized as critical elements in research translation. Process models to develop CEnR partnerships in rural and underserved communities are needed.

Limited accuracy of administrative data for the identification and classification of adult congenital heart disease

Background Administrative data sets utilize billing codes for research and quality assessment. Previous data suggest that such codes can accurately identify adults with congenital heart disease (CHD) in the cardiology clinic, but their use has yet to be validated in a larger population. Methods and Results All administrative codes from an entire health system were queried for a single year. Adults with a CHD diagnosis code (International Classification of Diseases, Ninth Revision, (ICD‐9) codes 745–747) defined the cohort. A previously validated hierarchical algorithm was used to identify diagnoses and classify patients. All charts were reviewed to determine a gold standard diagnosis, and comparisons were made to determine accuracy. Of 2399 individuals identified, 206 had no CHD by the algorithm or were deemed to have an uncertain diagnosis after provider review. Of the remaining 2193, only 1069 had a confirmed CHD diagnosis, yielding overall accuracy of 48.7% (95% confidence interval, 47–51%). When limited to those with moderate or complex disease (n=484), accuracy was 77% (95% confidence interval, 74–81%). Among those with CHD, misclassification occurred in 23%. The discriminative ability of the hierarchical algorithm (C statistic: 0.79; 95% confidence interval, 0.77–0.80) improved further with the addition of age, encounter type, and provider (C statistic: 0.89; 95% confidence interval, 0.88–0.90). Conclusions ICD codes from an entire healthcare system were frequently erroneous in detecting and classifying CHD patients. Accuracy was higher for those with moderate or complex disease or when coupled with other data. These findings should be taken into account in future studies utilizing administrative data sets in CHD.

Randomized Outpatient Trial of Single- and Dual-Hormone Closed-Loop Systems That Adapt to Exercise Using Wearable Sensors

OBJECTIVE Automated insulin delivery is the new standard for type 1 diabetes, but exercise-related hypoglycemia remains a challenge. Our aim was to determine whether a dual-hormone closed-loop system using wearable sensors to detect exercise and adjust dosing to reduce exercise-related hypoglycemia would outperform other forms of closed-loop and open-loop therapy. RESEARCH DESIGN AND METHODS Participants underwent four arms in randomized order: dual-hormone, single-hormone, predictive low glucose suspend, and continuation of current care over 4 outpatient days. Each arm included three moderate-intensity aerobic exercise sessions. The two primary outcomes were percentage of time in hypoglycemia (<70 mg/dL) and in a target range (70–180 mg/dL) assessed across the entire study and from the start of the in-clinic exercise until the next meal. RESULTS The analysis included 20 adults with type 1 diabetes who completed all arms. The mean time (SD) in hypoglycemia was the lowest with dual-hormone during the exercise period: 3.4% (4.5) vs. 8.3% (12.6) single-hormone (P = 0.009) vs. 7.6% (8.0) predictive low glucose suspend (P < 0.001) vs. 4.3% (6.8) current care where pre-exercise insulin adjustments were allowed (P = 0.49). Time in hypoglycemia was also the lowest with dual-hormone during the entire 4-day study: 1.3% (1.0) vs. 2.8% (1.7) single-hormone (P < 0.001) vs. 2.0% (1.5) predictive low glucose suspend (P = 0.04) vs. 3.1% (3.2) current care (P = 0.007). Time in range during the entire study was the highest with single-hormone: 74.3% (8.0) vs. 72.0% (10.8) dual-hormone (P = 0.44). CONCLUSIONS The addition of glucagon delivery to a closed-loop system with automated exercise detection reduces hypoglycemia in physically active adults with type 1 diabetes.

Reliability of Trained Dogs to Alert to Hypoglycemia in Patients With Type 1 Diabetes

Background: We examined the reliability of trained dogs to alert to hypoglycemia in individuals with type 1 diabetes. Methods: Patients with type 1 diabetes who currently used diabetes alert dogs participated in this exploratory study. Subjects reported satisfaction, perceived dog glucose sensing ability and reasons for obtaining a trained dog. Reliability of dog alerts was assessed using capillary blood glucose (CBG) and blinded continuous glucose monitoring (CGM) as comparators in 8 subjects (age 4-48). Hypoglycemia was defined as CBG or CGM <70 mg/dL. Results: Dog users were very satisfied (8.9/10 on a Likert-type scale) and largely confident (7.9/10) in their dog’s ability to detect hypoglycemia. Detection of hypoglycemia was the primary reason for obtaining a trained dog. During hypoglycemia, spontaneous dog alerts occurred at a rate 3.2 (2.0-5.2, 95% CI) times higher than during euglycemia (70-179 mg/dL). Dogs provided timely alerts in 36% (sensitivity) of all hypoglycemia events (n = 45). Due to inappropriate alerts, the PPV of a dog alert for hypoglycemia was 12%. When there was concurrence of a hypoglycemic event between the dog alert and CGM (n = 30), CGM would have alerted prior to the dog in 73% of events (median 22-minute difference). Conclusions: This is the first study evaluating reliability of trained dogs to alert to hypoglycemia under real-life conditions. Trained dogs often alert a human companion to otherwise unknown hypoglycemia; however due to high false-positive rate, a dog alert alone is unlikely to be helpful in differentiating hypo-/hyper-/euglycemia. CGM often detects hypoglycemia before a trained dog by a clinically significant margin.

Milk Options Observation (MOO): A Mixed-Methods Study of Chocolate Milk Removal on Beverage Consumption and Student/Staff Behaviors in a Rural Elementary School

Providing flavored milk in school lunches is controversial, with conflicting evidence on its impact on nutritional intake versus added sugar consumption and excess weight gain. Nonindustry-sponsored studies using individual-level analyses are needed. Therefore, we conducted this mixed-methods study of flavored milk removal at a rural primary school between May and June 2012. We measured beverage selection/consumption pre- and post-chocolate milk removal and collected observation field notes. We used linear and logistic mixed models to assess beverage waste and identified themes in staff and student reactions. Our analysis of data from 315 unique students and 1,820 beverages choices indicated that average added sugar intake decreased by 2.8 g postremoval, while average reductions in calcium and protein consumption were negligible (12.2 mg and 0.3 g, respectively). Five thematic findings emerged, including concerns expressed by adult staff about student rebellion following removal, which did not come to fruition. Removing flavored milk from school-provided lunches may lower students’ daily added sugar consumption without considerably decreasing calcium and protein intake and may promote healthy weight.

Women's Health Policies Associated with Obesity, Diabetes, High Blood Pressure, and Smoking: A Follow-Up on the Women's Health Report Card

ABSTRACT This study sought to elucidate associations between state-level policies related to womens health and state prevalence of obesity, smoking, high blood pressure, and diabetes among women. Using data from national sources compiled for Making the Grade on Womens Health: A National and State-by-State Report Card, state policies on key womens health issues were evaluated on the degree to which policies adequately protected womens health. Blocked regressions assessed the policies associated with state outcomes. Anti-discrimination policies were prominent for high blood pressure, smoking, and obesity; models accounted for significant variance for all outcomes. State policies that support women may improve womens health.

Effect of a Pragmatic, Cluster-randomized Controlled Trial on Patient Experience With Care: The Transforming Outcomes for Patients Through Medical Home Evaluation and reDesign (TOPMED) Study.

Background:Health reform programs like the patient-centered medical home are intended to improve the triple aim. Previous studies on patient-centered medical homes have shown mixed effects, but high value elements (HVEs) are expected to improve the triple aim. Objective:The aim of this study is to understand whether focusing on HVEs would improve patient experience with care. Methods:Eight clinics were cluster-randomized in a year-long trial. Both arms received practice facilitation, IT-based reporting, and financial incentives. Intervention practices were encouraged to choose HVEs for quality improvement goals. To assess patient experience, 1597 Consumer Assessment of Healthcare Providers and Systems surveys were sent pretrial and posttrial to a stratified random sample of patients. Difference-in-difference multivariate analysis was used to compare patient responses from intervention and control practices, adjusting for confounders. Results:The response rate was 43% (n=686). Nonrespondent analysis showed no difference between arms, although differences were seen by risk status and age. The overall difference in difference was 2.8%, favoring the intervention. The intervention performed better in 9 of 11 composites. The intervention performed significantly better in follow-up on test results (P=0.091) and patients’ rating of the provider (P=0.091), whereas the control performed better in access to care (P=0.093). Both arms also had decreases, including 4 of 11 composites for the intervention, and 8 of 11 for the control. Discussion:Practices that targeted HVEs showed significantly more improvement in patient experience of care. However, contemporaneous trends may have affected results, leading to declines in patient experience in both arms.