Anne M. Tomolo

Aging is associated with increased HbA1c levels, independently of glucose levels and insulin resistance, and also with decreased HbA1c diagnostic specificity.

To determine whether using HbA1c for screening and management could be confounded by age differences, whether age effects can be explained by unrecognized diabetes and prediabetes, insulin resistance or postprandial hyperglycaemia, and whether the effects of aging have an impact on diagnostic accuracy.

Weight loss and incidence of diabetes with the Veterans Health Administration MOVE! lifestyle change programme: an observational study

BACKGROUND Programmes for lifestyle change are aimed at improving health but little is known about their effectiveness in clinical settings. The Veterans Health Administration (VA) MOVE! lifestyle change programme is the largest in the USA. We investigated whether participation in MOVE! is associated with reduced incidence of diabetes. METHODS We did a retrospective observational analysis of data from VA databases in overweight patients and obese patients with a weight-related disorder who had undergone at least 3 years of continuous outpatient care in 2005-12. We used generalised estimating equations to assess characteristics associated with MOVE! participation, and Coxs proportional hazards regression to analyse the association between participation and diabetes incidence. FINDINGS Of 1·8 million eligible individuals, 238 540 (13%) participated in the MOVE! programme. 19 367 (1% overall, 8% of participants) met criteria for intense and sustained participation (at least eight sessions within 6 months over at least a 4-month span), which was associated with greater weight loss at 3 years than low-intensity or no participation (-2·2% vs -0·64% or 0·46%). Compared with non-participation, incidence of diabetes was reduced by intense and sustained participation (hazard ratio 0·67, 95% CI 0·61-0·74) and low-intensity participation (0·80, 0·77-0·83) in MOVE!. These patterns were consistent across sex, ethnic origin, and age. Participation was most beneficial in patients with high BMI or high random glucose concentrations at baseline (both pinteraction<0·0001). INTERPRETATION Participation in the MOVE! programme was associated with weight loss and reduced incidence of diabetes, but the rate of participation was low and, therefore, selection bias could have exaggerated these effects. FUNDING US Department of Veterans Affairs, National Institutes of Health.

Translating What Works A New Approach to Improve Diabetes Management

Background: The most efficacious strategies to improve diabetes control include case management, health care team changes, patient education, and facilitated transmission of patient data to clinicians (“facilitated relay”), but these strategies have not been translated to permit general use in clinical practice. Methods: A web-based decision support program was developed to include these features, and assessed in patients who had A1c ≥7.0% despite using metformin with/without sulfonylureas or insulin. Staff entered patients’ glucose data, obtained management recommendations, reviewed the plan with a clinician, and discussed the new plan with patients. Results: 113 subjects were 96% male and 32% black, with average age 65.6 years and BMI 32.8. During prior primary care, A1c averaged 8.32 ± 0.16% (SEM). In all patients, baseline A1c was 8.18 ± 0.11%, and decreased to 7.54 ± 0.12%, 7.16 ± 0.13%, and 7.54 ± 0.16% at 3, 6, and 12 months, respectively, all P < .001. In 42 subjects who provided glucose data and made requested changes in medications, A1c was 8.12 ± 0.09% at baseline and fell to 7.29 ± 0.11%, 6.98 ± 0.10%, and 7.05 ± 0.10% at 3, 6, and 12 months, respectively, all P < .001. Chart review of 16 subjects followed for 12 months demonstrated that hypoglycemia (symptoms and/or glucose <70 mg/dl) averaged less than 1 episode/patient/month, and there was no severe hypoglycemia. Conclusions: A novel decision support program improved A1c with little hypoglycemia. Use of this approach should allow primary care teams to keep patients well controlled, and reduce the need for specialist referrals.

Glucose challenge test screening for prediabetes and early diabetes

To test the hypothesis that a 50‐g oral glucose challenge test with 1‐h glucose measurement would have superior performance compared with other opportunistic screening methods.

Reduced Cardiovascular Disease Incidence With a National Lifestyle Change Program

INTRODUCTION Lifestyle change programs implemented within healthcare systems could reach many Americans, but their impact on cardiovascular disease (CVD) remains unclear. The MOVE! program is the largest lifestyle change program implemented in a healthcare setting in the U.S. This study aimed to determine whether MOVE! participation was associated with reduced CVD incidence. METHODS This retrospective cohort study, analyzed in 2013-2015, used national Veterans Health Administration databases to identify MOVE! participants and eligible non-participants for comparison (2005-2012). Patients eligible for MOVE!-obese or overweight with a weight-related health condition, and no baseline CVD-were examined (N=1,463,003). Of these, 169,248 (12%) were MOVE! PARTICIPANTS Patients were 92% male, 76% white, with mean age 52 years and BMI of 32. The main outcome was incidence of CVD (ICD-9 and procedure codes for coronary artery disease, cerebrovascular disease, peripheral vascular disease, and heart failure). RESULTS Adjusting for age, race, sex, BMI, statin use, and baseline comorbidities, over a mean 4.9 years of follow-up, MOVE! participation was associated with lower incidence of total CVD (hazard ratio [HR]=0.83, 95% CI=0.80, 0.86); coronary artery disease (HR=0.81, 95% CI=0.77, 0.86); cerebrovascular disease (HR=0.87, 95% CI=0.82, 0.92); peripheral vascular disease (HR=0.89, 95% CI=0.83, 0.94); and heart failure (HR=0.78, 95% CI=0.74, 0.83). The association between MOVE! participation and CVD incidence remained significant when examined across categories of race/ethnicity, BMI, diabetes, hypertension, smoking status, and statin use. CONCLUSIONS Although participation was limited, MOVE! was associated with reduced CVD incidence in a nationwide healthcare setting.

Participation in a National Lifestyle Change Program is associated with improved diabetes Control outcomes.

AIMS Clinical trials show lifestyle change programs are beneficial, yet large-scale, successful translation of these programs is scarce. We investigated the association between participation in the largest U.S. lifestyle change program, MOVE!, and diabetes control outcomes. METHODS This longitudinal, retrospective cohort study used Veterans Health Administration databases of patients with diabetes who participated in MOVE! between 2005 and 2012, or met eligibility criteria (BMI ≥25kg/m2) but did not participate. Main outcomes were diabetic eye disease, renal disease, and medication intensification. RESULTS There were 400,170 eligible patients with diabetes, including 87,366 (22%) MOVE! PARTICIPANTS Included patients were 96% male, 77% white, with mean age 58years and BMI 34kg/m2. Controlling for baseline measurements and age, race, sex, BMI, and antidiabetes medications, MOVE! participants had lower body weight (-0.6kg), random plasma glucose (-2.8mg/dL), and HbA1c (-0.1%) at 12months compared to nonparticipants (each p<0.001). In multivariable Cox models, MOVE! participants had lower incidence of eye disease (hazard ratio 0.80, 95% CI 0.75-0.84) and renal disease (HR 0.89, 95% CI 0.86-0.92) and reduced medication intensification (HR 0.82, 95% CI 0.80-0.84). CONCLUSIONS If able to overcome participation challenges, lifestyle change programs in U.S. health systems may improve health among the growing patient population with diabetes.

IMPLEMENTATION OF A RAI-FRAILTY SCREENING ACROSS SURGICAL CLINICS: A QUALITY IMPROVEMENT INITIATIVE

Background The Risk Analysis Index (RAI) is a pre-operative screening tool to identify frail patients at risk for post-operative complications and mortality. Prior to this initiative, no frailty screening existed within the local setting. Objectives The global aim is to standardize pre-operative screening for frailty across surgical clinics by utilizing the RAI. The specific aim is to attain 80% RAI-Frailty screenings by surgery providers within 12 weeks of implementation for patients scheduled for elective surgery. Methods This initiative was piloted within a vascular surgery clinic at a southeastern medical center. Implementation strategies were developed, and the Model for Improvement with sequential Plan-Do-Study-Act (PDSA) cycles was utilized to achieve project aim. A cause-and-effect diagram was completed to understand the lack of RAI-Frailty screenings and identify areas for improvement among surgery providers. Interventions included: audit and feedback, RAI REDCap link integration within Electronic Health Record (EHR), and creation of a RAI-Frailty screening template. Results PDSA Cycle 1 demonstrated a below target mean of 26.3%. During PDSA Cycle 2 and 3, a special cause variation was demonstrated by a shift of 15 points above the mean prompting the split of P-chart control limits. After splitting the control limits, a common cause variation revealed the change was sustained. Conclusions RAI-Frailty screening was successfully implemented and sustained in the vascular surgery clinic utilizing audit and feedback and EHR modifications as strategies for change. Based on these findings, this screening initiative is being implemented across surgical clinics. Outcome measures are being evaluated to determine the clinical implications of this screening initiative. Figure 1 Figure 2

Geographic Localization The Need for Better Reporting and Outcome Selection

Geographic localization, sometimes called geographic units, is a method of care delivery for hospitalized patients in which all or most of a provider’s patients are admitted to a single unit. Under the traditional care model, hospitalists and advanced practice providers often have patients on multiple units, resulting in repeated daily travel to various units and interaction with different health care professionals and staff. We attempted to synthesize the evidence on geographic localization in order to understand the implications of this intervention. However, we found it difficult to determine its generalizability, which may be related to the contextual nature of system changes in health care. As a result, we identified 2 areas that could be improved to strengthen this body of work and make results more informative for those looking to implement geographic localization: (1) use of guidelines for reporting key elements of these studies, such as the Standards for Quality Improvement Reporting Excellence (SQUIRE) 2.0 and (2) selection of outcomes that may be better indicators of the effectiveness of the intervention. Implementing geographic localization is a large interdisciplinary undertaking. Key changes required could include overhauling the bed assignment process, reassigning personnel, adjusting physician and advanced practice provider staffing levels, restructuring inpatient units, and determining benchmarks for continuity of care. Instituting geographic localization requires a strong commitment by organizational leaders and investment of resources. As health systems implement and study this model of care delivery, it is important to gain insight from their experiences by reporting on elements central to systems change. The SQUIRE 2.0 guidelines (available at http://www. squire-statement.org) provide a framework for reporting on quality improvement efforts and contain core areas that should be included, such as the local problem, context, and intervention. Use of the SQUIRE guidelines would improve reporting on studies of geographic localization, as they are studies of a system change, so that others might adapt all or some components of the intervention to their own setting. We found that the studies reviewed reported on SQUIRE core areas with varying degrees of detail. For example, Toomath et al provide an excellent description of the local problem, and readers can clearly see how this led to the changes they made within their system. A description of the local problem will help those who are looking to implement geographic localization because it allows readers to compare their local problem with that of the study to determine components that may need to be adapted. A thorough description of the context and the intervention is important as this also provides needed information for adaptation to other settings. For geographic localization specifically, it is necessary to know what percentage of a team’s patients was geographically localized, the composition of the health care team, and type of patients admitted to the unit. Although studies did report on the physician/advanced practice provider teams providing care, it would be useful to understand the characteristics of nurse and allied health staffing, as these team members are integral to unit-based care. The existing culture of interprofessional collaboration and/or intentions to institute measures to change the culture also is worth considering. On a broader level, it is important to know how this unit functions within the hospital as a whole. Including the aforementioned details will allow others to compare their local context and problem to determine if transitioning to geographic localization would be a feasible and worthwhile investment of resources. The second area that could be improved is the selection of outcomes when evaluating geographic localization. We found that many studies reported on outcomes such as length of stay (LOS), readmission rates, and/or mortality rates. These measures have multiple contributing factors and may be difficult to affect by care on a specific unit. This may explain study results demonstrating varying degrees of impact on these measures. In 640352 AJMXXX10.1177/1062860616640352American Journal of Medical QualityJohnson-Martinez et al research-article2016