Health Beliefs Associated With Metformin Use Among Insured Adults With Prediabetes.

OBJECTIVE: To use the framework of the Health Belief Model (HBM) to explore factors associated with metformin use among adults with prediabetes. RESEARCH DESIGN AND METHODS: We analyzed survey data from 200 metformin users and 1,277 nonmetformin users with prediabetes identified from a large, insured workforce. All subjects were offered the National Diabetes Prevention Program (DPP) at no out-of-pocket cost. We constructed bivariate and multivariate models to investigate how perceived threat, perceived benefits, self-efficacy, and cues to action impacted metformin use and how demographic, clinical, sociopsychological, and structural variables impacted the associations. RESULTS: Adults with prediabetes who used metformin were younger and more likely to be women and to have worse self-rated health and higher BMIs than those with prediabetes who did not use metformin. Those who used metformin were also more likely to be aware of their prediabetes and to have a personal history of gestational diabetes mellitus or a family history of diabetes. After consideration of perceived threat, perceived benefits, self-efficacy, and cues to action, the only independent predictors of metformin use were younger age, female sex, higher BMI, and cues to action, most specifically, a doctor offering metformin therapy. CONCLUSIONS: Demographic and clinical factors and cues to action impact the likelihood of metformin use for diabetes prevention. Perceived threat, perceived benefits, and self-efficacy were not independently associated with metformin use. These results highlight the importance of patient-centered primary care and shared decision-making in diabetes prevention. Clinicians should proactively offer metformin to patients with prediabetes to facilitate effective diabetes prevention.

Domains from the health belief model predict enrollment in the National Diabetes Prevention Program among insured adults with prediabetes.

AIMS: To examine enrollment in the National Diabetes Prevention Program (DPP) by insured adults with prediabetes according to domains of the Health Belief Model (HBM). METHODS: Between 2015 and 2019, University of Michigan employees, retirees, and dependents with prediabetes were offered the National DPP at no out-of-pocket cost. Individuals with prediabetes were identified and mailed letters encouraging them to enroll. We surveyed those who enrolled and a random sample of those who did not using the HBM as a framework to examine factors associated with enrollment. Analyses were performed using multivariable logistic regression models. RESULTS: Of 64,131 employees, retirees, and dependents, 8131 were identified with prediabetes and 776 (9.5%) enrolled in the National DPP. Of those surveyed, 532 of 776 National DPP enrollees and 945 of 2673 non-enrollees responded to the survey (adjusted response rates 74% and 43%, respectively). Among survey respondents, factors associated with National DPP enrollment included older age, female sex, higher BMI, prediabetes awareness, greater perceived benefits of health-protective action, and one or more cues to action. CONCLUSIONS: Optimizing National DPP enrollment among adults with prediabetes will require identifying individuals with prediabetes, increasing personal awareness of the diagnosis, increasing perceived benefits of enrollment, and providing strong cues to action.

Use of metformin following a population-level intervention to encourage people with pre-diabetes to enroll in the National Diabetes Prevention Program.

INTRODUCTION: The National Diabetes Prevention Program (NDPP) and metformin are interventions to slow progression from pre-diabetes to type 2 diabetes. When coverage for the NDPP was offered by a public research university's health insurance plan, proactive strategies were used to combat historically low enrollment. Although not specifically targeted by these strategies, metformin use was higher than expected, leading to this evaluation. RESEARCH DESIGN AND METHODS: We used insurance enrollment, claims, pharmacy, and laboratory data for 64 131 adult employees, dependents, and retirees to identify individuals with pre-diabetes and invite them to enroll in the NDPP at no out-of-pocket cost. The characteristics of individuals with pre-diabetes who used metformin before and after their invitation were compared with NDPP enrollees. RESULTS: 8131 individuals with pre-diabetes were identified. Of these, 776 (9.5%) enrolled in a NDPP and 802 (9.9%) used metformin. Metformin users were younger, had higher body mass index, were more likely to have comorbidities, and had higher baseline hemoglobin A1c levels than non-users. Timing of metformin use varied with 107 (13%) discontinuing, 426 (53%) continuing, and 269 (34%) initiating metformin use after their NDPP invitation. Of NDPP enrollees, 13 (2%) discontinued, 56 (7%) continued, and 34 (4%) initiated metformin use when they enrolled. CONCLUSIONS: Despite no active encouragement, use of metformin was similar to the rate of enrollment in the NDPP. Metformin use was higher for individuals with higher likelihood of responding. With the proven cost-effectiveness of metformin, targeted strategies to increase metformin use in individuals with pre-diabetes who are likely to respond, but not willing to enroll in a lifestyle intervention, are needed.

Clinical Courses of HeartMate II Left Ventricular Assist Device Thrombosis.

We observed different temporal patterns of HeartMate II left ventricular assist device (LVAD) thrombosis regarding clinical manifestations and lactate dehydrogenase (LDH) trends. We propose nomenclature for classification of LVAD thrombosis and compare patient characteristics and outcomes in each pattern of LVAD thrombosis. We reviewed electronic medical records of 362 consecutive HeartMate II devices implanted at Cleveland Clinic from October 2008 to July 2014. We categorized clinical courses of confirmed device thrombosis based on clinical manifestations and LDH patterns. Patients' characteristics, clinical variables, and outcomes were collected for comparison. From a total of 19 confirmed device thromboses, we categorized the patterns of thrombosis into three distinctive types; abrupt thrombosis (Type 1), subacute thrombosis (Type 2), and asymptomatic hemolysis (Type 3). Abrupt thrombosis (Type 1) tended to be the most morbid clinical course with acute-onset thrombosis at 56.5 (interquartile range: 44-71) days, all New York Heart Association functional class III or IV at presentation. Death and need for surgical intervention were not different in each pattern. Asymptomatic hemolysis had unique comorbidities of preexisting cardiac thrombi and preoperative bacteremia. Confirmed LVAD thrombosis has different patterns of clinical presentation and each pattern of thrombosis has unique characteristics and clinical manifestations.

Dynamic prediction of left ventricular assist device pump thrombosis based on lactate dehydrogenase trends.

AIMS: The risk of HeartMate II (HMII) left ventricular assist device (LVAD) thrombosis has been reported, and serum lactate dehydrogenase (LDH), a biomarker of haemolysis, increases secondary to LVAD thrombosis. This study evaluated longitudinal measurements of LDH post-LVAD implantation, hypothesizing that LDH trends could timely predict future LVAD thrombosis. METHODS AND RESULTS: From October 2004 to October 2014, 350 HMIIs were implanted in 323 patients at Cleveland Clinic. Of these, patients on 339 HMIIs had at least one post-implant LDH value (7996 total measurements). A two-step joint model combining longitudinal biomarker data and pump thrombosis events was generated to assess the effect of changing LDH on thrombosis risk. Device-specific LDH trends were first smoothed using multivariate boosted trees, and then used as a time-varying covariate function in a multiphase hazard model to analyse time to thrombosis. Pre-implant variables associated with time-varying LDH values post-implant using boostmtree were also investigated. Standardized variable importance for each variable was estimated as the difference between model-based prediction error of LDH when the variable was randomly permuted and prediction error without permuting the values. The larger this difference, the more important a variable is for predicting the trajectory of post-implant LDH. Thirty-five HMIIs (10%) had either confirmed (18) or suspected (17) thrombosis, with 15 (43%) occurring within 3 months of implant. LDH was associated with thrombosis occurring both early and late after implant (P < 0.0001 for both hazard phases). The model demonstrated increased probability of HMII thrombosis as LDH trended upward, with steep changes in LDH trajectory paralleling trajectories in probability of pump thrombosis. The most important baseline variables predictive of the longitudinal pattern of LDH were higher bilirubin, higher pre-implant LDH, and older age. The effect of some pre-implant variables such as sodium on the post-implant LDH longitudinal pattern differed across time. CONCLUSIONS: Longitudinal trends in surveillance LDH for patients on HMII support are useful for dynamic prediction of pump thrombosis, both early after implant and late. Incorporating upward and downward trends in LDH that dynamically update a model of LVAD thrombosis risk provides a useful tool for clinical management and decisions.