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OBJECTIVE: As part of a multidisciplinary team managing patients with type-2 diabetes, pharmacists need a consistent approach of identifying and prioritizing patients at highest risk of adverse outcomes. Our objective was to identify which predictors of adverse outcomes among type-2 diabetes patients were significant and common across 7 outcomes and whether these predictors improved the performance of risk prediction models. Identifying such predictors would allow pharmacists and other health care providers to prioritize their patient panels. RESEARCH DESIGN AND METHODS: Our study population included 120,256 adults aged 65 years or older with type-2 diabetes from a large integrated health system. Through an observational retrospective cohort study design, we assessed which risk factors were associated with 7 adverse outcomes (hypoglycemia, hip fractures, syncope, emergency department visit or hospital admission, death, and 2 combined outcomes). We split (50:50) our study cohort into a test and training set. We used logistic regression to model outcomes in the test set and performed k-fold validation (k = 5) of the combined outcome (without death) within the validation set. RESULTS: The most significant predictors across the 7 outcomes was: age, number of medicines, prior history of outcome within the past 2 years, chronic kidney disease, depression, and retinopathy. Experiencing an adverse outcome within the prior 2 years was the strongest predictor of future adverse outcomes (odds ratio range: 4.15–7.42). The best performing models across all outcomes included: prior history of outcome, physiological characteristics, comorbidities and pharmacy-specific factors (c-statistic range: 0.71–0.80). CONCLUSIONS: Pharmacists and other health care providers can use models with prior history of adverse event, number of medicines, chronic kidney disease, depression and retinopathy to prioritize interventions for elderly patients with type-2 diabetes.

Purpose Suspected diabetogenic effects or drug indication may increase testing for diabetes mellitus (DM), resulting in measurement bias when evaluating diabetogenic drug effects. We sought to evaluate the validity of electronic health record data in determining DM risk. Methods We used time-dependent Cox proportional hazard models within a retrospective cohort design to assess associations between use of antihypertensives, statins, atypical antipsychotics, and antidepressants, and two endpoints: (i) DM onset defined as fasting blood glucose (BG) ≥126 mg/dl, random BG ≥200 mg/dl, HbA1c ≥7.0%, or antidiabetic drug initiation; and (ii) first negative DM test. We used Poisson regression to assess the influence of these drugs on DM testing rates. Patients aged 35–64 years enrolled in Kaiser Permanente Northwest between 1997 and 2010 entered the cohort at the first negative BG test after ≥6 months without manifest DM. Results All drug classes showed significant associations not only with DM onset but also with first negative BG test and with DM testing rates. Antipsychotics had the greatest diabetogenic risk (adjusted hazard ratio [HR] = 1.73 [1.44–2.08]), the greatest propensity for a first negative test (adjusted HR = 1.87 [1.74–2.01]), and the highest testing rate (adjusted rate ratio = 1.76 [1.72–1.81]. Although renin–angiotensin system blockers and calcium channel blockers have shown no diabetogenic risk in clinical trials, both were associated with DM (HR = 1.19 [1.12–1.26] and 1.27 [1.17–1.38]), a negative glucose test (1.38 [1.35–1.41] and 1.24 [1.20–1.28]), and increased testing rates (rate ratio = 1.26 [1.24–1.27] and 1.27 [1.25–1.28]). Conclusion Caution should be used when diabetogenic risk is evaluated using data that rely on DM testing in general practice. Copyright © 2014 John Wiley & Sons, Ltd.

Advances in health information technology and widespread use of electronic health data offer new opportunities for development of large scale multisite disease-specific patient registries. Such registries use existing data, can be constructed at relatively low cost, include large numbers of patients, and once created can be used to address many issues with a short time between posing a question and obtaining an answer. Potential applications include comparative effectiveness research, public health surveillance, mapping and improving quality of clinical care, and others. This paper describes selected conceptual and practical challenges related to development of multisite diabetes and asthma registries, including development of case definitions, validation of case identification methods, variation in electronic health data sources; representativeness of registry populations, including the impact of attrition. Specific challenges are illustrated with data from actual registries.

Background Integrated health care delivery systems devote considerable resources to developing quality improvement (QI) interventions. Clinics serving vulnerable populations rarely have the resources for such development but might benefit greatly from implementing approaches shown to be effective in other settings. Little trial-based research has assessed the feasibility and impact of such cross-setting translation and implementation in community health centers (CHCs). We hypothesized that it would be feasible to implement successful QI interventions from integrated care settings in CHCs and would positively impact the CHCs. Methods We adapted Kaiser Permanente’s successful intervention, which targets guideline-based cardioprotective prescribing for patients with diabetes mellitus (DM), through an iterative, stakeholder-driven process. We then conducted a cluster-randomized pragmatic trial in 11 CHCs in a staggered process with six “early” CHCs implementing the intervention one year before five “‘late” CHCs. We measured monthly rates of patients with DM currently prescribed angiotensin converting enzyme (ACE)-inhibitors/statins, if clinically indicated. Through segmented regression analysis, we evaluated the intervention’s effects in June 2011–May 2013. Participants included ~6500 adult CHC patients with DM who were indicated for statins/ACE-inhibitors per national guidelines. Results Implementation of the intervention in the CHCs was feasible, with setting-specific adaptations. One year post-implementation, in the early clinics, there were estimated relative increases in guideline-concordant prescribing of 37.6 % (95 % confidence interval (CI); 29.0–46.2 %) among patients indicated for both ACE-inhibitors and statins and 38.7 % (95 % CI; 23.2–54.2 %) among patients indicated for statins. No such increases were seen in the late (control) clinics in that period. Conclusions To our knowledge, this was the first clinical trial testing the translation and implementation of a successful QI initiative from a private, integrated care setting into CHCs. This proved feasible and had significant impact but required considerable adaptation and implementation support. These results suggest the feasibility of adapting diverse strategies developed in integrated care settings for implementation in under-resourced clinics, with important implications for efficiently improving care quality in such settings. ClinicalTrials.gov NCT02299791.

The STRIDE study assessed whether a lifestyle intervention, tailored for individuals with serious mental illnesses, reduced weight and diabetes risk. The authors hypothesized that the STRIDE intervention would be more effective than usual care in reducing weight and improving glucose metabolism.The study design was a multisite, parallel two-arm randomized controlled trial in community settings and an integrated health plan. Participants who met inclusion criteria were ≥18 years old, were taking antipsychotic agents for ≥30 days, and had a body mass index ≥27. Exclusions were significant cognitive impairment, pregnancy/breastfeeding, recent psychiatric hospitalization, bariatric surgery, cancer, heart attack, or stroke. The intervention emphasized moderate caloric reduction, the DASH (Dietary Approaches to Stop Hypertension) diet, and physical activity. Blinded staff collected data at baseline, 6 months, and 12 months.Participants (men, N=56; women, N=144; mean age=47.2 years [SD=10.6]) were randomly assigned to usual care (N=96) or a 6-month weekly group intervention plus six monthly maintenance sessions (N=104). A total of 181 participants (90.5%) completed 6-month assessments, and 170 (85%) completed 12-month assessments, without differential attrition. Participants attended 14.5 of 24 sessions over 6 months. Intent-to-treat analyses revealed that intervention participants lost 4.4 kg more than control participants from baseline to 6 months (95% CI=-6.96 kg to -1.78 kg) and 2.6 kg more than control participants from baseline to 12 months (95% CI=-5.14 kg to -0.07 kg). At 12 months, fasting glucose levels in the control group had increased from 106.0 mg/dL to 109.5 mg/dL and decreased in the intervention group from 106.3 mg/dL to 100.4 mg/dL. No serious adverse events were study-related; medical hospitalizations were reduced in the intervention group (6.7%) compared with the control group (18.8%).Individuals taking antipsychotic medications can lose weight and improve fasting glucose levels. Increasing reach of the intervention is an important future step.

Suspected diabetogenic effects or drug indication may increase testing for diabetes mellitus (DM), resulting in measurement bias when evaluating diabetogenic drug effects. We sought to evaluate the validity of electronic health record data in determining DM risk.We used time-dependent Cox proportional hazard models within a retrospective cohort design to assess associations between use of antihypertensives, statins, atypical antipsychotics, and antidepressants, and two endpoints: (i) DM onset defined as fasting blood glucose (BG) ≥126 mg/dl, random BG ≥200 mg/dl, HbA1c ≥7.0%, or antidiabetic drug initiation; and (ii) first negative DM test. We used Poisson regression to assess the influence of these drugs on DM testing rates. Patients aged 35-64 years enrolled in Kaiser Permanente Northwest between 1997 and 2010 entered the cohort at the first negative BG test after ≥6 months without manifest DM.All drug classes showed significant associations not only with DM onset but also with first negative BG test and with DM testing rates. Antipsychotics had the greatest diabetogenic risk (adjusted hazard ratio [HR] = 1.73 [1.44-2.08]), the greatest propensity for a first negative test (adjusted HR = 1.87 [1.74-2.01]), and the highest testing rate (adjusted rate ratio = 1.76 [1.72-1.81]. Although renin-angiotensin system blockers and calcium channel blockers have shown no diabetogenic risk in clinical trials, both were associated with DM (HR = 1.19 [1.12-1.26] and 1.27 [1.17-1.38]), a negative glucose test (1.38 [1.35-1.41] and 1.24 [1.20-1.28]), and increased testing rates (rate ratio = 1.26 [1.24-1.27] and 1.27 [1.25-1.28]).Caution should be used when diabetogenic risk is evaluated using data that rely on DM testing in general practice.

Although cardiovascular disease causes substantial morbidity and mortality, how individual and groups of risk factors contribute to cardiovascular outcomes is incompletely understood. This study evaluated cardiometabolic risk factors and their relationship to prevalent diagnosis of acute myocardial infarction (AMI) and stroke.We used retrospective data from 3 integrated health-care systems that systematically collect and store detailed patient-level data. Adult enrollees were eligible for inclusion if they had all of the following clinical measurements: weight, height, blood pressure, high density lipoproteins, triglycerides, and fasting blood glucose or evidence of diabetes from July 1, 2003, to June 30, 2005. We used National Cholesterol Education Program Adult Treatment Panel III guidelines to determine qualifying levels for cardiometabolic risk factors.A total of 170,648 persons met the inclusion/exclusion criteria; 11,757 had no qualifying risk factors, 25,684 had 1, 38,176 had 2, and 95,031 had 3 or more risk factors. Compared to those without risk factors, persons with any 1 risk factor were 2.21 (95% confidence interval [CI], 1.78-2.74) times more likely to have had a diagnosis of AMI or stroke. The risk increased to 2.79 (95% CI, 2.26-3.42) for persons with 2, 3.45 (95% CI, 2.80-4.24) for persons with 3, 4.35 (95% CI, 3.54-5.35) for persons with 4, and 5.73 (95% CI, 4.65-7.07) for persons with 5 risk factors. The highest risk was conferred by having the combination of risk factors of diabetes, hypertension, and dyslipidemia, with or without weight risk.This study demonstrates a direct association between an increasing number of cardiometabolic risk factors and prevalent diagnosis of AMI and stroke. The combination of risk factors conferring the highest risk was diabetes, hypertension, and dyslipidemia.

This study compared prevalent health utilization and costs for persons with and without metabolic syndrome and investigated the independent associations of the various factors that make up metabolic syndrome.Subjects were enrollees of three health plans who had all clinical measurements (blood pressure, fasting plasma glucose, body mass index, triglycerides, and high-density lipoprotein cholesterol) necessary to determine metabolic syndrome risk factors over the 2-year study period (n = 170,648). We used clinical values, International Classification of Diseases, Ninth Revision (ICD-9) diagnoses, and medication dispensings to identify risk factors. We report unadjusted mean annual utilization and modeled mean annual costs adjusting for age, sex, and co-morbidity.Subjects with metabolic syndrome (n = 98,091) had higher utilization and costs compared to subjects with no metabolic syndrome (n = 72,557) overall, and when stratified by diabetes (P0.001). Average annual total costs between subjects with metabolic syndrome versus no metabolic syndrome differed by a magnitude of 1.6 overall ($5,732 vs. $3,581), and a magnitude of 1.3 when stratified by diabetes (diabetes, $7,896 vs. $6,038; no diabetes, $4,476 vs. $3,422). Overall, total costs increased by an average of 24% per additional risk factor (P0.001). Costs and utilization differed by risk factor clusters, but the more prevalent clusters were not necessarily the most costly. Costs for subjects with diabetes plus weight risk, dyslipidemia, and hypertension were almost double the costs for subjects with prediabetes plus similar risk factors ($8,067 vs. $4,638).Metabolic syndrome, number of risk factors, and specific combinations of risk factors are markers for high utilization and costs among patients receiving medical care. Diabetes and certain risk clusters are major drivers of utilization and costs.