https://caltcm.memberclicks.net/assets/documents/acc_aha_chf_2013_guidelines.pdf

2013 ACCF/AHA Guideline for the Management of Heart Failure: A Report of the American College of Cardiology Foundation/American Heart Association Task Force on Practice Guidelines

XI. STRATEGIES FOR IMPROVING DIABETES CARE D iabetes is a chronic illness that requires continuing medical care and patient self-management education to prevent acute complications and to reduce the risk of long-term complications. Diabetes care is complex and requires that many issues, beyond glycemic control, be addressed. A large body of evidence exists that supports a range of interventions to improve diabetes outcomes. These standards of care are intended to provide clinicians, patients, researchers, payors, and other interested individuals with the components of diabetes care, treatment goals, and tools to evaluate the quality of care. While individual preferences, comorbidities, and other patient factors may require modification of goals, targets that are desirable for most patients with diabetes are provided. These standards are not intended to preclude more extensive evaluation and management of the patient by other specialists as needed. For more detailed information, refer to Bode (Ed.): Medical Management of Type 1 Diabetes (1), Burant (Ed): Medical Management of Type 2 Diabetes (2), and Klingensmith (Ed): Intensive Diabetes Management (3). The recommendations included are diagnostic and therapeutic actions that are known or believed to favorably affect health outcomes of patients with diabetes. A grading system (Table 1), developed by the American Diabetes Association (ADA) and modeled after existing methods, was utilized to clarify and codify the evidence that forms the basis for the recommendations. The level of evidence that supports each recommendation is listed after each recommendation using the letters A, B, C, or E.

Standards of Medical Care in Diabetes

The propensity score is the probability of treatment assignment conditional on observed baseline characteristics. The propensity score allows one to design and analyze an observational (nonrandomized) study so that it mimics some of the particular characteristics of a randomized controlled trial. In particular, the propensity score is a balancing score: conditional on the propensity score, the distribution of observed baseline covariates will be similar between treated and untreated subjects. I describe 4 different propensity score methods: matching on the propensity score, stratification on the propensity score, inverse probability of treatment weighting using the propensity score, and covariate adjustment using the propensity score. I describe balance diagnostics for examining whether the propensity score model has been adequately specified. Furthermore, I discuss differences between regression-based methods and propensity score-based methods for the analysis of observational data. I describe different causal average treatment effects and their relationship with propensity score analyses.

https://www.tandfonline.com/doi/pdf/10.1080/00273171.2011.568786

An Introduction to Propensity Score Methods for Reducing the Effects of Confounding in Observational Studies

Global estimates of the prevalence of diabetes for 2010 and 2030.

Background Audit and feedback continues to be widely used as a strategy to improve professional practice. It appears logical that healthcare professionals would be prompted to modify their practice if given feedback that their clinical practice was inconsistent with that of their peers or accepted guidelines. Yet, audit and feedback has not been found to be consistently effective. Objectives To assess the effects of audit and feedback on the practice of healthcare professionals and patient outcomes. Search strategy We searched the Cochrane Effective Practice and Organisation of Care Group's register up to January 2001. This was supplemented with searches of MEDLINE and reference lists, which did not yield additional relevant studies. Selection criteria Randomised trials of audit and feedback (defined as any summary of clinical performance over a specified period of time) that reported objectively measured professional practice in a healthcare setting or healthcare outcomes. Data collection and analysis Two reviewers independently extracted data and assessed study quality. Quantitative (meta-regression), visual and qualitative analyses were undertaken. Main results We included 85 studies, 48 of which have been added to the previous version of this review. There were 52 comparisons of dichotomous outcomes from 47 trials with over 3500 health professionals that compared audit and feedback to no intervention. The adjusted RDs of non-compliance with desired practice varied from 0.09 (a 9% absolute increase in non-compliance) to 0.71 (a 71% decrease in non-compliance) (median = 0.07, inter-quartile range = 0.02 to 0.11). The one factor that appeared to predict the effectiveness of audit and feedback across studies was baseline non-compliance with recommended practice. Reviewer's conclusions Audit and feedback can be effective in improving professional practice. When it is effective, the effects are generally small to moderate. The absolute effects of audit and feedback are more likely to be larger when baseline adherence to recommended practice is low.

Audit and feedback: effects on professional practice and healthcare outcomes

OBJECTIVE —In vitro evidence shows that immune function is compromised in people with diabetes. Although certain rare infections are more common and infection-related mortality is higher, the risk of acquiring an infectious disease for diabetic patients has never been quantified. RESEARCH DESIGN AND METHODS —A retrospective cohort study using administrative data compared all people with diabetes in Ontario, Canada, on 1 April 1999 to matched nondiabetic people ( n = 513,749 in each group). The risk ratios of having an infectious disease and of death attributable to infectious disease between those with and without diabetes were calculated. Secondary analysis individually examined common infectious diseases. The study was repeated using a second pair of cohorts defined in 1996 to confirm stability of the estimates. RESULTS —Nearly half of all people with diabetes had at least one hospitalization or physician claim for an infectious disease in each cohort year. The risk ratio for diabetic versus nondiabetic people was 1.21 (99% CI 1.20–1.22) in both cohort years. The risk ratio for infectious disease-related hospitalization was up to 2.17 (99% CI 2.10–2.23). The risk ratio for death attributable to infection was up to 1.92 (1.79–2.05). Many individual infections were more common in people with diabetes, especially serious bacterial infections. CONCLUSIONS —Diabetes confers an increased risk of developing and dying from an infectious disease, corroborating both in vitro evidence and commonly held clinical belief. In addition to microvascular and macrovascular sequelae, clinicians should consider infection a complication of diabetes.

https://care.diabetesjournals.org/content/diacare/26/2/510.full.pdf

Quantifying the Risk of Infectious Diseases for People With Diabetes

https://www.thelancet.com/pdfs/journals/lancet/PIIS0140-6736(07)60361-4.pdf

Trends in diabetes prevalence, incidence, and mortality in Ontario, Canada 1995–2005: a population-based study

Propensity Score Methods Gave Similar Results to Traditional Regression Modeling in Observational Studies: A Systematic Review

Background: It is generally appreciated that gestational diabetes is a risk factor for type 2 diabetes. However, the precise relation between these 2 conditions remains unknown. We sought to determine the incidence of diabetes mellitus after diagnosis of gestational diabetes. Methods: We used a population-based database to identify all deliveries in the province of Ontario over the 7-year period from Apr. 1, 1995, to Mar. 31, 2002. We linked these births to mothers who had been given a diagnosis of gestational diabetes through another administrative database that records people with diabetes on the basis of either physician service claims or hospital admission records. We examined database records for these women from the time of delivery until Mar. 31, 2004, a total of 9 years. We determined the presence of diabetes mellitus according to a validated administrative database definition for this condition. Results: We identified 659 164 pregnant women who had no pre-existing diabetes. Of these, 21 823 women (3.3%) had a diagnosis of gestational diabetes. The incidence of gestational diabetes rose significantly over the 9-year study period, from 3.2% in 1995 to 3.6% in 2001 (p Interpretation: In this large population-based study, the rate of development of diabetes after gestational diabetes increased over time and was almost 20% by 9 years. This estimate should be used by clinicians to assist in their counselling of pregnant women and by policy-makers to target these women for screening and prevention

/pdf/risk-of-development-of-diabetes-mellitus-after-diagnosis-of-382muwqdg7.pdf

Risk of development of diabetes mellitus after diagnosis of gestational diabetes

OBJECTIVE—Diabetic patients with inadequate glycemic control ought to have their management intensified. Failure to do so can be termed “clinical inertia.” Because data suggest that specialist care results in better control than primary care, we evaluated whether specialists demonstrated less clinical inertia than primary care physicians. RESEARCH DESIGN AND METHODS—Using administrative data, we studied all non-insulin-requiring diabetic patients in eastern Ontario aged 65 or older who had A1c results >8% between September 1999 and August 2000. Drug intensification was measured by comparing glucose-lowering drug regimens in 4-month blocks before and after the elevated A1c test and was defined as 1) the addition of a new oral drug, 2) a dose increase of an existing oral drug, or 3) the initiation of insulin. Propensity score-based matching was used to control for confounding between groups. RESULTS—There were 591 patients with specialist care and 1,911 with exclusively primary care. In the matched cohorts, 45.1% of patients with specialist care versus 37.4% with primary care had drug intensification (P = 0.009). Most of this difference was attributed to specialists’ more frequent initiation of insulin in response to elevated A1c. CONCLUSIONS—Fewer than one-half of patients with high A1c levels had intensification of their medications, regardless of specialty of their physician. Specialists were more aggressive with insulin initiation than primary care physicians, which may contribute to the lower A1c levels seen with specialist care. Interventions assisting patients and physicians to recognize and overcome clinical inertia should improve diabetes care in the population.

https://care.diabetesjournals.org/content/diacare/28/3/600.full.pdf

Janet E. Hux

Papers

Quantifying the Risk of Infectious Diseases for People With Diabetes

Trends in diabetes prevalence, incidence, and mortality in Ontario, Canada 1995–2005: a population-based study

Propensity Score Methods Gave Similar Results to Traditional Regression Modeling in Observational Studies: A Systematic Review

Risk of development of diabetes mellitus after diagnosis of gestational diabetes

Clinical inertia in response to inadequate glycemic control: do specialists differ from primary care physicians?