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

Background
Decision aids are intended to help people participate in decisions that involve weighing the benefits and harms of treatment options often with scientific uncertainty.

Objectives
To assess the effects of decision aids for people facing treatment or screening decisions.

Search methods
For this update, we searched from 2009 to June 2012 in MEDLINE; CENTRAL; EMBASE; PsycINFO; and grey literature. Cumulatively, we have searched each database since its start date including CINAHL (to September 2008).

Selection criteria
We included published randomized controlled trials of decision aids, which are interventions designed to support patients' decision making by making explicit the decision, providing information about treatment or screening options and their associated outcomes, compared to usual care and/or alternative interventions. We excluded studies of participants making hypothetical decisions.

Data collection and analysis
Two review authors independently screened citations for inclusion, extracted data, and assessed risk of bias. The primary outcomes, based on the International Patient Decision Aid Standards (IPDAS), were:

A) 'choice made' attributes;

B) 'decision-making process' attributes.

Secondary outcomes were behavioral, health, and health-system effects. We pooled results using mean differences (MD) and relative risks (RR), applying a random-effects model.

Main results
This update includes 33 new studies for a total of 115 studies involving 34,444 participants. For risk of bias, selective outcome reporting and blinding of participants and personnel were mostly rated as unclear due to inadequate reporting. Based on 7 items, 8 of 115 studies had high risk of bias for 1 or 2 items each.

Of 115 included studies, 88 (76.5%) used at least one of the IPDAS effectiveness criteria: A) 'choice made' attributes criteria: knowledge scores (76 studies); accurate risk perceptions (25 studies); and informed value-based choice (20 studies); and B) 'decision-making process' attributes criteria: feeling informed (34 studies) and feeling clear about values (29 studies).

A) Criteria involving 'choice made' attributes:

Compared to usual care, decision aids increased knowledge (MD 13.34 out of 100; 95% confidence interval (CI) 11.17 to 15.51; n = 42). When more detailed decision aids were compared to simple decision aids, the relative improvement in knowledge was significant (MD 5.52 out of 100; 95% CI 3.90 to 7.15; n = 19). Exposure to a decision aid with expressed probabilities resulted in a higher proportion of people with accurate risk perceptions (RR 1.82; 95% CI 1.52 to 2.16; n = 19). Exposure to a decision aid with explicit values clarification resulted in a higher proportion of patients choosing an option congruent with their values (RR 1.51; 95% CI 1.17 to 1.96; n = 13).

B) Criteria involving 'decision-making process' attributes:

Decision aids compared to usual care interventions resulted in:

a) lower decisional conflict related to feeling uninformed (MD -7.26 of 100; 95% CI -9.73 to -4.78; n = 22) and feeling unclear about personal values (MD -6.09; 95% CI -8.50 to -3.67; n = 18);

b) reduced proportions of people who were passive in decision making (RR 0.66; 95% CI 0.53 to 0.81; n = 14); and

c) reduced proportions of people who remained undecided post-intervention (RR 0.59; 95% CI 0.47 to 0.72; n = 18).

Decision aids appeared to have a positive effect on patient-practitioner communication in all nine studies that measured this outcome. For satisfaction with the decision (n = 20), decision-making process (n = 17), and/or preparation for decision making (n = 3), those exposed to a decision aid were either more satisfied, or there was no difference between the decision aid versus comparison interventions. No studies evaluated decision-making process attributes for helping patients to recognize that a decision needs to be made, or understanding that values affect the choice.

C) Secondary outcomes

Exposure to decision aids compared to usual care reduced the number of people of choosing major elective invasive surgery in favour of more conservative options (RR 0.79; 95% CI 0.68 to 0.93; n = 15). Exposure to decision aids compared to usual care reduced the number of people choosing to have prostate-specific antigen screening (RR 0.87; 95% CI 0.77 to 0.98; n = 9). When detailed compared to simple decision aids were used, fewer people chose menopausal hormone therapy (RR 0.73; 95% CI 0.55 to 0.98; n = 3). For other decisions, the effect on choices was variable.

The effect of decision aids on length of consultation varied from 8 minutes shorter to 23 minutes longer (median 2.55 minutes longer) with 2 studies indicating statistically-significantly longer, 1 study shorter, and 6 studies reporting no difference in consultation length. Groups of patients receiving decision aids do not appear to differ from comparison groups in terms of anxiety (n = 30), general health outcomes (n = 11), and condition-specific health outcomes (n = 11). The effects of decision aids on other outcomes (adherence to the decision, costs/resource use) were inconclusive.

Authors' conclusions
There is high-quality evidence that decision aids compared to usual care improve people's knowledge regarding options, and reduce their decisional conflict related to feeling uninformed and unclear about their personal values. There is moderate-quality evidence that decision aids compared to usual care stimulate people to take a more active role in decision making, and improve accurate risk perceptions when probabilities are included in decision aids, compared to not being included. There is low-quality evidence that decision aids improve congruence between the chosen option and the patient's values.

New for this updated review is further evidence indicating more informed, values-based choices, and improved patient-practitioner communication. There is a variable effect of decision aids on length of consultation. Consistent with findings from the previous review, decision aids have a variable effect on choices. They reduce the number of people choosing discretionary surgery and have no apparent adverse effects on health outcomes or satisfaction. The effects on adherence with the chosen option, cost-effectiveness, use with lower literacy populations, and level of detail needed in decision aids need further evaluation. Little is known about the degree of detail that decision aids need in order to have a positive effect on attributes of the choice made, or the decision-making process.

/pdf/decision-aids-for-people-facing-health-treatment-or-1j1706q17l.pdf

Decision aids for people facing health treatment or screening decisions

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

2018 AHA/ACC/AACVPR/AAPA/ABC/ACPM/ADA/AGS/APhA/ASPC/NLA/PCNA Guideline on the Management of Blood Cholesterol: A Report of the American College of Cardiology/American Heart Association Task Force on Clinical Practice Guidelines

How physicians choose drugs.

https://pure.rug.nl/ws/files/17596084/Title_and_contents_.pdf

Medication beliefs, treatment complexity, and non-adherence to different drug classes in patients with type 2 diabetes

Background
Patients with diabetes mellitus are at increased risk of developing cardiovascular disease. Controlling lipid levels has a preventive effect on the occurrence of major cardiovascular and cerebrovascular events. Individual trials have shown varying data on the efficacy of treatment with lipid-lowering statin therapy in the primary prevention of such events in diabetes.

Primary prevention of major cardiovascular and cerebrovascular events with statins in diabetic patients: a meta-analysis.

SUMMARY Background Several measures using prescription data have been developed for estimating medication refill adherence. Few studies have made direct comparisons, and little is known about the accuracy of these measures in patients on a multiple-drug regimen. Purpose To compare different calculation methods using prescription data for assessing refill adherence. Method An observational cohort study among type 2 diabetes patients was conducted. Adherence to oral glucose-lowering, antihypertensive and lipid-lowering medication was assessed for 2004. We calculated medication possession ratios in a flexible period (MPRF), per calendar year (MPRY) and gaps between refills (GAP) at drug class and therapeutic level. Individual review of drug prescription profiles was conducted to validate identified cases of suboptimal refill adherence. Differences in Area Under the Curve (AUC) of ROC-curves were calculated to compare the methods. Results Of the 3877 patients, 2969 (77%) patients received oral glucose-lowering medication, 2715 (70%) antihypertensives and 1797 (46%) lipid-lowering medication. Using cutoffs for MPR 30 days, overall rates of suboptimal adherence for these drug classes were 32, 35 and 23% respectively. AUC for measures calculated at drug class level (range 0.85‐0.90) were significantly larger than those calculated at therapeutic level (0.72‐0.90). For oral glucose-regulating medication and antihypertensives, AUCs were largest for the MPRY and GAP measures (0.87‐0.88). For lipid-lowering medication, the AUC was largest for the GAP measure (0.90). Conclusions Differences between adherence measures were small and favoured calculation on drug class level. For multiple drug use, both MPRY and GAP were good measures for identifying suboptimal refill adherence. Copyright # 2008 John Wiley & Sons, Ltd. key words—patient compliance; diabetes mellitus type 2; administrative data; primary health care; drug utilization

/pdf/comparison-of-various-measures-for-assessing-medication-1cqjs6zysc.pdf

Comparison of various measures for assessing medication refill adherence using prescription data

https://pure.rug.nl/ws/files/6706124/Voorham_2007_J_Am_Med_Inform_Assoc.pdf

Petra Denig

Papers

How physicians choose drugs.

Medication beliefs, treatment complexity, and non-adherence to different drug classes in patients with type 2 diabetes

Primary prevention of major cardiovascular and cerebrovascular events with statins in diabetic patients: a meta-analysis.

Comparison of various measures for assessing medication refill adherence using prescription data

Computerized Extraction of Information on the Quality of Diabetes Care from Free Text in Electronic Patient Records of General Practitioners