Literature Update October 2009

Melissa Brouwers, Steven Hanna, Mona Abdel-Motagally, Jennifer Yee. Clinicians’ evaluations of, endorsements of, and intentions to use practice guidelines change over time: a retrospective analysis from an organized guideline program Implementation Science 2009; 4:34

Purpose: Clinical practice guidelines (CPGs) can improve clinical care but uptake and application are inconsistent. Objectives were: to examine temporal trends in clinicians’ evaluations of, endorsements of, and intentions to use cancer CPGs developed by an established CPG program; and to evaluate how predictor variables (clinician characteristics, beliefs, and attitudes) are associated with these trends.

Design and methods: Between 1999 and 2005, 756 clinicians evaluated 84 Cancer Care Ontario CPGs, yielding 4,091 surveys that targeted four CPG quality domains (rigour, applicability, acceptability, and comparative value), clinicians’ endorsement levels, and clinicians’ intentions to use CPGs in practice.

Results Time: In contrast to the applicability and intention to use in practice scores, there were small but statistically significant annual net gains in ratings for rigour, acceptability, comparative value, and CPG endorsement measures (p < 0.05 for all rating categories). Predictors: In 17 comparisons, ratings were significantly higher among clinicians having the most favourable beliefs and most positive attitudes and lowest for those having the least favourable beliefs and most negative attitudes (p < 0.05). Interactions Time × Predictors: Over time, differences in outcomes among clinicians decreased due to positive net gains in scores by clinicians whose beliefs and attitudes were least favorable.

Conclusion: Individual differences among clinicians largely explain variances in outcomes measured. Continued engagement of clinicians least receptive to CPGs may be worthwhile because they are the ones showing most significant gains in CPG quality ratings, endorsement ratings, and intentions to use in practice ratings.

Jan P. Vandenbroucke. STREGA, STROBE, STARD, SQUIRE, MOOSE, PRISMA,GNOSIS, TREND, ORION, COREQ, QUOROM, REMARK and CONSORT: for whom does the guideline toll? Journal of Clinical Epidemiology 2009; 62(6):594-596.

Erica L. Rosenberger, David C. Goff Jr., Caroline S. Blackwell, Dustin T. Williams, O. Lenore Crago, Shellie D. Ellis, Alain G. Bertoni, Denise E. Bonds. Implementing a palm pilot intervention for primary care providers: Lessons learned. Contemporary Clinical Trials 2009; 30(4): 321-325

The Personal Digital Assistance for Guideline Adherence (GLAD Heart) study was designed to test a strategy to improve quality of care through increased adherence to ATPIII cholesterol guidelines. This paper describes the overall study design including the multi-faceted intervention and outcome measures. Sixty-one primary care practices in NC were recruited and randomized to either a personal digital assistant-based cholesterol management intervention or an intervention similar in intensity and frequency of contact but focused on a hypertension clinical practice guideline. Installation and implementation of the technology intervention was challenging. Over the course of the study, there were 74 technical issues requiring assistance for the palm pilot from 23 participating practices. The GLAD Heart project was completed successfully with some impact on cholesterol management. Technology has the potential to improve the quality of care provided in the healthcare setting.However, potentially expensive interventions such as that conducted in GLAD Heart should undergo rigorous testing to assure their efficacy before widespread adoption.

Susan L. Norris. Clinical Practice Guidelines and Scientific Evidence. JAMA. 2009;302(2):142 (doi:10.1001/jama.2009.908)

George A. Diamond, Sanjay Kaul. Bayesian Classification of Clinical Practice Guidelines. Arch Intern Med 2009; 169(15):1431-1435.

Clinical practice guidelines are generally constructed from an admixture of expert consensus opinion, case-control studies, and randomized controlled trials (RCTs) and are categorized according to the methodological quality of the underlying data (level of evidence) and the trade-off between the benefits and risks of treatment (class of recommendation). The process is driven principally by conventional statistical significance and does not specifically consider the clinical importance of the alternative treatment effects. We herein propose a more formal quantitative algorithm for the construction of guidelines using Bayes’s theorem to integrate the clinical trial evidence with a range of prior belief representing the skeptical point of view embodied in the null hypothesis (to the effect that treatment can be expected to produce no reduction in risk), and the enthusiastic point of view embodied in the alternative hypothesis (to the effect that treatment can be expected to produce a specified clinically important reduction in risk). The operative practical utility of this algorithm is illustrated by application to a representative meta-analysis of RCTs. We conclude that this quantitative schema has the potential to improve the quality and cost of evidencebased clinical management.

Sharon E. Straus, Jacqueline Tetroe, and Ian Graham. Defining knowledge translation. CMAJ 2009; 181(3-4): 165-168.

Christopher AKY Chong, Ing-je Chen, Gary Naglie and Murray D. Krahn. How Well Do Guidelines Incorporate Evidence on Patient Preferences? Journal of General Internal Medicine 2009; 24(8): 977-982.

Background: Clinical practice guidelines (CPG) are meant to consider important values such as patient preferences.

Objective: To assess how well clinical practice guidelines (CPGs) integrate evidence on patient preferences compared with that on treatment effectiveness.

Design: A cross-sectional review of a listing in 2006 of CPGs judged to be the best in their fields by an external joint government and medical association body.

Study Selection: Exclusion criterion was unavailability in electronic format. Sixty-five of 71 listed CPGs met selection criteria.

Measurements: Two instruments originally constructed to evaluate the overall quality of CPGs were adapted to specifically assess the quality of integrating information on patient preference vs. treatment effectiveness. Counts of words and references in each CPG associated with patient preferences vs. treatment effectiveness were performed. Two reviewers independently assessed each CPG.

Main Results: Based on our adapted instruments, CPGs scored significantly higher (p < 0.001) on the quality of integrating treatment effectiveness compared with patient preferences evidence (mean instrument one scores on a scale of 0.25 to 1.00: 0.65 vs. 0.43; mean instrument two scores on a scale of 0 to 1: 0.58 vs. 0.18). The average percentage of the total word count dedicated to treatment effectiveness was 24.2% compared with 4.6% for patient preferences. The average percentage of references citing treatment effectiveness evidence was 36.6% compared with 6.0% for patient preferences.

Conclusion: High quality CPGs poorly integrate evidence on patient preferences. Barriers to incorporating preference evidence into CPGs should be addressed.

Lugtenberg M, Zegers-van Schaick JM, Westert GP, Burgers JS. Why don’t physicians adhere to guideline recommendations in practice? An analysis of barriers among Dutch general practitioners. Implementation Science 2009; 4:54

Background: Despite wide distribution and promotion of clinical practice guidelines, adherence among Dutch general practitioners (GPs) is not optimal. To improve adherence to guidelines, an analysis of barriers to implementation is advocated. Because different recommendations within a guideline can have different barriers, in this study we focus on key recommendations rather than guidelines as a whole, and explore the barriers to implementation perceived by Dutch GPs.

Methods: A qualitative study using six focus groups was conducted, in which 30 GPs participated, with an average of seven per session. Fifty-six key recommendations were derived from twelve national guidelines. In each focus group, barriers to the implementation of the key recommendations of two clinical practice guidelines were discussed. Focus group discussions were audiotaped and transcribed verbatim. Data was analysed by using an existing framework of barriers.

Results: The barriers varied largely within guidelines, with each key recommendation having a unique pattern of barriers. The most perceived barriers were lack of agreement with the recommendations due to lack of applicability or lack of evidence (68% of key recommendations), environmental factors such as organisational constraints (52%), lack of knowledge regarding the guideline recommendations (46%), and guideline factors such as unclear or ambiguous guideline recommendations (43%).

Conclusion: Our study findings suggest a broad range of barriers. As the barriers largely differ within guidelines, tailored and barrier-driven implementation strategies focusing on key recommendations are needed to improve adherence in practice. In addition, guidelines should be more transparent concerning the underlying evidence and applicability, and further efforts are needed to address complex issues such as comorbidity in guidelines. Finally, it might be useful to include focus groups in continuing medical education as an innovative medium for guideline education and implementation

Gordon H. Guyatt, Mark Helfand, Regina Kunz. Comparing the USPSTF and GRADE Approaches to Recommendations (Letter). Annals of Internal Medicine 2009; 151(5): 363.

Diana B. Petitti, Steven M. Teutsch, Mary B. Barton, George F. Sawaya, Judith K. Ockene, and Thomas DeWitt. (Response). Annals of Internal Medicine 2009;
151(5): 364

Agathe Bajard, Sylvie Chabaud, David Pérol, Jean-Pierre Boissel and Patrice Nony. Revisiting the level of evidence in randomized controlled clinical trials: A simulation approach. Contemporary Clinical Trials 2009; 30(5): 400-410.

Background: The Evidence Based Medicine (EBM) paradigm requires that results from Randomized Controlled Trials (RCTs) must be assessed for validity before being assimilated. However, evaluating available evidence is often still based on intuitive processes rather than on rigorous scientific analysis. Objective To establish a hierarchy among the different factors influencing the level of evidence of RCT results, using a Monte Carlo simulation.

Methods: The complete RCT model involved three submodels: i) the input–output submodel for the prediction of events (using the sigmoid dose–response relationship as the basic model), ii) the execution submodel for deviations from a randomized, controlled two-arm parallel trial related to either patient-specific or investigator-specific elements or both: placebo or nocebo effect, errors of measurement, effect of concomitant therapy, regression to the mean phenomenon, blinding process, loss to follow-up and randomization process, iii) the covariate distribution submodel.

Results: The most important factors influencing discrepancies in the true-to-observed odds ratio were the blinding process, the measurement errors (affecting either the therapeutic or the adverse effects), the placebo effect, the effect of concomitant therapies and to a less extent the randomization process. Whereas the randomization process remained the only relevant factor in double-blinded trials, the hierarchy of other factors was modified according to the type of blinding.

Conclusion: In RCTs, the hierarchy of confounding factors differs according to the type of blinding and the current short list of components of the strength of evidence (poorly concealed randomization and lack of blinding) appears to be incomplete.