Gordon H. Guyatt

Bio: Gordon H. Guyatt is an academic researcher from McMaster University. The author has contributed to research in topics: Randomized controlled trial & Evidence-based medicine. The author has an hindex of 231, co-authored 1620 publications receiving 228631 citations. Previous affiliations of Gordon H. Guyatt include Memorial Sloan Kettering Cancer Center & Cayetano Heredia University.

Guideline for opioid therapy and chronic noncancer pain

Abstract: Chronic noncancer pain includes any painful condition that persists for at least three months and is not associated with malignant disease.[1][1] According to seven national surveys conducted between 1994 and 2008, 15%–19% of Canadian adults live with chronic noncancer pain.[2][2] Chronic

Clinical expertise in the era of evidence-based medicine and patient choice.

Abstract: You are caring for a 68 year old man who has hypertension (intermittently controlled) with a remote gastrointestinal bleed and non-valvular atrial fibrillation (NVAF) for 3 months, and an enlarged left atrium (so cardioversion is unlikely). The patient has no history of stroke or transient ischaemic attack. His father experienced a debilitating stroke several years ago and when he learns that his atrial fibrillation places him at higher risk for a stroke, he is visibly distressed. The concepts of evidence-based medicine are evolving as limitations of early models are addressed. In this editorial, we present a new model for evidence-based clinical decision making based on patients' circumstances, patients' preferences and actions, and best research evidence, with a central role for clinical expertise to integrate these components. Traditionally, clinicians have been credited with clinical acumen according to their skills in making a diagnosis and prescribing or administering a treatment. The advent of major investments in biomedical research, leading to new and better tests and treatments, has spurred the development of critical appraisal of the medical literature and evidence-based medicine,1 and application of current best evidence from healthcare research is now an expected adjunct to clinical acumen. Initially, evidence-based medicine focused mainly on determining the best research evidence relevant to a clinical problem or decision and applying that evidence to resolve the issue. This early formulation de-emphasised traditional determinants of clinical decisions, including physiological rationale and individual clinical experience. Subsequent versions of evidence-based decision making have emphasised that research evidence alone is not an adequate guide to action. Rather, clinicians must apply their expertise to assess the patient's problem and must also incorporate the research evidence and the patient's preferences or values before making a management recommendation (figure 1).2 Figure 1 Early model of the key elements for evidence-based clinical decisions Figure …

Use of GRADE for assessment of evidence about prognosis: rating confidence in estimates of event rates in broad categories of patients.

Abstract: Introduction The term prognosis refers to the likelihood of future health outcomes in people with a given disease or health condition or with particular characteristics such as age, sex, or genetic profile. Patients and healthcare providers may be interested in prognosis for several reasons, so prognostic studies may have a variety of purposes,1–4 including establishing typical prognosis in a broad population, establishing the effect of patients’ characteristics on prognosis, and developing a prognostic model (often referred to as a clinical prediction rule) (Table 1). Considerations in determining the trustworthiness of estimates of prognosis arising from these types of studies differ. This article covers studies answering questions about the prognosis of a typical patient from a broadly defined population; we will consider prognostic studies assessing risk factors and clinical prediction guides in subsequent papers. Knowing the likely course of their disease may help patients to come to terms with, and plan for, the future. Knowledge of the risk of adverse outcomes or the likelihood of spontaneous resolution of symptoms is critical in predicting the likely effect of treatment and planning diagnostic investigations.5 If the probability of facing an adverse outcome is very low or the spontaneous remission of the disease is high (“good prognosis”), the possible absolute benefits of treatment will inevitably be low and serious adverse effects related to treatment or invasive diagnostic tests, even if rare, will loom large in any decision. If instead the probability of an adverse outcome is high (“bad prognosis”), the impact of new diagnostic information or of effective treatment may be large and patients may be ready to accept higher risks of diagnostic investigation and treatment related adverse effects. Inquiry into the credibility or trustworthiness of prognostic estimates has, to date, largely focused on individual studies of prognosis. Systematic reviews of the highest quality evidence including all the prognostic studies assessing a particular clinical situation are, however, gaining increasing attention, including the Cochrane Collaboration’s work (in progress) to define a template for reviews of prognostic studies (http://prognosismethods.cochrane.org/scope-ourwork). Trustworthy systematic reviews will not only ensure comprehensive collection, summarization, and critique of the primary studies but will also conduct optimal analyses. Matters that warrant consideration in such analyses include the method used to pool rates and whether analyses account for all the relevant covariates; the literature provides guidance on both questions.6 7 In this article, we consider how to establish degree of confidence in estimates from such bodies of evidence. The guidance in this article is directed primarily at researchers conducting systematic reviews of prognostic studies. It will also be useful to anyone interested in prognostic estimates and their associated confidence (including guideline developers) when evaluating a body of evidence (for example, a guideline panel using baseline risk estimates to estimate the absolute effect of Summary poIntS

II. Meta-Analysis of Alendronate for the Treatment of Postmenopausal Women

Abstract: Objective To review the effect of alendronate on bone density and fractures in postmenopausal women. Data source We searched MEDLINE, EMBASE, Current Contents, and the Cochrane Controlled trials registry from 1980 to 1999, and we examined citations of relevant articles and proceedings of international meetings. Study selection We included 11 trials that randomized women to alendronate or placebo and measured bone density for at least 1 yr. Data extraction For each trial, three independent reviewers assessed the methodological quality and abstracted data. Data synthesis The pooled relative risk (RR) for vertebral fractures in patients given 5 mg or more of alendronate was 0.52 [95% confidence interval (CI), 0.43-0.65]. The RR of nonvertebral fractures in patients given 10 mg or more of alendronate was 0.51 (95% CI 0.38-0.69), an appreciably greater effect than for the 5 mg dose. We found a similar reduction in RR across nonvertebral fracture types; in particular, RR reductions for fractures traditionally thought to be "osteoporotic," such as hip and forearm, were very similar to RR reductions for "nonosteoporotic" fractures. Individual studies showed similar results, reflected in the P values of the test of heterogeneity (P = 0.99 for vertebral and 0.88 for nonvertebral fractures). Alendronate produced positive effects on the percentage change in bone density, which increased with both dose and time. After 3 yr of treatment with 10 mg of alendronate or more, the pooled estimate of the difference in percentage change between alendronate and placebo was 7.48% (95% CI 6.12-8.85) for the lumbar spine (2-3 yr), 5.60% (95% CI 4.80-6.39) for the hip (3-4 yr), 2.08% (95% CI 1.53-2.63) for the forearm (2-4 yr), and 2.73% (95% CI 2.27-3.20) for the total body (3 yr). Heterogeneity of the treatment effect of alendronate was not consistently explained by any of our a priori hypotheses; in particular, the effect was very similar in prevention and treatment studies. The pooled RR for discontinuing medication due to adverse effects for 5 mg or greater of alendronate was 1.15 (95% CI 0.93-1.42). The pooled RR for discontinuing medication due to gastro-intestinal (GI) side effects for 5 mg or greater was 1.03 (0.81-1.30, P = 0.83), and the pooled RR for GI adverse effects with continuation of medication was 1.03 (0.98 to 1.07) P = 0.23. Conclusions Alendronate increases bone density in both early postmenopausal women and those with established osteoporosis while reducing the rate of vertebral fracture over 2-3 yr of treatment. Reductions in nonvertebral fractures are evident among postmenopausal women without prevalent fractures and have bone mineral density (BMD) levels below the World Health Organization threshold for osteoporosis. The impact on fractures appears consistent across all fracture types, casting doubt on traditional distinctions between osteoporotic and nonosteoporotic fractures.

Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement

Abstract: David Moher and colleagues introduce PRISMA, an update of the QUOROM guidelines for reporting systematic reviews and meta-analyses

Preferred reporting items for systematic reviews and meta-analyses: the PRISMA Statement.

Abstract: Systematic reviews and meta-analyses have become increasingly important in health care. Clinicians read them to keep up to date with their field,1,2 and they are often used as a starting point for developing clinical practice guidelines. Granting agencies may require a systematic review to ensure there is justification for further research,3 and some health care journals are moving in this direction.4 As with all research, the value of a systematic review depends on what was done, what was found, and the clarity of reporting. As with other publications, the reporting quality of systematic reviews varies, limiting readers' ability to assess the strengths and weaknesses of those reviews. Several early studies evaluated the quality of review reports. In 1987, Mulrow examined 50 review articles published in 4 leading medical journals in 1985 and 1986 and found that none met all 8 explicit scientific criteria, such as a quality assessment of included studies.5 In 1987, Sacks and colleagues6 evaluated the adequacy of reporting of 83 meta-analyses on 23 characteristics in 6 domains. Reporting was generally poor; between 1 and 14 characteristics were adequately reported (mean = 7.7; standard deviation = 2.7). A 1996 update of this study found little improvement.7 In 1996, to address the suboptimal reporting of meta-analyses, an international group developed a guidance called the QUOROM Statement (QUality Of Reporting Of Meta-analyses), which focused on the reporting of meta-analyses of randomized controlled trials.8 In this article, we summarize a revision of these guidelines, renamed PRISMA (Preferred Reporting Items for Systematic reviews and Meta-Analyses), which have been updated to address several conceptual and practical advances in the science of systematic reviews (Box 1). Box 1 Conceptual issues in the evolution from QUOROM to PRISMA

Measuring inconsistency in meta-analyses

Abstract: Cochrane Reviews have recently started including the quantity I 2 to help readers assess the consistency of the results of studies in meta-analyses. What does this new quantity mean, and why is assessment of heterogeneity so important to clinical practice? Systematic reviews and meta-analyses can provide convincing and reliable evidence relevant to many aspects of medicine and health care.1 Their value is especially clear when the results of the studies they include show clinically important effects of similar magnitude. However, the conclusions are less clear when the included studies have differing results. In an attempt to establish whether studies are consistent, reports of meta-analyses commonly present a statistical test of heterogeneity. The test seeks to determine whether there are genuine differences underlying the results of the studies (heterogeneity), or whether the variation in findings is compatible with chance alone (homogeneity). However, the test is susceptible to the number of trials included in the meta-analysis. We have developed a new quantity, I 2, which we believe gives a better measure of the consistency between trials in a meta-analysis. Assessment of the consistency of effects across studies is an essential part of meta-analysis. Unless we know how consistent the results of studies are, we cannot determine the generalisability of the findings of the meta-analysis. Indeed, several hierarchical systems for grading evidence state that the results of studies must be consistent or homogeneous to obtain the highest grading.2–4 Tests for heterogeneity are commonly used to decide on methods for combining studies and for concluding consistency or inconsistency of findings.5 6 But what does the test achieve in practice, and how should the resulting P values be interpreted? A test for heterogeneity examines the null hypothesis that all studies are evaluating the same effect. The usual test statistic …

Bias in meta-analysis detected by a simple, graphical test

Abstract: Objective: Funnel plots (plots of effect estimates against sample size) may be useful to detect bias in meta-analyses that were later contradicted by large trials. We examined whether a simple test of asymmetry of funnel plots predicts discordance of results when meta-analyses are compared to large trials, and we assessed the prevalence of bias in published meta-analyses. Design: Medline search to identify pairs consisting of a meta-analysis and a single large trial (concordance of results was assumed if effects were in the same direction and the meta-analytic estimate was within 30% of the trial); analysis of funnel plots from 37 meta-analyses identified from a hand search of four leading general medicine journals 1993-6 and 38 meta-analyses from the second 1996 issue of the Cochrane Database of Systematic Reviews . Main outcome measure: Degree of funnel plot asymmetry as measured by the intercept from regression of standard normal deviates against precision. Results: In the eight pairs of meta-analysis and large trial that were identified (five from cardiovascular medicine, one from diabetic medicine, one from geriatric medicine, one from perinatal medicine) there were four concordant and four discordant pairs. In all cases discordance was due to meta-analyses showing larger effects. Funnel plot asymmetry was present in three out of four discordant pairs but in none of concordant pairs. In 14 (38%) journal meta-analyses and 5 (13%) Cochrane reviews, funnel plot asymmetry indicated that there was bias. Conclusions: A simple analysis of funnel plots provides a useful test for the likely presence of bias in meta-analyses, but as the capacity to detect bias will be limited when meta-analyses are based on a limited number of small trials the results from such analyses should be treated with considerable caution. Key messages Systematic reviews of randomised trials are the best strategy for appraising evidence; however, the findings of some meta-analyses were later contradicted by large trials Funnel plots, plots of the trials9 effect estimates against sample size, are skewed and asymmetrical in the presence of publication bias and other biases Funnel plot asymmetry, measured by regression analysis, predicts discordance of results when meta-analyses are compared with single large trials Funnel plot asymmetry was found in 38% of meta-analyses published in leading general medicine journals and in 13% of reviews from the Cochrane Database of Systematic Reviews Critical examination of systematic reviews for publication and related biases should be considered a routine procedure

Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries.

Abstract: This article provides an update on the global cancer burden using the GLOBOCAN 2020 estimates of cancer incidence and mortality produced by the International Agency for Research on Cancer. Worldwide, an estimated 19.3 million new cancer cases (18.1 million excluding nonmelanoma skin cancer) and almost 10.0 million cancer deaths (9.9 million excluding nonmelanoma skin cancer) occurred in 2020. Female breast cancer has surpassed lung cancer as the most commonly diagnosed cancer, with an estimated 2.3 million new cases (11.7%), followed by lung (11.4%), colorectal (10.0 %), prostate (7.3%), and stomach (5.6%) cancers. Lung cancer remained the leading cause of cancer death, with an estimated 1.8 million deaths (18%), followed by colorectal (9.4%), liver (8.3%), stomach (7.7%), and female breast (6.9%) cancers. Overall incidence was from 2-fold to 3-fold higher in transitioned versus transitioning countries for both sexes, whereas mortality varied <2-fold for men and little for women. Death rates for female breast and cervical cancers, however, were considerably higher in transitioning versus transitioned countries (15.0 vs 12.8 per 100,000 and 12.4 vs 5.2 per 100,000, respectively). The global cancer burden is expected to be 28.4 million cases in 2040, a 47% rise from 2020, with a larger increase in transitioning (64% to 95%) versus transitioned (32% to 56%) countries due to demographic changes, although this may be further exacerbated by increasing risk factors associated with globalization and a growing economy. Efforts to build a sustainable infrastructure for the dissemination of cancer prevention measures and provision of cancer care in transitioning countries is critical for global cancer control.