Guidelines 2.0: systematic development of a comprehensive checklist for a successful guideline enterprise.
Humans; Data Collection/standards; Checklist; Evidence-Based Medicine/methods; Practice Guidelines as Topic/standards; Statistics as Topic/standards
BACKGROUND: Although several tools to evaluate the credibility of health care guidelines exist, guidance on practical steps for developing guidelines is lacking. We systematically compiled a comprehensive checklist of items linked to relevant resources and tools that guideline developers could consider, without the expectation that every guideline would address each item. METHODS: We searched data sources, including manuals of international guideline developers, literature on guidelines for guidelines (with a focus on methodology reports from international and national agencies, and professional societies) and recent articles providing systematic guidance. We reviewed these sources in duplicate, extracted items for the checklist using a sensitive approach and developed overarching topics relevant to guidelines. In an iterative process, we reviewed items for duplication and omissions and involved experts in guideline development for revisions and suggestions for items to be added. RESULTS: We developed a checklist with 18 topics and 146 items and a webpage to facilitate its use by guideline developers. The topics and included items cover all stages of the guideline enterprise, from the planning and formulation of guidelines, to their implementation and evaluation. The final checklist includes links to training materials as well as resources with suggested methodology for applying the items. INTERPRETATION: The checklist will serve as a resource for guideline developers. Consideration of items on the checklist will support the development, implementation and evaluation of guidelines. We will use crowdsourcing to revise the checklist and keep it up to date.
2014-02
Schunemann HJ; Wiercioch W; Etxeandia I; Falavigna M; Santesso N; Mustafa RA; Ventresca M; Brignardello-Petersen R; Laisaar Kaja-Triin; Kowalski S; Baldeh T; Zhang Y; Raid U; Neumann I; Norris S; Thornton J; Harbour R; Treweek S; Guyatt G; Alonso-Coello P; Reinap M; Brozek J; Oxman A; Akl EA
Canadian Medical Association Journal
2014
Article information provided for research and reference use only. PedPalASCNET does not hold any rights over the resource listed here. All rights are retained by the journal listed under publisher and/or the creator(s).
Journal Article
<a href="http://doi.org/10.1503/cmaj.131237" target="_blank" rel="noreferrer">10.1503/cmaj.131237</a>
Methodological survey of designed uneven randomization trials (DU-RANDOM): a protocol
Background Although even randomization (that is, approximately 1:1 randomization ratio in study arms) provides the greatest statistical power, designed uneven randomization (DUR), (for example, 1:2 or 1:3) is used to increase participation rates. Until now, no convincing data exists addressing the impact of DUR on participation rates in trials. The objective of this study is to evaluate the epidemiology and to explore factors associated with DUR. Methods We will search for reports of RCTs published within two years in 25 general medical journals with the highest impact factor according to the Journal Citation Report (JCR)-2010. Teams of two reviewers will determine eligibility and extract relevant information from eligible RCTs in duplicate and using standardized forms. We will report the prevalence of DUR trials, the reported reasons for using DUR, and perform a linear regression analysis to estimate the association between the randomization ratio and the associated factors, including participation rate, type of informed consent, clinical area, and so on. Discussion A clearer understanding of RCTs with DUR and its association with factors in trials, for example, participation rate, can optimize trial design and may have important implications for both researchers and users of the medical literature.
2014-01
Wu Darong; Akl EA; Guyatt G; Devereaux PJ; Brignardello-Petersen R; Prediger B; Patel K; Patel N; Lu Taoying; Zhang Y; Falavigna M; Santesso N; Mustafa RA; Zhou Qi; Briel M; Schunemann HJ
Trials
2014
Article information provided for research and reference use only. PedPalASCNET does not hold any rights over the resource listed here. All rights are retained by the journal listed under publisher and/or the creator(s).
Journal Article
<a href="http://doi.org/10.1186/1745-6215-15-33" target="_blank" rel="noreferrer">10.1186/1745-6215-15-33</a>
GRADE guidelines: 15. Going from evidence to recommendation-determinants of a recommendation's direction and strength
Humans; United States; Canada; Practice Guidelines as Topic; Treatment Outcome; Research Design; Risk Assessment; Evidence-Based Medicine; Treatment Failure; Germany; Pulmonary Disease; Health Care; Quality Assurance; Chronic Obstructive
In the GRADE approach, the strength of a recommendation reflects the extent to which we can be confident that the composite desirable effects of a management strategy outweigh the composite undesirable effects. This article addresses GRADE's approach to determining the direction and strength of a recommendation. The GRADE describes the balance of desirable and undesirable outcomes of interest among alternative management strategies depending on four domains, namely estimates of effect for desirable and undesirable outcomes of interest, confidence in the estimates of effect, estimates of values and preferences, and resource use. Ultimately, guideline panels must use judgment in integrating these factors to make a strong or weak recommendation for or against an intervention.
2013-07
Andrews JC; Schunemann HJ; Oxman A; Pottie K; Meerpohl Joerg J; Coello PA; Rind D; Montori VM; Brito JP; Norris S; Elbarbary M; Post P; Nasser M; Shukla V; Jaeschke R; Brozek J; Djulbegovic B; Guyatt G
Journal Of Clinical Epidemiology
2013
Article information provided for research and reference use only. PedPalASCNET does not hold any rights over the resource listed here. All rights are retained by the journal listed under publisher and/or the creator(s).
Journal Article
<a href="http://doi.org/10.1016/j.jclinepi.2013.02.003" target="_blank" rel="noreferrer">10.1016/j.jclinepi.2013.02.003</a>
Patient-reported outcomes in meta-analyses-part 2: methods for improving interpretability for decision-makers
Systematic reviews and meta-analyses of randomized trials that include patient-reported outcomes (PROs) often provide crucial information for patients, clinicians and policy-makers facing challenging health care decisions. Based on emerging methods, guidance on improving the interpretability of meta-analysis of patient-reported outcomes, typically continuous in nature, is likely to enhance decision-making. The objective of this paper is to summarize approaches to enhancing the interpretability of pooled estimates of PROs in meta-analyses. When differences in PROs between groups are statistically significant, decision-makers must be able to interpret the magnitude of effect. This is challenging when, as is often the case, clinical trial investigators use different measurement instruments for the same construct within and between individual randomized trials. For such cases, in addition to pooling results as a standardized mean difference, we recommend that systematic review authors use other methods to present results such as relative (relative risk, odds ratio) or absolute (risk difference) dichotomized treatment effects, complimented by presentation in either: natural units (e.g. overall depression reduced by 2.4 points when measured on a 50-point Hamilton Rating Scale for Depression); minimal important difference units (e.g. where 1.0 unit represents the smallest difference in depression that patients, on average, perceive as important the depression score was 0.38 (95% CI 0.30 to 0.47) units less than the control group); or a ratio of means (e.g. where the mean in the treatment group is divided by the mean in the control group, the ratio of means is 1.27, representing a 27% relative reduction in the mean depression score).
2013
Johnston BC; Patrick DL; Thorlund K; Busse JW; da Costa BR; Schunemann HJ; Guyatt G
Health And Quality Of Life Outcomes
2013
Article information provided for research and reference use only. PedPalASCNET does not hold any rights over the resource listed here. All rights are retained by the journal listed under publisher and/or the creator(s).
Journal Article
<a href="http://doi.org/10.1186/1477-7525-11-211" target="_blank" rel="noreferrer">10.1186/1477-7525-11-211</a>
How well do parents know their children? Implications for proxy reporting of child health-related quality of life
PedPal Lit; 26 and 11% of the parents gave > or = 3 and 6 > or = DK responses to 33 items comprising the PPQ. DK responses were associated with child's age and clinical co ndition; Adolescent Chi-Square Distribution ChildChild Welfare Female Humans MaleOral Health Parents/psychologyProxyQuality of Life Questionnaires Research Support; given that parental and child reports are measuring different realities; imputation of the value zero and adjustment of scores to account for items with DK responses. RESULTS: Respectively; information provided by parents is useful even if it is incomplete.; item mean imputation; Non-U.S. Gov't%X OBJECTIVES: This study examined parental knowledge of their children's oral-health-related quality of life (OHRQoL) (Objective 1)
2004
Jokovic A; Locker D; Guyatt G
Quality of Life Research
2004
Article information provided for research and reference use only. PedPalASCNET does not hold any rights over the resource listed here. All rights are retained by the journal listed under publisher and/or the creator(s).
Journal Article
<a href="http://doi.org/10.1023/b:qure.0000037480.65972.eb" target="_blank" rel="noreferrer">10.1023/b:qure.0000037480.65972.eb</a>
Clinician predictions of intensive care unit mortality
Female; Humans; Male; Medical Staff; Hospital Mortality; Prognosis; Prospective Studies; Middle Aged; Respiration; Severity of Illness Index; Survival Analysis; Risk Factors; Predictive Value of Tests; Chi-Square Distribution; Proportional Hazards Models; Nursing Staff; Artificial; Intensive Care Units/statistics & numerical data; APACHE; Nursing Assessment/standards; Likelihood Functions; Clinical Competence/standards; Critical Illness/mortality/therapy; Hospital/standards; Multiple Organ Failure/classification/mortality
OBJECTIVE: Predicting outcomes for critically ill patients is an important aspect of discussions with families in the intensive care unit. Our objective was to evaluate clinical intensive care unit survival predictions and their consequences for mechanically ventilated patients. DESIGN: Prospective cohort study. SETTING: Fifteen tertiary care centers. PATIENTS: Consecutive mechanically ventilated patients > or = 18 yrs of age with expected intensive care unit stay > or = 72 hrs. INTERVENTIONS: We recorded baseline characteristics at intensive care unit admission. Daily we measured multiple organ dysfunction score (MODS), use of advanced life support, patient preferences for life support, and intensivist and bedside intensive care unit nurse estimated probability of intensive care unit survival. MEASUREMENTS AND MAIN RESULTS: The 851 patients were aged 61.2 (+/- 17.6, mean + SD) yrs with an Acute Physiology and Chronic Health Evaluation (APACHE) II score of 21.7 (+/- 8.6). Three hundred and four patients (35.7%) died in the intensive care unit, and 341 (40.1%) were assessed by a physician at least once to have a < 10% intensive care unit survival probability. Independent predictors of intensive care unit mortality were baseline APACHE II score (hazard ratio, 1.16; 95% confidence interval, 1.08-1.24, for a 5-point increase) and daily factors such as MODS (hazard ratio, 2.50; 95% confidence interval, 2.06-3.04, for a 5-point increase), use of inotropes or vasopressors (hazard ratio, 2.14; 95% confidence interval, 1.66-2.77), dialysis (hazard ratio, 0.51; 95% confidence interval, 0.35-0.75), patient preference to limit life support (hazard ratio, 10.22; 95% confidence interval, 7.38-14.16), and physician but not nurse prediction of < 10% survival. The impact of physician estimates of < 10% intensive care unit survival was greater for patients without vs. those with preferences to limit life support (p < .001) and for patients with less vs. more severe organ dysfunction (p < .001). Mechanical ventilation, inotropes or vasopressors, and dialysis were withdrawn more often when physicians predicted < 10% probability of intensive care unit survival (all ps < .001). CONCLUSIONS: Physician estimates of intensive care unit survival < 10% are associated with subsequent life support limitation and more powerfully predict intensive care unit mortality than illness severity, evolving or resolving organ dysfunction, and use of inotropes or vasopressors.
2004
Rocker G; Cook D; Sjokvist P; Weaver B; Finfer S; McDonald E; Marshall J; Kirby A; Levy M; Dodek P; Heyland D; Guyatt G; Level of Care Study Investigators; Canadian Critical Care Trials Group
Critical Care Medicine
2004
Article information provided for research and reference use only. PedPalASCNET does not hold any rights over the resource listed here. All rights are retained by the journal listed under publisher and/or the creator(s).
Journal Article
<a href="http://doi.org/10.1097/01.ccm.0000126402.51524.52" target="_blank" rel="noreferrer">10.1097/01.ccm.0000126402.51524.52</a>