FORM: An Australian method for formulating and grading recommendations in evidence-based clinical guidelines

Extent: 8p.BACKGROUND: Clinical practice guidelines are an important element of evidence-based practice. Considering an often complicated body of evidence can be problematic for guideline developers, who in the past may have resorted to using levels of evidence of individual studies as a quasi-indicator for the strength of a recommendation. This paper reports on the production and trial of a methodology and associated processes to assist Australian guideline developers in considering a body of evidence and grading the resulting guideline recommendations. METHODS: In recognition of the complexities of clinical guidelines and the multiple factors that influence choice in health care, a working group of experienced guideline consultants was formed under the auspices of the Australian National Health and Medical Research Council (NHMRC) to produce and pilot a framework to formulate and grade guideline recommendations. Consultation with national and international experts and extensive piloting informed the process. RESULTS: The FORM framework consists of five components (evidence base, consistency, clinical impact, generalisability and applicability) which are used by guideline developers to structure their decisions on how to convey the strength of a recommendation through wording and grading via a considered judgement form. In parallel (but separate from the grading process) guideline developers are asked to consider implementation implications for each recommendation. CONCLUSIONS: The framework has now been widely adopted by Australian guideline developers who find it to be a logical and intuitive way to formulate and grade recommendations in clinical practice guidelines.Susan Hillier, Karen Grimmer-Somers, Tracy Merlin, Philippa Middleton, Janet Salisbury, Rebecca Tooher and Adele Westo

Percutaneous versus surgical strategy for tracheostomy: protocol for a systematic review and meta-analysis of perioperative and postoperative complications

Background: Tracheostomy is one of the most frequently performed procedures in intensive care medicine. The two main approaches to form a tracheostoma are the open surgical tracheotomy (ST) and the interventional strategy of percutaneous dilatational tracheotomy (PDT). It is particularly important to the critically ill patients that both procedures are performed with high success rates and low complication frequencies. Therefore, the aim of this systematic review is to summarize and analyze existing and relevant evidence for peri- and postoperative parameters of safety. Methods/design: A systematic literature search will be conducted in The Cochrane Library, MEDLINE, LILACS, and Embase to identify all randomized controlled trials (RCTs) comparing peri- and postoperative complications between the two strategies and to define the strategy with the lower risk of potentially life-threatening events. A priori defined data will be extracted from included studies, and methodological quality will be assessed according to the recommendations of the Cochrane Collaboration. Discussion: The findings of this systematic review with proportional meta-analysis will help to identify the strategy with the lowest frequency of potentially life-threatening events. This may influence daily practice, and the data may be implemented in treatment guidelines or serve as the basis for planning further randomized controlled trials. Considering the critical health of these patients, they will particularly benefit from evidence-based treatment. Systematic review registration: PROSPERO CRD4201502196

Comparative kinome analysis to identify putative colon tumor biomarkers

Kinase domains are the type of protein domain most commonly found in genes associated with tumorigenesis. Because of this, the human kinome (the protein kinase component of the genome) represents a promising source of cancer biomarkers and potential targets for novel anti-cancer therapies. Alterations in the human colon kinome during the progression from normal colon (NC) through adenoma (AD) to adenocarcinoma (AC) were investigated using integrated transcriptomic and proteomic datasets. Two hundred thirty kinase genes and 42 kinase proteins showed differential expression patterns (fold change ≥ 1.5) in at least one tissue pair-wise comparison (AD vs. NC, AC vs. NC, and/or AC vs. AD). Kinases that exhibited similar trends in expression at both the mRNA and protein levels were further analyzed in individual samples of NC (n = 20), AD (n = 39), and AC (n = 24) by quantitative reverse transcriptase PCR. Individual samples of NC and tumor tissue were distinguishable based on the mRNA levels of a set of 20 kinases. Altered expression of several of these kinases, including chaperone activity of bc1 complex-like (CABC1) kinase, bromodomain adjacent to zinc finger domain protein 1B (BAZ1B) kinase, calcium/calmodulin-dependent protein kinase type II subunit delta (CAMK2D), serine/threonine-protein kinase 24 (STK24), vaccinia-related kinase 3 (VRK3), and TAO kinase 3 (TAOK3), has not been previously reported in tumor tissue. These findings may have diagnostic potential and may lead to the development of novel targeted therapeutic interventions for colorectal cancer

Can Survival Prediction Be Improved By Merging Gene Expression Data Sets?

BACKGROUND:High-throughput gene expression profiling technologies generating a wealth of data, are increasingly used for characterization of tumor biopsies for clinical trials. By applying machine learning algorithms to such clinically documented data sets, one hopes to improve tumor diagnosis, prognosis, as well as prediction of treatment response. However, the limited number of patients enrolled in a single trial study limits the power of machine learning approaches due to over-fitting. One could partially overcome this limitation by merging data from different studies. Nevertheless, such data sets differ from each other with regard to technical biases, patient selection criteria and follow-up treatment. It is therefore not clear at all whether the advantage of increased sample size outweighs the disadvantage of higher heterogeneity of merged data sets. Here, we present a systematic study to answer this question specifically for breast cancer data sets. We use survival prediction based on Cox regression as an assay to measure the added value of merged data sets. RESULTS:Using time-dependent Receiver Operating Characteristic-Area Under the Curve (ROC-AUC) and hazard ratio as performance measures, we see in overall no significant improvement or deterioration of survival prediction with merged data sets as compared to individual data sets. This apparently was due to the fact that a few genes with strong prognostic power were not available on all microarray platforms and thus were not retained in the merged data sets. Surprisingly, we found that the overall best performance was achieved with a single-gene predictor consisting of CYB5D1. CONCLUSIONS:Merging did not deteriorate performance on average despite (a) The diversity of microarray platforms used. (b) The heterogeneity of patients cohorts. (c) The heterogeneity of breast cancer disease. (d) Substantial variation of time to death or relapse. (e) The reduced number of genes in the merged data sets. Predictors derived from the merged data sets were more robust, consistent and reproducible across microarray platforms. Moreover, merging data sets from different studies helps to better understand the biases of individual studies and can lead to the identification of strong survival factors like CYB5D1 expression

GRADE Guidelines 30: the GRADE approach to assessing the certainty of modeled evidence—An overview in the context of health decision-making

Objectives: The objective of the study is to present the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) conceptual approach to the assessment of certainty of evidence from modeling studies (i.e., certainty associated with model outputs). / Study Design and Setting: Expert consultations and an international multidisciplinary workshop informed development of a conceptual approach to assessing the certainty of evidence from models within the context of systematic reviews, health technology assessments, and health care decisions. The discussions also clarified selected concepts and terminology used in the GRADE approach and by the modeling community. Feedback from experts in a broad range of modeling and health care disciplines addressed the content validity of the approach. / Results: Workshop participants agreed that the domains determining the certainty of evidence previously identified in the GRADE approach (risk of bias, indirectness, inconsistency, imprecision, reporting bias, magnitude of an effect, dose–response relation, and the direction of residual confounding) also apply when assessing the certainty of evidence from models. The assessment depends on the nature of model inputs and the model itself and on whether one is evaluating evidence from a single model or multiple models. We propose a framework for selecting the best available evidence from models: 1) developing de novo, a model specific to the situation of interest, 2) identifying an existing model, the outputs of which provide the highest certainty evidence for the situation of interest, either “off-the-shelf” or after adaptation, and 3) using outputs from multiple models. We also present a summary of preferred terminology to facilitate communication among modeling and health care disciplines. / Conclusion: This conceptual GRADE approach provides a framework for using evidence from models in health decision-making and the assessment of certainty of evidence from a model or models. The GRADE Working Group and the modeling community are currently developing the detailed methods and related guidance for assessing specific domains determining the certainty of evidence from models across health care–related disciplines (e.g., therapeutic decision-making, toxicology, environmental health, and health economics)

GRADE Guidelines 30: The GRADE Approach to Assessing the Certainty of Modelled Evidence - an Overview in the Context of Health Decision-making.

OBJECTIVES: To present the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) conceptual approach to the assessment of certainty of evidence from modelling studies (i.e. certainty associated with model outputs). STUDY DESIGN AND SETTING: Expert consultations and, an international multi-disciplinary workshop informed development of a conceptual approach to assessing the certainty of evidence from models within the context of systematic reviews, health technology assessments, and health care decisions. The discussions also clarified selected concepts and terminology used in the GRADE approach and by the modelling community. Feedback from experts in a broad range of modelling and health care disciplines addressed the content validity of the approach. RESULTS: Workshop participants agreed, that the domains determining the certainty of evidence previously identified in the GRADE approach (risk of bias, indirectness, inconsistency, imprecision, reporting bias, magnitude of an effect, dose-response relation, and the direction of residual confounding) also apply when of assessing the certainty of evidence from models. The assessment depends on the nature of model inputs and the model itself and on whether one is evaluating evidence from a single model or multiple models. We propose a framework for selecting the best available evidence from models: 1) developing de novo a model specific to the situation of interest, 2) identifying an existing model the outputs of which provide the highest certainty evidence for the situation of interest, either "off the shelf" or after adaptation, and 3) using outputs from multiple models. We also present a summary of preferred terminology to facilitate communication among modelling and health care disciplines. CONCLUSIONS: This conceptual GRADE approach provides a framework for using evidence from models in health decision making and the assessment of certainty of evidence from a model or models. The GRADE Working Group and the modelling community are currently developing the detailed methods and related guidance for assessing specific domains determining the certainty of evidence from models across health care-related disciplines (e.g. therapeutic decision-making, toxicology, environmental health, health economics)

Surviving Sepsis Campaign: International guidelines for management of severe sepsis and septic shock: 2008

SCOPUS: ar.jinfo:eu-repo/semantics/publishe