Literature DB >> 31982539

GRADE Guidelines 28: Use of GRADE for the assessment of evidence about prognostic factors: rating certainty in identification of groups of patients with different absolute risks.

Farid Foroutan1, Gordon Guyatt2, Victoria Zuk2, Per Olav Vandvik3, Ana Carolina Alba4, Reem Mustafa5, Robin Vernooij6, Ingrid Arevalo-Rodriguez7, Zachary Munn8, Pavel Roshanov9, Richard Riley10, Stefan Schandelmaier2, Ton Kuijpers11, Reed Siemieniuk2, Carlos Canelo-Aybar6, Holger Schunemann2, Alfonso Iorio2.   

Abstract

OBJECTIVE: The objective of this study was to provide guidance on the use of the Grading of Recommendations Assessment, Development and Evaluation (GRADE) approach to determine certainty in estimates of association between prognostic factors and future outcomes. STUDY DESIGN AND
SETTING: We developed our guidance through an iterative process that involved review of published systematic reviews and meta-analyses of prognostic factors, consultation with members, feedback, presentation, and discussion at the GRADE Working Group meetings.
RESULTS: For questions of prognosis, a body of observational evidence (potentially including patients enrolled in randomized controlled trials) begins as high certainty in the evidence. The five domains of GRADE for rating down certainty in the evidence, that is, risk of bias, imprecision, inconsistency, indirectness, and publication bias, as well as the domains for rating up, also apply to estimates of associations between prognostic factors and outcomes. One should determine if their ratings do not consider (noncontextualized) or consider (contextualized) the clinical context as this will may result in variable judgments on certainty of the evidence.
CONCLUSIONS: The same principles GRADE proposed for bodies of evidence addressing treatment and overall prognosis work well in assessing individual prognostic factors, both in noncontextualized and contextualized settings.
Copyright © 2020 Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Certainty in evidence; GRADE; Guideline; Prognosis; Prognostic factor; Subgroup; Systematic review

Mesh:

Year:  2020        PMID: 31982539     DOI: 10.1016/j.jclinepi.2019.12.023

Source DB:  PubMed          Journal:  J Clin Epidemiol        ISSN: 0895-4356            Impact factor:   6.437


  49 in total

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Authors:  O O Babatunde; M Bucknall; C Burton; J J Forsyth; N Corp; S Gwilym; Z Paskins; D A van der Windt
Journal:  Osteoporos Int       Date:  2021-11-11       Impact factor: 4.507

Review 2.  Impact of residual disease as a prognostic factor for survival in women with advanced epithelial ovarian cancer after primary surgery.

Authors:  Andrew Bryant; Shaun Hiu; Patience T Kunonga; Ketankumar Gajjar; Dawn Craig; Luke Vale; Brett A Winter-Roach; Ahmed Elattar; Raj Naik
Journal:  Cochrane Database Syst Rev       Date:  2022-09-26

3.  Extravascular lung water levels are associated with mortality: a systematic review and meta-analysis.

Authors:  Francesco Gavelli; Rui Shi; Jean-Louis Teboul; Danila Azzolina; Pablo Mercado; Mathieu Jozwiak; Michelle S Chew; Wolfgang Huber; Mikhail Y Kirov; Vsevolod V Kuzkov; Tobias Lahmer; Manu L N G Malbrain; Jihad Mallat; Samir G Sakka; Takashi Tagami; Tài Pham; Xavier Monnet
Journal:  Crit Care       Date:  2022-07-06       Impact factor: 19.334

4.  Association between sepsis survivorship and long-term cardiovascular outcomes in adults: a systematic review and meta-analysis.

Authors:  Leah B Kosyakovsky; Federico Angriman; Emma Katz; Neill K Adhikari; Lucas C Godoy; John C Marshall; Bruno L Ferreyro; Douglas S Lee; Robert S Rosenson; Naveed Sattar; Subodh Verma; Augustin Toma; Marina Englesakis; Barry Burstein; Michael E Farkouh; Margaret Herridge; Dennis T Ko; Damon C Scales; Michael E Detsky; Lior Bibas; Patrick R Lawler
Journal:  Intensive Care Med       Date:  2021-08-09       Impact factor: 17.440

Review 5.  Systematic review and meta-analysis of the proportion and associated mortality of polymicrobial (vs monomicrobial) pulmonary and bloodstream infections by Acinetobacter baumannii complex.

Authors:  Stamatis Karakonstantis; Evangelos I Kritsotakis
Journal:  Infection       Date:  2021-07-14       Impact factor: 3.553

6.  Prognostic models for predicting relapse or recurrence of major depressive disorder in adults.

Authors:  Andrew S Moriarty; Nicholas Meader; Kym Ie Snell; Richard D Riley; Lewis W Paton; Carolyn A Chew-Graham; Simon Gilbody; Rachel Churchill; Robert S Phillips; Shehzad Ali; Dean McMillan
Journal:  Cochrane Database Syst Rev       Date:  2021-05-06

7.  Optimizing a literature surveillance strategy to retrieve sound overall prognosis and risk assessment model papers.

Authors:  Patricia L Kavanagh; Francine Frater; Tamara Navarro; Peter LaVita; Rick Parrish; Alfonso Iorio
Journal:  J Am Med Inform Assoc       Date:  2021-03-18       Impact factor: 4.497

8.  Gestational diabetes mellitus in women born small or preterm: Systematic review and meta-analysis.

Authors:  Yasushi Tsujimoto; Yuki Kataoka; Masahiro Banno; Shunsuke Taito; Masayo Kokubo; Yuko Masuzawa; Yoshiko Yamamoto
Journal:  Endocrine       Date:  2021-11-02       Impact factor: 3.633

9.  The effectiveness of Salvianolate injection for in-stent restenosis after percutaneous coronary intervention: A protocol for systematic review and meta-analysis.

Authors:  Miao Zhang; Yue Yuan; Ying Gao; Ruozhu Lu; Yue Deng
Journal:  Medicine (Baltimore)       Date:  2022-04-22       Impact factor: 1.817

10.  Prognostic factors for chronic post-surgical pain after lung or pleural surgery: a protocol for a systematic review and meta-analysis.

Authors:  Pascal Richard David Clephas; Sanne Elisabeth Hoeks; Marialena Trivella; Christian S Guay; Preet Mohinder Singh; Markus Klimek; Michael Heesen
Journal:  BMJ Open       Date:  2021-06-15       Impact factor: 2.692

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