UMBRELLA protocol: systematic reviews of multivariable biomarker prognostic models developed to predict clinical outcome
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(2020) 4:13
PROTOCOL
Diagnostic and Prognostic Research
Open Access
UMBRELLA protocol: systematic reviews of multivariable biomarker prognostic models developed to predict clinical outcomes in patients with heart failure Maria D. L. A. Vazquez-Montes1, Thomas P. A. Debray2,3, Kathryn S. Taylor1, Benjamin Speich2,4, Nicholas Jones1, Gary S. Collins2,5, F. D. R. Richard Hobbs1, Emmanuella Magriplis2,6, Hugo Maruri-Aguilar7, Karel G. M. Moons2,3, John Parissis8, Rafael Perera1, Nia Roberts9, Clare J. Taylor1, Nikolaos P. E. Kadoglou2†, Marialena Trivella2*† and on behalf of the proBHF group
Abstract Background: Heart failure (HF) is a chronic and common condition with a rising prevalence, especially in the elderly. Morbidity and mortality rates in people with HF are similar to those with common forms of cancer. Clinical guidelines highlight the need for more detailed prognostic information to optimise treatment and care planning for people with HF. Besides proven prognostic biomarkers and numerous newly developed prognostic models for HF clinical outcomes, no risk stratification models have been adequately established. Through a number of linked systematic reviews, we aim to assess the quality of the existing models with biomarkers in HF and summarise the evidence they present. Methods: We will search MEDLINE, EMBASE, Web of Science Core Collection, and the prognostic studies database maintained by the Cochrane Prognosis Methods Group combining sensitive published search filters, with no language restriction, from 1990 onwards. Independent pairs of reviewers will screen and extract data. Eligible studies will be those developing, validating, or updating any prognostic model with biomarkers for clinical outcomes in adults with any type of HF. Data will be extracted using a piloted form that combines published good practice guidelines for critical appraisal, data extraction, and risk of bias assessment of prediction modelling studies. Missing information on predictive performance measures will be sought by contacting authors or estimated from available information when possible. If sufficient high quality and homogeneous data are available, we will metaanalyse the predictive performance of identified models. Sources of between-study heterogeneity will be explored through meta-regression using pre-defined study-level covariates. Results will be reported narratively if study quality is deemed to be low or if the between-study heterogeneity is high. Sensitivity analyses for risk of bias impact will be performed. (Continued on next page)
* Correspondence: [email protected] † Nikolaos P. E. Kadoglou and Marialena Trivella contributed equally to this work. 2 Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, Botnar Research Centre, University of Oxford, Windmill Road, Oxford OX3 7LD, UK Full list of author information is available at the end of the article © The Author(s). 2020 Open Access This article is licensed under a Creative Commons Attr
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