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2024 Revision of the level of evidence grading system for ESC clinical practice guideline recommendations II: diagnostic tests and prediction models

Identificadores del recurso

Di Angelantonio E, Pennells L, Abdelhamid M, Aboyans V, Asteggiano R, Čelutkienė J, et al. 2024 Revision of the level of evidence grading system for ESC clinical practice guideline recommendations II: diagnostic tests and prediction models. Eur Heart J. 2025 Mar 21.

1522-9645

https://hdl.handle.net/20.500.13003/24526

10.1093/eurheartj/ehaf016

40116738

Procedencia

(Docusalut. Repositorio institucional del sistema sanitario público de las Islas Baleares)

Ficha

Título:: 2024 Revision of the level of evidence grading system for ESC clinical practice guideline recommendations II: diagnostic tests and prediction models
Tema:: Cardiovascular disease; Clinical Practice Guidelines; Diagnostic ability; Diagnostic tests; European Society of Cardiology; Evidence grading; Prediction models; Predictive ability; Study quality
Descrición:: The level of evidence (LOE) grading system for European Society of Cardiology (ESC) Clinical Practice Guidelines (CPG) classifies the quality of the evidence supporting a recommendation. However, the current taxonomy does not fully consider the optimal study design necessary to establish evidence for different types of recommendations in ESC guidelines. Therefore, two separate task forces of clinical and methodological experts were appointed by the CPG Committee, with the first tasked with updating the LOE grading system for therapy and prevention and the second responsible for developing a LOE grading system for diagnosis and prediction. This report from the second of these Task Forces develops a new system for diagnostic tests and prediction models which maintains the three-level grading structure to classify the quality of the evidence but introduces new definitions specific for diagnosis and prediction. For diagnostic tests, LOE A represents conclusive evidence of adequate diagnostic ability from at least two high-quality studies. Level of evidence B represents suggestive evidence from one high-quality or at least two moderate-quality studies. Level of evidence C represents preliminary evidence not classified as A or B, including evidence from less than two moderate-quality studies, or from expert consensus. For prediction models, LOE A represents conclusive evidence of adequate predictive ability from at least one high-quality derivation and two or more external validation studies of at least moderate quality. Level of evidence B represents suggestive evidence in one or more derivation studies and one or more external validation studies of at least moderate quality. Level of evidence C represents preliminary evidence not classified as A or B, including evidence from a derivation study of at least moderate quality, but with low quality or no external validation, or a derivation study of low quality.; E.D.A. and L.P. were supported by funding from the British Heart Foundation (RG/18/13/33946: RG/F/23/110103), NIHR Cambridge Biomedical Research Centre (NIHR203312) [*], BHF Chair Award (CH/12/2/29428), Cambridge BHF Centre of Research Excellence (RE/24/130011, RE/18/1/34212) and by Health Data Research UK, which is funded by the UK Medical Research Council, Engineering and Physical Sciences Research Council, Economic and Social Research Council, Department of Health and Social Care (England), Chief Scientist Office of the Scottish Government Health and Social Care Directorates, Health and Social Care Research and Development Division (Welsh Government), Public Health Agency (Northern Ireland), British Heart Foundation and the Wellcome Trust. *The views expressed are those of the authors and not necessarily those of the NIHR or the Department of Health and Social Care.
Idioma:: English
Relación:: RG/18/13/33946: RG/F/23/110103; NIHR203312; CH/12/2/29428; RE/24/130011, RE/18/1/34212; https://doi.org/10.1093/eurheartj/ehaf016
Autor/Productor:: Di Angelantonio, Emanuele; Pennells, Lisa; Abdelhamid, Magdy; Aboyans, Victor; Asteggiano, Riccardo; Čelutkienė, Jelena; Grobbee, Diederick E; Iung, Bernard; Jüni, Peter; McEvoy, John William; Rakisheva, Amina; Rosselló, Xavier; Visseren, Frank L J; Baigent, Colin; Prescott, Eva B
Editor:: Oxford
Dereitos:: Attribution-NonCommercial-NoDerivatives 4.0 International; http://creativecommons.org/licenses/by-nc-nd/4.0/; open access
Data:: 2025-03-27T07:29:52Z; 2025-03-21
Tipo de recurso:: research article
Formato:: application/pdf

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