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Do population-level risk prediction models that use routinely collected health data reliably predict individual risks?

The objective of this study was to assess the reliability of individual risk predictions based on routinely collected data considering the heterogeneity between clinical sites in data and populations. Cardiovascular disease (CVD) risk prediction with QRISK3 was used as exemplar. The study included 3...

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Autores principales: Li, Yan, Sperrin, Matthew, Belmonte, Miguel, Pate, Alexander, Ashcroft, Darren M., van Staa, Tjeerd Pieter
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6677736/
https://www.ncbi.nlm.nih.gov/pubmed/31375726
http://dx.doi.org/10.1038/s41598-019-47712-5
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author Li, Yan
Sperrin, Matthew
Belmonte, Miguel
Pate, Alexander
Ashcroft, Darren M.
van Staa, Tjeerd Pieter
author_facet Li, Yan
Sperrin, Matthew
Belmonte, Miguel
Pate, Alexander
Ashcroft, Darren M.
van Staa, Tjeerd Pieter
author_sort Li, Yan
collection PubMed
description The objective of this study was to assess the reliability of individual risk predictions based on routinely collected data considering the heterogeneity between clinical sites in data and populations. Cardiovascular disease (CVD) risk prediction with QRISK3 was used as exemplar. The study included 3.6 million patients in 392 sites from the Clinical Practice Research Datalink. Cox models with QRISK3 predictors and a frailty (random effect) term for each site were used to incorporate unmeasured site variability. There was considerable variation in data recording between general practices (missingness of body mass index ranged from 18.7% to 60.1%). Incidence rates varied considerably between practices (from 0.4 to 1.3 CVD events per 100 patient-years). Individual CVD risk predictions with the random effect model were inconsistent with the QRISK3 predictions. For patients with QRISK3 predicted risk of 10%, the 95% range of predicted risks were between 7.2% and 13.7% with the random effects model. Random variability only explained a small part of this. The random effects model was equivalent to QRISK3 for discrimination and calibration. Risk prediction models based on routinely collected health data perform well for populations but with great uncertainty for individuals. Clinicians and patients need to understand this uncertainty.
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spelling pubmed-66777362019-08-08 Do population-level risk prediction models that use routinely collected health data reliably predict individual risks? Li, Yan Sperrin, Matthew Belmonte, Miguel Pate, Alexander Ashcroft, Darren M. van Staa, Tjeerd Pieter Sci Rep Article The objective of this study was to assess the reliability of individual risk predictions based on routinely collected data considering the heterogeneity between clinical sites in data and populations. Cardiovascular disease (CVD) risk prediction with QRISK3 was used as exemplar. The study included 3.6 million patients in 392 sites from the Clinical Practice Research Datalink. Cox models with QRISK3 predictors and a frailty (random effect) term for each site were used to incorporate unmeasured site variability. There was considerable variation in data recording between general practices (missingness of body mass index ranged from 18.7% to 60.1%). Incidence rates varied considerably between practices (from 0.4 to 1.3 CVD events per 100 patient-years). Individual CVD risk predictions with the random effect model were inconsistent with the QRISK3 predictions. For patients with QRISK3 predicted risk of 10%, the 95% range of predicted risks were between 7.2% and 13.7% with the random effects model. Random variability only explained a small part of this. The random effects model was equivalent to QRISK3 for discrimination and calibration. Risk prediction models based on routinely collected health data perform well for populations but with great uncertainty for individuals. Clinicians and patients need to understand this uncertainty. Nature Publishing Group UK 2019-08-02 /pmc/articles/PMC6677736/ /pubmed/31375726 http://dx.doi.org/10.1038/s41598-019-47712-5 Text en © The Author(s) 2019 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
spellingShingle Article
Li, Yan
Sperrin, Matthew
Belmonte, Miguel
Pate, Alexander
Ashcroft, Darren M.
van Staa, Tjeerd Pieter
Do population-level risk prediction models that use routinely collected health data reliably predict individual risks?
title Do population-level risk prediction models that use routinely collected health data reliably predict individual risks?
title_full Do population-level risk prediction models that use routinely collected health data reliably predict individual risks?
title_fullStr Do population-level risk prediction models that use routinely collected health data reliably predict individual risks?
title_full_unstemmed Do population-level risk prediction models that use routinely collected health data reliably predict individual risks?
title_short Do population-level risk prediction models that use routinely collected health data reliably predict individual risks?
title_sort do population-level risk prediction models that use routinely collected health data reliably predict individual risks?
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6677736/
https://www.ncbi.nlm.nih.gov/pubmed/31375726
http://dx.doi.org/10.1038/s41598-019-47712-5
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