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The value of time-dependent risk predictions in a screening context - a comprehensive simulation analysis validated on German cancer registry data

BACKGROUND: Risk-prediction tools allow classifying individuals into risk groups based on risk thresholds. Such risk categorization is often used to inform screening schemes by offering screening only to individuals at increased risk of harmful events. Adding information concerning an individual’s r...

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Autores principales: Voelkel, Vinzenz, Draeger, Teresa, van Mossel, Sietse, Siesling, Sabine, Koffijberg, Hendrik
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9464381/
https://www.ncbi.nlm.nih.gov/pubmed/36088300
http://dx.doi.org/10.1186/s12874-022-01718-2
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author Voelkel, Vinzenz
Draeger, Teresa
van Mossel, Sietse
Siesling, Sabine
Koffijberg, Hendrik
author_facet Voelkel, Vinzenz
Draeger, Teresa
van Mossel, Sietse
Siesling, Sabine
Koffijberg, Hendrik
author_sort Voelkel, Vinzenz
collection PubMed
description BACKGROUND: Risk-prediction tools allow classifying individuals into risk groups based on risk thresholds. Such risk categorization is often used to inform screening schemes by offering screening only to individuals at increased risk of harmful events. Adding information concerning an individual’s risk development over time would allow assessing not just who to screen but also when to screen. This paper illustrates the value of personalised, time-dependent risk predictions to optimize risk-based screening schemes. METHODS: In a simulation analysis, two different time-dependent risk-based screening approaches are compared to another risk-based, but time-independent approach regarding their impact on screening efficiency. For this purpose, 81 scenarios featuring 5000 patients with five consecutive annual risk estimations for a hypothetical disease D are simulated, using different parameters to model disease progression and risk distribution. This simulation analysis is validated using a real-world clinical case study based on German breast cancer patients and the INFLUENCE-nomogram for locoregional breast cancer recurrence. RESULTS: If individual risk estimations were used to personalise screening for a disease D aiming at detecting a 90% of curable cases, more than 20% of screening examinations could be avoided relative to a conventional uninformed approach, depending on the simulated scenario. Whereas an individual but time-independent approach is associated with acceptable saving potentials in case of a relatively homogenous risk distribution, the time-dependent approaches are superior when the complexity of a scenario increases. With slowly progressing diseases, risk-accumulation over time needs to be considered to achieve the highest screening efficiency on population level, for rapidly progressing diseases, an interval-specific approach is superior. The possible benefits of time-dependent risk-based screening were confirmed in the real-world clinical case study. CONCLUSIONS: Appropriate approaches to use time-dependent risk predictions may considerably enhance screening efficiency on individual and population level. Therefore, predicting risk development over time should be supported by future prediction tools and be incorporated in decision algorithms. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12874-022-01718-2.
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spelling pubmed-94643812022-09-12 The value of time-dependent risk predictions in a screening context - a comprehensive simulation analysis validated on German cancer registry data Voelkel, Vinzenz Draeger, Teresa van Mossel, Sietse Siesling, Sabine Koffijberg, Hendrik BMC Med Res Methodol Research BACKGROUND: Risk-prediction tools allow classifying individuals into risk groups based on risk thresholds. Such risk categorization is often used to inform screening schemes by offering screening only to individuals at increased risk of harmful events. Adding information concerning an individual’s risk development over time would allow assessing not just who to screen but also when to screen. This paper illustrates the value of personalised, time-dependent risk predictions to optimize risk-based screening schemes. METHODS: In a simulation analysis, two different time-dependent risk-based screening approaches are compared to another risk-based, but time-independent approach regarding their impact on screening efficiency. For this purpose, 81 scenarios featuring 5000 patients with five consecutive annual risk estimations for a hypothetical disease D are simulated, using different parameters to model disease progression and risk distribution. This simulation analysis is validated using a real-world clinical case study based on German breast cancer patients and the INFLUENCE-nomogram for locoregional breast cancer recurrence. RESULTS: If individual risk estimations were used to personalise screening for a disease D aiming at detecting a 90% of curable cases, more than 20% of screening examinations could be avoided relative to a conventional uninformed approach, depending on the simulated scenario. Whereas an individual but time-independent approach is associated with acceptable saving potentials in case of a relatively homogenous risk distribution, the time-dependent approaches are superior when the complexity of a scenario increases. With slowly progressing diseases, risk-accumulation over time needs to be considered to achieve the highest screening efficiency on population level, for rapidly progressing diseases, an interval-specific approach is superior. The possible benefits of time-dependent risk-based screening were confirmed in the real-world clinical case study. CONCLUSIONS: Appropriate approaches to use time-dependent risk predictions may considerably enhance screening efficiency on individual and population level. Therefore, predicting risk development over time should be supported by future prediction tools and be incorporated in decision algorithms. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12874-022-01718-2. BioMed Central 2022-09-10 /pmc/articles/PMC9464381/ /pubmed/36088300 http://dx.doi.org/10.1186/s12874-022-01718-2 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis 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 licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence 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 licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Voelkel, Vinzenz
Draeger, Teresa
van Mossel, Sietse
Siesling, Sabine
Koffijberg, Hendrik
The value of time-dependent risk predictions in a screening context - a comprehensive simulation analysis validated on German cancer registry data
title The value of time-dependent risk predictions in a screening context - a comprehensive simulation analysis validated on German cancer registry data
title_full The value of time-dependent risk predictions in a screening context - a comprehensive simulation analysis validated on German cancer registry data
title_fullStr The value of time-dependent risk predictions in a screening context - a comprehensive simulation analysis validated on German cancer registry data
title_full_unstemmed The value of time-dependent risk predictions in a screening context - a comprehensive simulation analysis validated on German cancer registry data
title_short The value of time-dependent risk predictions in a screening context - a comprehensive simulation analysis validated on German cancer registry data
title_sort value of time-dependent risk predictions in a screening context - a comprehensive simulation analysis validated on german cancer registry data
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9464381/
https://www.ncbi.nlm.nih.gov/pubmed/36088300
http://dx.doi.org/10.1186/s12874-022-01718-2
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