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Predictors for independent external validation of cardiovascular risk clinical prediction rules: Cox proportional hazards regression analyses

BACKGROUND: Clinical prediction rules (CPRs) should be externally validated by independent researchers. Although there are many cardiovascular CPRs, most have not been externally validated. It is not known why some CPRs are externally validated by independent researchers and others are not. METHODS:...

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Autores principales: Ban, Jong-Wook, Stevens, Richard, Perera, Rafael
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6460844/
https://www.ncbi.nlm.nih.gov/pubmed/31093553
http://dx.doi.org/10.1186/s41512-018-0025-6
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author Ban, Jong-Wook
Stevens, Richard
Perera, Rafael
author_facet Ban, Jong-Wook
Stevens, Richard
Perera, Rafael
author_sort Ban, Jong-Wook
collection PubMed
description BACKGROUND: Clinical prediction rules (CPRs) should be externally validated by independent researchers. Although there are many cardiovascular CPRs, most have not been externally validated. It is not known why some CPRs are externally validated by independent researchers and others are not. METHODS: We analyzed cardiovascular risk CPRs included in a systematic review. Independent external validations were identified by forward citation searches of derivation studies. Time between the publication of a cardiovascular CPR and the first independent external validation was calculated. We assessed Kaplan-Meier estimates of the probability to have an independent external validation. Using univariable Cox regression, we explored whether characteristics of derivation (design, location, sample size, number of predictors, presentation format, validation in derivation), reporting (participants, predictors, outcomes, performance measure, information for risk calculation), and publication (journal impact factor) are associated with time to the first independent external validation. RESULTS: Of 125 cardiovascular risk CPRs, 29 had an independent external validation. The median follow-up was 118 months (95% CI, 99–130). The 25th percentile of event time was 122 months (95% CI, 91–299). Cardiovascular risk CPRs from the USA were 4.15 times (95% CI, 1.89–9.13) more likely to have an independent external validation. Increasing the sample size of derivation by ten times was associated with a 2.32-fold (95% CI, 1.37–3.91) increase in the probability of having an independent external validation. CPRs presented with an internal validation tend to get an independent external validation sooner (HR = 1.73, 95% CI, 0.77–3.93). CPRs reporting all the information necessary for calculating individual risk were 2.65 (95% CI, 1.01–6.96) times more likely to have an independent external validation. Publishing a cardiovascular risk CPR in a journal that has one unit higher impact factor was associated with a 6% (95% CI, 3–9) higher likelihood of an independent external validation. CONCLUSIONS: The probability for cardiovascular risk CPRs to get an independent external validation was low even many years after their derivations. Authors of new cardiovascular risk CPRs should consider using adequate sample size, conducting an internal validation, and reporting all the information needed for risk calculation as these features were associated with an independent external validation. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s41512-018-0025-6) contains supplementary material, which is available to authorized users.
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spelling pubmed-64608442019-05-15 Predictors for independent external validation of cardiovascular risk clinical prediction rules: Cox proportional hazards regression analyses Ban, Jong-Wook Stevens, Richard Perera, Rafael Diagn Progn Res Research BACKGROUND: Clinical prediction rules (CPRs) should be externally validated by independent researchers. Although there are many cardiovascular CPRs, most have not been externally validated. It is not known why some CPRs are externally validated by independent researchers and others are not. METHODS: We analyzed cardiovascular risk CPRs included in a systematic review. Independent external validations were identified by forward citation searches of derivation studies. Time between the publication of a cardiovascular CPR and the first independent external validation was calculated. We assessed Kaplan-Meier estimates of the probability to have an independent external validation. Using univariable Cox regression, we explored whether characteristics of derivation (design, location, sample size, number of predictors, presentation format, validation in derivation), reporting (participants, predictors, outcomes, performance measure, information for risk calculation), and publication (journal impact factor) are associated with time to the first independent external validation. RESULTS: Of 125 cardiovascular risk CPRs, 29 had an independent external validation. The median follow-up was 118 months (95% CI, 99–130). The 25th percentile of event time was 122 months (95% CI, 91–299). Cardiovascular risk CPRs from the USA were 4.15 times (95% CI, 1.89–9.13) more likely to have an independent external validation. Increasing the sample size of derivation by ten times was associated with a 2.32-fold (95% CI, 1.37–3.91) increase in the probability of having an independent external validation. CPRs presented with an internal validation tend to get an independent external validation sooner (HR = 1.73, 95% CI, 0.77–3.93). CPRs reporting all the information necessary for calculating individual risk were 2.65 (95% CI, 1.01–6.96) times more likely to have an independent external validation. Publishing a cardiovascular risk CPR in a journal that has one unit higher impact factor was associated with a 6% (95% CI, 3–9) higher likelihood of an independent external validation. CONCLUSIONS: The probability for cardiovascular risk CPRs to get an independent external validation was low even many years after their derivations. Authors of new cardiovascular risk CPRs should consider using adequate sample size, conducting an internal validation, and reporting all the information needed for risk calculation as these features were associated with an independent external validation. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s41512-018-0025-6) contains supplementary material, which is available to authorized users. BioMed Central 2018-02-06 /pmc/articles/PMC6460844/ /pubmed/31093553 http://dx.doi.org/10.1186/s41512-018-0025-6 Text en © The Author(s) 2018 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided 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 Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research
Ban, Jong-Wook
Stevens, Richard
Perera, Rafael
Predictors for independent external validation of cardiovascular risk clinical prediction rules: Cox proportional hazards regression analyses
title Predictors for independent external validation of cardiovascular risk clinical prediction rules: Cox proportional hazards regression analyses
title_full Predictors for independent external validation of cardiovascular risk clinical prediction rules: Cox proportional hazards regression analyses
title_fullStr Predictors for independent external validation of cardiovascular risk clinical prediction rules: Cox proportional hazards regression analyses
title_full_unstemmed Predictors for independent external validation of cardiovascular risk clinical prediction rules: Cox proportional hazards regression analyses
title_short Predictors for independent external validation of cardiovascular risk clinical prediction rules: Cox proportional hazards regression analyses
title_sort predictors for independent external validation of cardiovascular risk clinical prediction rules: cox proportional hazards regression analyses
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6460844/
https://www.ncbi.nlm.nih.gov/pubmed/31093553
http://dx.doi.org/10.1186/s41512-018-0025-6
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