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Data on prognostic factors associated with 3-month and 1-year mortality from infective endocarditis
This article describes supplementary tables and figures associated with the research paper entitled “Impact of referral bias on prognostic studies outcomes: insights from a population-based cohort study on infective endocarditis”. The aforementioned paper is a secondary analysis of data from the EI...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7666320/ https://www.ncbi.nlm.nih.gov/pubmed/33225027 http://dx.doi.org/10.1016/j.dib.2020.106478 |
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author | Collonnaz, Magali Erpelding, Marie-Line Alla, François Goehringer, François Delahaye, François Iung, Bernard Le Moing, Vincent Hoen, Bruno Selton-Suty, Christine Agrinier, Nelly |
author_facet | Collonnaz, Magali Erpelding, Marie-Line Alla, François Goehringer, François Delahaye, François Iung, Bernard Le Moing, Vincent Hoen, Bruno Selton-Suty, Christine Agrinier, Nelly |
author_sort | Collonnaz, Magali |
collection | PubMed |
description | This article describes supplementary tables and figures associated with the research paper entitled “Impact of referral bias on prognostic studies outcomes: insights from a population-based cohort study on infective endocarditis”. The aforementioned paper is a secondary analysis of data from the EI 2008 cohort on infective endocarditis and aimed at characterising referral bias. A total of 497 patients diagnosed with definite infective endocarditis between January 1(st) and December 31(st) 2008 were included in EI 2008. Data were collected from hospital medical records by trained clinical research assistants. Patients were divided into three groups: admitted to a tertiary hospital (group T), admitted to a non-tertiary hospital and referred secondarily to a tertiary hospital (group NTT) or admitted to a non-tertiary hospital and not referred (group NT). The pooled (NTT+T) group mimicked studies recruiting patients in tertiary hospitals only. Two different starting points were considered for follow up: date of first hospital admission and date of first admission to a tertiary hospital if any (hereinafter referred to as “referral time”). Referral bias is a type of selection bias which can occur due to recruitment of patients in tertiary hospitals only (excluding those who are admitted to non-tertiary hospitals and not referred to tertiary hospitals). This bias may impact the description of patients’ characteristics, survival estimates as well as prognostic factors identification. The six tables presented in this paper illustrate how patients’ selection (population-based sample [pooled (NT+NTT+T) group] versus recruitment in tertiary hospitals only [pooled (NTT+T) group]) might impact Hazards Ratios values for prognostic factors. Crude and adjusted Cox regression analyses were first performed to identify prognostic factors associated with 3-month and 1-year mortality in the whole sample using inclusion as the starting point. Analyses were then performed in the pooled (NTT+T) group first using inclusion as the starting point and finally using referral time as the starting point. Figures 1 to 3 illustrate how HR increase with time for covariates that were considered as time-varying covariates (covariate*time interaction). |
format | Online Article Text |
id | pubmed-7666320 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
spelling | pubmed-76663202020-11-20 Data on prognostic factors associated with 3-month and 1-year mortality from infective endocarditis Collonnaz, Magali Erpelding, Marie-Line Alla, François Goehringer, François Delahaye, François Iung, Bernard Le Moing, Vincent Hoen, Bruno Selton-Suty, Christine Agrinier, Nelly Data Brief Data Article This article describes supplementary tables and figures associated with the research paper entitled “Impact of referral bias on prognostic studies outcomes: insights from a population-based cohort study on infective endocarditis”. The aforementioned paper is a secondary analysis of data from the EI 2008 cohort on infective endocarditis and aimed at characterising referral bias. A total of 497 patients diagnosed with definite infective endocarditis between January 1(st) and December 31(st) 2008 were included in EI 2008. Data were collected from hospital medical records by trained clinical research assistants. Patients were divided into three groups: admitted to a tertiary hospital (group T), admitted to a non-tertiary hospital and referred secondarily to a tertiary hospital (group NTT) or admitted to a non-tertiary hospital and not referred (group NT). The pooled (NTT+T) group mimicked studies recruiting patients in tertiary hospitals only. Two different starting points were considered for follow up: date of first hospital admission and date of first admission to a tertiary hospital if any (hereinafter referred to as “referral time”). Referral bias is a type of selection bias which can occur due to recruitment of patients in tertiary hospitals only (excluding those who are admitted to non-tertiary hospitals and not referred to tertiary hospitals). This bias may impact the description of patients’ characteristics, survival estimates as well as prognostic factors identification. The six tables presented in this paper illustrate how patients’ selection (population-based sample [pooled (NT+NTT+T) group] versus recruitment in tertiary hospitals only [pooled (NTT+T) group]) might impact Hazards Ratios values for prognostic factors. Crude and adjusted Cox regression analyses were first performed to identify prognostic factors associated with 3-month and 1-year mortality in the whole sample using inclusion as the starting point. Analyses were then performed in the pooled (NTT+T) group first using inclusion as the starting point and finally using referral time as the starting point. Figures 1 to 3 illustrate how HR increase with time for covariates that were considered as time-varying covariates (covariate*time interaction). Elsevier 2020-11-01 /pmc/articles/PMC7666320/ /pubmed/33225027 http://dx.doi.org/10.1016/j.dib.2020.106478 Text en © 2020 The Author(s) http://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). |
spellingShingle | Data Article Collonnaz, Magali Erpelding, Marie-Line Alla, François Goehringer, François Delahaye, François Iung, Bernard Le Moing, Vincent Hoen, Bruno Selton-Suty, Christine Agrinier, Nelly Data on prognostic factors associated with 3-month and 1-year mortality from infective endocarditis |
title | Data on prognostic factors associated with 3-month and 1-year mortality from infective endocarditis |
title_full | Data on prognostic factors associated with 3-month and 1-year mortality from infective endocarditis |
title_fullStr | Data on prognostic factors associated with 3-month and 1-year mortality from infective endocarditis |
title_full_unstemmed | Data on prognostic factors associated with 3-month and 1-year mortality from infective endocarditis |
title_short | Data on prognostic factors associated with 3-month and 1-year mortality from infective endocarditis |
title_sort | data on prognostic factors associated with 3-month and 1-year mortality from infective endocarditis |
topic | Data Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7666320/ https://www.ncbi.nlm.nih.gov/pubmed/33225027 http://dx.doi.org/10.1016/j.dib.2020.106478 |
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