Data on prognostic factors associated with 3-month and 1-year mortality from infective endocarditis

Bibliographic Details
Title: Data on prognostic factors associated with 3-month and 1-year mortality from infective endocarditis
Authors: Magali Collonnaz, Marie-Line Erpelding, François Alla, François Goehringer, François Delahaye, Bernard Iung, Vincent Le Moing, Bruno Hoen, Christine Selton-Suty, Nelly Agrinier
Source: Data in Brief, Vol 33, Iss , Pp 106478- (2020)
Publisher Information: Elsevier, 2020.
Publication Year: 2020
Collection: LCC:Computer applications to medicine. Medical informatics
LCC:Science (General)
Subject Terms: Infective endocarditis, Referral bias, Tertiary hospitals, Prognostic factors, Survival, Selection bias, Computer applications to medicine. Medical informatics, R858-859.7, Science (General), Q1-390
More Details: 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 1st and December 31st 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).
Document Type: article
File Description: electronic resource
Language: English
ISSN: 2352-3409
Relation: http://www.sciencedirect.com/science/article/pii/S2352340920313603; https://doaj.org/toc/2352-3409
DOI: 10.1016/j.dib.2020.106478
Access URL: https://doaj.org/article/d1f95fd15adb4fd09edd30b14451c127
Accession Number: edsdoj.1f95fd15adb4fd09edd30b14451c127
Database: Directory of Open Access Journals
More Details
ISSN:23523409
DOI:10.1016/j.dib.2020.106478
Published in:Data in Brief
Language:English