AI Article Synopsis

  • The article discusses supplementary tables and figures related to a research paper that analyzes referral bias in patients with infective endocarditis, using data from a population-based cohort study.
  • It includes 497 patients diagnosed in 2008, categorized into three groups based on their hospital admissions, to examine the effects of referral bias on patient outcomes.
  • The findings suggest that referral bias—favoring patients from tertiary hospitals—can significantly alter survival estimates and the identification of prognostic factors, as depicted in the six tables that compare selection impacts.

Article Abstract

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 cohort on infective endocarditis and aimed at characterising referral bias. A total of 497 patients diagnosed with definite infective endocarditis between January 1 and December 31 2008 were included in . 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).

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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7666320PMC
http://dx.doi.org/10.1016/j.dib.2020.106478DOI Listing

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