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Multiple time scales in modeling the incidence of infections acquired in intensive care units

Overview of attention for article published in BMC Medical Research Methodology, September 2016
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Title
Multiple time scales in modeling the incidence of infections acquired in intensive care units
Published in
BMC Medical Research Methodology, September 2016
DOI 10.1186/s12874-016-0199-y
Pubmed ID
Authors

Martin Wolkewitz, Ben S. Cooper, Mercedes Palomar-Martinez, Francisco Alvarez-Lerma, Pedro Olaechea-Astigarraga, Adrian G. Barnett, Martin Schumacher

Abstract

When patients are admitted to an intensive care unit (ICU) their risk of getting an infection will be highly depend on the length of stay at-risk in the ICU. In addition, risk of infection is likely to vary over calendar time as a result of fluctuations in the prevalence of the pathogen on the ward. Hence risk of infection is expected to depend on two time scales (time in ICU and calendar time) as well as competing events (discharge or death) and their spatial location. The purpose of this paper is to develop and apply appropriate statistical models for the risk of ICU-acquired infection accounting for multiple time scales, competing risks and the spatial clustering of the data. A multi-center data base from a Spanish surveillance network was used to study the occurrence of an infection due to Methicillin-resistant Staphylococcus aureus (MRSA). The analysis included 84,843 patient admissions between January 2006 and December 2011 from 81 ICUs. Stratified Cox models were used to study multiple time scales while accounting for spatial clustering of the data (patients within ICUs) and for death or discharge as competing events for MRSA infection. Both time scales, time in ICU and calendar time, are highly associated with the MRSA hazard rate and cumulative risk. When using only one basic time scale, the interpretation and magnitude of several patient-individual risk factors differed. Risk factors concerning the severity of illness were more pronounced when using only calendar time. These differences disappeared when using both time scales simultaneously. The time-dependent dynamics of infections is complex and should be studied with models allowing for multiple time scales. For patient individual risk-factors we recommend stratified Cox regression models for competing events with ICU time as the basic time scale and calendar time as a covariate. The inclusion of calendar time and stratification by ICU allow to indirectly account for ICU-level effects such as local outbreaks or prevention interventions.

Twitter Demographics

The data shown below were collected from the profiles of 2 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 30 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 30 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 7 23%
Student > Master 6 20%
Researcher 6 20%
Student > Doctoral Student 2 7%
Lecturer 2 7%
Other 2 7%
Unknown 5 17%
Readers by discipline Count As %
Medicine and Dentistry 7 23%
Social Sciences 4 13%
Mathematics 4 13%
Agricultural and Biological Sciences 2 7%
Immunology and Microbiology 2 7%
Other 6 20%
Unknown 5 17%

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 24 October 2017.
All research outputs
#18,469,995
of 22,886,568 outputs
Outputs from BMC Medical Research Methodology
#1,748
of 2,023 outputs
Outputs of similar age
#258,239
of 337,400 outputs
Outputs of similar age from BMC Medical Research Methodology
#39
of 47 outputs
Altmetric has tracked 22,886,568 research outputs across all sources so far. This one is in the 11th percentile – i.e., 11% of other outputs scored the same or lower than it.
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