Publication: Multilevel competing risk models to evaluate the risk of nosocomial infection
Issued Date
2014-04-08
Resource Type
ISSN
1466609X
13648535
13648535
DOI
Other identifier(s)
2-s2.0-84901399579
Rights
Mahidol University
Rights Holder(s)
SCOPUS
Bibliographic Citation
Critical Care. Vol.18, No.2 (2014)
Suggested Citation
Martin Wolkewitz, Ben S. Cooper, Mercedes Palomar-Martinez, Francisco Alvarez-Lerma, Pedro Olaechea-Astigarraga, Adrian G. Barnett, Stephan Harbarth, Martin Schumacher Multilevel competing risk models to evaluate the risk of nosocomial infection. Critical Care. Vol.18, No.2 (2014). doi:10.1186/cc13821 Retrieved from: https://repository.li.mahidol.ac.th/handle/20.500.14594/34252
Research Projects
Organizational Units
Authors
Journal Issue
Thesis
Title
Multilevel competing risk models to evaluate the risk of nosocomial infection
Other Contributor(s)
Abstract
Introduction: Risk factor analyses for nosocomial infections (NIs) are complex. First, due to competing events for NI, the association between risk factors of NI as measured using hazard rates may not coincide with the association using cumulative probability (risk). Second, patients from the same intensive care unit (ICU) who share the same environmental exposure are likely to be more similar with regard to risk factors predisposing to a NI than patients from different ICUs. We aimed to develop an analytical approach to account for both features and to use it to evaluate associations between patient- and ICU-level characteristics with both rates of NI and competing risks and with the cumulative probability of infection.Methods: We considered a multicenter database of 159 intensive care units containing 109,216 admissions (813,739 admission-days) from the Spanish HELICS-ENVIN ICU network. We analyzed the data using two models: an etiologic model (rate based) and a predictive model (risk based). In both models, random effects (shared frailties) were introduced to assess heterogeneity. Death and discharge without NI are treated as competing events for NI.Results: There was a large heterogeneity across ICUs in NI hazard rates, which remained after accounting for multilevel risk factors, meaning that there are remaining unobserved ICU-specific factors that influence NI occurrence. Heterogeneity across ICUs in terms of cumulative probability of NI was even more pronounced. Several risk factors had markedly different associations in the rate-based and risk-based models. For some, the associations differed in magnitude. For example, high Acute Physiology and Chronic Health Evaluation II (APACHE II) scores were associated with modest increases in the rate of nosocomial bacteremia, but large increases in the risk. Others differed in sign, for example respiratory vs cardiovascular diagnostic categories were associated with a reduced rate of nosocomial bacteremia, but an increased risk.Conclusions: A combination of competing risks and multilevel models is required to understand direct and indirect risk factors for NI and distinguish patient-level from ICU-level factors. © 2014 Wolkewitz et al.; licensee BioMed Central Ltd.