Publication:
Multilevel competing risk models to evaluate the risk of nosocomial infection

dc.contributor.authorMartin Wolkewitzen_US
dc.contributor.authorBen S. Cooperen_US
dc.contributor.authorMercedes Palomar-Martinezen_US
dc.contributor.authorFrancisco Alvarez-Lermaen_US
dc.contributor.authorPedro Olaechea-Astigarragaen_US
dc.contributor.authorAdrian G. Barnetten_US
dc.contributor.authorStephan Harbarthen_US
dc.contributor.authorMartin Schumacheren_US
dc.contributor.otherUniversitats Klinikum Freiburg und Medizinische Fakultaten_US
dc.contributor.otherUniversitat Freiburg im Breisgauen_US
dc.contributor.otherMahidol Universityen_US
dc.contributor.otherHospital Universitari Arnau de Vilanova de Lleidaen_US
dc.contributor.otherService of Intensive Care Medicineen_US
dc.contributor.otherHospital de Galdakaoen_US
dc.contributor.otherQueensland University of Technology QUTen_US
dc.contributor.otherHopitaux universitaires de Geneveen_US
dc.date.accessioned2018-11-09T02:37:51Z
dc.date.available2018-11-09T02:37:51Z
dc.date.issued2014-04-08en_US
dc.description.abstractIntroduction: 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.en_US
dc.identifier.citationCritical Care. Vol.18, No.2 (2014)en_US
dc.identifier.doi10.1186/cc13821en_US
dc.identifier.issn1466609Xen_US
dc.identifier.issn13648535en_US
dc.identifier.other2-s2.0-84901399579en_US
dc.identifier.urihttps://repository.li.mahidol.ac.th/handle/20.500.14594/34252
dc.rightsMahidol Universityen_US
dc.rights.holderSCOPUSen_US
dc.source.urihttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84901399579&origin=inwarden_US
dc.subjectMedicineen_US
dc.titleMultilevel competing risk models to evaluate the risk of nosocomial infectionen_US
dc.typeArticleen_US
dspace.entity.typePublication
mu.datasource.scopushttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84901399579&origin=inwarden_US

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