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

Content Provider	Springer Nature : BioMed Central
Author	Wolkewitz, Martin Cooper, Ben S Palomar-Martinez, Mercedes Alvarez-Lerma, Francisco Olaechea-Astigarraga, Pedro Barnett, Adrian G Harbarth, Stephan Schumacher, Martin
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.
Related Links	https://ccforum.biomedcentral.com/counter/pdf/10.1186/cc13821.pdf
Ending Page	11
Page Count	11
Starting Page	1
File Format	HTM / HTML
ISSN	13648535
DOI	10.1186/cc13821
Journal	Critical Care
Issue Number	2
Volume Number	18
Language	English
Publisher	BioMed Central
Publisher Date	2014-04-08
Access Restriction	Open
Subject Keyword	Intensive Critical Care Medicine Emergency Medicine Intensive Care Unit Hazard Rate Intensive Care Unit Stay Frailty Model Cumulative Incidence Function
Content Type	Text
Resource Type	Article
Subject	Critical Care and Intensive Care Medicine
Journal Impact Factor	8.8/2023
5-Year Journal Impact Factor	10.4/2023

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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