Multilevel competing risk models to evaluate the risk of nosocomial infection

Abstract

IntroductionRisk 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.MethodsWe 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.ResultsThere 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.ConclusionsA 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.

Individual participant data meta‐analysis of intervention studies with time‐to‐event outcomes: A review of the methodology and an applied example

“…5,12,44,78 If competing risks are present & absolute risks are of interest, apply competing risks models. 105,108,109,111,170 Multiple imputation of missing covariates must account for clustering & time-toevent, [136][137][138]140,143 using the event indicator and the Nelson-Aalen cumulative hazard.…”

Accounting for Competing Events in Multivariate Analyses of Hospital-Acquired Infection Risk Factors

“…In this case, a simpler risk metric analysis via logistic regression and risk odds ratios yielded similar results. 4 The phenomenon can be explained as follows: Even though patients with antibiotic treatment acquire less nosocomial bacteremia per ICU day at risk (nosocomial bacteremia HR, 0.83), their extended at-risk time in the ICU (discharge HR, 0.70) results eventually in an equal cumulative infection risk. Notably, the discharge hazard is much larger than the death hazard and is therefore the main determinant for the at-risk time.…”

“…For instance, in a multilevel competing risk analysis, 4 we showed that intensive care unit (ICU) patients who received antibiotic treatment 48 h before and/or after ICU admission had a lower hazard of acquiring a primary or secondary nosocomial bacteremia of any pathogen (hazard ratio [HR], 0.83; 95% CI, 0.77-0.88). For the competing events, antibiotic treatment is also associated with an increased death hazard (HR, 1.08; 95% CI, 1.04-1.13) and a reduced discharge hazard (HR, 0.70; 95% CI, 0.69-0.71), meaning that patients with antibiotic treatment remain at risk longer.…”

“…Patients who acquire a HAI are then censored at the time of infection onset. 4 Such competing risk analyses are not only very informative, they might also explain phenomena due to the 2 metrics. We believe that competing risk analyses are necessary since ignoring the potential effect of exposures on the competing events can easily lead to incorrect conclusions.…”

“…Thus, only the use of both metrics can provide a complete picture in multivariate analyses of HAI risk factors. 4,5 However, in the presence of time-dependent exposures, the rate metric approaches are very suitable, 6 but risk metric approaches have still challenging limitations in their interpretation. Potential conflicts of interest: The author reports no conflicts of interest relevant to this article.…”