To study the impact of duration of mechanical ventilation, hospitalization and multiple ventilation episodes on the development of pneumonia while accounting for extubation as a competing event.

A multicenter data base from a Spanish surveillance network was used to conduct a retrospective analysis of prospectively collected intensive care patients followed from admission to discharge.

Spanish intensive care units (ICUs).

Mechanically ventilated adult patients from 158 ICUs with 45,486 admissions, 48,705 ventilation episodes, and 314,196 ventilator days.

Competing-risk models were applied to account for extubation plus 48 hours as a competing event for acquiring ventilator-associated pneumonia (VAP).

Time in the ICU before mechanical ventilation was associated with an increased VAP hazard rate and with longer intubation time. This indirect prolongation of intubation increased the cumulative risk to eventually acquire VAP. For instance, comparing 3–4 versus 0 days, the adjusted VAP hazard ratio was 1.40 (95% confidence interval [CI], 1.19–1.64) and the adjusted extubation hazard ratio was 0.64 (95% CI, 0.61–0.68), which leads to an adjusted VAP subdistribution hazard ratio (sHR) of 2.13 (95% CI, 1.83–2.50). Similarly, due to prolonged intubation, multiple ventilation episodes increase the risk for VAP; the adjusted sHR is 1.52 (95% CI, 1.35–1.72) for the second episode compared to the first episode, and the adjusted sHR is 1.54 (95% CI, 1.03–2.30) for the third episode compared to the first episode. The Kaplan-Meier method produced an upward biased estimated cumulative risk for VAP.

A competing-risk analysis is necessary to receive unbiased risk estimates and to quantify the indirect effect of intubation time on the cumulative VAP risk. Our findings may guide physicians to improve medical decisions related to the harms and benefits of the duration of ventilation.

Competing risks are a necessary consideration when analyzing risk factors for nosocomial infections (NIs). In this article, we identify additional information that a competing risks analysis provides in a hospital setting. Furthermore, we improve on established methods for nested case-control designs to acquire this information.

Using data from 2 Spanish intensive care units and model simulations, we show how controls selected by time-dynamic sampling for NI can be weighted to perform risk-factor analysis for death or discharge without infection. This extension not only enables hazard rate analysis for the competing risk, it also enables prediction analysis for NI.

The estimates acquired from the extension were in good agreement with the results from the full (real and simulated) cohort dataset. The reduced dataset results averted any false interpretation common in a competing-risks setting.

Using additional information that is routinely collected in a hospital setting, a nested case-control design can be successfully adapted to avoid a competing risks bias. Furthermore, this adapted method can be used to reanalyze past nested case-control studies to enhance their findings.

More than 10% of patients admitted to intensive care units (ICUs) experience a severe, healthcare-associated infection, such as ventilator-associated pneumonia (VAP) or bloodstream infection (BSI). What could be a public health target for prevention is hotly debated, because properly adjusting for intrinsic risk factors in the patient population is difficult. We aimed to estimate the proportion of ICU-acquired VAP and BSI cases that are amenable to prevention in routine conditions.

We analyzed routine data collected prospectively according to the European standard protocol for patient-based surveillance of healthcare-acquired infections in ICUs. We computed the number of infections to be expected if, after adjustment for case mix, the infection incidence in ICUs with higher infection rates could be reduced to that of the top-tenth-percentile-ranked ICU. Computations came from model-based simulation of individual patient profiles over time in the ICU. The preventable proportion was computed as the number of observed cases minus the number of expected cases divided by the number of observed cases.

Data for 78,222 patients admitted for more than 2 days to 525 ICUs in 6 European countries from 2005 to 2008 were available for analysis. We calculated that 52% of VAP and 69% of BSI was preventable.

Our pragmatic, if highly conservative, estimates quantify the potential for prevention of VAP and BSI in routine conditions, assuming that variation in infection incidence between ICUs can be eliminated with improved quality of care, apart from variation attributable to differential case mix.

To assess the influence of nosocomial infection on length of stay in the intensive care unit (ICU) and to determine the relative effect of other factors on extra length of hospitalization associated with nosocomial infection.

Prospective cohort multicenter study in the context of the ENVIN-UCI project.

Medical or surgical ICUs of 49 different hospitals in Spain.

All consecutive patients (N = 6,593) admitted to ICUs of the participating hospitals who stayed for more than 24 hours during a 3-month period (from January 15 to April 15, 1996) were included. Length of ICU stay was compared between patients with and without nosocomial infections.

Uninfected patients (N = 5,868) had a median stay in the ICU of 3 days, whereas the median for infected patients (N = 725) was 17 days (P < .001). The median for infected patients with one episode of nosocomial infection was 13 days. The greatest length of stay (40 days) was among patients admitted to the ICU because of medical diseases, with an infection acquired before admission to the ICU, and with the largest number of nosocomial infection episodes. In extended stays, nosocomial infection was significantly associated with length of hospitalization (day 21; odds ratio, 22.38; 95% confidence interval, 16.6 to 30.4), whereas an effect of variables related to severity of illness on admission (Acute Physiology and Chronic Health Evaluation II score, urgent surgery, and infection prior to ICU admission) was not found.

The presence of nosocomial infection and the number of infection episodes were the variables with the strongest association with prolonged hospital stay among ICU patients.