Highlights of the Reappraisal
In a recent multicenter observational study published in Intensive Care Medicine, researchers have provided new evidence regarding the impact of ventilator-associated events (VAEs) on patient outcomes. The key findings include:
1. VAEs were significantly associated with a twofold increase in 30-day hospital mortality (HR 2.00; 95% CI 1.23-3.26).
2. The study utilized a marginal structural model to account for time-dependent confounders, specifically changes in patient severity that occur after the initiation of mechanical ventilation.
3. The incidence of VAEs was 10.0 per 1,000 ventilation days, with an identified population attributable risk fraction (PARF) of 8.8% for in-hospital deaths.
4. Beyond mortality, VAEs were linked to significantly longer durations of hospital and ICU stays.
The Evolving Definition of Ventilator-Associated Complications
For decades, clinician-scientists focused on Ventilator-Associated Pneumonia (VAP) as the primary indicator of complications related to mechanical ventilation. However, VAP definitions often suffered from subjectivity and poor inter-rater reliability due to the ambiguity of chest radiograph interpretations. In response, the US Centers for Disease Control and Prevention (CDC) introduced the Ventilator-Associated Event (VAE) surveillance definition in 2013. VAEs prioritize objective, automated data—primarily changes in the fraction of inspired oxygen (FiO2) and positive end-expiratory pressure (PEEP)—to identify respiratory deterioration.
Despite the widespread adoption of VAEs as a quality metric, a persistent clinical question remained: Are VAEs a direct cause of death, or are they simply a marker for patients who are already sicker and thus more likely to die? Previous studies often failed to account for the dynamic nature of patient severity, leading to concerns that VAEs were merely a surrogate for overall clinical decline rather than an independent driver of poor outcomes.
Methodological Innovation: The Marginal Structural Model
To address the limitations of prior research, Nakahashi and the Japan VAE Study Investigators Group employed a marginal structural model (MSM) with inverse probability weighting (IPW). In traditional regression models, adjusting for variables that are both consequences of the exposure (the VAE) and predictors of the outcome (mortality) can introduce bias. For instance, a patient’s fluid balance or sedation level may change over time, influencing both the risk of a VAE and the risk of death.
By using an MSM, the researchers were able to create a ‘pseudo-population’ where the probability of experiencing a VAE was independent of time-varying confounders. This approach allows for a more accurate estimation of the causal effect of VAEs on mortality, effectively ‘re-appraising’ the relationship by adjusting for the fluctuations in patient severity that occur during an ICU stay.
Study Design and Population
This multicenter observational study was conducted across 18 intensive care units (ICUs) in Japan between May 2020 and December 2022. The study included 1,094 adult and adolescent patients (aged ≥ 12 years) who required mechanical ventilation for at least three consecutive days.
VAEs were defined according to the CDC criteria, which require a baseline period of stability or improvement followed by a sustained increase in daily minimum PEEP (≥ 3 cm H2O) or daily minimum FiO2 (≥ 20 points) for at least two calendar days. The primary endpoint was 30-day in-hospital mortality, with secondary endpoints including ICU mortality and length of stay (LOS).
Key Findings: Quantifying the Impact of VAEs
Among the 1,094 subjects, 106 VAEs were identified, representing an incidence rate of 9.7%. This translates to roughly 10 events for every 1,000 days of mechanical ventilation, a rate consistent with global benchmarks.
Mortality and Survival Analysis
The most striking result was the doubling of the mortality risk. Even after rigorous adjustment for time-dependent severity using the MSM, the hazard ratio (HR) for 30-day in-hospital mortality was 2.00 (95% CI 1.23-3.26). Similarly, the HR for ICU mortality was 1.92 (95% CI 1.03-3.57). These figures suggest that the occurrence of a VAE is not merely a bystander in the patient’s clinical course but a significant event that independently jeopardizes survival.
Fig Forest plot of hazard ratios for clinical outcomes comparing VAEs versus non-VAEs groups. VAEs ventilator-associated events, HR hazard ratio, CI confidence interval, ICU intensive care unit, MV mechanical ventilation
Fig Forest plot of hazard ratios for clinical outcomes stratified by VAEs subtypes. VAEs ventilator-associated events, VAC ventilator-associated condition, IVAC infection-related ventilator-associated complication, PVAP possible ventilator-associated pneumonia, HR hazard ratio, CI confidence interval, ICU intensive care unit
Resource Utilization and Length of Stay
VAEs also imposed a substantial burden on healthcare resources. Patients who experienced a VAE had significantly longer ICU and hospital stays. The HR for discharge alive from the hospital was 0.72 (95% CI 0.54-0.97), and for discharge from the ICU, it was 0.47 (95% CI 0.23-0.96). A lower hazard ratio in this context indicates a reduced probability of being discharged, thereby reflecting a prolonged stay.
Population Attributable Risk
The researchers calculated the Population Attributable Risk Fraction (PARF), which estimates the proportion of deaths that could theoretically be avoided if VAEs were entirely prevented. The PARF was 8.8% for in-hospital deaths and 8.2% for ICU deaths. This highlights VAE prevention as a high-yield target for quality improvement initiatives in the ICU.
Expert Commentary: Bridging Quality and Causality
The findings of Nakahashi et al. are a significant step forward in critical care epidemiology. By utilizing the marginal structural model, the investigators have largely silenced the argument that VAEs are merely indicators of baseline illness.
However, it is important to note the nuances of the VAE definition. VAE is an umbrella term that includes Ventilator-Associated Conditions (VAC), Infection-related Ventilator-Associated Conditions (IVAC), and Possible Ventilator-Associated Pneumonia (PVAP). While this study confirms the association for the broad VAE category, the specific mechanisms—whether inflammatory, infectious, or related to barotrauma—remain multifaceted.
One limitation of the study is its observational nature; while MSM provides a robust adjustment for confounding, it cannot entirely replace the insights from a randomized controlled trial. Furthermore, the data was collected in Japan, and while the multicenter design is a strength, differences in ICU practices (such as sedation protocols and weaning strategies) across different countries might influence VAE rates and their subsequent impact.
Clinical Takeaways for the Intensivist
For clinicians, this study reinforces the importance of VAE surveillance as more than just a regulatory requirement. It is a direct reflection of patient safety and prognosis. To mitigate the risk of VAEs, ICUs should double down on established evidence-based practices, including:
1. Minimal Sedation: Prioritizing wakefulness and daily spontaneous breathing trials to reduce the duration of ventilation.
2. Lung-Protective Ventilation: Strict adherence to low tidal volume strategies to prevent barotrauma and volutrauma.
3. Fluid Management: Avoiding excessive positive fluid balance, which can lead to pulmonary edema and subsequent respiratory deterioration.
4. Early Mobilization: Reducing the physical deconditioning that often accompanies prolonged mechanical ventilation.
Conclusion
The reappraisal of VAEs using the marginal structural model confirms that these events are independent predictors of mortality and prolonged hospitalization. With nearly 9% of in-hospital deaths in ventilated patients attributable to VAEs, focusing on the prevention of these respiratory complications is not only a matter of quality tracking but a vital strategy for improving patient survival in the intensive care unit.
References
1. Nakahashi S, Suzuki K, Nakashima T, et al. A reappraisal of association between ventilator-associated events and mortality among critically ill patients using marginal structural model: multicenter observational study. Intensive Care Med. 2025;51(10):1764-1774.
2. Magill SS, Klompas M, Balk R, et al. Developing a new, objective, surveillance paradigm for ventilator-associated harm. Crit Care Med. 2013;41(11):2467-2475.
3. Robins JM, Hernán MA, Brumback B. Marginal structural models and causal inference in epidemiology. Epidemiology. 2000;11(5):550-560.
4. Klompas M. Ventilator-associated events: what they are and what they are not. Respir Care. 2019;64(8):953-961.