A Method to Explore Variations of Ventilator-Associated Event Surveillance Definitions in Large Critical Care Databases in the United States

Abstract

The Centers for Disease Control has well-established surveillance programs to monitor preventable conditions in patients supported by mechanical ventilation (MV). The aim of the study was to develop a data-driven methodology to examine variations in the first tier of the ventilator-associated event surveillance definition, described as a ventilator-associated condition (VAC). Further, an interactive tool was designed to illustrate the effect of changes to the VAC surveillance definition, by applying different ventilator settings, time-intervals, demographics, and selected clinical criteria.

Design: Retrospective, multicenter, cross-sectional analysis.

Setting: Three hundred forty critical care units across 209 hospitals, comprising 261,910 patients in both the electronic Intensive Care Unit Clinical Research Database and Medical Information Mart for Intensive Care III databases.

Patients: A total of 14,517 patients undergoing MV for 4 or more days.

Measurements and main results: We designed a statistical analysis framework, complemented by a custom interactive data visualization tool to depict how changes to the VAC surveillance definition alter its prognostic performance, comparing patients with and without VAC. This methodology and tool enable comparison of three clinical outcomes (hospital mortality, hospital length-of-stay, and ICU length-of-stay) and provide the option to stratify patients by six criteria in two categories: patient population (dataset and ICU type) and clinical features (minimum Fio₂, minimum positive end-expiratory pressure, early/late VAC, and worst first-day respiratory Sequential Organ Failure Assessment score). Patient population outcomes were depicted by heatmaps with mortality odds ratios. In parallel, outcomes from ventilation setting variations and clinical features were depicted with Kaplan-Meier survival curves.

Conclusions: We developed a method to examine VAC using information extracted from large electronic health record databases. Building upon this framework, we developed an interactive tool to visualize and quantify the implications of variations in the VAC surveillance definition in different populations, across time and critical care settings. Data for patients with and without VAC was used to illustrate the effect of the application of this method and visualization tool.

Keywords: data science; mechanical; pneumonia; surveillance; ventilator-associated; ventilator-associated condition; ventilator-associated event; ventilators.

Figure 1.

Flow diagram. eICU-CRD = electronic ICU - Clinical Research Database, MIMIC-III = Medical Information Mart for Intensive Care III, MV = mechanical ventilation, PEEP = positive end-expiratory pressure, VAC = ventilator-associated condition.

Figure 2.

Illustration of the interactive visualization tool for a select set of ventilation criteria: minimum Fio₂ (ΔminFio₂) greater than or equal to 20%, minimum positive end-expiratory pressure (ΔminPEEP) greater than or equal to 3 (n = 14,517; ventilator-associated condition [VAC] = 1,366; no VAC = 13,151). On each heatmap, the cell surrounded by a blue box corresponds to the criteria selected in this example (ΔminFio₂ ≥ 20%, ΔminPEEP ≥ 3). The upper left graph represents the Kaplan-Meier survival curves among patients with VAC versus patients without VAC (“no VAC”) resulting from the selected set of clinical criteria, whereas the upper right panel illustrates variability in mortality odds ratios across changes in ΔminFio₂ and ΔminPEEP. In addition, the lower panels showcase differences in ICU (left) and hospital (right) length of stay outcomes. Although the estimated probabilities of survival clearly distinguish VAC patients from others until the 15th day following the start of mechanical ventilation, they fully overlap from the 16th day onward (p = 0.063).

Figure 3.

Illustration of the interactive visualization tool for a select set of ventilation criteria: minimum Fio₂ (ΔminFio₂) greater than or equal to 20%, minimum positive end-expiratory pressure (ΔminPEEP) greater than or equal to 6 (n = 14,517; ventilator-associated condition [VAC] = 274; no ventilator-associated condition [VAC] = 14,243). On each heatmap, the cell surrounded by a blue box corresponds to the criteria selected in this example (ΔminFio₂ ≥ 20, ΔminPEEP ≥ 6). Of note, the detrimental effects of VAC occurrence during the ICU stay are more pronounced than in the preceding example (ΔminFio₂ ≥ 20, ΔminPEEP ≥ 3)—across all three outcomes. The upper left plot represents the Kaplan-Meier survival curves among patients with VAC versus patients without VAC (non-VAC) resulting from the selected set of clinical criteria, whereas the upper right heatmap illustrates variability in mortality odds ratios across changes in PEEP and Fio₂. In addition, the lower panels showcase differences in ICU (left) and hospital (right) length of stay outcomes. Although the estimated Kaplan-Meier survival curve pertaining to VAC patients presents wider confidence intervals than in the first/previous/preceding example, the estimated probabilities of survival for VAC and non-VAC patients clearly separate until the end of follow-up (i.e. 28 d, p < 0.001).