Evidence-based algorithms for diagnosing and treating ventilator-associated pneumonia

Abstract

Background: Ventilator-associated pneumonia (VAP) is widely recognized as a serious and common complication associated with high morbidity and high costs. Given the complexity of caring for heterogeneous populations in the intensive care unit (ICU), however, there is still uncertainty regarding how to diagnose and manage VAP.

Objective: We recently conducted a national collaborative aimed at reducing health care-associated infections in ICUs of hospitals operated by the Hospital Corporation of America (HCA). As part of this collaborative, we developed algorithms for diagnosing and treating VAP in mechanically ventilated patients. In the current article, we (1) review the current evidence for diagnosing VAP, (2) describe our approach for developing these algorithms, and (3) illustrate the utility of the diagnostic algorithms using clinical teaching cases.

Design: This was a descriptive study, using data from a national collaborative focused on reducing VAP and catheter-related bloodstream infections.

Setting: The setting of the study was 110 ICUs at 61 HCA hospitals.

Intervention: None.

Measurements and results: We assembled an interdisciplinary team that included infectious disease specialists, intensivists, hospitalists, statisticians, critical care nurses, and pharmacists. After reviewing published studies and the Centers for Disease Control and Prevention VAP guidelines, the team iteratively discussed the evidence, achieved consensus, and ultimately developed these practical algorithms. The diagnostic algorithms address infant, pediatric, immunocompromised, and adult ICU patients.

Conclusions: We present practical algorithms for diagnosing and managing VAP in mechanically ventilated patients. These algorithms may provide evidence-based real-time guidance to clinicians seeking a standardized approach to diagnosing and managing this challenging problem.