Abstract
Ventilator-associated pneumonia (VAP) affects up to 20% of critically ill patients and induces significant antibiotic prescription pressure, accounting for half of all antibiotic use in the ICU. VAP significantly increases hospital length of stay and healthcare costs yet is also associated with long-term morbidity and mortality. The diagnosis of VAP continues to present challenges and pitfalls for the currently available clinical, radiological and microbiological diagnostic armamentarium. Biomarkers and artificial intelligence offer an innovative potential direction for ongoing future research. In this Review, we summarise the pathobiological heterogeneity and diagnostic challenges associated with VAP.
Key Points
- Prevalence and Impact: VAP affects 20–36% of ICU patients, increasing hospital stays, healthcare costs, and mortality rates.
- Pathobiology: VAP arises from altered airway defenses, microbial dysbiosis, and impaired immune responses due to ICU interventions such as intubation and sedation.
- Microbial Landscape: VAP is predominantly caused by antibiotic-resistant gram-negative bacteria, with growing concerns over fungal and viral infections in ICU settings.
- Diagnostic Challenges: Clinical, radiological, and microbiological criteria for diagnosing VAP lack sensitivity and specificity, leading to potential over- or under-treatment.
- Role of Biomarkers: Emerging biomarkers like procalcitonin, CRP, and cytokines show potential for guiding diagnosis and treatment but lack standardization for routine use.
- Imaging Modalities: CT scans provide high accuracy but are impractical in critically ill patients, while chest X-rays and lung ultrasounds offer bedside alternatives with variable reliability.
- Microbiological Methods: Techniques like bronchoalveolar lavage (BAL) provide specificity but are invasive; non-invasive options like endotracheal aspirates are less reliable.
- Preventative Measures: Interventions such as subglottic suctioning, proper oral care, and ventilator management reduce VAP incidence but require strict adherence to protocols.
- AI Integration: Artificial intelligence offers promising advancements in diagnostics, phenotyping, and predictive modeling for VAP, though it requires further validation.
- Healthcare Inequities: Resource-limited settings face higher VAP incidence and mortality rates, with limited access to advanced diagnostics and preventative measures.
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