"SWALLOWING AND ASPIRATION: HOW TO EVALUATE AND TREAT SWALLOWING DISORDERS ASSOCIATED WITH ASPIRATION PNEUMONIA IN OLDER PERSONS".

BACKGROUND Aspiration pneumonia (AP) is the most severe complication of oropharyngeal dysphagia (OD). It is highly underdiagnosed and undertreated among older patients hospitalized with community-acquired pneumonia (CAP). Our aim is to review the state of the art in the diagnosis and treatment of swallowing disorders associated with AP. METHODOLOGY We performed a narrative review, including our experience with prior studies at Hospital de Mataró, on the diagnosis and treatment of AP. RESULTS AP refers to pneumonia occurring in patients with swallowing disorders, frequently coinciding with poor oral health and vulnerability. Its main risk factors include oropharyngeal aspiration, impaired health status, malnutrition, frailty, immune dysfunction and oral colonization by respiratory pathogens. Incidence is estimated at between 5%-15% of cases of CAP but it is highly underdiagnosed. Diagnostic criteria for AP have not been standardized but should include its main pathophysiological element, oropharyngeal aspiration. Recently, a clinical algorithm was proposed, based on the recommendations of the Japanese Respiratory Society (JRS), that includes aspiration risk factors and clinical evaluation of OD. To facilitate the task for healthcare professionals, new AI-based screening tools for OD combined with validated clinical methods such as the volume-viscosity swallowing test (V-VST) for the detection of AP are being validated. Prevention and treatment of AP require multimodal interventions aimed to cover the main risk factors: textural adaptation of fluids and diets to avoid oropharyngeal aspiration; nutritional support to avoid malnutrition; and oral hygiene to reduce oral bacterial load. CONCLUSIONS The diagnosis of AP must be based on standardized criteria providing evidence on the main etiological factor, oropharyngeal aspiration. Clinical algorithms are valid in the diagnosis of AP and the identification of its main risk factors. Combination of AI-based tools with V-VST can lead to massive screening of OD and save resources and improve efficiency in the detection AP.