Assessing the prevalence and the underdiagnosis of aspiration pneumonia among older hospitalized patients with community-acquired pneumonia using an artificial intelligence algorithm.

Abstract

Introduction: Aspiration pneumonia (AP) in older persons is associated with oropharyngeal dysphagia (OD) and is estimated to account for 5-15% of cases of community-acquired pneumonia (CAP). Artificial Intelligence Massive Screening for OD (AIMS-OD) is an algorithm for identifying OD in older patients on hospital admission using data from electronic health records (EHR). We aimed to assess the prevalence of OD among older patients hospitalized with pneumonia and thus estimate the underdiagnosis of AP based on AIMS-OD.

Materials and methods: A retrospective observational study included 15,603 patients older than 65 years who were admitted for pneumonia to a general hospital between 2013 and 2022. Clinical data were obtained from EHR. AIMS-OD is an accurate diagnostic algorithm (AUCROC > 0.79, specificity 0.92, PPV 0.86, NPV 0.58) for OD using AI and machine learning.

Results: a) AP prevalence following traditional clinical practice (ICD-10 J69.0, AP codification) on discharge was 15.57% (n=2,430, 86.73±7.43 years); b) Estimated AP prevalence related to OD identified with AIMS-OD, was 25.32% (n=3,951, 85.11±8.78 years); c) AIMS-OD identified 84.77% (n=2,060, 87.17±7.09 years) of clinically diagnosed patients (ICD-10 J69.0), and 1,891 additional cases of AP (82.87±9.84 years) undetected by clinical practice, distinguishing them from pneumonia patients without OD in seconds.  CONCLUSION: The prevalence of AP following traditional clinical practice among older patients hospitalized with pneumonia was 15.57%. AIMS-OD revealed a potential prevalence of AP of 25.32%. AIMS-OD allows to increase by 62.6% the detection of AP related to OD versus traditional clinical practice among older patients hospitalized with pneumonia. AIMS-OD allows massive, immediate, and accurate identification of OD on hospital admission, from which AP cases can be identified, enabling early and specific treatment to improve the poor clinical outcomes of these unrecognized patients with AP and prevent its recurrence.