Evidence map›Paper›PMID 40993813›Full record

ArticlePneumonia (Nathan Qld.)2025

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

Alberto Martín-Martínez, Clàudia Sitges-Milà, Jaume Miró, Cristina Amadó, Ramon Boixeda, Yuki Yoshimatsu, Dorte Melgaard, Pere Clavé, Omar Ortega

Abstract read
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Article in Pneumonia (Nathan Qld.), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

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3citing papers in PubMed
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1 · What the graph read from it

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3 · Its place in the literature

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3 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

9 authors.

Alberto Martín-Martínez *Gastrointestinal Physiology Laboratory, Department of Surgery, Hospital Universitari de Mataró, Consorci Sanitari del Maresme, Universitat Autònoma de Barcelona, Mataró, Spain.
Clàudia Sitges-Milà *Gastrointestinal Physiology Laboratory, Department of Surgery, Hospital Universitari de Mataró, Consorci Sanitari del Maresme, Universitat Autònoma de Barcelona, Mataró, Spain.
Jaume MiróGastrointestinal Physiology Laboratory, Department of Surgery, Hospital Universitari de Mataró, Consorci Sanitari del Maresme, Universitat Autònoma de Barcelona, Mataró, Spain.
Cristina AmadóGastrointestinal Physiology Laboratory, Department of Surgery, Hospital Universitari de Mataró, Consorci Sanitari del Maresme, Universitat Autònoma de Barcelona, Mataró, Spain.
Ramon BoixedaInternal Medicine Department, Hospital Universitari de Mataró, Consorci Sanitari del Maresme, Mataró, Spain.
Yuki YoshimatsuGuy's and St Thomas' NHS Foundation Trust, London, United Kingdom.
Dorte MelgaardFaculty of Clinical Medicine, Aalborg University, Aalborg, Denmark.
Pere ClavéGastrointestinal Physiology Laboratory, Department of Surgery, Hospital Universitari de Mataró, Consorci Sanitari del Maresme, Universitat Autònoma de Barcelona, Mataró, Spain. pere.clave@ciberehd.org.
Omar OrtegaGastrointestinal Physiology Laboratory, Department of Surgery, Hospital Universitari de Mataró, Consorci Sanitari del Maresme, Universitat Autònoma de Barcelona, Mataró, Spain. oortega@csdm.cat.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionAspiration 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

methodsA 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.

resultsa) 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. 

conclusionThe 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.

Indexed as

Artificial intelligenceAspiration pneumoniaCommunity-acquired pneumoniaOropharyngeal aspirationOropharyngeal dysphagiaSwallowing disorders

Identifiers

PMID40993813
PMCPMC12462020

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.