ArticleJMIR AI2026
Using Machine Learning to Improve Screening for Oropharyngeal Dysphagia in Hospitalized Versus Primary Care Adult Patients With COVID-19 Disease: Retrospective Observational Study.
Article in JMIR AI, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Background: Oropharyngeal dysphagia (OD) commonly occurs in patients with COVID-19 disease, posing diagnostic challenges due to isolation protocols. Objective: This study aimed at evaluating Artificial Intelligence Massive Screening for Oropharyngeal Dysphagia (AIMS-OD), a machine learning software for real-time OD screening, comparing OD prevalence and clinical outcomes using OD ICD-10 (International Statistical Classification of Diseases, Tenth Revision) R13 codes (R13-OD) and high-risk AIMS-OD (H-AIMS-OD) scores (>0.5), in hospital and primary care patients with COVID-19 disease. It explored clinical characteristics, OD risk factors, and clinical outcomes. Methods: This retrospective, observational study analyzed patients with SARS-CoV-2 aged 18 years and older in Catalonia from January 1 to August 31, 2020, including hospital and primary care data on clinical information, International Classification of Diseases, Tenth Revision (ICD-10) codes, hospital stay, discharge destination, and mortality. AIMS-OD assessed OD risk, stratifying patients by age (aged 18-69 years and 70 years and older). Results: Among 257,541 patients with COVID-19 disease, 59.3% (152,721/257,541) were aged 18-69 years and 40.7% (104,820/257,541) were aged 70 years and older. Hospital and primary care R13-OD prevalence was 3.5% and 4.3%, respectively; AIMS-OD showed 34.8% and 15.4%, with True prevalence at 16.7% and 7.4%. Patients aged 70 years and older had worse clinical outcomes and worse prognosis. Patients in R13-OD experienced significantly worse clinical outcomes than patients with H-AIMS-OD, who in turn fared worse than those with no R13-OD and with low AIMS-OD risk. Risk factors for patients with COVID-19 R13-OD included age, neuroleptic use, stroke, dementia, and delirium. Conclusions: AIMS-OD screening revealed high prevalence and significant underdiagnosis in patients with COVID-19 disease across settings. Early detection and risk stratification using AIMS-OD could improve clinical decision-making, diagnosis, and management, particularly in older patients with comorbidities.
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