Quantifying clinical indicators for identifying and prognosticating aspiration pneumonia in Chinese geriatric patients: a retrospective cohort study integrating multivariable regression and interpretable machine learning
Abstract
Objective Aspiration pneumonia (AP) substantially increases mortality risk among elderly patients, necessitating prompt diagnostic recognition and prognostic stratification. However, standardized diagnostic criteria and prognostic markers remain elusive. This investigation seeks to quantify high-contribution clinical parameters for the diagnosis and evaluation of AP prognosis, elucidate nonlinear risk interactions, and establish a comprehensive risk stratification framework tailored for Chinese geriatric populations. Methods This retrospective cohort analysis (2017–2018) enrolled 295 patients with pneumonia (aged 65–98 years) from Guangzhou First People’s Hospital. The exposure variable was the diagnosis of AP, with the primary endpoints being 3-month mortality and recurrence rates. The adjustment variables encompassed demographic characteristics, comorbidities, biomarkers, and functional status. A dual-modeling approach was implemented: (1) multivariable multinomial logistic regression; (2) interpretable random forest algorithms incorporating SHAP/PDP analyses. Results Conventional analytical approaches demonstrated significantly elevated 3-month mortality (17.3% vs. 5.9%) and recurrence rates (42.3% vs. 17.8%) in the AP cohort (both p 15 days and Barthel Index ≤2, along with nonlinear interaction effects between antibiotic duration and Barthel score; antibiotic duration was interpreted as a clinical-course marker rather than a causal factor. Additionally, prognostic…
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Evidence Hub reads this as a cohort study, done in people. Its evidence level is 3 of 5 (Moderate · trial / cohort / secondary analysis). Trials without a large randomized design, cohorts and secondary analyses give moderate evidence. Reported size: 295 participants or records.
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