Quantifying clinical indicators for identifying and prognosticating aspiration pneumonia in Chinese geriatric patients: a retrospective cohort study integrating multivariable regression and interpretable machine learning.

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 Conclusion: This dual-framework methodology amalgamates epidemiological rigor with artificial intelligence interpretability, allowing AP identification and prognostic evaluation through routine clinical parameters. This approach establishes a foundation for dynamic risk stratification in AP patients and contributes to improved clinical outcomes.