Nonlinear Association of Length of Stay with In-Hospital Mortality in Alzheimer's Disease Hospitalizations: Admission-Only vs. Inpatient-Course Prediction Using Explainable Machine Learning.

Background: Hospitalizations among patients with Alzheimer's disease (AD) carry substantial mortality risk, but length of stay (LOS) is time-dependent and may reflect heterogeneous inpatient trajectories. We examined unadjusted and adjusted LOS-mortality patterns and compared admission-only versus inpatient-course prediction using explainable machine learning. Methods: Using the full 2017 Nationwide Readmissions Database (NRD), we identified hospitalizations among adults aged ≥60 years with an ICD-10-CM G30.x AD code in any diagnosis position. Records with missing in-hospital mortality status were excluded. LOS was summarized in clinically interpretable bins and modeled using restricted cubic splines. Model A excluded explicit inpatient-course measures, whereas Model B added LOS, procedure count, and total charges. Performance was evaluated using patient-grouped 5-fold out-of-fold validation and summarized by AUROC and AUPRC; SHAP was used for interpretation. Results: Among 249,507 AD hospitalizations, 12,666 in-hospital deaths occurred (5.08%; weighted mortality 4.97%). Unadjusted mortality was highest at LOS 0-1 day (13.00%), lowest at 4-6 days (3.47%), and increased to 7.77% at ≥22 days. After multivariable adjustment, LOS remained strongly nonlinear, but adjusted predicted mortality declined across the modeled LOS range. Model A achieved AUROC/AUPRC of 0.780/0.180, whereas Model B improved to 0.828/0.329. Sepsis, diagnostic burden, acute kidney injury, age, stroke, and pneumonia were stable predictors; LOS and procedure burden added prognostic information in Model B. Conclusions: The crude LOS-mortality pattern was U-shaped, whereas the adjusted pattern suggests that the late-stay increase in unadjusted mortality is partly explained by patient complexity and evolving inpatient-course factors. Admission-only prediction provides meaningful early risk stratification, while inpatient-course information improves prognostic assessment as hospitalization evolves.