As China enters an aging society, the incidence of Alzheimer's disease (AD) is increasing annually. Concurrently, these patients are susceptible to developing community-acquired pneumonia (CAP), a complication that ultimately leads to adverse outcomes. Therefore, establishing an accurate risk prediction model can assist healthcare professionals in early identification and intervention for high-risk patients, thereby alleviating familial and societal burdens.

To develop a predictive model for the risk of adverse prognosis in AD patients with CAP, analyze the high-risk factors for poor outcomes in this patient population, and provide a basis for formulating targeted treatment measures.

Data retrieval was conducted using the dementia database platform established by Hubei Provincial Hospital of Traditional Chinese Medicine. Hospitalized patients diagnosed with AD and CAP from January 2020 to August 2025 were included as study subjects (n=371). Data collected included demographic and baseline characteristics, comorbidities and behavioral risk factors, assessment scales, and laboratory indicators. The data were randomly split into a training set (n=259) and a validation set (n=112) in a 7∶3 ratio. Subsequently, least absolute shrinkage and selection operator (LASSO) regression with 10-fold cross-validation was employed to screen for optimal predictors. Define the composite outcome of patient death or respiratory failure during hospitalization as an adverse event. Following this screening process, Logistic regression was used to develop the final prediction model, and a nomogram was constructed based on this model. The model performance was evaluated using multiple metrics, including the area under the receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA).

The incidence of adverse outcomes in AD patients with CAP was 27.76% (103/371). Using LASSO regression, four potential predictors were selected from 20 candidate variables: CDR score, CURB-65 score, ADL score, and dysphagia. Subsequent multivariate Logistic regression analysis confirmed that these four factors were independent influencing factors for adverse outcomes (P<0.05), with the following odds ratios: CDR score (OR=2.304, 95%CI=1.486-3.572), CURB-65 score (OR=2.263, 95%CI=1.477-3.468), ADL score (OR=2.337, 95%CI=1.033-5.287), and dysphagia (OR=2.042, 95%CI=1.036-4.023). A clinical prediction model (nomogram) was constructed based on these four predictive variables. The area under the ROC curve for the prediction model was 0.835 (95%CI=0.777-0.892) in the training set and 0.902 (95%CI=0.835-0.968) in the validation set, indicating good discriminative ability. The Hosmer-Lemeshow test results showed χ²=7.046 (P=0.531) for the training set and χ²=7.781 (P=0.455) for the validation set, suggesting good model fit. The Brier scores for the calibration curves were 0.137 and 0.108 for the two datasets, further indicating good consistency between predicted and actual values. DCA results showed that using the nomogram model to predict the risk of adverse outcomes provides clinical benefit when the threshold probability ranges from 0.10 to 0.75.

The nomogram model developed in this study effectively predicts the risk of adverse outcomes in AD patients with CAP. This tool facilitates early identification of high-risk populations by clinicians, thereby enabling the formulation of individualized interventions to improve patient prognosis.