阿尔茨海默病患者跌倒相关住院风险预测模型的构建与验证研究OA

Background Falls are common in patients with Alzheimer's disease(AD)and are associated with increased risks of severe trauma,infection,disability,and death,often leading to unplanned hospitalization and placing a heavy burden on families and society.Objective To develop and validate a risk prediction model for fall-related hospitalization in patients with AD,and to identify the risk factors for fall-related hospitalization,thereby assisting physicians in accurately identifying high-risk individuals and implementing early interventions.Methods A total of 987 patients with AD from the dementia database of Hubei Provincial Hospital of Traditional Chinese Medicine between January 2020 and October 2025 were included.They were randomly divided into a training set(n=690)and a validation set(n=297)at a ratio of 7:3.Data on general characteristics,assessment scales,laboratory indicators,and medication use were extracted.Fall-related hospitalization was the outcome variable.Potential predictors were selected using LASSO regression.A nomogram prediction model was established using multivariable Logistic regression.Model discrimination,calibration,and clinical utility were evaluated using the area under the receiver operating characteristic curve(AUC),calibration curves,and decision curve analysis(DCA),respectively.Results Among the 987 patients with AD,276(27.96%)experienced fall-related hospitalization.Multivariable Logistic regression based on variables selected by LASSO regression showed that disease duration(OR=2.843,95%CI=1.862-4.342),Clinical Dementia Rating(CDR)score(OR=1.275,95%CI=1.010-1.610),history of falls(OR=7.779,95%CI=3.515-17.213),osteoarthritis(OR=1.757,95%CI=1.068-2.892),osteoporosis(OR=2.481,95%CI=1.692-3.638),high-risk behavioral and psychological symptoms of dementia(BPSD)(OR=2.193,95%CI=1.229-3.914),albumin(ALB)level(OR=1.781,95%CI=1.128-2.814),and high-risk medication use(OR=1.466,95%CI=1.191-1.805)were independent risk factors for fall-related hospitalization in patients with AD(P<0.05).Receiver operating characteristic analysis showed that the AUCs of the training set and validation set were 0.753(95%CI=0.711-0.795)and 0.794(95%CI=0.734-0.853),respectively.Calibration curves showed good agreement between the predicted and ideal curves in both sets.DCA showed that the nomogram provided a net benefit greater than 0 when the predicted probability of fall-related hospitalization ranged from 0.1 to 0.8.Conclusion Disease duration,CDR score,history of falls,osteoarthritis,osteoporosis,high-risk BPSD,ALB level,and high-risk medication use were identified as risk factors for fall-related hospitalization in patients with AD.The nomogram model constructed in this study can be used to predict the risk of fall-related hospitalization in this population.