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Introduction <p>Delirium, a serious geriatric syndrome, is prevalent in nursing homes. This study aims to identify the prevalence, associated factors for delirium among older residents in Chinese nursing homes, and to develop and validate a nomogram for identifying high-risk individuals for delirium.</p> Methods <p>A cross-sectional survey was conducted in eight nursing homes in Xi'an, Shaanxi Province, China, using the 3-Minute Diagnostic Interview for CAM-defined Delirium (3D-CAM). Data from the first six nursing homes were utilized as the training dataset, while data from the remaining two nursing homes were employed for external validation. Initially, variable selection was performed using LASSO regression on the training set, followed by identification of significant predictors of delirium through multivariate logistic regression. Subsequently, a prediction model was developed based on the selected factors and evaluated for its performance on the training set. Finally, external validation of the model was conducted using the validation set.</p> Results <p>The study included a total of 406 older nursing home residents, of whom 125 (30.79%) were diagnosed with delirium. Using LASSO and multivariate logistic regression analysis, factors dementia, MNA, ADL, frailty score, and antipsychotics were identified as significantly associated with the presence of delirium in older residents. The AUC values of the prediction model were 0.836 (95% CI 0.788–0.884) for the training set and 0.768 (95% CI 0.676–0.860) for the external validation set. Additionally, the model demonstrated good calibration and clinical utility.</p> Conclusion <p>This nomogram exhibited good predictive performance for delirium in older nursing home residents, facilitating the early identification of high-risk individuals and potentially enhancing clinical outcomes. Future multi-center studies with larger sample sizes are required to confirm the associations between individual variables and delirium, as well as to further validate the model.</p>

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Prevalence, associated factors, and predictive nomogram of delirium among older nursing home residents in China

  • 期刊论文

  • 发表日期：

    2026年3月1日

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  • Rui Ma,
  • Ziying Wen,
  • Conglei Hu,
  • Peng Wang,
  • Fengxia Han,
  • Cui Li,
  • Huan Li,
  • Yang Chen,
  • Shiren Sun,
  • Xiaoxuan Ning

摘要

Introduction

Delirium, a serious geriatric syndrome, is prevalent in nursing homes. This study aims to identify the prevalence, associated factors for delirium among older residents in Chinese nursing homes, and to develop and validate a nomogram for identifying high-risk individuals for delirium.

Methods

A cross-sectional survey was conducted in eight nursing homes in Xi'an, Shaanxi Province, China, using the 3-Minute Diagnostic Interview for CAM-defined Delirium (3D-CAM). Data from the first six nursing homes were utilized as the training dataset, while data from the remaining two nursing homes were employed for external validation. Initially, variable selection was performed using LASSO regression on the training set, followed by identification of significant predictors of delirium through multivariate logistic regression. Subsequently, a prediction model was developed based on the selected factors and evaluated for its performance on the training set. Finally, external validation of the model was conducted using the validation set.

Results

The study included a total of 406 older nursing home residents, of whom 125 (30.79%) were diagnosed with delirium. Using LASSO and multivariate logistic regression analysis, factors dementia, MNA, ADL, frailty score, and antipsychotics were identified as significantly associated with the presence of delirium in older residents. The AUC values of the prediction model were 0.836 (95% CI 0.788–0.884) for the training set and 0.768 (95% CI 0.676–0.860) for the external validation set. Additionally, the model demonstrated good calibration and clinical utility.

Conclusion

This nomogram exhibited good predictive performance for delirium in older nursing home residents, facilitating the early identification of high-risk individuals and potentially enhancing clinical outcomes. Future multi-center studies with larger sample sizes are required to confirm the associations between individual variables and delirium, as well as to further validate the model.