ArticleNeuropsychiatric disease and treatment2023
Development and Validation of a Delirium Risk Prediction Model for Elderly Patients Undergoing Elective Orthopedic Surgery.
Article in Neuropsychiatric disease and treatment, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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6 citing papers in PubMed, 6 citations in OpenAlex.
- Article
- A machine-learning-assisted logistic regression model for predicting post-operative delirium in older adults undergoing total knee arthroplasty.Frontiers in neurology · 2026Article
- SURGE-ahead postoperative delirium prediction: external validation and open-source library.European geriatric medicine · 2025Article
- Determining the ground truth for the prediction of delirium in adult patients in acute care: a scoping review.JAMIA open · 2025Review
- Risk factors and prediction model of delirium in elderly patients after hip arthroplasty.Pakistan journal of medical sciences · 2024Article
- The use of drugs to prevent postoperative delirium in elderly patients with radical esophagectomy.Esophagus : official journal of the Japan Esophageal Society · 2024Article
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Authors and funding
10 authors at 1 institution in 1 country.
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Abstract
Purpose: This study aimed to develop and validate a post-operative delirium (POD) nomogram in a population of elderly patients undergoing elective orthopedic surgery. Patients and Methods: A predictive model was developed based on a training dataset of 474 elderly patients undergoing elective orthopedic surgery from March 2021 to May 2022. POD was identified using the Confusion Assessment Methods (CAM). The least absolute shrinkage and selection operator (LASSO) method was used to screen risk factors, and prediction models were created by combining the outcomes with logistic regression analysis. We employ bootstrap validation for internal validation to examine the model's repeatability. The results were validated using a prospective study on 153 patients operated on from January 2022 to May 2022 at another institution. Results: The predictors in the POD nomogram included age, the Mini-Mental State Examination(MMSE), sleep disorder, neurological disorders, preoperative serum creatinine (Pre-SCR), and ASA classification. The c-index of the model was 0.928 (95% confidence interval 0.898 ~ 0.957) and the bootstrap validation still achieved a high c-index of 0.912. The c-index of the external validation was 0.921. The calibration curve for the diagnostic probability showed good agreement between prediction by nomogram and actual observation. Conclusion: By combining preoperative and intraoperative clinical risk factors, we created a POD risk nomogram to predict the probability of POD in elderly patients who undergo elective orthopedic surgery. It could be a tool for guiding individualized interventions.
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