以质量求发展,以服务铸品牌

Journal of Nursing ›› 2023, Vol. 30 ›› Issue (7): 48-52.doi: 10.16460/j.issn1008-9969.2023.07.048

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Risk prediction models for postoperative delirium in elderly patients with hip fracture: a systematic review

YE Lei1a, ZHANG Ai-qin2, RONG Yun1a, XIA Guang-hui1b   

  1. 1a. Dept. of Critical Care Medicine; 1b. Dept. of Nursing Administration, Brain Hospital Affiliated to Nanjing Medical University, Nangjing 210029, China;
    2. Dept. of Burn and Plastic Surgery, General Hospital of Eastern Theater Command, Nanjing 210002, China
  • Received:2022-10-28 Published:2023-05-12

Abstract: Objective To systematically evaluate the risk prediction model for postoperative delirium in elderly patients with hip fracture. Methods We searched the databases of PubMed, Embase, Web of Science, The Cochrane Library, China knowledge Network, Wanfang and VIP from the inception to May 2022 for eligible literature. Two researchers independently extracted the data and PROBAST was used for quality evaluation. Results Eleven studies were included and the area under the ROC curve was 0.67~0.94. The most common predisposing factors of postoperative delirium were age, ASA grading and decreased cognitive reserve, and the promoting factors were waiting time for operation and hypoproteinemia before operation. The prediction performance of 11 models was good, but there was certain bias, mainly ignoring the missing data processing. Most of the predictive factor screening was not combined with clinical professional knowledge, lacking external verification. There were differences in the evaluation tools and time of postoperative delirium in some studies. Conclusion Good prediction performance, low risk of applicability and high risk of bias of the existing models are found. It is still necessary to improve the statistical analysis details such as variable screening, missing data processing, and model performance evaluation, and carry out prospective studies to conduct research on existing models.

Key words: hip fracture, postoperative delirium, risk prediction model, systematic review

CLC Number: 

  • R473.6
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