以质量求发展,以服务铸品牌

护理学报 ›› 2023, Vol. 30 ›› Issue (7): 48-52.doi: 10.16460/j.issn1008-9969.2023.07.048

• 循证护理 • 上一篇    下一篇

老年髋部骨折患者术后谵妄风险预测模型的系统评价

叶磊1a, 张爱琴2, 荣芸1a, 夏广惠1b   

  1. 1.南京医科大学附属脑科医院 a.重症医学科;b.护理部,江苏 南京 210029;
    2.东部战区总医院 烧伤整形科,江苏 南京 210002
  • 收稿日期:2022-10-28 发布日期:2023-05-12
  • 通讯作者: 夏广惠(1969-),女,江苏南京人,本科学历,主任护师,护理部主任。E-mail:755411389@qq.com
  • 作者简介:叶磊(1993-),男,安徽滁州人,硕士,护师。

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

摘要: 目的 系统评价老年髋部骨折术后谵妄风险预测模型。方法 检索PubMed、Embase、Web of Science、The Cochrane Library、中国知网、万方、维普数据库关于老年髋部骨折术后谵妄风险预测模型的研究,检索时限为建库至2022年5月。由2名研究者独立筛选文献和提取数据,采用PROBAST评估工具对纳入文献进行质量评价。结果 共纳入11项研究,ROC曲线下面积为0.67~0.94。常见的术后谵妄易感因素为年龄、ASA分级和认知功能储备减少;促发因素为术前等待时间症、低蛋白血症。11个模型的预测性能较好,但均存在一定的偏倚,主要为未报告数据缺失处理方法,大部分模型预测因子筛选未结合临床专业知识,且缺少模型外部验证,部分研究术后谵妄评估工具、时间存在差异。结论 现有模型整体预测性能较好,适用性风险较低,但偏倚风险较高,仍需完善变量筛选、缺失数据处理及模型效能评价等统计分析细节,开展前瞻性研究,对现有模型进行外部验证。

关键词: 髋部骨折, 术后谵妄, 预测模型, 系统评价

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

中图分类号: 

  • R473.6
[1] 刘杨,罗健,朱佩佩,等. 髋部手术患者术后康复指南的质量评价及内容分析[J]. 护理学报,2021,28(12):56-61.DOI:10.16460/j.issn1008-9969.2021.12.056.
[2] 王天沛,蔡永松,郭华,等.老年髋部骨折患者个体化术后谵妄风险预测模型的构建及验证[J]. 陆军军医大学学报,2022,44(6):563-570.2015.02.002. DOI:10.16016/j.2097-0927.202110045.
[3] 孟恬宇,尹战海,李萌,等.老年髋部骨折术后谵妄的管理[J]. 中华老年多器官疾病杂志,2021,20(9):716-720.DOI:10.11915/J.issn.1671-5403.2021.09.150.
[4] Marcantonio ER.Delirium in hospitalized older adults[J]. N Engl J Med, 2017, 377(15):1456-1466. DOI:10.1056/NEJMcp1605501.
[5] 董碧蓉,岳冀蓉. 老年患者术后谵妄防治中国专家共识[J]. 中华老年医学杂志, 2016, 35(12):1257-1262.DOI:10.3760/cma.J.issn.0254-9026.2016.12.001.
[6] Wang Y, Zhao L, Zhang C,et al.Identification of risk factors for postoperative delirium in elderly patients with hip fractures by a risk stratification index model: a retrospective study[J]. Brain Behav, 2021, 11(12):e32420.DOI:10.1002/brb3.2420.
[7] 陈香萍, 张奕, 庄一渝, 等. PROBAST:诊断或预后多因素预测模型研究偏倚风险的评估工具[J]. 中国循证医学杂志,2020,20(6):737-744.DOI:10.7057/1672-2351.201910087.
[8] Oberai T, Oosterhoff JHF, Woodman R, et al.Development of a dostoperative delirium risk scoring tool using data from the Australian and New Zealand hip fracture registry: an analysis of 6672 patients 2017-2018[J]. Arch Gerontol Geriatr,2021,94:104368.DOI:10.1016/j.archger.2021.104368.
[9] Kim EM, Li G, Kim M.Development of a risk score to predict postoperative delirium in patients with hip fracture[J]. Anesth Analg,2020,130(1):79-86.DOI:10.1213/ANE.0000000000004386.
[10] Uzoigwe CE, O'Leary L, Nduka J, et al. Factors associated with delirium and cognitive decline following hip fracture surgery[J]. Bone Joint J,2020,102-B(12):1675-1681. DOI:10.1302/0301-620X.102B12.BJJ-2019-1537.R3.
[11] Zhang X, Tong DK, Ji F, et al.Predictive nomogram for postoperative delirium in elderly patients with a hip fracture[J]. Injury,2019,50(2):392-397. DOI:10.1016/j.injury.2018.10.034.
[12] Zhao H, You J, Peng Y, et al. Machine learning algorithm using electronic chart-derived data to predict delirium after elderly hip fracture surgeries: a retrospective case-control study[J]. Front Surg, 2021, 13;8:634629. DOI:10.3389/fsurg.2021.634629.
[13] 胡玲,胡三莲,钱会娟. 老年髋部骨折患者术后谵妄发生现况及危险因素分析[J].中国护理管理,2019,19(2):204-210.DOI:10.3969/j.issn.1672-1756.2019.02.010.
[14] 张明媚,朱星波,黄立新. 老年髋部骨折患者术后谵妄的预测模型构建及初步应用[J]. 天津医药,2021,49(6):641-645.DOI:10.11958/20201943.
[15] 熊春红,熊淑明,王小云,等. 结合术前营养评估结果的老年髋部骨折术后谵妄Nomogram模型构建[J]. 护理研究,2020,34(14):2457-2462.DOI:10.12102/j.issn.1009-6493.2020.14.005.
[16] 苏保童,王翰宇,许忆浪,等. 基于医院病历资料构建老年髋部骨折术后谵妄Nomogram预测模型[J]. 中国组织工程研究,2021,25(24):3844-3849. DOI:2095-4344(2021)24-03844-06.
[17] 王树相,陈鑫磊,徐超. 个体化预测老年髋部骨折患者术后谵妄风险[J]. 中国矫形外科杂志,2019,27(6):542-548.DOI:1005-8478(2019)06-0542-07.
[18] Bai J, Liang Y, Zhang P, et al.Association between postoperative delirium and mortality in elderly patients undergoing hip fractures surgery: a Meta-analysis[J]. Osteoporos Int, 2020,31(2):317-326.DOI:10.1007/s00198-019-05172-7.
[19] Hölttä EH, Laurila JV, Laakkonen ML, et al.Precipitating factors of delirium: stress response to multiple triggers among patients with and without dementia[J]. Exp Gerontol, 2014, 59:42-46. DOI:10.1016/j.exger.2014.04.014.
[20] Hongisto MT, Nuotio MS, Luukkaala T, et al.Delay to surgery of less than 12 hours is associated with improved short- and long-term survival in moderate- to high-risk hip fracture patients[J].Geriatr Orthop Surg Rehabil,2019,10:2151459319853142.DOI:10.1177/2151459319853142.
[21] Devinney MJ, Mathew JP, Miles B.Postoperative delirium and postoperative cognitive dysfunction[J]. Anesthesiology, 2018, 129(3):389-391. DOI:10.1097/ALN.0000000000002338.
[22] Clemmesen CG, Palm H, Foss NB.Delay in detection and treatment of perioperative anemia in hip fracture surgery and its impact on postoperative outcomes[J]. Injury,2019,50(11):2034-2039. DOI:10.1016/j.injury.2019.09.001.
[23] 张菊明. 术前营养状态与老年髋部骨折患者术后谵妄的相关性分析[J]. 中国老年保健医学,2021, 19(1):63-66.DOI:10.3969/j.issn.1672-2671.2021.01.020.
[24] 陈俊杉,余金甜,赵思雨,等. ICU患者谵妄风险预测模型研究进展[J]. 护理学报,2019,26(5):15-19. DOI:10.16460/j.issn1008-9969.2019.05.015.
[25] Zhou ZR, Wang WW, Li Y, et al.In-depth mining of clinical data: the construction of clinical prediction model with R[J]. Ann Transl Med, 2019, 7(23):796. DOI:10.21037/atm.2019.08.63.
[26] Collins GS, Reitsma JB, Altman DG, Moons KG.Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis(TRIPOD): the TRIPOD statement[J].BMJ,2015,350:g7594.DOI:10.1136/bmj.g7594.
[27] Li QH, Yu L, Yu ZW, et al.Relation of postoperative serum S100A12 levels to delirium and cognitive dysfunction occurring after hip fracture surgery in elderly patients[J]. Brain Behav, 2019, 9(1):e01176. DOI:10.1002/brb3.1176.
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