以质量求发展，以服务铸品牌

护理学报 ›› 2023, Vol. 30 ›› Issue (19): 45-50.doi: 10.16460/j.issn1008-9969.2023.19.045

• 循证护理 • 上一篇    下一篇

老年人认知衰弱风险预测模型的系统评价

周飞洋1,2, 邓露1, 龙柯宇2, 杨婷婷2, 谢琳琳2, 吕倩1, 郭春波1   

  1. 1.中南大学湘雅二医院临床护理教研室,湖南 长沙 410011;
    2.中南大学湘雅护理学院,湖南 长沙 410013
  • 收稿日期:2023-09-10 出版日期:2023-10-10 发布日期:2023-11-07
  • 通讯作者: 郭春波（1985-）,女,湖南长沙人,硕士,主管护师。E-mail: 154780593@qq.com
  • 作者简介:周飞洋（1996-）,女,湖南岳阳人,本科学历,硕士研究生在读,护师。
  • 基金资助:
    湖南省财政厅2022年高校改革发展项目湘财教指[（2022）75号]; 中南大学湘雅二医院护理科研基金[2022-HLKY-19,院行字（2017）88号]

Risk prediction model for cognitive frailty in elderly: a systematic review

ZHOU Fei-yang1,2, DENG Lu1, LONG Ke-yu2, YANG Ting-ting2, XIE Lin-lin2, LV Qing1, GUO Chun-bo1   

  1. 1. Teaching and Research Office of Clinical Nursing, the Second Xiangya Hospital of Central South University, Changsha 410011, China;
    2. Xiangya Nursing School, Central South University, Changsha 410013, China
  • Received:2023-09-10 Online:2023-10-10 Published:2023-11-07

PDF (PC)

202

赞

摘要： 目的 系统评价老年人认知衰弱风险预测模型,以期为认知衰弱的风险预测提供证据支持。方法 系统检索PubMed、Web of Science、Cochrane Library、Embase、CINAHL、中国知网、万方数据库、中国生物医学文献数据库发表的老年人认知衰弱风险预测模型相关文献。检索时限为建库至2023年8月,语种限定为中文和英文。根据纳入与排除标准进行文献筛选,由2名研究者独立进行纳入文献的数据提取和质量评价。提取作者、发表年份、国家、随访时间、研究对象、研究类型、预测因子、模型构建方法及预测效能等数据。结果 共纳入16项研究,老年人认知衰弱等发生率为4.8%~65.3%。纳入研究的适用性较好,但总体偏倚风险较高。多个模型重复报告的预测因子有：年龄、营养、活动能力和多病等。结论 老年人认知衰弱风险预测模型尚处于发展阶段,部分模型存在显著的方法学缺陷和高偏倚风险。未来的研究中应借助大数据,构建内容全面的老年人认知衰弱风险预测模型并加以验证,形成可行性高的信息化风险预警系统。

关键词: 老年, 认知衰弱, 预测模型, 风险评估, 系统评价

Abstract: Objective To systematically review the risk prediction models of cognitive frailty in the elderly, and to provide evidence support for the risk prediction of cognitive frailty. Methods Systematic search of the literature regarding risk prediction models of cognitive frailty in the elderly in PubMed, Web of Science, the Cochrane Library, Embase, CINAHL, CNKI, Wanfang database, and SinoMed was conducted. The search time was from the inception of the database to August 2023, and the language was limited to Chinese and English. Literature screening was conducted according to the inclusion and exclusion criteria, and data extraction and quality evaluation of the included literature were carried out by two researchers independently. The information of author, year of publication, country, follow-up time, study object, study type, predictors, model construction method, and predictive efficacy was extracted. Results Sixteen studies were included, all of which were studies on model development. The prevalence of cognitive frailty in the elderly ranged from 4.8% to 65.3%. The applicability was good in included studies, but the overall risk of bias was high. Predictors reported repeatedly by multiple models were age, nutrition, activity of daily living and multimorbidity. Conclusion Risk prediction model of cognitive frailty in the elderly is still in the developmental stage, and some models have significant methodological flaws and a high risk of bias. Future research should focus on the establishment of comprehensive risk prediction models and their validation with the help of big data to form an informative risk warning system with high feasibility.

Key words: elderly people, cognitive frailty, prediction model, risk assessment, systematic review

中图分类号: 

  • R473.59
[1] Kelaiditi E, Cesari M, Canevelli M, et al.Cognitive frailty: rational and definition from an (I.A.N.A./I.A.G.G.) international consensus group[J]. J Nutr Health Aging, 2013, 17(9):726-734.DOI:10.1007/s12603-013-0367-2.
[2] Bu ZH, Huang AL, Xue MT, et al.Cognitive frailty as a predictor of adverse outcomes among older adults: a systematic review and Meta-analysis[J]. Brain Behav, 2021, 11(1): e01926.DOI:10.1002/brb3.1926.
[3] Xu WH, Li Y, Hu Y, et al.Association of frailty with recovery from disability among community-dwelling Chinese older adults: China health and retirement longitudinal study[J]. BMC Geriatr, 2020, 20(1):1-7.DOI:10.1186/s12877-020-01519-6.
[4] Zhang XM, Jiao J, Zhu C, et al.Cognitive frailty and 30-day mortality in a national cohort of older Chinese inpatients[J]. Clin Interv Aging, 2021, 16(1):389-401.DOI:10.2147/CIA.S294106.
[5] Zhang XM, Yuan L, Quo N, et al.Cognitive frailty and falls in a national cohort of older Chinese inpatients[J]. J Nutr Health Aging, 2021, 25(8):993-998.DOI:10.1007/s12603-021-1670-y.
[6] Zheng LF, Li GC, Gao DW, et al.Cognitive frailty as a predictor of dementia among older adults: a systematic review and Meta-analysis[J]. Arch Gerontol Geriatr, 2020, 87(3): 103997.DOI:10.1016/j.archger.2019.103997.
[7] De Roeck EE, van der VA, Engelborghs S, et al. Exploring cognitive frailty: prevalence and associations with other frailty domains in older people with different degrees of cognitive impairment[J]. Gerontology, 2020, 66(1):55-64.DOI:10.1159/000501168.
[8] Mantovani E, Zucchella C, Schena F, et al.Towards a redefinition of cognitive frailty[J]. J Alzheimers Dis, 2020, 76(3): 831-843.DOI:10.3233/jad-200137.
[9] 陶立元, 刘珏, 曾琳, 等. 针对个体的预后或诊断多因素预测模型报告规范(TRIPOD)解读[J]. 中华医学杂志, 2018, 98(44):3556-3560. DOI:10.3760/cma.j.issn.0376-2491.2018.44.002.
[10] 张莹莹, 张广清, 李佳雨, 等. 基于循证构建ICU亚谵妄综合征早期识别及管理方案[J]. 护理学报, 2022, 29(17): 7-12.DOI:10.16460/j.issn1008-9969.2022.17.007.
[11] Wolff RF, Moons KG M, Riley RD, et al.PROBAST: a tool to assess the risk of bias and qpplicability of prediction model studies[J]. Ann Intern Med, 2019, 170(1): 51-58.DOI:10.7326/m18-1376.
[12] Tseng SH, Liu LK, Peng LN, et al.Development and validation of a tool to screen for cognitive frailty among community-dwelling elders[J]. J Nutr Health Aging, 2019, 23(9):904-909.DOI:10.1007/s12603-019-1235-5.
[13] Navarro-Pardo E, Facal D, Campos-Magdaleno M, et al.Prevalence of cognitive frailty,do psychosocial-related factors matter?[J].Brain Sci,2020,10(12).DOI:10.3390/brainsci10120968.
[14] Rivan NFM, Shahar S, Rajab NF, et al.Incidence and predictors of cognitive frailty among older adults: a community-based longitudinal study[J]. Int J Environ Res Public Health, 2020, 17(5):1547.DOI:10.3390/ijerph17051547.
[15] Sargent L, Nalls M, Amella EJ, et al.Shared mechanisms for cognitive impairment and physical frailty: a model for complex systems[J]. Alzheimers Dement (N Y), 2020, 6(1): e12027.DOI:10.1002/trc2.12027.
[16] 问芳芳, 程苗苗, 赵翠芬, 等. 老年稳定性冠心病患者认知衰弱风险预测模型的建立[J]. 护理学杂志, 2021, 36(10):21-26.DOI:10.3870/j.issn.1001-4152.2021.10.021.
[17] 杨振, 张会君. 社区老年慢性病患者认知衰弱风险预测模型的构建及验证[J]. 护理学杂志, 2021, 36(12): 86-89.DOI:10.3870/j.issn.1001-4152.2021.12.086.
[18] 陈颖勇, 张正敏, 左倩倩, 等. 社区老年人认知衰弱风险预测模型的构建及验证[J]. 中华护理杂志, 2022, 57(2): 197-203.DOI:10.3761/j.issn.0254-1769.2022.02.012.
[19] 王彦, 刘媛. 住院老年高血压患者认知衰弱影响因素及列线图模型构建[J]. 实用心脑肺血管病杂志, 2022, 30(7): 54-59.DOI:10.12114/j.issn.1008-5971.2022.00.167.
[20] Yuan MQ, Xu CH, Fang Y.The transitions and predictors of cognitive frailty with multi-state Markov model: a cohort study[J].BMC Geriatr, 2022,22(1):550.DOI:10.1186/s12877-022-03220-2.
[21] Luo B, Luo Z, Zhang X, et al.Status of cognitive frailty in elderly patients with chronic kidney disease and construction of a risk prediction model: a cross-sectional study[J]. BMJ Open, 2022, 12(12):e060633.DOI:10.1136/bmjopen-2021-060633.
[22] 陈程程, 伍丹智, 孙苞苞, 等. 老年人骨科术后认知衰弱风险预测模型的构建[J]. 中国基层医药, 2023, 30(2): 287-291.DOI:10.3760/cma.j.cn341190-20220802-00627.
[23] 李梅. 老年维持性血液透析病人认知衰弱风险预测模型的建立[J]. 全科护理, 2023, 21(10):1392-1396.DOI:10.12104/j.issn.1674-4748.2023.10.025.
[24] 秦僮, 李海娜, 王纪哲, 等. 维持性血液透析患者认知衰弱风险列线图模型构建与验证[J]. 齐鲁护理杂志, 2023,29(7):13-18.DOI:10.3969/j.issn.1006-7256.2023.07.004.
[25] Bai A, Zhao M, Zhang T, et al.Development and validation of a nomogram-assisted tool to predict potentially reversible cognitive frailty in Chinese community-living older adults[J]. Aging Clin Exp Res, 2023(7):1-11.DOI:10.1007/s40520-023-02494-9.
[26] Lee SY, Nyunt MS,Gao Q, et al. Risk factors of progression to cognitive frailty: Singapore longitudinal ageing study 2[J/OL].Gerontology2023,[2023-09-05]. https://doi.org/10.1159/000533635.
[27] Peng SZ, Juan Z, Shuzhen X, et al.Construction and validation of cognitive frailty risk prediction model for elderly patients with multimorbidity in Chinese community based on non-traditional factors[J]. BMC Psychiatry, 2023, 23(1): 266.DOI:https://doi.org/10.1186/s12888-023-04736-6.
[28] Ogundimu EO, Altman DG, Collins GS.Adequate sample size for developing prediction models is not simply related to events per variable[J]. J Clin Epidemiol, 2016, 76(8): 175-182.DOI:10.1016/j.jclinepi.2016.02.031.
[29] 王伟华, 寇京莉, 张佟, 等. 老年科住院患者认知衰弱状况调查及其影响因素分析[J]. 中华现代护理杂志, 2022, 28(3):296-301.DOI:10.3760/cma.j.cn115682-20210604-02426.
[30] 刘泳秀, 余莉, 韩婷, 等. 乌鲁木齐市住院老年患者认知衰弱现状及影响因素研究[J]. 中国实用护理杂志, 2021, 37(6):424-430.DOI:10.3760/cma.j.cn211501-20200615-02775.
[31] 瞿茜, 代玲, 张萍, 等. 老年人认知衰弱影响因素的Meta分析[J]. 现代临床护理, 2022,21(2):54-62.DOI:10.3969/j.issn.1671-8283.2022.02.010.
[32] 刘玥婷, 范俊瑶, 赵慧敏, 等. 老年人认知衰弱与失能关系的研究进展[J]. 实用老年医学, 2020, 34(2):190-193.DOI:10.3969/j.issn.1003-9198.2020.02.024.
[33] 徐传海, 袁满琼, 方亚. 我国老年人认知衰弱转移规律及其影响因素研究[J]. 中华流行病学杂志, 2022, 43(5):722-727.DOI:10.3760/cma.j.cn112338-20211013-00792.
[34] 姜文慧, 李烨, 贾敏, 等. 抑郁障碍与精神分裂症患者认知功能损害分析[J]. 临床精神医学杂志, 2022, 32(4): 301-303.DOI:10.3969/j.issn.1005-3220.2022.04.014.
[35] 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:10.7507/1672-2531.201910087.
[36] Arai H, Satake S, Kozaki K.Cognitive frailty in geriatrics[J]. Clin Geriatr Med, 2018, 34(4):667-675.DOI:10.1016/j.cger.2018.06.011.
[37] Ruan Q, Yu Z, Chen M, et al.Cognitive frailty, a novel target for the prevention of elderly dependency[J].Ageing Res Rev, 2015, 20(2):1-10.DOI:10.1016/j.arr.2014.12.004.
[1] 周梦娟, 朱晓丽, 张态, 段剑锋, 罗宇梅, 马维莉, 陆涵, 史婷婷, 杨怡霖, 李婷, 查耀蕾, 赵媛. 急性胰腺炎非重症患者住院时间延长预测模型的构建及验证[J]. 护理学报, 2023, 30(9): 7-12.
[2] 周祎敏, 姚宇哲, 叶旭春. 社区老年人对照护机器人感知与期望的Meta整合[J]. 护理学报, 2023, 30(9): 43-48.
[3] 孙小卫, 王燕, 闵春燕, 段菲, 肖江琴. 322例老年COPD患者生活空间现状及影响因素分析[J]. 护理学报, 2023, 30(8): 1-6.
[4] 王美慧, 李雨馨, 潘一佳, 张琼琼, 汤胜男, 王庆妍. 元宇宙技术应用于老年痴呆患者照顾者的研究进展[J]. 护理学报, 2023, 30(8): 44-47.
[5] 朱明月, 丁晓彤, 史纪元, 李峥. 自我感知老化对老年人认知功能影响的系统评价[J]. 护理学报, 2023, 30(8): 52-57.
[6] 任影, 于卫华, 张利, 郑俊俊, 邓曼, 杨侠, 张雨溪. 社区老年人生活空间水平现状及影响因素分析[J]. 护理学报, 2023, 30(7): 11-15.
[7] 叶磊, 张爱琴, 荣芸, 夏广惠. 老年髋部骨折患者术后谵妄风险预测模型的系统评价[J]. 护理学报, 2023, 30(7): 48-52.
[8] 贺丽春. 失智症老年人蒙台梭利型非药物干预研究进展[J]. 护理学报, 2023, 30(5): 38-41.
[9] 张红霞, 杨巧巧, 党晨珀, 张文芳, 张晓敏, 邵转兰. 老年性骨质疏松患者骨折风险预测工具的范围综述[J]. 护理学报, 2023, 30(3): 57-62.
[10] 周巧, 吴俊琪, 张翠娥, 游君, 许湘华. 老年脑卒中幸存者团体接纳承诺疗法干预方案构建与应用[J]. 护理学报, 2023, 30(19): 7-12.
[11] 董岩, 朱思颖, 王虹, 范甜甜, 黎玉梅, 韩媛. 青少年癌症幸存者重返校园体验质性研究的Meta整合[J]. 护理学报, 2023, 30(19): 56-61.
[12] 李娜, 黄懿炘, 刘美兰, 邓银辉, 邹小芳. 老年人健康促进行为量表的汉化及信效度评价[J]. 护理学报, 2023, 30(17): 5-10.
[13] 刘雪琴, 刘晓辉, 平智广. 维持性血液透析患者营养不良风险列线图模型及在线计算器的构建与验证研究[J]. 护理学报, 2023, 30(16): 12-18.
[14] 王若仙, 何斌, 王玉环, 刘柯汝, 唐雪婷, 赵淑华, 魏杉杉. 社区老年人骨质疏松性骨折风险感知偏差现况及影响因素分析[J]. 护理学报, 2023, 30(15): 1-5.
[15] 彭仁梅, 钱荣, 李亚军, 段缓, 陈莲芳, 刘玉文. ICU转出患者睡眠障碍风险列线图预测模型的构建与验证[J]. 护理学报, 2023, 30(15): 16-22.
Viewed
Full text


Abstract

Cited
  Shared   
[1] 骆佳慧, 罗园园, 方庆虹, 叶艳欣, 刘素婷, 杨智慧, 缪景霞, 张立力. 肺癌患者认知功能的现状及影响因素分析[J]. 护理学报, 2023, 30(18): 1 -5 .
[2] 冯小莹, 吴培香, 孙雪琪, 苑潇敏, 陈兰波. 冠心病经皮冠状动脉介入术后患者中西医结合康复护理质量评价指标体系的构建[J]. 护理学报, 2023, 30(19): 1 -6 .
[3] 周巧, 吴俊琪, 张翠娥, 游君, 许湘华. 老年脑卒中幸存者团体接纳承诺疗法干预方案构建与应用[J]. 护理学报, 2023, 30(19): 7 -12 .
[4] 廖少娜, 宋慧娟, 龚雪, 黄文嫣, 杨晨, 王丽, 陈赟. 广东省三级甲等综合医院1 972名护士对自身医院磁性水平的感知[J]. 护理学报, 2023, 30(19): 13 -18 .
[5] 杨梅, 王美, 张秀婷, 房文杰, 张艺琳, 范秀珍. 本科实习护生职业探索行为的潜在剖面分析及影响因素研究[J]. 护理学报, 2023, 30(19): 19 -23 .
[6] 伦冰, 杨晓宇, 孙丹丹, 任丽杰, 马硕. 助产专科护士原位模拟培训模式的构建及应用[J]. 护理学报, 2023, 30(19): 24 -27 .
[7] 刘源源, 邱文慧, 张艺雄. 人际信任及班级氛围在中医药院校护生知识隐藏行为与创新能力之间的链式中介作用[J]. 护理学报, 2023, 30(19): 28 -31 .
[8] 余卫卫, 夏美霞, 谢庆磊, 丁世云, 戚方圆, 王桂花. 构建腹膜透析患者腹腔压力测量方案的品管圈实践[J]. 护理学报, 2023, 30(19): 32 -36 .
[9] 万丽丽, 宋春美, 郑翠霞. 消化性溃疡患者自我报告结局评估工具的研究进展[J]. 护理学报, 2023, 30(19): 37 -40 .
[10] 张馥丽, 王丹妮, 叶红, 蔡姣芝. 胃癌患者报告结局评估工具的研究进展[J]. 护理学报, 2023, 30(19): 41 -44 .