Journal of Nursing ›› 2023, Vol. 30 ›› Issue (7): 48-52.doi: 10.16460/j.issn1008-9969.2023.07.048
Previous Articles Next Articles
YE Lei1a, ZHANG Ai-qin2, RONG Yun1a, XIA Guang-hui1b
CLC Number:
[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. |
[1] | ZHENG Xiao-jing, YAN Hong-hong, LI Hui-jing, CHEN Si-juan, CHEN Xiu-mei. Construction and verification of risk prediction model for hypoglycemia with hepatocellular carcinoma [J]. Journal of Nursing, 2025, 32(4): 53-58. |
[2] | ZHOU Yue, ZHANG Jie, PAN Yu-fan, DAI Yu, SUN Yu-jian, XIAO Yi, YU Yu-feng. Prediction models for risk of acquired weakness in mechanically ventilated patients: a systematic review [J]. Journal of Nursing, 2024, 31(6): 56-61. |
[3] | WU Lin-mei, LIANG Zhi-jin, LIU Rui-jie, ZHONG Jing-jing, QIU Yu-hua. Barriers to and facilitators of exercise rehabilitation in patients with COPD: a CFIR-based systematic review [J]. Journal of Nursing, 2024, 31(5): 44-49. |
[4] | WANG Wei, ZHOU Yan-ling, XUE Wen-ping, ZHANG Lin-lin, LIN Shu-qiu. Frailty assessment tools for elderly patients with hip fracture [J]. Journal of Nursing, 2024, 31(4): 42-47. |
[5] | WANG Yue, LIU Guo-qing, NIU Cong-ying, ZHANG Zhen-wei, SUN Jian, CHU You-ai, QIN Han-zhi. Risk prediction models of 30-day mortality after surgery in patients with hip fractures: a scoping review [J]. Journal of Nursing, 2024, 31(24): 51-56. |
[6] | YANG Li-na, HUANG Rong, YAO Mei-qi, WANG Zhu-qing, XU Yi-Ting, XIE Pei-min. Risk prediction models for postpartum breastfeeding behavior interruption: a systematic review [J]. Journal of Nursing, 2024, 31(23): 59-65. |
[7] | WANG Ai, ZHOU Bing-qian, CAO Hong. Application of Fracture Liaison Service in Patients with Osteoporotic Hip Fracture:a Scoping Review [J]. Journal of Nursing, 2024, 31(22): 48-53. |
[8] | ZHANG Yi-ran, WANG Kang-mei, ZHU Sheng-cai, ZHU Lu, HUANG Yi. COSMIN guideline-based systematic review of Chinese version of self-management assessment tool for hypertension patients [J]. Journal of Nursing, 2024, 31(19): 52-57. |
[9] | ZHOU Meng-juan, ZHU Xiao-li, ZHANG Tai, DUAN Jian-feng, LUO Yu-mei, MA Wei-li, LU Han, SHI Ting-ting, YANG Yi-lin, LI Ting, CHA Yao-lei, ZHAO Yuan. Construction and verification of predictive model for prolonged length of stay in patients with non-severe acute pancreatitis [J]. Journal of Nursing, 2023, 30(9): 7-12. |
[10] | ZHU Ming-yue, DING Xiao-tong, SHI Ji-yuan, Li Zheng. Effects of self-perception of aging on cognitive function in elderly people: a systematic review [J]. Journal of Nursing, 2023, 30(8): 52-57. |
[11] | YANG Nan-nan, JIANG Hui-ping, SHI Ting-qi. Machine learning-based a risk predictive model for deep vein thrombosis in hospitalized patients: a systematic review [J]. Journal of Nursing, 2023, 30(23): 44-49. |
[12] | WEI Jia-hao, BU Meng-ru, BAI Zi-ye, ZHOU Jin. Measurement property of traditional Chinese medicine clinical effectiveness evaluation scales for lung cancer: a systematic review [J]. Journal of Nursing, 2023, 30(22): 56-62. |
[13] | WANG Yao, GUAN Yu-xiang, ZHENG Jing, XU Juan, WANG Chao. Systematic reviews of continuous glucose monitoring in patients with diabetes: an overview [J]. Journal of Nursing, 2023, 30(21): 39-45. |
[14] | ZHU Ming-yue, SHI Ji-yuan, LI Zheng. Effectiveness of computerized cognitive training for patients with cognitive dysfunction: an overview of systematic reviews [J]. Journal of Nursing, 2023, 30(21): 46-53. |
[15] | ZHOU Fei-yang, DENG Lu, LONG Ke-yu, YANG Ting-ting, XIE Lin-lin, LV Qing, GUO Chun-bo. Risk prediction model for cognitive frailty in elderly: a systematic review [J]. Journal of Nursing, 2023, 30(19): 45-50. |
|