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心力衰竭患者常合并衰弱症状，对心力衰竭患者的衰弱状态进行早期评估，可有效改善其预后。然而，传统的衰弱评估工具存在主观性强、针对性不足等问题。机器学习作为人工智能的重要分支，在心力衰竭患者衰弱的精准评估中展现出临床价值。本研究综述了机器学习在该领域的应用现状，具体包括构建衰弱评估指数、预测衰弱评估量表得分、评估不同衰弱表型，以及结合可穿戴设备、语音生物标志物识别衰弱，并探讨了其在应用中存在的挑战，旨在为未来推动多模态数据融合，建立心力衰竭合并衰弱患者的精准化、智能化评估体系提供参考。
Abstract：Patients with heart failure are often comorbid with frailty symptoms. Early assessment of frailty status in these patients can effectively improve their prognosis. However, traditional frailty assessment tools have issues such as strong subjectivity and insufficient specificity for heart failure patients. As an important branch of artificial intelligence, machine learning has demonstrated clinical value in the precise assessment of frailty in heart failure patients. This study reviews the current application status of machine learning in this field, specifically including the construction of frailty assessment indices, prediction of frailty assessment scale scores, evaluation of different frailty phenotypes, and identification of frailty by combining wearable devices and voice biomarkers. It also discusses the challenges in its application, aiming to provide a reference for promoting multimodal data fusion and establishing a precise and intelligent assessment system for patients comorbid with heart failure and frailty in the future.
[1]MCDONAGH J, FERGUSON C, NEWTON P J. Frailty assessment in heart failure:an overview of the multi-domain approach[J]. Curr Heart Fail Rep, 2018,15(1):17-23.
[2]RAMONFAUR D, BUCKLEY L F, ARTHUR V, et al. High throughput plasma proteomics and risk of heart failure and frailty in late life[J]. JAMA Cardiol, 2024,9(7):649-658.
[3]TANG M, ZHAO R, LV Q. Status and influencing factors of frailty in hospitalized patients with chronic heart failure:a cross-sectional study[J]. J Clin Nurs, 2025,34(1):194-203.
[4]宋健屏，柯羽婷，金金花.心力衰竭患者衰弱评估工具的范围综述[J].全科医学临床与教育，2025,23(2):164-168.
[5]国家卫生健康委员会办公厅，国家中医药局综合司，国家疾病预防控制局综合司.卫生健康行业人工智能应用场景参考指引[EB/OL].(2024-11-06)[2025-06-10]. https：//www. nhc.gov. cn/guihuaxxs/c100133/202411/3dee425b8dc34f739d63483c4e5c334c. shtml.
[6]REHMAN M U, NASEEM S, BUTT A U R, et al. Predicting coronary heart disease with advanced machine learning classifiers for improved cardiovascular risk assessment[J]. Sci Rep, 2025,15(1):13361.
[7]HAMID A, SEGAR M W, BOZKURT B, et al. Machine learning in the prevention of heart failure[J]. Heart Fail Rev, 2025,30(1):117-129.
[8]KOKORI E, PATEL R, OLATUNJI G, et al. Machine learning in predicting heart failure survival:a review of current models and future prospects[J]. Heart Fail Rev, 2025,30(2):431-442.
[9]DU C, ZHANG Z, LIU B, et al. Explainable machine learning model for pre-frailty risk assessment in community-dwelling older adults[J]. Health Care Sci, 2024,3(6):426-437.
[10]OBERMEYER Z, EMANUEL E J. Predicting the future-big data, machine learning, and clinical medicine[J]. N Engl J Med,2016,375(13):1216-1219.
[11]RICHENS J G, LEE C M, JOHRI S. Improving the accuracy of medical diagnosis with causal machine learning[J]. Nat Commun, 2020,11(1):3923.
[12]REDDY B S. Advancements in machine learning:a comprehensive exploration of methods, applications,and future perspectives[J]. Int J Sci Eng Technol, 2024,4(1):1-4.
[13]NAEEM S, ALI A, ANAM S, et al. An unsupervised machine learning algorithms:comprehensive review[J]. Int J Comput Digit Syst, 2023,13(1):911-921.
[14]SMITI A. When machine learning meets medical world:current status and future challenges[J]. Comput Sci Rev, 2020,37:100280.
[15]葛恭豪.机器学习算法原理及效率分析[J].电子世界，2018,40(1):65-66.
[16]侯钊，双卫兵.机器学习算法的概述及其在生物医学中的应用[J].中国医学工程，2025,33(3):72-78.
[17]SZCZEPANOWSKI R, UCHMANOWICZ I, PASIECZNA-DIXIT A H, et al. Application of machine learning in predicting frailty syndrome in patients with heart failure[J]. Adv Clin Exp Med,2024,33(3):309-315.
[18]MITNITSKI A B, MOGILNER A J, ROCKWOOD K. Accumulation of deficits as a proxy measure of aging[J]. ScientificWorldJournal, 2001,1:323-336.
[19]DANIELS R, VAN ROSSUM E, BEURSKENS A, et al. The predictive validity of three self-report screening instruments for identifying frail older people in the community[J]. BMC Public Health, 2012,12:69.
[20]RAZJOUYAN J, HORSTMAN M J, ORKABY A R, et al. Developing a parsimonious frailty index for older, multimorbid adults with heart failure using machine learning[J]. Am J Cardiol,2023,190:75-81.
[21]UELAND T, GULLESTAD L, NYMO S H, et al. Inflammatory cytokines as biomarkers in heart failure[J]. Clin Chim Acta,2015,443:71-77.
[22]ANKER S D, NEGASSA A, COATS A J, et al. Prognostic importance of weight loss in chronic heart failure and the effect of treatment with angiotensin-convertingenzyme inhibitors:an observational study[J]. Lancet, 2003,361(9363):1077-1083.
[23]JU C, ZHOU J, LEE S, et al. Derivation of an electronic frailty index for predicting short-term mortality in heart failure:a machine learning approach[J]. ESC Heart Fail, 2021,8(4):2837-2845.
[24]陈木欣，梁好，赵怡迪，等.不同衰弱评估工具在老年术前衰弱筛查中的应用效果比较研究[J].中国全科医学，2024,27(30):3790-3796.
[25]NOZAKI K, KAMIYA K, HAMAZAKI N, et al. Validity and utility of the questionnaire-based FRAIL scale in older patients with heart failure:findings from the FRAGILE-HF[J]. J Am Med Dir Assoc, 2021,22(8):1621-1626.
[26]王丹蕾，张蓉，吴琪，等.基于Tilburg衰弱评估量表评价的老年慢性心力衰竭合并衰弱的危险因素探讨及其对患者生活质量和预后的影响[J].现代生物医学进展，2023,23(16):3077-3082.
[27]NISSEN S K, FOURNAISE A, LAURIDSEN J T, et al. Crosssectoral inter-rater reliability of the clinical frailty scale:a Danish translation and validation study[J]. BMC Geriatr, 2020,20(1):443.
[28]MIZUGUCHI Y, NAKAO M, NAGAI T, et al. Machine learningbased gait analysis to predict clinical frailty scale in elderly patients with heart failure[J]. Eur Heart J Digit Health, 2023,5(2):152-162.
[29]YUN V S, ENJUANES G C, CALERO M E, et al. Frailty phenotypes in patients with heart failure in the early post-discharge period:insights from the iCOR randomised controlled trial and a machine learning-based clustering analysis[J]. Eur Heart J, 2021,42(Supplement_1):1058.
[30]GIGGINS O M, VAVASOUR G, DOYLE J. Unsupervised assessment of frailty status using wearable sensors:a feasibility study among community-dwelling older adults[J]. Adv Rehabil Sci Pract, 2025,14:27536351241311845.
[31]KIM T, CHOI J Y, KO M J, et al. Development and validation of a machine learning method using vocal biomarkers for identifying frailty in community-dwelling older adults:cross-sectional study[J]. JMIR Med Inform, 2025,13:e57298.
[32]CHAI C L, WANG J Y, LUO Y Y, et al. Data management for machine learning:a survey[J]. IEEE Trans Knowl Data Eng,2023,35(5):4646-4667.
[33]PINSKY M R, BEDOYA A, BIHORAC A, et al. Use of artificial intelligence in critical care:opportunities and obstacles[J]. Crit Care, 2024,28(1):113.
[34]师庆科，李楠，叶枫.机器学习在临床研究中的应用进展[J].中国数字医学，2025,20(3):1-10.
[35]ADAMS R, HENRY K E, SRIDHARAN A, et al. Prospective,multi-site study of patient outcomes after implementation of the TREWS machine learning-based early warning system for sepsis[J]. Nat Med, 2022,28(7):1455-1460.
[36]中国政府网.中共中央国务院印发《“健康中国2030”规划纲要》[EB/OL].(2016-11-20)[2025-06-16]. https：///www. gov.cn/zhengce/202203/content_3635233. htm.
基本信息:
中图分类号:R541.6
引用信息:
[1]郑博文,王笑蕾,花木森.机器学习在心力衰竭患者衰弱评估中的研究进展[J].老年医学研究,2025,6(06):33-37.
2025-12-25
2025-12-25