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Nair D, Liu CK, Raslan R, McAdams-DeMarco M, Hall RK. Frailty in Kidney Disease: A Comprehensive Review to Advance Its Clinical and Research Applications. Am J Kidney Dis 2024:S0272-6386(24)00836-9. [PMID: 38906506 DOI: 10.1053/j.ajkd.2024.04.018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2024] [Revised: 04/05/2024] [Accepted: 04/20/2024] [Indexed: 06/23/2024]
Abstract
Frailty is a multisystem syndrome of decreased physiologic reserve that has been shown to strongly and independently predict morbidity and mortality. Frailty is prevalent in patients living with kidney disease and occurs earlier in individuals with kidney disease as compared to the general population. In this comprehensive review, we examine clinical and research applications of frailty in kidney disease populations. Specifically, we clarify the definition of frailty and address common misconceptions, review the mechanisms and epidemiology of frailty in kidney disease, discuss challenges and limitations in frailty measurement, and provide updated evidence related to risk factors for frailty, its associated adverse outcomes, and interventions. We further add to the literature in this topic by highlighting the potential applications of frailty measurement in the care of patients with kidney disease and conclude with our recommendations for future research related to this important syndrome.
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Affiliation(s)
- Devika Nair
- Vanderbilt University Medical Center, Division of Nephrology and Hypertension, Nashville, Tennessee; Vanderbilt Center for Health Services Research, Nashville, Tennessee; Tennessee Valley Veterans Affairs Healthcare System, Nashville, Tennessee
| | - Christine K Liu
- Geriatric Research and Education Clinical Center, Veteran Affairs Palo Alto Health Care System, Palo Alto, California; Section of Geriatric Medicine, Division of Primary Care and Population Health, School of Medicine, Stanford University, Stanford, California
| | - Rasha Raslan
- Department of Medicine, School of Medicine, Duke University, Durham, North Carolina
| | - Mara McAdams-DeMarco
- Department of Surgery, Langone Health and Grossman School of Medicine, New York University, New York, New York; Department of Population Health, Langone Health and Grossman School of Medicine, New York University, New York, New York
| | - Rasheeda K Hall
- Department of Medicine, School of Medicine, Duke University, Durham, North Carolina; Durham Veterans Affairs Medical Center, Durham, North Carolina.
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Long Q, Yang M, Long J. Application of intelligent nursing system based on big data in maintenance hemodialysis patients. SLAS Technol 2024:100160. [PMID: 38901761 DOI: 10.1016/j.slast.2024.100160] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2024] [Revised: 05/22/2024] [Accepted: 06/18/2024] [Indexed: 06/22/2024]
Abstract
Maintenance hemodialysis (MHD) is one of the most important renal replacement therapies for patients with end-stage renal disease. However, long-term and frequent treatment not only damages the physiological functions of patients but also leads to serious economic burdens and mental stress. This can easily cause a series of psychological disorders in patients, resulting in severe rejection and fear of MHD. To reduce patient resistance and improve the quality of life of MHD, this article built an intelligent nursing system based on big data and then used the constructed intelligent nursing system to research MHD. Through experiments, it has been found that compared to self-efficacy intervention, intelligent nursing systems can control the concurrent rate of MHD patients below 14 %, and self-efficacy intervention methods can control the concurrent rate of MHD patients above 13 %. Moreover, using intelligent nursing systems can improve the ability of MHD patients to self-care. At the same time, before the use of intelligent nursing systems, the nursing satisfaction of MHD patients also varied greatly, with the overall satisfaction rate after use being 70 % higher than before. Using intelligent nursing systems can improve the satisfaction of MHD patients with nursing outcomes.
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Affiliation(s)
- Qijun Long
- School of Public Health and Administration, Youjiang Medical University for Nationalities, Baise 533000, Guangxi, China; College of Innovation and Entrepreneurship, Youjiang Medical University for Nationalities, Baise 533000, Guangxi, China
| | - Meichun Yang
- Affiliated Hospital, Youjiang Medical University for Nationalities, Baise 533000, Guangxi, China; College for International Education, Philippine Christian University, Manila 1004, Philippines.
| | - Jiahui Long
- Medical College, Xinjiang University of Science & Technology, Korla 841009, Xinjiang, China
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Lv W, Liao H, Wang X, Yu S, Peng Y, Li X, Fu P, Yuan H, Chen Y. A machine learning-based assistant tool for early frailty screening of patients receiving maintenance hemodialysis. Int Urol Nephrol 2024; 56:223-235. [PMID: 37227677 DOI: 10.1007/s11255-023-03640-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2023] [Accepted: 05/13/2023] [Indexed: 05/26/2023]
Abstract
PURPOSE To develop an assistant tool based on machine learning for early frailty screening in patients receiving maintenance hemodialysis. METHODS This is a single-center retrospective study. 141 participants' basic information, scale results and laboratory findings were collected and the FRAIL scale was used to assess frailty. Then participants were divided into the frailty group (n = 84) and control group (n = 57). After feature selection, data split and oversampling, ten commonly used binary machine learning methods were performed and a voting classifier was developed. RESULTS The grade results of Clinical Frailty Scale, age, serum magnesium, lactate dehydrogenase, comorbidity and fast blood glucose were considered to be the best feature set for early frailty screening. After abandoning models with overfitting or poor performance, the voting classifier based on Support Vector Machine, Adaptive Boosting and Naive Bayes achieved a good screening performance (sensitivity: 68.24% ± 8.40%, specificity:72.50% ± 11.81%, F1 score: 72.55% ± 4.65%, AUC:78.38% ± 6.94%). CONCLUSION A simple and efficient early frailty screening assistant tool for patients receiving maintenance hemodialysis based on machine learning was developed. It can provide assistance on frailty, especially pre-frailty screening and decision-making tasks.
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Affiliation(s)
- Wenmei Lv
- Department of Nephrology, West China Hospital, Sichuan University/West China School of Nursing, Sichuan University, Chengdu, 610041, Sichuan, China
| | - Hualong Liao
- Department of Applied Mechanics, College of Architecture and Environment, Sichuan University, Chengdu, 610065, Sichuan, China
| | - Xue Wang
- Department of Nephrology, West China Hospital, Sichuan University/West China School of Nursing, Sichuan University, Chengdu, 610041, Sichuan, China
| | - Shaobin Yu
- National Clinical Research Center for Geriatrics, West China Hospital of Sichuan University, Chengdu, 610041, Sichuan, China
| | - Yuan Peng
- Department of Nephrology, West China Hospital, Sichuan University/West China School of Nursing, Sichuan University, Chengdu, 610041, Sichuan, China
| | - Xianghong Li
- Department of Nephrology, West China Hospital, Sichuan University/West China School of Nursing, Sichuan University, Chengdu, 610041, Sichuan, China
| | - Ping Fu
- Department of Nephrology, Kidney Research Institute, West China Hospital of Sichuan University, Chengdu, 610041, Sichuan, China
| | - Huaihong Yuan
- Department of Nephrology, West China Hospital, Sichuan University/West China School of Nursing, Sichuan University, Chengdu, 610041, Sichuan, China.
| | - Yu Chen
- Department of Applied Mechanics, College of Architecture and Environment, Sichuan University, Chengdu, 610065, Sichuan, China.
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Clinical frailty assessment might be associated with mortality in incident dialysis patients. Sci Rep 2022; 12:17651. [PMID: 36271111 PMCID: PMC9587224 DOI: 10.1038/s41598-022-22483-8] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Accepted: 10/14/2022] [Indexed: 01/18/2023] Open
Abstract
Frailty is associated with mortality in maintenance dialysis patients. For incident dialysis patients, we used the clinical frailty scale (CFS) to investigate frailty as related to mortality or hospitalization within 2 years. We retrospectively reviewed the medical records of patients initiating hemodialysis or peritoneal dialysis during 2016-2018. Based on those records, two dialysis nurses independently used a 9-point CFS (1 = "Very fit" to 9 = "Terminally ill") to assess each patient's frailty at dialysis initiation. Patients with a mean CFS value of 5 or higher were classified into the frail group. The 2-year survival rates or hospitalization-free rates after the initiation of dialysis were compared between the frail (mean CFS score ≥ 5) and non-frail (mean CFS score < 5) groups. The analysis included 155 incident dialysis patients with mean age of 66.7 ± 14.1 (71% male). Frailty was inferred for 39 (25%) patients at dialysis initiation. Kaplan-Meier analyses showed that the survival rate and hospitalization-free rate within 2 years were significantly lower in the frail group than in the non-frail group (p < 0.01). Cox proportional hazards regression analyses revealed the CFS score as associated with the occurrence of a composite outcome, independently of age (hazard ratio 1.34, 95% confidence interval 1.04-1.72). Frailty assessment based on clinical judgment using CFS might predict adverse outcomes in dialysis-initiated patients.
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Kalantar-Zadeh K, Moore LW. Renal Telenutrition for Kidney Health: Leveraging Telehealth and Telemedicine for Nutritional Assessment and Dietary Management of Patients With Kidney Disorders. J Ren Nutr 2020; 30:471-474. [DOI: 10.1053/j.jrn.2020.09.003] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2020] [Accepted: 09/28/2020] [Indexed: 12/14/2022] Open
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