Abstract
Objective
To explore the impact of cognitive frailty on falls, readmissions, and mortality in aged patients undergoing Maintenance Hemodialysis (MHD).
Design
A prospective cohort study.
Methods
We collected baseline data from September 2023 to January 2024. We assessed cognitive frailty at baseline using the Frailty Scale and Montreal Cognitive Assessment. Older adults receiving MHD were followed for 12 months, with assessments at 6 and 12 months. Mortality was the primary outcome; falls and readmissions were secondary outcomes. We assessed adverse outcomes through multiple methods: mortality data were obtained via hospital electronic medical record system queries, dialysis registry platform verification, and confirmation by medical staff; falls and readmissions were ascertained through face-to-face interviews with patients during dialysis sessions, supplemented by hospital medical record system queries. We used the χ² test to compare group differences in adverse outcome rates, the Kaplan–Meier method with the log-rank test to estimate monthly cumulative event probabilities, and Cox regression to assess the association between cognitive frailty and the three adverse outcomes.
Results
Of 1,023 older adults on MHD, 300 (29.3%) had cognitive frailty. The other 723 (70.7%) did not. In 12 months, there were 101 deaths (9.8%), 190 falls (18.6%), and 299 readmissions (29.2%). The cognitive frailty group had higher incidences of death (59 [19.7%] vs. 42 [5.8%]), falls (94 [31.3%] vs. 95 [13.1%]), and readmissions (129 [43.0%] vs. 170 [23.5%]). After adjusting for confounders, cognitive frailty remained an independent risk factor for mortality (HR = 2.716, 95% CI 1.754–4.205, P < 0.001), falls (HR = 2.021, 95% CI 1.471–2.777, P < 0.001), and readmissions (HR = 1.996, 95% CI 1.585–2.514, P < 0.001).
Conclusions
Cognitive frailty independently increases risk for mortality, falls, and hospital readmission in older adults undergoing MHD. Strengthen screening and assessment for cognitive frailty to help identify high-risk patients early and inform targeted interventions.
Supplementary Information
The online version contains supplementary material available at https://doi.org/10.1186/s12877-026-07930-9.
Keywords: Cognitive Frailty, Adverse Outcomes, Aged, Maintenance Hemodialysis
Introduction
Global aging has emerged as a critical public health challenge over the past few decades. The World Health Organization’s 2023 Global Report on Aging projects substantial demographic transformations worldwide within the next thirty years [1]. This demographic shift has led to increased prevalence of multiple chronic conditions among older adults, subsequently raising the incidence of geriatric syndromes, including mild cognitive impairment (MCI) and physical frailty - conditions that significantly compromise quality of life [2].
Current research reveals important interconnections between frailty and MCI, demonstrating shared risk factors and pathological mechanisms [3]. These conditions exhibit a mutually reinforcing relationship that creates a detrimental cycle, even though they have historically been investigated as distinct entities. A pivotal 2013 international consensus meeting established the integrated concept of cognitive frailty [4], defining it as the co-occurrence of physical frailty and MCI while requiring differentiation from Alzheimer’s disease and other dementia subtypes [5]. This conceptual advancement moves beyond traditional single-domain assessments by incorporating both physiological and cognitive markers of decline, providing a more comprehensive framework for understanding aging processes.
Cognitive frailty is strongly associated with numerous adverse outcomes, including falls, increased healthcare utilization, and mortality [6], making it a crucial focus area for healthy aging initiatives [7]. In patients with end-stage renal disease (ESRD), MHD is a primary renal replacement therapy [8]. Aged MHD patients face particularly elevated risks for developing cognitive frailty due to multiple interacting factors: progressive renal disease, metabolic disturbances, hemodynamic instability, vascular pathology, uremic toxin accumulation, and the metabolic demands of chronic dialysis therapy [9]. Epidemiological studies document cognitive frailty prevalence of 25.9% in aged MHD populations - nearly triple the 9.0% rate observed in community-dwelling older adults [10], highlighting this population’s exceptional vulnerability.
Recent studies [11, 12] show cognitive frailty predicts adverse outcomes better than frailty or MCI alone. Most MHD research studies these conditions separately. Evidence confirms that frailty predicts several risks in MHD patients. These include longer hospital stays, higher fall risk, and increased mortality [13]. MCI also shows similar associations with these adverse outcomes [14]. Notably, their co-occurrence appears to synergistically worsen prognosis for chronic kidney disease patients [15]. Adverse outcomes, including falls, hospital readmissions, and mortality, have garnered significant clinical attention in older MHD patients due to their high prevalence and severe consequences. Epidemiological data reveal that cognitively frail MHD patients experience particularly alarming fall rates reaching 82.6% in a cross-sectional survey of elderly hemodialysis patients conducted in China, with post-fall disability rates substantially compromising quality of life [16], with post-fall disability rates substantially compromising quality of life. Unplanned readmissions serve as critical indicators of disease progression, representing frequent clinical events that not only escalate healthcare expenditures but also strain medical resources. Mortality, as the definitive endpoint for all disease trajectories, warrants particular emphasis in risk assessment protocols. The literature review reveals that while existing studies have examined the association between cognitive frailty and adverse outcomes in community-dwelling older adults and hospitalized chronic disease patients, research specifically targeting older MHD patients remains predominantly focused on documenting the prevalence of cognitive frailty and analyzing its contributing factors, with a notable lack of investigation into the underlying mechanisms by which it precipitates adverse clinical outcomes. This population demonstrates unique risk profiles characterized by advanced age, cognitive impairment, multimorbidity, and social isolation - factors that may exacerbate cognitive frailty severity and consequently elevate risks for adverse clinical outcomes while significantly impairing quality of life [17]. Based on these considerations, there is an urgent need for targeted research to investigate the impact of cognitive frailty on the incidence of falls, readmission rates, and mortality in older adults undergoing MHD. The findings from such research will be crucial for enabling healthcare professionals to implement precision interventions, improve patient prognoses, and enhance quality of life.
Methodology
Aims
The aim of this paper is to explore the relationship between cognitive frailty and adverse outcomes in older patients undergoing MHD.
Design
A prospective cohort study design was used.
Setting
Eleven hemodialysis units in the Chengdu region of Sichuan Province, China.
Participants
NCSS-PASS 11.0 was used to calculate the sample size. Given a prevalence of cognitive frailty in patients undergoing MHD of 20.6%, a type I error rate of 5%, and a power of 90%, 853 patients undergoing MHD were required for the analysis. Considering 20% of cases as invalid, the required sample size for recruitment was 880. 1023 patients undergoing MHD from 11 Hemodialysis Units in Chengdu City, Sichuan Province, China were recruited for the study using convenience sampling. The inclusion criteria were as follows: (1) aged ≥ 60 years and (2) MHD≥3months. The exclusion criteria were as follows: (1) a clear diagnosis of Alzheimer’s disease or other types of dementia, (2) completely disabled and bedridden, or suffering from serious diseases that prevented them from completing physical tests, and (3) acute and critical conditions, such as concurrent active malignancy and acute infection.
Data collection
Baseline data were collected between September 2023 and January 2024, with follow-up assessments conducted at 6 and 12 months after baseline. The survey was conducted by two professionally trained postgraduate nursing students across 11 different hemodialysis units. Before the questionnaires were administered, the objectives and significance of the study were fully and truthfully explained to the participants and their guardians.
Authorization for conducting the survey was initially obtained from each hemodialysis unit. A consistent script was utilized to elucidate the study’s rationale to the respondents. After obtaining informed consent from the patients, the researchers facilitated the completion of the questionnaire by reading each question aloud and assisting in its completion. The questionnaires were collected immediately upon completion. Information on diseases in the general information section was gathered from electronic medical records.
Baseline measurements
A general information questionnaire
In the development of our study, we meticulously crafted a general information questionnaire informed by a comprehensive review of pertinent literature. This instrument encompassed an array of socio-demographic characteristics, namely age, gender, marital status, educational attainment, average monthly income on a per capita basis, and method of medical payment. Additionally, lifestyle factors were captured through data on smoking history (categorized as never/smoking/quit), and alcohol consumption history [categorized as never/occasionally (1–2 times per week)/quit]. Dialysis-related variables included in the questionnaire were specific to the age at which dialysis commenced. Health status was evaluated through indicators such as impaired vision and hearing, body weight, height, Body Mass Index (BMI), history of falls within the preceding year, and comorbidities encompassing hypertension, diabetes mellitus, cardiac disease, peripheral vascular disease, osteoporosis, cerebrovascular disease, pulmonary disease, and secondary hyperthyroidism. Furthermore, laboratory parameters integral to the survey comprised measurements of hemoglobin, serum albumin, serum calcium, serum phosphorus, serum sodium, whole parathyroid hormone, total cholesterol, triglycerides, and serum creatinine levels.
Criteria for screening for cognitive frailty
In alignment with the diagnostic criteria for cognitive frailty promulgated by the International Consensus Group, the present study employed the following criteria for screening cognitive frailty:
The individual must meet the Fried criteria for frailty, which include a frailty phenotype (FP) assessment score of ≥ 3, indicative of physical frailty.
The individual must score within the range of 14 to 25 on MoCA, suggestive of mild cognitive impairment.
Frailty phenotype
This scale is a standardized debility assessment tool developed by Fried et al., based on the theory of the debilitating cycle, which is currently the most extensively utilized and well tested debilitating tool for measuring frailty [18]. The assessment consisted of the following five components: weight loss, slow walking speed, low grip strength, low physical activity and fatigue. Each item was answered with “yes/no,” with “yes” scoring 1 point and “no” scoring 0 point. The aggregate score ranges from 0 to 5.
Montreal cognitive assessment
MOCA was initially formulated by Nasreddine et al. as a screening instrument, primarily intended for MCI detection [19]. The Chinese version of the MOCA, which is predominantly employed in China, was translated and culturally adapted by Wei-Wang et al. [20]. This version encompasses cognitive assessments across seven domains including visuospatial and executive functions, naming and attention, with a maximum score of 30. In light of the low educational attainment and prevalence of illiteracy within the population under scrutiny and drawing upon the comprehensive AD diagnostic guidelines [21] and normative findings from relevant scholars, the present study in Beijing has established the following cutoff scores for the Chinese rendition of the MoCA scale: ≤13 points indicative of AD, 14–25 points suggestive of MCI and ≥ 26 points for normal cognitive functioning.All cognitive assessments were conducted 30–60 min before the start of dialysis to minimize the acute effects of hemodynamic changes on cognitive function. Furthermore, the original English version of the scale design allows an additional point to be added to the total score if an individual’s educational tenure is ≥ 12 years. All assessors received standardized training.
Malnutrition-inflammation score (MIS)
MIS was employed to assess the nutritional status of the patients [22], utilizing a scoring system ranging from 0 to 30 points. According to this scale, scores ranging from 0 to 8 points indicate mild malnutrition, scores from 9 to 18 points signify moderate malnutrition, and scores exceeding 18 points denote severe malnutrition. It is important to note that higher scores on the MIS correlate with increased severity of malnutrition and inflammation.
Geriatric Depressions Scale-15 (GDS-15)
The GDS-15 was administered to evaluate the psychological status of the patients, specifically targeting the assessment of negative mood experienced by the elderly over the preceding week [23]. Composed of 15 items, each prompting a binary “yes/no” response, the scale assigns a score of 1 for responses indicative of depressive symptomatology and 0 for responses that are non-indicative. Scoring ranges from 0 to 15, with a threshold of ≥ 8 points suggestive of depressive symptoms. Furthermore, there is a positive correlation between the score and the severity of depressive symptoms; higher scores reflect a more pronounced degree of depression.
Adverse outcomes
The research team used a study-specific clinical adverse outcome record form to document the occurrence and timing of adverse outcomes. An English version of this form is provided in Supplementary Table 1. For each participant, only the first occurrence of each type of adverse outcome was recorded, and subsequent events of the same category were not counted. This approach was adopted to ensure clear endpoint definition and avoid repeated counting in the statistical analysis.
Primary adverse outcome
Mortality served as the primary adverse outcome. Defined as mortality from any cause during the study period. Information on deaths among elderly MHD patients was obtained through multiple verification methods, including hospital electronic medical record system queries, dialysis registry platform verification, and confirmation by medical staff. Out-of-hospital deaths were confirmed by: regularly searching hospital electronic medical records and the dialysis registry platform at fixed three-month intervals, combined with telephone follow-up with healthcare workers at the dialysis center and family members.
Secondary adverse outcomes
Falls and hospital readmissions were evaluated as secondary adverse outcomes. According to the WHO definition [24], a fall is defined as an unintentional, sudden descent to the ground or a position lower than the initial posture. Information on fall incidents was collected through face-to-face interviews with patients during dialysis sessions. Readmissions refer to unplanned hospital readmission due to any cause, excluding scheduled surgeries or examinations. Data on readmissions events were obtained through hospital medical record system queries and supplemented by face-to-face interviews with patients during dialysis sessions.
Selection of follow-up time points
The National Health Commission of China’s 2021 Blood Purification Standards [25] recommend 3-to-6-month dialysis adequacy assessments for MHD patients. Due to study constraints and recall limitations, we chose 6- and 12-month follow-ups.
Ethical considerations
The study protocol underwent rigorous ethical review and was approved by the Ethics Committee of Chengdu Seventh People’s Hospital (Approval No. KY2022-028-02). The study was initiated only after ethical filing procedures had been completed at the other ten participating hospitals. This research was carried out in accordance with the World Medical Association Declaration of Helsinki: Ethical principles for medical research involving human subjects. Prior to their participation, all individuals were fully briefed on the objectives, significance, potential risks, and methodologies involved in the study. They were also made aware that they retained the right to withdraw from the study at any juncture without incurring any adverse effects. A written informed consent was meticulously obtained from each participant following a comprehensive explanation of the study’s details, ensuring their autonomous decision-making and voluntary involvement in the research.
Data analysis
Excel was used for double checking after data entry. SPSS statistics 25.0 was employed for the statistical analysis of the dataset. The covariates in this study were selected based on clinical relevance from the literature. Differences in the occurrence of adverse outcomes between groups were compared using the χ2 test. The Kaplan-Meier method was used to plot survival curves and statistical differences in the cumulative incidence of adverse outcomes between the two groups were assessed by the Log-rank test. To further explore the correlation between cognitive frailty and the risk of adverse outcomes, Cox proportional risk regression models were constructed to calculate the independent effects of cognitive frailty on the risk of falls, readmissions and mortality, respectively.
Results
General information and status of cognitive frailty in older MHD patients
The study population consisted of 1,023 older patients undergoing MHD, aged 60 to 94 years, with a mean age of 69.52 years (SD 7.52). The duration of dialysis ranged from 3 to 348 months, with a median period of 32 months. Within this cohort, the prevalence of cognitive frailty was identified in 300 patients, representing 29.3% of the sample, while 723 patients, constituting 70.7%, were categorized as non-cognitive frailty. Additional general information pertaining to the study sample is presented in Tables 1 and 2.
Table 1.
Univariate analysis of count data on cognitive frailty in older patients with MHD(N=1023)
| Variables | Total Cohort(%) | Cognitive frailty (N = 300) |
Non-Cognitive frailty (N = 723) |
Statistical value | p-value |
|---|---|---|---|---|---|
| Age | 155.838a | <0.001 | |||
| 60 ~ 69 | 544(53.2) | 75(25) | 469(64.9) | ||
| 70 ~ 79 | 355(34.7) | 147(49) | 208(28.8) | ||
| ≥ 80 | 124(12.1) | 78(26) | 46(6.4) | ||
| Sex | 15.397a | <0.001 | |||
| Male | 594(58.1) | 146(48.7) | 448(62) | ||
| Female | 429(42.9) | 154(51.3) | 275(38) | ||
| Educational level | 28.275b | <0.001 | |||
| Primary and below | 372(36.4) | 142(47.3) | 230(31.8) | ||
| Junior high school | 391(38.2) | 109(36.3) | 282(39) | ||
| High school or junior college | 144(14.1) | 28(9.3) | 116(16) | ||
| College and above | 116(11.3) | 21(7) | 95(13.1) | ||
| Marital status | 27.176a | <0.001 | |||
| Married | 828(80.9) | 213(71) | 615(85.1) | ||
| Other | 195(19.1) | 87(29) | 108(14.9) | ||
| Living style | 0.035a | 0.852 | |||
| Live with family | 947(92.6) | 277(92.3) | 670(92.7) | ||
| Other(Living alone/institutionalized) | 76(7.4) | 23(7.7) | 53(7.3) | ||
| Medical payment | 3.143a | 0.076 | |||
| Employee medical insurance | 518(50.6) | 139(46.3) | 379(52.4) | ||
| Other (Resident’s medical insurance/new rural cooperative/self-payment/commercial insurance) | 505(49.4) | 161(53.7) | 344(47.6) | ||
| Visually impaired | 8.341a | 0.004 | |||
| Yes | 399(39.1) | 145(48.3) | 254(35.1) | ||
| No | 624(60.9) | 155(51.7) | 469(64.9) | ||
| Hearing impaired | 24.333a | <0.001 | |||
| Yes | 217(21.2) | 93(31) | 124(17.2) | ||
| No | 806(78.8) | 207(69) | 599(82.8) | ||
| Falls within a year | 37.169 a | <0.001 | |||
| Yes | 320(31.3) | 135(45) | 185(25.6) | ||
| No | 703(68.7) | 165(55) | 538(74.4) | ||
| Complication | 5.081b | 0.079 | |||
| 0 | 77(7.5) | 16(5.3) | 61(8.4) | ||
| 1 ~ 2 | 682(66.7) | 199(66.3) | 483(66.8) | ||
| 3 ~ 4 | 227(22.2) | 68(22.7) | 159(22) | ||
| ≥ 5 | 37(3.6) | 17(5.7) | 20(2.8) | ||
| Depression | 23.472a | <0.001 | |||
| Yes | 181(17.7) | 80(26.7) | 101(14) | ||
| No | 842(82.3) | 220(73.3) | 622(86) |
a represents the x²-value and b represents the Z-value
Table 2.
Univariate analysis of measurement data of cognitive frailty in older patients with MHD(N=1023)
| Variables | Cognitive frailty (N = 300) |
Non-Cognitive frailty (N = 723) |
Statistical value | P- value |
|---|---|---|---|---|
| Weight(kg) | 55.1(49, 63.45) | 58.5(51.25, 66.25) | -3.844b | <0.001 |
| Height(m) | 1.595(1.54, 1.65) | 1.62(1.56, 1.68) | -4.665b | <0.001 |
| BMI (kg/m²) | 22(19.9, 24.375) | 22.41(20.1, 24.8) | -1.292b | 0.196 |
| Hemoglobin (g/L) | 111(97.5, 123.5) | 110(98, 121) | -1.184b | 0.236 |
| MIS (mark) | 8(5, 11) | 5(3, 8) | -8.659 b | <0.001 |
| Serum albumin(g/L) | 38.6(35.55, 40.75) | 39.5(36.75, 42) | -4.165b | <0.001 |
| Serum calcium(mmol/L) | 2.18(2.07, 2.32) | 2.18(2.05, 2.31) | -0.19b | 0.849 |
| Serum sodium(mmol/L) | 138.3(135.725, 140.2) | 139(136.8, 141) | -3.222b | 0.001 |
| Serum phosphorus (mmol/L) | 1.63(1.2425, 2.02) | 1.7(1.3275, 2.1) | -2.408b | 0.001 |
| Parathyroid hormone (pg/ml) | 236(121.7, 394.5) | 266.9(155.9, 433.9) | -1.865b | 0.062 |
| Total cholesterol (mmol/L) | 3.5(2.765, 4.22) | 3.75(3.13, 4.29) | -3.323b | 0.001 |
| Triglyceride (mmol/L) | 1.475(0.98, 2.065) | 1.66(1.1, 2.28) | -2.675b | 0.007 |
| Serum creatinine (µmol/L) |
709.7 (489.6, 884.5) |
711.5 (480.6, 922.7) |
-5.595b | <0.001 |
a represents the t-value and b represents the Z-value
Survey completion and reasons for dropout
During the follow-up phase, a total of 1,023 older MHD patients were included for the survey. The first follow-up, at 6 months, 990 MHD older patients completed the 6-month follow-up, with 33 patients dropped out. The specific reasons for dropout among older people were as follows: 15 cases refused to continue participation, 5 cases showed significant disease progression and were unable to cooperate, 5 cases discontinued dialysis, 3 cases were transferred to other hospitals, and 5 cases could not be recontacted. The second follow-up, at 12 months, 944 MHD older patients completed the final 12-month follow-up, with 46 patients dropped out. The specific reasons for dropout among older people were as follows: 18 cases refused to continue participation, 8 cases discontinued dialysis, 7 cases were transferred to other hospitals, and 13 cases could not be recontacted. The total dropout rate for the 12-month follow-up was 7.7%. It is mentioned in the medical follow-up study design and considerations that loss of visits cannot be avoided during the follow-up process. It is generally required that the rate of loss of visits be kept within 10%.The loss to follow-up rate in this study is within an acceptable range, indicating that the study data are well representative.
Incidence of adverse outcomes in older MHD patients
The follow-up results revealed that, during the 12-month period, 101 patients (9.8%) died, 190 patients (18.6%) experienced falls, and 299 patients (29.2%) were hospitalized. Among the cognitively frail group, 59 patients (19.7%) died, 94 patients (31.3%) had falls, and 129 patients (43.0%) were readmitted. In the non-cognitively frail group, 42 patients (5.8%) died, 95 patients (13.1%) had falls, and 170 patients (23.5%) were readmitted. The chi-square test results indicated that, compared to the non-cognitively frail group, the cognitively frail group had a significantly higher risk of falls (χ²=46.596, P < 0.001), rehospitalization (χ²=38.923, P < 0.001), and death (χ²=45.757, P < 0.001), with the differences being statistically significant. Details are presented in Table 3.
Table 3.
Incidence of Adverse Outcomes in aged MHD Patients by Group(N=1023)
| Adverse Outcomes | Cognitively Frailty (N = 300) |
Non-Cognitively Frailty (N = 723) | χ2 | P-value |
|---|---|---|---|---|
| Mortality | 45.757 | <0.001 | ||
| Occurred | 59(19.7) | 42(5.8) | ||
| Not occurred | 241(80.3) | 681(94.2) | ||
| Falls | 46.596 | <0.001 | ||
| Occurred | 94(31.3) | 95(13.1) | ||
| Not occurred | 206(68.7) | 628(86.9) | ||
| Readmission | 38.923 | <0.001 | ||
| Occurred | 129(43.0) | 170(23.5) | ||
| Not occurred | 171(57.0) | 553(76.5) |
The relationship between cognitive frailty and all-cause mortality in older MHD patients
The Kaplan-Meier survival analysis with 12-month follow-up revealed significantly lower survival probabilities in cognitively frail versus non-cognitively frail older MHD patients (Log-rank χ²=43.914, P < 0.001), demonstrating substantially higher mortality risk in the cognitive frailty group (Fig. 1). Univariate cox regression identified hearing impairment [HR = 2.007, 95%CI 1.330–3.030, P < 0.001], serum albumin [HR = 0.618, 95%CI 0.407–0.940, P = 0.024], regular dialysis adherence [HR = 0.125, 95%CI 0.017–0.906, P = 0.040], and walking aid use [HR = 2.307, 95%CI 1.535–3.468, P < 0.001] as significant mortality predictors. Cognitive frailty showed particularly strong unadjusted mortality association [HR = 3.487, 95%CI 2.347–5.180, P < 0.001], which remained significant after multivariate adjustment for hearing, albumin, dialysis regularity, and mobility aids [adjusted HR = 2.716, 95%CI 1.754–4.205, P < 0.001], establishing cognitive frailty as an independent mortality predictor conferring 2.716-fold higher risk (Tables 4 and 5).
Fig. 1.
Survival curves for mortality during the 12-month follow-up period for both groups of older patients with MHD
Table 4.
One-way Cox proportional risk regression analysis of general information and mortality in older patients with MHD
| Items | β | SE | Wald χ 2 | P | HR | 95%CI | |
|---|---|---|---|---|---|---|---|
| Lower limit | Upper limit | ||||||
| Hearing Impairment | 0.697 | 0.210 | 10.992 | 0.001 | 2.007 | 1.330 | 3.031 |
| Serum Albumin | -0.481 | 0.214 | 5.062 | 0.024 | 0.618 | 0.407 | 0.940 |
| Regular Dialysis | -2.083 | 1.013 | 4.231 | 0.040 | 0.125 | 0.017 | 0.906 |
| Use of Walking Aids | 0.836 | 0.208 | 16.176 | <0.001 | 2.307 | 1.535 | 3.468 |
Table 5.
Cox proportional risk regression analysis of cognitive frailty and mortality in older patients with MHD
| Items | Adjustment | β | SE | Waldχ2 | P | HR | 95% CI | |
|---|---|---|---|---|---|---|---|---|
| Lower limit | Upper limit | |||||||
| Cognitive Frailty | Unadjusted | 1.249 | 0.202 | 38.270 | ＜0.001 | 3.487 | 2.347 | 5.180 |
| Adjusted | 0.999 | 0.223 | 20.080 | ＜0.001 | 2.716 | 1.754 | 4.205 | |
The relationship between cognitive frailty and falls in older MHD patients
The Kaplan-Meier survival analysis demonstrated a significantly lower survival probability in the cognitively frail group compared to the non-cognitively frail group throughout the 12-month follow-up period, indicating a substantially higher risk of falls among cognitively impaired older MHD patients (Fig. 2). This difference in survival curves was confirmed to be statistically significant by the log-rank test (χ²=47.543, P < 0.001), establishing cognitive frailty as a strong independent predictor of falls in this patient population. Further supporting this finding, univariate Cox regression analysis revealed cognitive frailty was significantly associated with increased falls risk (HR = 2.604, 95% CI 1.958–3.464, P < 0.001), and this association remained robust after multivariate adjustment for potential confounders including age, sex, BMI and use of walking aids (adjusted HR = 2.021, 95% CI 1.471–2.777, P < 0.001), indicating cognitively frail patients had a 2.021-fold higher falls risk than their non-cognitively frail counterparts. These results collectively demonstrate that cognitive frailty serves as a significant and independent risk factor for falls in older MHD patients (Tables 6 and 7).
Fig. 2.
Survival curves for falls during the 12-month follow-up period in two groups of older patients with MHD
Table 6.
One-way Cox proportional risk regression analysis of general information and falls in older patients with MHD
| Items | β | SE | Wald χ 2 | P | HR | 95% CI | |
|---|---|---|---|---|---|---|---|
| Lower limit | Upper limit | ||||||
| Age | 0.029 | 0.01 | 7.974 | 0.005 | 1.03 | 1.009 | 1.051 |
| Gender | 0.38 | 0.147 | 6.712 | 0.01 | 1.463 | 1.097 | 1.95 |
| BMI | 0.044 | 0.011 | 16.821 | <0.001 | 1.045 | 1.023 | 1.067 |
| Use of walking aids | 0.561 | 0.171 | 10.75 | 0.001 | 1.752 | 1.253 | 2.451 |
Table 7.
Cox proportional risk regression analysis of cognitive frailty and falls in older patients with MHD
| m | Adjustment | β | SE | Waldχ 2 | P | HR | 95% CI | |
|---|---|---|---|---|---|---|---|---|
| Lower limit | Upper limit | |||||||
| Cognitive Frailty | Unadjusted | 0.957 | 0.146 | 43.228 | ＜0.001 | 2.604 | 1.958 | 3.464 |
| Adjusted | 0.704 | 0.162 | 18.834 | ＜0.001 | 2.021 | 1.471 | 2.777 | |
The relationship between cognitive frailty and readmissions in older MHD patients
The Kaplan-Meier survival analysis demonstrated a significantly lower survival probability in the cognitively frail group compared to the non-cognitively frail group throughout the 12-month follow-up period, indicating a substantially higher risk of readmissions among cognitively impaired older MHD patients (Fig. 3). The Kaplan-Meier survival analysis revealed significantly lower readmission-free survival rates in cognitively frail older MHD patients compared to non-cognitively frail patients during the 12-month follow-up period (Log-rank χ²=40.783, P < 0.001), indicating a substantially elevated readmission risk in the cognitive frailty group. Univariate Cox regression identified age [HR = 1.027, 95%CI 1.012–1.042, P < 0.001] and sex [HR = 1.343, 95%CI 1.070–1.685, P = 0.011] as significant predictors of readmission. Notably, cognitive frailty demonstrated a strong unadjusted association with readmission risk [HR = 2.050, 95%CI 1.630–2.578, P < 0.001], which remained statistically significant after multivariate adjustment for age and sex [adjusted HR = 1.996, 95%CI 1.585–2.514, P < 0.001], establishing cognitive frailty as an independent predictor conferring a 1.996-fold higher readmission risk in elderly MHD patients (Tables 8 and 9).
Fig. 3.
Survival curves for readmissions during the 12-month follow-up period for both groups of older patients with MHD
Table 8.
One-way Cox proportional risk regression analysis of general information and readmission in older patients with MHD
| Items | β | SE | Wald χ 2 | P | HR | 95% CI | |
|---|---|---|---|---|---|---|---|
| Lower limit | Upper limit | ||||||
| age | 0.026 | 0.007 | 12.385 | <0.001 | 1.027 | 1.012 | 1.042 |
| sex | 0.295 | 0.116 | 6.479 | 0.011 | 1.343 | 1.070 | 1.685 |
Table 9.
Cox proportional risk regression analysis of cognitive frailty and readmission in older patients with MHD
| Items | Adjustment | β | SE | Wald χ2 | P | HR | 95% CI | |
|---|---|---|---|---|---|---|---|---|
| Lower limit | Upper limit | |||||||
| Cognitive Frailty | Unadjusted | 0.718 | 0.117 | 37.711 | ＜0.001 | 2.05 | 1.63 | 2.578 |
| Adjusted | 0.691 | 0.118 | 34.47 | ＜0.001 | 1.996 | 1.585 | 2.514 | |
Discussion
This study investigated the status of cognitive frailty among 1,023 elderly patients receiving MHD in Chengdu, Sichuan Province. The results revealed that 300 patients (29.3%) presented with cognitive frailty. MHD patients commonly exhibit impaired cerebrovascular autoregulation; a mechanism associated with uremic toxin-mediated endothelial dysfunction and vascular calcification induced by calcium-phosphate metabolism disorders [26]. Furthermore, recurrent hemodynamic fluctuations during dialysis may lead to cerebral hypoperfusion, promoting neuroimaging changes such as white matter hyperintensities, thereby increasing the risk of cognitive frailty [17]. In addition, the synergistic effect of chronic kidney disease-mineral and bone disorder with sarcopenia and protein-energy wasting accelerates the decline in physical function [27]. Coupled with alterations in the immune system and a chronic microinflammatory state in MHD patients [28], these factors further elevate the risk of cognitive frailty. In summary, the findings indicate a concerning and non-negligible prevalence of cognitive frailty among elderly MHD patients.
Impact of cognitive frailty on mortality in older patients with MHD
Death remains the most critical outcome measure in clinical practice [29]. The results of this study revealed a statistically significant independent association between cognitive frailty and mortality risk. Elderly MHD patients with cognitive frailty demonstrated a 2.716-fold higher mortality risk compared to those without cognitive frailty, indicating that cognitive frailty serves as an independent prognostic factor for mortality in this patient population. For older MHD patients, cognitive frailty may increase mortality risk through multiple pathways. These patients often have multiple chronic conditions and weakened immunity [30]. Their kidney disease and other health problems interact with frailty, creating a vicious cycle. Poor nutritional status is common among older adults receiving maintenance hemodialysis and may contribute to adverse outcomes through metabolic dysregulation, impaired immune function, and heightened inflammatory activity [31]. These physiological derangements may increase susceptibility to infectious complications and cardiovascular events, which collectively contribute to progressive clinical deterioration and elevated mortality risk. Long-term cognitive frailty reduces social participation [32]. Patients may become socially isolated. Without proper support and interaction, they develop more depression and anxiety. This lowers quality of life and, indirectly, raises death risk [33]. Cognitive frailty affects treatment adherence [34]. Patients may struggle to follow complex treatment plans. Irregular dialysis attendance and suboptimal medication adherence may further compromise treatment effectiveness and increase the risk of adverse clinical events.
Impact of cognitive frailty on falls in older patients with MHD
Falls represent a significant health challenge for older adults, serving as a major contributing factor to fractures, functional impairment, disability, and even mortality, with severe adverse consequences for both physical and psychological well-being [35]. Previous studies have demonstrated that cognitive frailty can predict falls. The results of this study demonstrate that cognitive frailty is independently associated with fall risk. Specifically, compared to non-cognitive frailty group older MHD patients, those with cognitive frailty had a 2.021-fold higher risk of falls. This finding suggests that cognitive frailty may be an independent risk factor for falls in older MHD patients.
The link between cognitive frailty and fall risk in elderly MHD patients involves unique mechanisms. First, uremic toxin buildup accelerates brain degeneration, weakening cognitive function and safety awareness [36]. Dialysis-related brain problems plus normal aging further hurt balance control - even simple moves need extra mental effort, making patients slower to spot and react to dangers [37]. When distracted, their walking and coordination worsen sharply, increasing the risk of falls [38]. Second, these patients often suffer from poor nutrition, leading to rapid muscle loss. Those over 70 typically lose 2.1% of bone density and 10–15% of muscle mass each year [39]. Overactive parathyroid glands make this worse, increasing the risk of weak muscles and brittle bones. Together, these issues damage strength and balance [40]. Cognitive frailty is also associated with greater body inflammation, which breaks down muscles faster while slowing their regrowth [41]. Third, long-term dialysis brings other problems like anemia, heart disease, and amyloid buildup [42]. The amyloid can cause hand numbness and joint damage, further limiting movement. All these factors combine to make falls much more likely [43].
Impact of cognitive frailty on readmission in older patients with MHD
Readmissions often indicate acute illness or other health concerns requiring attention. For aged MHD patients specifically, readmissions may further increase the risk of nosocomial infections and lead to a series of adverse consequences. The findings of this study demonstrate a significant independent association between cognitive frailty and readmission risk. Older MHD patients with cognitive frailty exhibited a 1.99-fold higher risk of hospital readmission compared to their non-cognitively frail counterparts, indicating that cognitive frailty serves as an independent predictor for hospital readmissions in this patient population. Several factors may explain this link. First, these patients often have multiple chronic diseases [44] and take many medications. Their mobility tends to be poor. These problems combine to speed up physical decline [45]. Long-term illness also damages blood vessels in the brain. This worsens thinking skills, making it harder to handle health problems [46]. Both physical and mental decline raise readmission risks. Second, poor nutrition plays a role. Many older MHD patients have low albumin levels. This causes metabolism problems that weaken immunity [31]. Their bodies respond too strongly to inflammation. Compared to healthy seniors, they get sick more easily [47]. When stressed, they can’t recover well. Inflammation increases [48], thereby increasing the likelihood of readmission. They also have more falls [49], which lead to more hospital stays. Third, depression matters too. Research shows that cognitively impaired patients have higher levels of inflammation markers such as IL-6 and CRP [50]. These substances affect brain function. They can cause brain inflammation that leads to depression. A higher degree of depression exacerbates the negative impact on mental health. This creates a cycle that increases the likelihood of readmission.
Strengths and limitations
This study represents the first prospective cohort study to use a multicenter dataset to analyze the impact of cognitive frailty on 12-month adverse outcomes (including mortality, falls, and readmissions) in aged MHD patients, with rigorous adjustment for a comprehensive list of confounding factors to ensure robust results. However, several limitations should be noted. First, self-reported data on falls and rehospitalizations may be subject to recall bias, potentially affecting the accuracy of the findings. This study only recorded the first occurrence of each adverse outcome and did not include recurrent events, which may underestimate the true incidence and disease burden of falls and hospital readmissions. Further studies may adopt recurrent event models such as the Andersen–Gill model for further verification.Second, the 12-month follow-up period, constrained by time and resource limitations, may restrict the comprehensive evaluation of cognitive frailty’s long-term effects. Third, the use of convenience sampling from 11 hemodialysis units in the Chengdu region of Sichuan Province may limit the generalizability of our findings to other geographic regions or healthcare settings in China, particularly given regional variations in healthcare resources, dialysis practices, and patient demographics. Finally, the lack of biomarker assessments, such as β-amyloid and tau protein, limits the exploration of underlying pathological mechanisms. In addition, Dialysis adequacy, such as Kt/V, was not available for all participants and therefore could not be incorporated into the multivariable models. This may have resulted in residual confounding.Future studies should extend the follow-up duration and incorporate biomarker analysis to better understand the long-term prognostic impact of cognitive frailty in this population and provide more robust evidence for developing targeted interventions.
Conclusion
Cognitive frailty is significantly associated with adverse outcomes such as falls, readmissions, and mortality, and serves as an independent predictor for these adverse outcomes. This study emphasizes the importance of assessing cognitive function in MHD older patients. It is recommended that clinical practice enhance the monitoring of cognitive function in MHD older patients to promptly identify those with cognitive frailty and implement effective intervention measures, thereby reducing the risk of adverse outcomes and improving patients’ quality of life.
Supplementary Information
Acknowledgements
The authors sincerely acknowledge the support and participation of all hemodialysis centers and participants involved in this study.
Authors’ contributions
SL made substantial contributions to the conception and design of the study. YC drafted the manuscript and revised it critically for important intellectual content. JD contributed to data acquisition. JH and LY contributed to data analysis and interpretation. LY and JZ supervised the study. SL and YC contributed equally to this work. All authors read and approved the final manuscript and agreed to be accountable for all aspects of the work, ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.
Funding
This work was supported by grants from the Key Project of Chengdu Medical College - Chengdu Seventh People’s Hospital Joint Research Fund (grant numbers 2022LHJYZD-04) and the 2024 Sichuan Nursing Association Grassroots Nursing Special Research Project (grant numbers H24030).
Data availability
The data set of this study is available from the corresponding author upon reasonable request.
Declarations
Ethics approval and consent to participate
The study protocol was reviewed and approved by the Ethics Committee of Chengdu Seventh People’s Hospital (Approval No. KY2022-028-02). Study initiation at the other ten participating hospitals proceeded only after completion of the required ethical filing procedures. The study was conducted in accordance with the Declaration of Helsinki, as revised in 2013. All participants were fully informed of the study purpose and procedures, and written informed consent was obtained from each participant prior to enrollment.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Siyu Li and Yuanmin Chen contributed equally to this work.
Contributor Information
Liwei Yang, Email: yangllwei@163.com.
Jing Zeng, Email: Zengjinger@163.com.
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Associated Data
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Supplementary Materials
Data Availability Statement
The data set of this study is available from the corresponding author upon reasonable request.