Associated factors of frailty in patients with chronic heart failure: A systematic review and meta-analysis

doi:10.21203/rs.3.rs-3772507/v1

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Research Article

Associated factors of frailty in patients with chronic heart failure: A systematic review and meta-analysis

https://doi.org/10.21203/rs.3.rs-3772507/v1

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Background: Although some researchers have explored the influencing factors of frailty in chronic heart failure (CHF) patients, frailty is affected by many factors, and their conclusions are inconsistent. This review aims to systematically evaluate the factors affecting frailty in CHF patients to provide an evidence-based basis for risk prediction, treatment plans, and the prevention of health outcomes in clinical settings.

Methods: EMBASE, the Cochrane Library, PubMed, Web of Science, CINAHL, Chinese Biological Medicine (CBM), CNKI, and Wan Fang databases were searched up to August 10, 2023, to identify observational studies to assemble the factors affecting frailty in CHF patients. Two independent reviewers assessed the quality of included studies using corresponding assessment tools. RevMan 5.4 was used for meta-analysis and sensitivity analysis. Stata 18 MP was used for publication bias assessment.

Results: 14 articles including 4310 patients were included. 16 influencing factors were identified, and the factors with statistical significance were age (OR=1.11, 95% CI=1.07-1.16, p<0.001), NYHA functional class (OR=3.15, 95% CI=2.46-4.04, p<0.001), albumin (OR=0.86, 95% CI=0.77-0.95, p=0.005), haemoglobin (OR=0.86, 95% CI=0.76-0.97, p=0.01), cerebrovascular accidents (OR=2.31, 95% CI=1.49-3.06, P<0.001), number of comorbidities (OR=1.24, 95%CI=1.05-1.47, P=0.01), left ventricular ejection fraction (LVEF) (OR=0.88, 95% CI=0.78-0.99, p=0.03), duration of hospitalization (OR=1.14, 95% CI=1.05-1.23, p=0.001) and left atrial diameter (OR=1.12, 95% CI=1.05-1.20, P=0.0006).

Conclusions: While this review and meta-analysis found that age, NYHA functional class, albumin, haemoglobin, cerebrovascular accidents, comorbidity, LVEF, duration of hospitalization, left atrial diameter were associated with frailty in patients with chronic heart failure, the study heterogeneity shows the need for better-designed studies to further clarify the influencing factors of frailty in CHF patients and develop disease prediction models based on an algorithm for predicting the risk of frailty more accurately.

heart failure

frailty

associated factors

prediction

meta-analysis

Heart failure (HF) is a complex clinical syndrome caused by a significant decrease in cardiac output volume and is the end point of various cardiovascular diseases [1]. Like other patients with chronic diseases, HF patients often have multiple morbidities, complex polypharmacy and inadequate self-management skills. Despite improvements in HF management, the incidence, mortality and early readmission rates of HF patients are still at a bottleneck to their cure. It has been reported that approximately 64.3 million people worldwide suffer from HF [2], and the readmission rates at 6 months after discharge among HF patients are as high as 50%. Within five years of a diagnosis of HF, the survival rate is only 20% [3]. It is well known that rehospitalization imposes a significant burden on the health care system and on individuals, and recurrent hospitalization due to chronic heart failure (CHF) has become an important epidemiological feature of the disease [4].

Frailty is a clinical state with increased vulnerability to stressors due to a decline in the function and reserves of multiple physiologic systems [5]. A recent systematic review reported that frailty at the time of hospital admission is a risk factor for a longer hospital stay, functional decline and hospital mortality [6]. The prevalence of frailty in HF is as high as 44.5% [7], and it is a significant risk factor for readmission in patients with CHF. Being frail increases the risk of falls, disability, arrhythmias, and functional aggravation and puts up barriers to the understanding and self-management skills conveyed by health professionals [8, 9]. Frailty has become a severe threat to the prognosis of patients with CHF, and early identification of factors affecting the development of frailty in CHF patients can facilitate timely intervention to prevent or delay it.

Some researchers have explored the influencing factors of frailty in CHF patients and found that age, gender [10], comorbidity [11], NYHA functional class, left ventricular ejection fraction (LVEF) and other factors play important roles in the occurrence and progression of frailty in CHF patients. However, the findings have been affected by many factors, such as study design, geographical area, and population characteristics, and their conclusions have been inconsistent. A prospective cohort study reported by Hamada et al indicated that body mass index (BMI) was not associated with frailty in patients with CHF [12], but a cross-sectional study showed that BMI ≥ 30 Kg/m² had influence on frailty in CHF patients [13]. Yang and her colleagues found that female gender in the Chinese population increased the risk of frailty in CHF patients [14], but Joyce et al suggested that female gender showed nonsignificant influence on frailty in American patients with HF [15].

This study aimed to perform a systematic review and meta-analysis to systematically evaluate the factors affecting frailty in CHF patients to provide an evidence-based basis for risk prediction, treatment plans, and prevention of health outcomes in clinical settings.

We conducted a systematic review using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) checklist [16]. We also followed standard criteria for meta-analyses of observational studies [17]. This study was registered in PROSPERO (registration number: CRD42023448771).

Search Strategy

We searched the Cochrane Library, PubMed, EMBASE, Web of Science, CINAHL, Chinese Biological Medicine (CBM), CNKI, and Wan Fang databases (up to August 10, 2023) to identify relevant reports, and we manually searched for articles from the reference lists of the retrieved articles to assemble a comprehensive collection of the factors reported to affect frailty in CHF patients. The search terms used were “heart failure,” “cardiac failure,” “frailty,” “frailty syndrome,” “physical frailty,” “cognitive frailty,” “debility,” “risk factor,” “contributing factor,” “impact factor,” “influencing factor,” “relevant factor,” “correlative factor,” “associated factor,” “predictor,” “heart disease risk factors,” “cardiometabolic risk factors.” The PubMed database search strategy is shown in (Supplementary material, Appendix A).

Selection Criteria

Inclusion criteria were as follows: (ⅰ) participants with CHF regardless of pathogenesis and age ≥ 18 years; (ⅱ) observational study including cohort study, case-control study and cross-sectional study; (ⅲ) investigation of the influencing factors or predictors of frailty in patients with CHF; (ⅳ) studies that used at least one of frailty assessments to screen patients with CHF; (v) the publication had to include estimates of the relative risk (RR: hazard ratio, risk ratio, odds ratio) with the 95% confidence intervals for at least one confounding factor had to be available; and (vi) English and Chinese publication languages only. Exclusion criteria were as follows: (ⅰ) full text not available; (ⅱ) review, grey literature, editorial, texts published in non-peer-reviewed journals, letters to the editor and study protocols; (ⅲ) data information that was difficult to extract completely or literatures had obvious mistakes; (ⅳ) corresponding outcomes not reported.

Data Collection and Quality Assessment

The reviewers extracted the following information from every included study: first author; publication year; country where the study was conducted; survey setting; study design and size; characteristics of participants (mean age, NYHA class, LVEF, B-type natriuretic peptide); the assessment tools or diagnostic methods of frailty, the incidence of frailty, the influencing factors involved, and RRs and their 95% CIs. We attempted to contact the authors to obtain the missing information. The quality of the cohort and case-control studies was assessed using the Newcastle–Ottawa scale [18]. This tool addressed three broad domains: the selection of the study groups, the comparability of the groups, and the ascertainment of either the exposure or outcome of interest for case-control or cohort studies, respectively. The cross-sectional studies were assessed using the risk of bias evaluation criteria recommended by the Agency for Health care Research and Quality (AHRQ) [19]. There were 11 items in the AHRQ that were answered with “yes,” “no,” or “unclear.”

Two reviewers (Si Liu, Meng-die Liu) independently screened, assessed the quality of each included study and extracted relevant data; differing opinions were resolved through discussion or a third reviewer’s judgement (Xiao-yun Xiong).

Statistical Analysis

We performed meta-analysis and sensitivity analysis using Revman 5.4 software, and publication bias using Stata 18 MP software. First, we conducted the chi-square test to determine whether there was heterogeneity among the studies. A result of p > 0.1, I² < 50% indicated no significant heterogeneity between studies; in such cases, we used the fixed-effects model for analysis. However, if P < 0.1, I² ≥ 50% and there was no clinical heterogeneity, we chose the random-effects model for analysis. If P < 0.1 and we were unable to judge the source of heterogeneity, we used descriptive analysis. For some of the factors we could not pool, we also used descriptive analysis to classify them into protective and risk factors. We calculated the odd risk (OR) and 95% CI for dichotomous variables. We considered a two-sided p value less than 0.05 to indicate statistical significance. Sensitivity analysis was performed on the influencing factors (more than two studies were included in the meta-analysis) on frailty in patients with HF based on the leave-one-out approach in turn, to assess the stability of the results and the source of heterogeneity. Publication bias was assessed using Egger’s regression asymmetry test. A level of < 0.05 was used to determine whether publication bias might be present.

Characteristics of Studies

We retrieved 4319 records through the database searches. After removing duplicates, we were left with 4111 articles, 4022 of which we excluded by reviewing the title and abstract using general screening criteria. After assessing 89 full-text articles for eligibility, we excluded 75 studies for the following reasons: they were review articles (n = 9); studies in which the results were presented as abstracts (n = 17); studies for which the full text was not available (n = 6); articles published in Spanish (n = 1); articles that were editorials (n = 1); articles that were not relevant to the topic (n = 8); articles that were not peer-reviewed (n = 8); studies that focused on the influencing factors of prognosis (n = 5); papers that had obvious mistakes (n = 3); papers whose data were difficult to extract (n = 13); and studies designed to make clinical prediction models (n = 4). We subjected the remaining 14 articles to systematic review and 13 articles for meta-analysis. Figure 1 shows the specific flow chart. Reasons for the final exclusion of the 75 studies are described in (Supplementary material, Appendix B).

Table 1 summarizes the details of the 14 included studies. The studies were conducted in different locations: China (n = 6), Korea (n = 2), Japan (n = 3), Italy (n = 2), and Brazil (n = 1). Three were cohort studies, four were case-control studies, and seven were cross-sectional studies. The 14 studies included 4310 patients, 1803 of whom were frail, the incidence rate of frailty was 41.83%. The number of patients in each included article ranged from 76 to 949, the mean ages ranged from 59 to 82, and the frailty phenotype was the most widely used evaluation tool.

Table 1

Characteristics of the included studies.
Author (year)	Study design	Country	Setting	Study size	Mean ages (years)	Incidence rate(%)	LVEF (%)	NT-proBNP/BNP (pg/mL)	NYHA function class	Evaluation tool	Influence factor
Hamada(2021)[12]	Prospective cohort study	Japan	Inpatient department	949	81(72–87)	53.70	≥ 50%: 244 vs. 204 (Frail vs. Non-frail)	BNP 278.9 (143.3-499.6)	Class III-IV	The Kihon Checklist	A B C D E F G H I J K L
Valdiviesso(2021)[13]	Cross-sectional study	Italy	Outpatient department	136	59	15.40	Normal: 37.9 ± 13.2 Prefrail: 37.0 ± 13.0 Frail: 41.2 ± 18.0	Not mention	Class I-III	Frailty phenotype	A C E M
Nozaki (2020) [20]	Retrospective cohort study	Japan	Inpatient department	387	74.9 ± 6.1	53.50	46.6 ± 15.8	BNP: 600 (251–1120)	Not mention	The frailty score	N
Komici (2020) [21]	Cross-sectional study	Italy	Inpatient department	128	69.2 ± 4.8	42.20	28.7 ± 8.5	NT-proBNP: 5922.4 ± 15099.9	Class III-IV	The Clinical Frailty Scale (CFS)	A B C I O E P Q H R S
Noda (2023) [22]	Retrospective cohort study	Japan	Inpatient department	922	72 (62–79)	49.90	39.0 (28.0-53.8)	BNP: 688.7 (325.0 -1204.9)	Class III-IV	The frailty score	A B C D G O R T
Son (2018) [23]	Cross-sectional Study	Korea	Outpatient department	190	70.3 ± 7.7	61.60	LVEF ≤ 40: 78 vs. 48 (Frail vs. Non-frail)	Not mention	Class I II IV	The Korean version of the 5-item FRAIL	A B U V W D E X e Y Z
Ribeiro (2022) [24]	Cross-sectional study	Brazil	Outpatient department	106	68 (63.0–74.0)	28.00	34.56 ± 11.87	Not mention	Class I-IV	Frailty phenotype	a Y b R
Son (2022) [25]	Cross-sectional study	Korea	Inpatient department	407	74.18 ± 7.17	28.30	58.87 ± 14.14	NT-proBNP: 342.35 ± 638.79	Class I-IV	The Korean version of the 5-item FRAIL	A E O
Tang (2023) [26]	Case-control study	China	Inpatient department	180	76.65 ± 6.64 vs. 70.20 ± 6.01 (Frail vs. Non-frail)	31.11	41. 19 ± 8. 82 vs. 47. 75 ± 8. 70 (Frail vs. Non-frail)	Not mention	Class II-IV	Frailty phenotype	A E c P H I
Quan (2017) [27]	Cross-sectional study	China	Inpatient department	371	80.5 ± 6.0	21.30	LVEF < 40% 53 vs. 192 (Frail vs. Non-frail)	Not mention	Class II-IV	Frailty phenotype	A I K O E
Gao (2022) [28]	Case-control study	China	Inpatient department	201	74.06 ± 6.26 vs. 73.08 ± 5.80 (Frail vs. Non-frail)	23.88	57.89 ± 9.97 vs. 62.43 ± 7.56 (Frail vs. Non-frail)	NT-proBNP: 659.23 ± 622.51 vs. 342.83 ± 246.57 (Frail vs. Non-frail)	Not mention	The FRAIL scale	A d X K e f Z H F Q h P g
Yang (2021) [29]	Case-control study	China	Inpatient department	95	82.5 ± 7.2	32.6	63.90 ± 4.87 vs. 64.05 ± 6.04 (Frail vs. Non-frail)	Not mention	Class II-III	Frailty phenotype	A i O F h
Yang (2022) [30]	Case‒control study	China	Inpatient department	76	76.21 ± 5.82 vs. 73.18 ± 4.35 (Frail vs. Non-frail)	28.95	51.59 ± 4.07 vs. 53.76 ± 4.22 (Frail vs. Non-frail)	NT-proBNP: 3 968.31 ± 637.24 VS 1 015.76 ± 328.85 (Frail vs. Non-frail)	Class II-IV	The FRAIL scale	A E O j H F P G
Wang (2022) [31]	Cross-sectional study	China	Inpatient department	162	75.68 ± 5.67 VS 70.50 ± 5.92	32.72	Not mention	Not mention	Class I-IV	Frailty phenotype	A U W k E O D
Notes: A: age; B:gender; C: body mass index BMI; D: prior HF hospitalization; E: NYHA function class; F: albumin; G: B-type natriuretic peptide BNP; H: haemoglobin; I: renal insufficiency; J: living alone; K: cerebrovascular accident CVA; L: atrial fibrillation; M: middle arm muscle circumference; N: rising time; O: comorbidity; P: LVEF; Q: NT-proBNP N-terminal pro-B-type natriuretic peptide; R: C-reactive phase protein CRP; S: galectin-3 Gal-3; T: hepato-renal dysfunction; U: education; V: no job; W: no spouse; X: hypertension; Y: no diuretics; Z: depressed; a: no β-blocker; b: functional capacity; c: aortic dimension; d: duration of hospitalization; e: diabetes; f: chronic obstructive pulmonary disease COPD; g: high-density lipoprotein cholesterol HDL-C; h: left atrial diameter; i: polypharmacy; j: nutrition risk; k: income/month

Quality Assessment

We evaluated the quality of all included studies with appropriate tools independently. The overall risk of bias was considered acceptable for most of the included studies. For details see (Supplementary material, Appendix C).

Meta-analysis and descriptive analysis of the influencing factors of frailty in CHF patients

Meta-analysis of the influencing factors of frailty in CHF patients

36 relevant factors were mentioned all together in the included studies. A total of 20 factors affecting frailty in CHF patients were extracted from 13 studies for meta-analysis. The meta-analysis identified nine influencing factors associated with frailty in CHF patients: age, NYHA functional class, albumin, haemoglobin, cerebrovascular accidents, number of comorbidities, LVEF, duration of hospitalization and left atrial diameter. The combined ORs of age (OR = 1.11, 95%CI = 1.07–1.16, I² = 80%) (Fig. 2A) and NYHA functional class (OR = 3.15, 95% CI = 2.46–4.04, I² = 27%) (Fig. 2B) increased the risk of frailty in CHF patients. Albumin (OR = 0.86, 95% CI = 0.77–0.95, I² = 0%) (Fig. 2C), haemoglobin (OR = 0.86, 95% CI = 0.76–0.97, I² = 62%) (Fig. 2D) and LVEF (OR = 0.88, 95% CI = 0.78–0.99, I² = 68%) (Fig. 3A) were associated with decreased odds of frailty in CHF patients. CHF patients who had a longer hospital length of stay (OR = 1.14, 95% CI = 1.05–1.23, I² = 0%) (Fig. 3B) and a wider left atrial diameter (OR = 1.12, 95% CI = 1.05–1.20, I² = 0%) (Fig. 3C) were more likely to suffer from frailty. The odds of frailty were higher among CHF patients with than without cerebrovascular accidents (OR = 2.13, 95% CI = 1.49–3.06, I² = 0%) (Fig. 3D) and more comorbidities (OR = 1.24, 95%CI = 1.05–1.47, I² = 41%) (Fig. 3E).

The combined results indicated that males (OR = 0.77, 95%CI = 0.35–1.65, I² = 60%), BMI (OR = 0.97, 95%CI = 0.91–1.03, I² = 60%), BNP (OR = 1.06, 95%CI = 0.90–1.24, I² = 63%), NT-proBNP (OR = 1.45, 95%CI = 0.62–3.35, I² = 84%), eGFR (OR = 0.99, 95%CI = 0.98-1.00, I² = 0%), CRP(OR = 1.30, 95%CI = 0.93–1.84, I² = 96%), hypertension(OR = 1.04, 95%CI = 0.32–3.38, I² = 68%), prior HF hospitalization (OR = 1.15, 95%CI = 0.88–1.51, I² = 65%), no spouse (OR = 1.66, 95%CI = 0.85–3.23, I² = 0%), depression (OR = 1.16, 95%CI = 0.06–20.89, I² = 91%), and no diuretics(OR = 0.98, 95%CI = 0.52–1.83, I² = 67%) showed non-significant results on frailty in patients with CHF. Details are shown in Table 2.

Table 2

**Pooled relevant factors of frailty in CHF patients**
Influence factor	Heterogeneity			Model	OR	95% CI	P-value
Influence factor	I²(%)	Q	P	Model	OR	95% CI	P-value
Male gender	60	5.00	0.08	Random	0.77	0.35, 1.65	0.50
BMI	60	7.53	0.06	Random	0.97	0.91, 1.03	0.31
BNP	63	5.35	0.07	Random	1.06	0.90, 1.24	0.49
NT-proBNP	84	6.40	0.01	Random	1.45	0.62, 3.35	0.39
eGFR	0	0.28	0.60	Fixed	0.99	0.98, 1.00	0.05
CRP	96	49.38	P < 0.001	Random	1.30	0.93, 1.84	0.13
Hypertension	68	3.15	0.08	Random	1.04	0.32, 3.38	0.95
Prior HF hospitalization	65	8.50	0.04	Random	1.15	0.88,1.51	0.32
No spouse	0	0.12	0.73	Fixed	1.66	0.85, 3.23	0.14
Depression	91	11.55	P < 0.001	Random	1.16	0.06, 20.89	0.92
No diuretics	67	3.02	0.08	Random	0.98	0.52, 1.83	0.94
Notes: BNP: B-type natriuretic peptide; NT-proBNP: N-terminal pro-B-type natriuretic peptide

Descriptive analysis of the influencing factors of frailty in CHF patients

A descriptive analysis of the other 16 factors indicated that two factors decreased the risk of frailty in CHF, and six factors increased the risk. (ⅰ) Middle arm muscle circumference and functional capacity were shown to be protective factors against frailty in patients with CHF; (ⅱ) The risk factors for frailty in patients with CHF included hepato-renal dysfunction, rising time, no β-blocker, polypharmacy, and malnutrition risk; (ⅲ) Education, living alone, no job, income/month, diabetes, COPD, HDL-C, aortic dimension, and atrial fibrillation showed nonsignificant effects on frailty in patients with CHF.

Sensitivity analysis and publication bias

Regarding the effect of age, male gender, BMI, CRP, prior HF hospitalization, BNP on frailty in patients with CHF, when we removed individual studies one by one, there was no significant change in the results of the heterogeneity test and meta-analysis.

Regarding the effect of haemoglobin and LVEF on frailty in CHF patients was modified when data from individual studies were removed from the analysis one at a time. Evidence of publication bias was observed through Egger’s test for the effect of age on frailty in patients with CHF (P < 0.05).

To the best of our knowledge, this paper is the first review, through descriptive analysis and meta-analysis, to systematically evaluate the influencing factors affecting frailty in CHF patients. As seen from the results of this systematic review and meta-analysis, the incidence rate of frailty in patients with CHF was 41.83%, and age, NYHA functional class, albumin, haemoglobin, cerebrovascular accidents, comorbidity, duration of hospitalization, LVEF, left atrial diameter were associated with frailty in patients with CHF. In previous reports, while some studies have explored the factors that influence frailty in CHF patients, the articles each focused on a single factor, and their conclusions were controversial [32, 33].

Our finding was consistent with the studies conducted by He et al and Meng et al [34, 35], indicating that age was a significant factor associated with frailty in patients with CHF. A systematic review supported this, revealing that older patients with HF were six to seven times more likely to be frail than non-frail individuals [36]. A systematic review also supports this, the risk of frailty increases with age in CHF patients, the older the CHF patients, the higher the risk of frailty [37, 38]. This can be attributed to a decline in activities of daily living, reduced handgrip strength, and slower gait speed that commonly occur with ageing. Combined with the lower activity tolerance of CHF patients and poor immune function, these changes eventually lead to frailty [39]. Ageing is also associated with DNA damage, impaired autophagy and elevated oxidative stress due to mitochondrial dysfunction, which could be considered an accelerated form of frailty in CHF patients [40].

Our research also suggests a strong correlation between a poor NYHA classification and frailty in CHF patients. Some studies [41–43] have demonstrated that changes in cardiac structure and function including elevated B-type natriuretic peptide, abnormal left ventricular structures and decrease in stroke volume worsen frailty. Cardiac function decline contributes to dysfunction of organ systems and functional losses. Meanwhile, patients with poor cardiac function are more likely to restrict their movements, leading to skeletal muscle atrophy, which increases the likelihood of developing frailty [7]. Studies have shown that engaging in physical activity [44, 45], such as resistance training, can potentially reverse or delay the muscle wasting process. This suggests that implementing rehabilitation training during a patient’s stable phase to improve functional capacity may help prevent or slow down the progression of frailty.

The systematic review and meta-analysis highlighted that nutritional management and nutritional support may be beneficial to reducing the risk of frailty in patients with CHF. We found that albumin, haemoglobin and MAMC were protective factors against frailty in individuals with heart failure. In contrast, CHF patients with malnutrition risk were more inclined to be frail than their counterparts. Chaves and colleagues [46] conducted a cross-sectional study that mildly low and low-normal haemoglobin levels were independently associated with increased frailty risk in community-dwelling older women, and cardiovascular diseases such as CHF decreased the ability of compensatory reactions to anaemia, leading to increased susceptibility. Additionally, malnutrition can lead to a decrease in protein synthesis and muscle mass, causing the risk of and progression of sarcopenia, which is an important aetiological factor in the development of frailty [47].

Frailty is considered the accumulation of biological deficits, and multiple chronic diseases are manifestations of biological deficit accumulation [48]. In this study, the CIRS-CI scale, comorbidity score and number of comorbidities contributed to frailty in patients with CHF, and the results were similar to the findings of Vetrano and colleagues [49]. Tazzeo et al found that in multimorbidity patterns, cardiovascular diseases, and neuropsychiatric diseases were more strongly associated with physical frailty than other diseases in both cross-sectional and longitudinal dimensions [50]. Our results also demonstrated that cerebrovascular accidents had a significant effect on the risk of frailty in CHF. Hence, it is crucial to establish an early warning mechanism for HF patients with cerebrovascular diseases and implement appropriate interventions to prevent frailty. CHF patients with multiple conditions usually rely on polypharmacy. Gnjidic and Hilmer have indicated that [51] there are plausible mechanisms by which drugs may affect the development of frailty, including weight loss, balance disorders, poor nutritional status, and functional deterioration. Observing the possible benefits of reducing polypharmacy and improving the precise medication rate to delay frailty need to be studied further.

In contrast to previous studies, our study did not find a significant difference in gender between frail and non-frail individuals. Conversely, this contradicts the majority of previous studies which reported a higher proportion of frailty among females than males [35, 52]. However, it is important to note that our study had strict limitations on the articles included for analysis, which may have affected our ability to observe gender differences in frailty over long periods of time. Future research using different study designs may provide further insights into the relationship between gender and frailty.

Previous studies showed [53, 54] a relationship between increased inflammation and frailty regardless of the presence of significant clinical comorbidity. However, the C-reactive phase protein also showed nonsignificant results on frailty in patients with HF in our study. Ribeiro et al [24] collected elevated sensitivity C-reactive protein by immunoturbidimetry analysis, and Noda analysed the relationship between log CRP with frailty [22]. The difference in the detection method and calculation method between the three studies brings heterogeneity.

Psychiatric disease conditions such as depression and anxiety have a negative effect on physical frailty in patients with HF in the findings of Fried et al [55], but depression was not associated with frailty in our study. A relatively small sample size would most likely have resulted in the finding of a nonsignificant association between frailty and depression.

In addition, the influence of haemoglobin and LVEF on frailty in CHF patients in our review should be treated with careful caution. The unstability of the results due to the limited number of studies with heterogeneity call for more robust evidence. there was possible evidence of publication bias in our systematic review and meta-analysis. Although we tried to minimize potential publication bias by a comprehensive collection of the factors reported to affect frailty in CHF patients. Reasons for this bias including firstly the nature of observational study design, the limited number of studies with heterogeneity were the origin of publication bias. Secondly, favourable results were more likely to be reported also caused the risk. Thirdly, the included population was not set as the elderly in the study of Valdiviesso et al [13], and the age of participants ranged from 24 to 81 years, which increased the likelihood of publication bias.

Limitations and strengths

This review features participants from a diverse range of study designs, geographical regions, and ethnic backgrounds, and we combine descriptive analysis with meta-analysis, to comprehensively recognize the influencing factors affecting frailty in CHF patients. Since we only retrieved published literature, articles published in English and Chinese, and the document collection may be incomplete. Future studies should consider other languages and grey literature. In addition, the relatively small size of some studies [29, 30], the moderate risk bias of two articles [25, 31], the heterogeneity in study design and the evidence of publication bias calls for robust, better-designed, and uniform standard studies. Third, the influencing factors included in the studies were scattered, so a meta-analysis of some of the influencing factors could not be performed in this study.

The results of this systematic review and meta-analysis showed that age, NYHA functional class, albumin, haemoglobin, cerebrovascular accidents, comorbidity, duration of hospitalization, LVEF, left atrial diameter were associated with frailty in patients with CHF. The findings of this study serve as a reminder of the pressing need to prioritize the identification and management of frailty in CHF patients. It is crucial to enhance frailty assessment and intervene in risk factors as early as possible. Additionally, large, multicentre, prospective cohort studies should be conducted to further clarify the influencing factors of frailty in CHF patients and develop disease prediction models based on algorithms for more accurate prediction of frailty risk.

CHF	Chronic heart failure
EMBASE	Excerpt Medica Database
CINAHL	Cumulative Index to Nursing and Allied Health Literature
CBM	Chinese Biological Medicine
CNKI	China national knowledge infrastructure
NYHA	New York Heart Association
LVEF	Left ventricular ejection fraction
HF	Heart failure
BMI	Body mass index
PRISMA	Preferred Reporting Items for Systematic Reviews and Meta-Analyses
PROSPERO	International prospective register of systematic reviews
RR	Relative risk
BNP	B-type natriuretic peptide
NT-proBNP	N-terminal pro-B-type natriuretic peptide
CI	Confidence interval
AHRQ	Agency for Health care Research and Quality
OR	Odd risk
CVA	Cerebrovascular accident
CRP	C-reactive phase protein
Gal-3	Galectin-3
COPD	Chronic obstructive pulmonary disease
HDL-C	High-density lipoprotein cholesterol
Scr	Serum creatinine
eGFR	Estimated Glomerular Filtration Rate
CIRS-CI	Comorbidity Index Disease Cumulative Score-Comorbidity Index
MAMC	Middle arm muscle circumference
DNA	Deoxyribonucleic acid

Acknowledgements

We would like to thank all authors of studies included in this systematic review and meta-analysis.

Authors’ contributions

Si Liu: Conceptualization, methodology, formal analysis and writing-original draft. Xiao-yun Xiong: Conceptualization, supervision, validation and writing revision. Meng-Die Liu: Methodology and formal analysis. Qin Xiang and Mei-jun Zhang: Data curation and validation. Hua Chen: interpretation of data. All authors read and approved the final manuscript.

Funding

This research received a grant from the National Natural Science Foundation of China (No. 72064028).

Availability of data and materials

All data analysed in this study are available from the corresponding author up on a reasonable request.

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Author details

¹Nursing Department, The Second Affiliated Hospital, Jiangxi Medical College, Nan Chang University, Nan Chang, Jiang Xi, People’s Republic of China. ²School of Nursing, Jiangxi Medical College, Nan Chang University, Nan Chang, Jiang Xi, People’s Republic of China.

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No competing interests reported.

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Associated factors of frailty in patients with chronic heart failure: A systematic review and meta-analysis

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Abstract

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Introduction

Methods

Search Strategy

Selection Criteria

Data Collection and Quality Assessment

Statistical Analysis

Results

Characteristics of Studies

Quality Assessment

Meta-analysis and descriptive analysis of the influencing factors of frailty in CHF patients

Meta-analysis of the influencing factors of frailty in CHF patients

Descriptive analysis of the influencing factors of frailty in CHF patients

Sensitivity analysis and publication bias

Discussion

Limitations and strengths

Conclusion

Abbreviations

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References

Additional Declarations

Supplementary Files

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