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Frailty and risks of all-cause and cause-specific death in community-dwelling adults: a systematic review and meta-analysis

BMC Geriatrics volume 22, Article number: 725 (2022) Cite this article

Abstract

Background

The associations of frailty with all-cause and cause-specific mortality remain unclear. Therefore, we performed this meta-analysis to fill this gap.

Methods

We searched the PubMed and Embase databases through June 2022. Prospective cohort studies or clinical trials examining frailty were evaluated, and the multiple adjusted risk estimates of all-cause and cause-specific mortality, such as death from cardiovascular disease (CVD), cancer, respiratory illness, dementia, infection, and coronavirus disease 2019 (COVID-19), were included. A random effects model was used to calculate the summary hazard ratio (HR).

Results

Fifty-eight studies were included for the qualitative systematic review, of which fifty-six studies were eligible for the quantitative meta-analysis, and the studies included a total of 1,852,951 individuals and more than 145,276 deaths. Compared with healthy adults, frail adults had a significantly higher risk of mortality from all causes (HR 2.40; 95% CI 2.17–2.65), CVD (HR 2.64; 95% CI 2.20–3.17), respiratory illness (HR 4.91; 95% CI 2.97–8.12), and cancer (HR 1.97; 95% CI 1.50–2.57). Similar results were found for the association between prefrail adults and mortality risk. In addition, based on the studies that have reported the HRs of the mortality risk per 0.1 and per 0.01 increase in the frailty index, we obtained consistent results.

Conclusions

The present study demonstrated that frailty was not only significantly related to an increased risk of all-cause mortality but was also a strong predictor of cause-specific mortality from CVD, cancer, and respiratory illness in community-dwelling adults. More studies are warranted to clarify the relationship between frailty and cause-specific mortality from dementia, infection, and COVID-19.

Trial registration

PROSPERO (CRD42021276021).

Peer Review reports

Background

Global ageing results in extensive concerns about various geriatric syndromes [1]. As one of the most common geriatric syndromes, frailty is a condition of an attenuated physiological reserve, which is characterized by an impaired response and an increased vulnerability to stressor events [2]. Considering that frailty is a frequently used clinical indicator of functional ageing, the prevalence of frailty varies from 4.0 to 59.1% in community-dwelling older people [3].

Based on the different theories, various tools have been established to assess frailty status. One of the most popular assessment tools is the frailty phenotype (FP), which categorizes the population into frail, prefrail, and robust or not frail according to five criteria (unintentional weight loss, self-reported exhaustion, low energy expenditure, a slow gait speed, and a weak grip strength) [4]. Another common tool is the frailty index (FI), which is measured as the proportion of accumulated deficits and defines frailty by predefined cut-points [5]. The FRAIL scale (FS) is also a widely used frailty screening tool that can recognize either a frail or a prefrail status quickly in terms of five self-reported items: fatigue, resistance, ambulation, illnesses, and loss of weight [6]. Despite conceptual differences, these tools have all been well validated in subsequent studies and have been widely used in clinical and scientific research [7].

Many studies have explored the association between frailty and various adverse health outcomes, such as falls, fractures, disabilities, institutionalization, hospitalization, and death, in the general population, especially in older adults [8,9,10]. To date, numerous studies have reached a consensus that frailty is a predictor of mortality. However, these studies were usually limited to mortality in specific populations, such as perioperative patients, [11] nursing home residents, [12] and patients with diseases such as tumours, [13] heart failure, [14] coronavirus disease 2019 (COVID-19), [15] etc. Furthermore, owing to the relatively small sample sizes used to evaluate cause-specific mortality in previous research, only all-cause mortality was regarded as the endpoint in most relevant systematic reviews, meta-analyses and umbrella reviews [16,17,18,19,20,21,22]. Although a 2017 meta-analysis investigated the associations of frailty with morbidity and mortality from cardiovascular disease (CVD), only 2 included studies provided data on CVD-related mortality, and these data were limited to adults older than 65 and survivors after an acute coronary syndrome [23].

An ageing society is associated with a higher risk of frailty and prefrailty in the community population [24]. In this context, emerging community-based studies have provided more evidence of frailty and all-cause mortality and cause-specific deaths from CVD, cancer, respiratory illness, dementia, infection, COVID-19, etc. [25,26,27,28] Nonetheless, the conclusion is still ambiguous. Previous meta-analyses mainly focused on single frailty assessment tools [16,17,18], and there is no systematic review and meta-analysis on the relationship between frailty status and cause-specific mortality thus far. Therefore, to quantify the associations of frailty status with all-cause and cause-specific mortality, we performed this meta-analysis.

Methods

We performed this systematic review and meta-analysis following the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) guidelines [29]. No ethics committee approval was required for this study. The study protocol was registered at PROSPERO (CRD42021276021) [30].

Literature search

We performed an electronic literature search of the PubMed and Embase databases from inception to August 2021 to identify the relevant studies using a combination of terms: “frailty” or “frail” and “mortality” or “death”. An updated literature search was performed in June 2022. The language was restricted to English. A detailed description of the search strategy is supplemented in Supplementary Table 1. We manually checked the references of pertinent articles for additional studies and contacted the original author when necessary.

Study selection

Two of the authors (Y.P. and G.C.Z.) independently screened the citations in accordance with the preset inclusion and exclusion criteria. Any disagreements were settled by consulting a third reviewer (L.Z.).

The included studies were required to fulfil the following criteria: 1) prospective cohort studies or clinical trials reporting all-cause or cause-specific mortality, such as deaths from CVD, cancer, respiratory illness, dementia, infection, and COVID-19; 2) the study participants were adults over the age of 18 in community-dwelling settings; 3) frailty status was defined by one of the three most commonly used tools (i.e., the FP, FI, or FS); 4) the multiple adjusted risk estimates with corresponding 95% confidence intervals (CIs) were available; and 5) when the same cohort was used in multiple publications, the latest published one with the largest number of events was included.

Studies were excluded if they 1) defined frailty status by other evaluation methods; 2) investigated the association of changes in frailty status or the combined impact of other factors with mortality; 3) focused on non-community participants, such as those in hospitals, nursing homes or patients with certain diseases; and 4) were conference abstracts, cross-sectional analyses, review articles, editorials, letters, or published errata.

Data extraction and quality assessment

Two investigators (Y.P. and G.C.Z.) independently extracted the data and evaluated the methodological quality of the selected studies. Disagreements were resolved by discussion with a third author (L.Z.). Using a predesigned data extraction form, the following information was recorded: the name of the first author, publication year, study location, mean age, follow-up years, sample size, sex, assessment tools, status of frailty, cause and number of deaths, outcome assessment, fully adjusted risk estimate and the corresponding 95% CIs, and adjustment factors. Methodological quality was evaluated through the Newcastle–Ottawa Quality Assessment Scale (NOS) [31]. Studies with a score of 6 or more points were deemed to be of high quality, and the maximum score was 9 for each study.

Statistical analysis

In our study, the hazard ratio (HR) was used as a common measure to estimate the combined effect size, and the odds ratio (OR) was regarded as equivalent. We conducted a random effects meta-analysis when more than 3 studies provided the same effect measure for all-cause or certain cause-specific mortality in terms of the following categories: robust, prefrail, and frail status. For some studies [32,33,34,35,36] that provided HRs categorized by sex, age range or severity of frailty status, we combined the HRs through a random effects model to yield a summary HR.

Since some studies provided HRs per 0.1 or 0.01 increase in the FI, we also combined the HRs for mortality per 0.1 or 0.01 FI increment. For one study [27] that evaluated the mortality from ischaemic heart disease and cerebrovascular disease and another study [28] that evaluated the mortality from stroke, heart attack, and other CVDs separately, we combined the HRs through a random effects model to yield a summary HR for the mortality from CVD, since these diseases are important components of CVD. Circulatory diseases, heart disease, and CVD were regarded as equivalent diseases in our analysis. Likewise, neoplasms were deemed as cancer.

The Q statistic (significance set at P < 0.10) and I2 statistic (I2 > 75.0%, 50.0–75.0, and < 50.0% signified substantial, moderate, and low heterogeneity, respectively) were adopted to quantify the heterogeneity across the studies. To clarify the potential source of heterogeneity, we conducted sensitivity analyses by omitting one study in turn, repeating the meta-analysis through a fixed effects model, and changing the eligibility criteria. It is worth noting that the sensitivity analyses were conducted only for all-cause and CVD mortality due to the limited number of studies that evaluated cancer and respiratory illness mortality.

We selected Begg’s and Egger’s tests to determine if there was publication bias in our meta-analysis. STATA software (version 15.0, StataCorp LP, College Station, Texas, USA) was used for all statistical analyses, and the statistical significance level was defined as P < 0.05 under a two-sided test.

Results

Search results

Through an initial systematic search, a total of 16,697 citations were identified from the databases, of which 12,685 citations remained after the removal of duplicate studies. After reviewing the titles and abstracts, 12,555 irrelevant articles were excluded. A total of 130 studies remained for further full-text assessment, and a list of studies excluded after a detailed assessment based on full text are presented in Supplementary Table 2. In addition, 7 additional studies were found through reference lists of pertinent articles. Finally, 58 studies were included for the qualitative systematic review, and 56 studies were eligible for the quantitative meta-analysis. The detailed selection process and reasons for exclusion are shown in the flowchart (Fig. 1).

Fig. 1
figure 1

Flowchart of the included studies

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Study characteristics and quality assessment

The main characteristics of the included studies are summarized in Table 1. The 58 studies included 1,852,951 individuals and more than 145,276 deaths, involving all-cause death and specific causes of death, including CVD, cancer, respiratory illness, dementia, infection, and COVID-19. The death-related information was available from sources such as the death registry, death certificates, National Death Index, structured interview, and standard report. The study locations were spread all around the world. Most of the included studies were prospective cohort studies, apart from the study by Farooqi et al., [37] which was a pooled analysis of prospective clinical trials. The follow-up duration ranged from 0.6 to 30 years. The mean age of the baseline population varied from 44·0 to 93.7 years. In addition to six studies [38,39,40,41,42,43] that only enrolled male or female individuals, the other studies all consisted of both sexes. Almost all the identified studies provided corresponding HRs for the risk of death, with potential adjustment factors including age and sex, but three studies [25, 44, 45] reported the OR.

Table 1 The characteristics of the included studies
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With respect to the assessment of frailty, a total of 24 studies adopted the FP. In terms of the five established criteria, individuals meeting three or more items were regarded as frail, those meeting one or two items as prefrail, and those with no items as not frail or as robust. The FI was used in 30 studies, which usually divided the participants into two groups (frail and non-frail), three groups (frail, prefrail, and robust), and four categories (mostly frail, moderately frail, prefrail, and robust) based upon the different total numbers of baseline deficits and the different cut-off points. Moreover, 17 of these studies provided the HRs per 0.01 or per 0.1 increase in the FI and per increase in one deficit, respectively. Seven studies reported the frailty status by the FS, in which the categories of robust, prefrail, and frail were defined as individuals who had 0, 1 or 2, and 3 to 5 items, respectively.

Regarding the methodological quality, all the included studies were generally of high quality according to the NOS scale. As shown in Supplementary Table 3, all the identified studies scored from 6 to 9 points.

Frailty and all-cause mortality

Fifty-four studies [4, 26,27,28, 32,33,34,35,36,37, 39,40,41,42,43,44,45,46,47,48,49, 51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76, 78,79,80,81,82,83,84,85] were included in the meta-analysis of the association between frailty status and all-cause mortality, and the summary HRs were calculated using a random-effects model. As depicted in Fig. 2, compared with the robust group, the frail group had a significantly higher risk of all-cause mortality (pooled HR = 2.40, 95% CI 2.17–2.65; I2 = 91.2%, Pheterogeneity < .001; 48 studies; Fig. 2A). Similarly, the prefrail group also displayed a higher all-cause death risk than the robust group (pooled HR = 1.42, 95% CI 1.34–1.51; I2 = 81.3%, Pheterogeneity < .001; 36 studies; Fig. 2B). In addition, based on seven studies [27, 33,34,35, 37, 40, 80] that reported HRs of the all-cause mortality risk per 0.1 increase in the FI (pooled HR = 1.47, 95% CI 1.29–1.67; I2 = 98.2%, Pheterogeneity < .001; 7 studies; Fig. 2C) and nine studies [46, 54, 55, 65, 67, 69, 76, 79, 83] that reported HRs of the all-cause mortality risk per 0.01 increase in the FI (pooled HR = 1.04, 95% CI 1.03–1.05; I2 = 87.9%, Pheterogeneity < .001; 9 studies; Fig. 2D), we also confirmed that frailty was a significant predictor of all-cause mortality. In addition, one study [32] demonstrated an increased risk of all-cause death per increase in one deficit (HR = 1.04, 95% CI 1.01–1.07 for men, HR = 1.08, 95% CI 1.06–1.11 for women). These studies consistently suggested that frailty status, as defined by the FI using various ways, was linked to an increased all-cause death risk.

Fig. 2
figure 2

Forest plots of the all-cause mortality risk according to the frailty status. A The pooled HR and 95% CI of the all-cause mortality in the frail group compared with the robust group; B The pooled HR and 95% CI of the all-cause mortality in the prefrail group compared with the robust group; C The pooled HR of the all-cause mortality risk per 0.1 increase in the frailty index score; D The pooled HR of the all-cause mortality risk per 0.01 increase in the frailty index score. CI = confidence interval, HR = hazard ratio

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Considering that there was a high degree of heterogeneity across the studies, we performed further sensitivity analyses. A fixed effects model and the removal of any single study had little effect on the overall pooling risk estimate. Of note, although a similar outcome was found in subgroup analyses categorized by different frailty assessment tools (FP, FI, and FS), we found markedly decreased heterogeneity when the included studies were restricted to those using the FS to assess frailty (Fig. 2).

Marginal evidence of publication bias was found for the association between frailty status and all-cause mortality by Begg’s test and Egger’s test (Begg’s test P = 0.004–0.463 and Egger’s test P = 0.030–0.918).

Frailty and CVD mortality

Nine studies [26,27,28, 34, 35, 37, 38, 48, 49] were included in the random effects meta-analysis of the effect of frailty status on the CVD mortality risk. As shown in Fig. 3, both the frail group (pooled HR = 2.64, 95% CI 2.20–3.17; I2 = 89.8%, Pheterogeneity < .001; 8 studies; Fig. 3A) and the prefrail group (pooled HR = 1.63, 95% CI 1.45–1.83; I2 = 85.3%, Pheterogeneity < .001; 8 studies; Fig. 3B) suggested an obviously increased risk for CVD death compared to the robust group with substantial heterogeneity. As expected, a similar result could be found by combining the HRs of the CVD mortality risk for each 0.1 increase in the FI (pooled HR = 1.50, 95% CI 1.30–1.74; I2 = 98.2%, Pheterogeneity < .001; 4 studies; Fig. 3C). In addition, one study [46] provided the HR of the CVD mortality risk for each 0.01 increase in the FI (HR = 1.05, 95% CI 1.03–1.06), and another study [50] reported that there was an increased CVD death risk with each increase in one deficit (HR = 1.03, 95% CI 0.99–1.08 for men, HR = 1.13, 95% CI 1.09–1.17 for women). The relationship between the frailty status as defined by the FI and the CVD mortality risk appeared to be sex-specific.

Fig. 3
figure 3

Forest plots of the CVD mortality risk according to the frailty status. A The pooled HR and 95% CI of the CVD mortality in the frail group compared with the robust group; B The pooled HR and 95% CI of the CVD mortality in the prefrail group compared with the robust group; C The pooled HR of the CVD mortality risk per 0.1 increase in the frailty index score. CI = confidence interval, CVD = cardiovascular disease, HR = hazard ratio

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In reference to the sensitivity analysis, none of the three abovementioned methods altered the initial results. Moreover, marginal evidence of publication bias was detected for the association between frailty status and CVD mortality (Begg’s test P = 0.37–1.00 and Egger’s test P = 0.009–0.026).

Frailty and cancer and respiratory illness mortality

Five individual studies [26,27,28, 34, 35] were eligible for the evaluation of the association between frailty status and cancer and respiratory illness mortality. The random-effects meta-analysis revealed that frailty could statistically increase the risk of death from cancer (pooled HR = 1.97, 95% CI 1.50–2.57; I2 = 82.9%, Pheterogeneity < .001; 4 studies; Fig. 4A) and respiratory illness (pooled HR =4.91, 95% CI 2.97–8.12; I2 = 87.2%, Pheterogeneity < .001; 4 studies; Fig. 5A). Similarly, compared with the robust group, the individuals in the prefrail group had a 1.37-fold higher risk of death from cancer (95% CI 1.10–1.71; I2 = 81.6%, Pheterogeneity = 0.001; 4 studies; Fig. 4B) and a 2.16-fold higher risk of death from respiratory illness (95% CI 1.68–2.79; I2 = 53.9%, Pheterogeneity = 0.089; 4 studies; Fig. 5B). In addition, with the three studies [27, 34, 35] that reported the HRs of the cancer and respiratory illness mortality risk per 0.1 increase in the FI, a pooled HR of 1.12 (95% CI = 1.04–1.21; I2 = 87.2%, Pheterogeneity < .001; 3 studies; Fig. 4C) for cancer death and 1.59 (95% CI = 1.02–2.46; I2 = 99.1%, Pheterogeneity < .001; 3 studies; Fig. 5C) for respiratory illness death was obtained.

Fig. 4
figure 4

Forest plots of the cancer mortality risk according to the frailty status. A The pooled HR and 95% CI of the cancer mortality in the frail group compared with the robust group; B The pooled HR and 95% CI of the cancer mortality in the prefrail group compared with the robust group; C The pooled HR of the cancer mortality risk per 0.1 increase in the frailty index score. CI = confidence interval, HR = hazard ratio

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Fig. 5
figure 5

Forest plots of the respiratory illness mortality risk according to the frailty status. A The pooled HR and 95% CI of the respiratory illness mortality in the frail group compared with the robust group; B The pooled HR and 95% CI of the respiratory illness mortality in the prefrail group compared with the robust group; C The pooled HR of the respiratory illness mortality risk per 0.1 increase in the frailty index score. CI = confidence interval, HR = hazard ratio

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Discussion

Previous evidence [19, 20, 22] has shown that there is a significant association between frailty and all-cause mortality. The present study revealed positive correlations between frailty, prefrailty, and all-cause mortality and further demonstrated that frailty was a strong predictor of cause-specific mortality from CVD, cancer, and respiratory illness. To the best of our knowledge, this is the first systematic review and meta-analysis to explore the influence that frailty exerts on cause-specific mortality among adults living in communities. Specifically, we found an almost 2-fold increased risk in the frail group and a 1.5-fold increased risk in the prefrail group for all-cause mortality, CVD mortality, and cancer mortality, respectively. Of note, the risk of respiratory illness mortality was approximately doubled in both the frail and prefrail groups, with 4.91- and 2.16-fold higher risks compared to the robust group. In addition, the all-cause and cause-specific mortality risk per 0.1 and per 0.01 increase in the FI showed consistently significant results, which indicated that the risk of death increased with the increase in the frailty status (i.e., from prefrailty worsening to frailty).

We did not perform a meta-analysis on the association between frailty status and mortality from dementia, infection, and COVID-19 due to the limited number of studies, but the research outcomes were still noteworthy. The study by Lohman et al. [26] suggested that frailty was associated with a 2.87 (95% CI = 1.47–5.59) times greater hazard of death from dementia, while prefrailty was not a predictor of dementia mortality. In addition, Jiang et al. [50] found that frailty, as defined by the FI, was not linked to dementia mortality in either sex. Two identified studies [27, 28] independently confirmed that frailty and prefrailty were associated with a higher risk of mortality due to infection. Nevertheless, the statistically significant association between prefrailty and the infection mortality disappeared after adjusting for all the confounding factors, as seen in the further analyses in one study [28]. During the global pandemic of COVID-19, a plethora of studies [15, 86, 87] have reported that an increased COVID-19 mortality risk has been associated with frailty. However, most of these studies focused on patients who were diagnosed with COVID-19 until a recent study by Mak et al., [25] which found that frailty was related to a higher COVID-19 mortality risk in a community population. A dramatically growing number of confirmed cases has raised public attention to determine the effect of frailty on COVID-19 mortality in the general population. The number of studies that evaluated the associations of frailty with dementia, infection, and COVID-19 mortality has been too small to yield reliable results. Thus, more congeneric studies are warranted.

Although substantial heterogeneity existed in our meta-analysis, we only found that the different frailty assessment tools might be the underlying effect factor in the sensitivity analysis. We included studies that defined frailty using one of three widely used tools: the FP, FI, and FS. In fact, when we restricted the includes studies to those only using the FS, the heterogeneity notably declined. There is no consensus regarding the gold standard to assess frailty to date. In recent decades, a vast variety of frailty assessment tools, [4, 77, 88,89,90] such as the FP, FI, FS, Groningen Frailty Indicator, Tilburg Frailty Indicator, Clinical Frailty Scale, etc., have been proposed and well validated. However, some comparative studies [7, 91] have also found substantive differences between these tools in their validity, feasibility, and ability to predict mortality. A 2017 umbrella review [21] examined five systematic reviews to compare the reliability, validity, accuracy, and predictive ability of 34 frailty screening tools in older adults, and found that the FI had good predictive ability and mostly acceptable validity and diagnostic accuracy. Notably, significant heterogeneity was found in the subgroups using the FI as the frailty assessment method (as shown in Fig. 2), which is consistent with a previous systematic review of the all-cause mortality risk according to the FI [16]. First, the FI was constructed based on the different numbers and types of deficits. In addition, the included studies defined frailty with different cut-off points for the FI. Therefore, to reduce the heterogeneity across studies, we need more studies with uniform frailty assessment tools.

Previous research has explored sex and age effects on the association between frailty and mortality risk, but the results are still in dispute. Some studies [17, 50, 92] have found a sex-specific impact of frailty on all-cause and cause-specific mortality. However, a recent meta-analysis found that there was no sex difference in the association of frailty with mortality [93]. In addition, shorter follow-up periods and younger age were found to be potentially associated with a higher mortality risk [16, 50]. However, neither sex nor age or follow-up duration showed any effect on the relationship between frailty and all-cause or cause-specific mortality in the current study. Hence, more large-scale studies are required to identify whether sex, the age threshold, or the follow-up duration can modify the frailty-mortality association.

Several limitations should be considered in our meta-analysis. First, significant heterogeneity was observed in the statistical analysis, which caused concerns about the reliability of the pooled results. However, through sensitivity analyses, we found that the sources of heterogeneity could be partially explained by the different assessment tools adopted to measure frailty. Although the different measuring methods and cut-off points across studies could possibly lead to a misclassification of frailty, the subgroup analysis based on the different frailty assessment tools showed consistent results. Additionally, irrespective of which tools were used to define frailty, both frailty and prefrailty were significantly associated with a higher mortality risk in previous studies [16,17,18]. Moreover, methodological heterogeneity was inevitable in all the meta-analyses, especially the meta-analyses based on observational studies. Second, frailty is a dynamic process that usually progresses to greater frailty (i.e., “worsening”) with ageing but could be reversible by effective interventions [94, 95]. However, because only the baseline frailty status was evaluated in the included studies, we could not overcome the confounding effects from the progression of frailty during the follow-up duration. In addition, even though we extracted the most fully adjusted risk estimates, residual confounding still existed. Third, most included studies ascertained the causes of death from a death registry or a national death database according to the International Classification of Diseases (ICD) codes; thus, inaccurate information under some circumstances might cause a misclassification bias. Fourth, we restricted our search to studies published in English, and this is a possible source of bias. Finally, the number of studies of cause-specific mortality was limited, especially for studies that included cancer and respiratory illness mortality. This hampered further analysis, given that these analysis results were potentially unreliable under the condition that the number of identified studies was less than 10, especially in the sensitivity, subgroup, or meta-regression analyses. Additionally, we only performed a systematic review but not a meta-analysis since there were fewer studies on the associations between frailty and dementia, infection, and COVID-19 mortality.

Conclusion

In conclusion, frailty was not only significantly associated with an increased risk of all-cause mortality but was also a strong predictor of cause-specific mortality from CVD, cancer, and respiratory illness in community-dwelling adults. These findings highlight the importance of frailty interventions in reducing the risk of death in the general population and indicate which population will benefit the most from efficient interventions. Early diagnosis of frailty can help identify high-risk older adults, helping to minimize the risk of prefrail status developing into frail status and even reverse frailty status. In addition, the implementation of therapeutic measures such as physical activity, nutrition support, comorbidities and polypharmacy management could reduce disability, institutionalization, hospitalization, the need for long-term care, medical and social costs, and death. Furthermore, knowing the increased risk stratified by cause of death allows us to make further targeted interventions regarding the natural development of frailty status as well as aid in designing disease-specific interventions to reduce mortality. Nonetheless, these results should be interpreted with caution due to the limited number of studies included in our meta-analysis; thus, more studies are warranted in the future to explore the association of frailty with cause-specific mortality.

Availability of data and materials

All data generated or analysed during this study are available from the included studies in this article.

Abbreviations

CIs:

Confidence intervals

COVID-19:

Coronavirus disease 2019

CVD:

Cardiovascular disease

FI:

Frailty index

FP:

Frailty phenotype

FS:

FRAIL scale

HR:

Hazard ratio

ICD:

International Classification of Diseases

NOS:

Newcastle-Ottawa Quality Assessment Scale

OR:

Odds ratio

References

  1. Inouye SK, Studenski S, Tinetti ME, Kuchel GA. Geriatric syndromes: clinical, research, and policy implications of a core geriatric concept. J Am Geriatr Soc. 2007;55(5):780–91.

    Article  PubMed  PubMed Central  Google Scholar 

  2. Clegg A, Young J, Iliffe S, Rikkert MO, Rockwood K. Frailty in elderly people. Lancet. 2013;381(9868):752–62.

    Article  PubMed  Google Scholar 

  3. Collard RM, Boter H, Schoevers RA, Oude Voshaar RC. Prevalence of frailty in community-dwelling older persons: a systematic review. J Am Geriatr Soc. 2012;60(8):1487–92.

    Article  PubMed  Google Scholar 

  4. Fried LP, Tangen CM, Walston J, Newman AB, Hirsch C, Gottdiener J, et al. Frailty in older adults: evidence for a phenotype. J Gerontol A Biol Sci Med Sci. 2001;56(3):146–56.

    Article  Google Scholar 

  5. Mitnitski AB, Mogilner AJ, Rockwood K. Accumulation of deficits as a proxy measure of aging. Sci World J. 2001;1:323–36.

    Article  CAS  Google Scholar 

  6. Abellan van Kan G, Rolland Y, Bergman H, Morley JE, Kritchevsky SB, Vellas B. The I.a.N.a task force on frailty assessment of older people in clinical practice. J Nutr Health Aging. 2008;12(1):29–37.

    Article  CAS  PubMed  Google Scholar 

  7. Theou O, Brothers TD, Mitnitski A, Rockwood K. Operationalization of frailty using eight commonly used scales and comparison of their ability to predict all-cause mortality. J Am Geriatr Soc. 2013;61(9):1537–51.

    Article  PubMed  Google Scholar 

  8. Chu W, Chang SF, Ho HY. Adverse health effects of frailty: systematic review and Meta-analysis of middle-aged and older adults with implications for evidence-based practice. Worldviews Evid-Based Nurs. 2021;18(4):282–9.

    Article  PubMed  Google Scholar 

  9. Chen KW, Chang SF, Lin PL. Frailty as a predictor of future fracture in older adults: a systematic review and Meta-analysis. Worldviews Evid-Based Nurs. 2017;14(4):282–93.

    Article  PubMed  Google Scholar 

  10. Hsu B, Naganathan V, Blyth FM, Hirani V, Le Couteur DG, Waite LM, et al. Frailty and cause-specific hospitalizations in community-dwelling older men. J Nutr Health Aging. 2020;24(6):563–9.

    Article  CAS  PubMed  Google Scholar 

  11. Oakland K, Nadler R, Cresswell L, Jackson D, Coughlin PA. Systematic review and meta-analysis of the association between frailty and outcome in surgical patients. Ann R Coll Surg Engl. 2016;98(2):80–5.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Zhang X, Dou Q, Zhang W, Wang C, Xie X, Yang Y, et al. Frailty as a predictor of all-cause mortality among older nursing home residents: a systematic review and Meta-analysis. J Am Med Dir Assoc. 2019;20(6):657–63.e4.

    Article  CAS  PubMed  Google Scholar 

  13. Torres-Perez P, Álvarez-Satta M, Arrazola M, Egaña L, Moreno-Valladares M, Villanua J, et al. Frailty is associated with mortality in brain tumor patients. Am J Cancer Res. 2021;11(6):3294–303.

    PubMed  PubMed Central  Google Scholar 

  14. Weng SC, Lin CS, Tarng DC, Lin SY. Physical frailty and long-term mortality in older people with chronic heart failure with preserved and reduced ejection fraction: a retrospective longitudinal study. BMC Geriatr. 2021;21(1):92.

    Article  PubMed  PubMed Central  Google Scholar 

  15. Zhang XM, Jiao J, Cao J, Huo XP, Zhu C, Wu XJ, et al. Frailty as a predictor of mortality among patients with COVID-19: a systematic review and meta-analysis. BMC Geriatr. 2021;21(1):186.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  16. Kojima G, Iliffe S, Walters K. Frailty index as a predictor of mortality: a systematic review and meta-analysis. Age Ageing. 2018;47(2):193–200.

    Article  PubMed  Google Scholar 

  17. Chang SF, Lin PL. Frail phenotype and mortality prediction: a systematic review and meta-analysis of prospective cohort studies. Int J Nurs Stud. 2015;52(8):1362–74.

    Article  PubMed  Google Scholar 

  18. Kojima G. Frailty defined by FRAIL scale as a predictor of mortality: a systematic review and Meta-analysis. J Am Med Dir Assoc. 2018;19(6):480–3.

    Article  PubMed  Google Scholar 

  19. Shamliyan T, Talley KM, Ramakrishnan R, Kane RL. Association of frailty with survival: a systematic literature review. Ageing Res Rev. 2013;12(2):719–36.

    Article  PubMed  Google Scholar 

  20. Vermeiren S, Vella-Azzopardi R, Beckwee D, Habbig AK, Scafoglieri A, Jansen B, et al. Frailty and the prediction of negative health outcomes: a meta-analysis. J Am Med Dir Assoc. 2016;17(12):1163 e1–1163.e17.

    Article  Google Scholar 

  21. Apostolo J, Cooke R, Bobrowicz-Campos E, Santana S, Marcucci M, Cano A, et al. Predicting risk and outcomes for frail older adults: an umbrella review of frailty screening tools. JBI Database System Rev Implement Rep. 2017;15(4):1154–208.

    Article  PubMed  PubMed Central  Google Scholar 

  22. Ekram A, Woods RL, Britt C, Espinoza S, Ernst ME, Ryan J. The association between frailty and all-cause mortality in community-dwelling older individuals: an umbrella review. J Frailty Aging. 2021;10(4):320–6.

    CAS  PubMed  Google Scholar 

  23. Veronese N, Cereda E, Stubbs B, Solmi M, Luchini C, Manzato E, et al. Risk of cardiovascular disease morbidity and mortality in frail and pre-frail older adults: results from a meta-analysis and exploratory meta-regression analysis. Ageing Res Rev. 2017;35:63–73.

    Article  PubMed  PubMed Central  Google Scholar 

  24. Ofori-Asenso R, Chin KL, Mazidi M, Zomer E, Ilomaki J, Zullo AR, et al. Global incidence of frailty and Prefrailty among community-dwelling older adults: a systematic review and meta-analysis. JAMA Network Open. 2019;2(8):e198398.

  25. Mak JKL, Kuja-Halkola R, Wang Y, Hägg S, Jylhävä J. Frailty and comorbidity in predicting community COVID-19 mortality in the U.K. biobank: the effect of sampling. J Am Geriatr Soc. 2021;69(5):1128–39.

    Article  PubMed  PubMed Central  Google Scholar 

  26. Lohman MC, Sonnega AJ, Resciniti NV, Leggett AN. Frailty phenotype and cause-specific mortality in the United States. J Gerontol A Biol Sci Med Sci. 2020;75(10):1935–42.

    Article  PubMed  PubMed Central  Google Scholar 

  27. Fan J, Yu C, Guo Y, Bian Z, Sun Z, Yang L, et al. Frailty index and all-cause and cause-specific mortality in Chinese adults: a prospective cohort study. Lancet Public Health. 2020;5(12):e650–e60.

    Article  PubMed  PubMed Central  Google Scholar 

  28. Grabovac I, Haider S, Mogg C, Majewska B, Drgac D, Oberndorfer M, et al. Frailty status predicts all-cause and cause-specific mortality in community dwelling older adults. J Am Med Dir Assoc. 2019;20(10):1230–5.e2.

    Article  PubMed  Google Scholar 

  29. Moher D, Liberati A, Tetzlaff J, Altman DG. Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. BMJ. 2009;339(jul21 1):2535.

    Article  Google Scholar 

  30. PROSPERO.https://www.crd.york.ac.uk/prospero/display_record.php? ID=CRD42021276021. Accessed 20 Aug 2021.

  31. GA W, Shea BO, D OC. The Newcastle–Ottawa Scale (NOS) for assessing the quality in nonrandomized studies in meta-analyses. http://www.ohri.ca/programs/clinical_epidemiology/oxford.asp. Accessed 26 Aug 2021.

  32. Hou Y, Xu C, Lu Q, Zhang Y, Cao Z, Li S, et al. Associations of frailty with cardiovascular disease and life expectancy: a prospective cohort study. Arch Gerontol Geriatr. 2022;99:104598.

    Article  PubMed  Google Scholar 

  33. Dallmeier D, Braisch U, Rapp K, Klenk J, Rothenbacher D, Denkinger M. Frailty index and sex-specific 6-year mortality in community-dwelling older people: the ActiFE study. J Gerontol A Biol Sci Med Sci. 2020;75(2):366–73.

    PubMed  Google Scholar 

  34. Li X, Ploner A, Karlsson IK, Liu X, Magnusson PKE, Pedersen NL, et al. The frailty index is a predictor of cause-specific mortality independent of familial effects from midlife onwards: a large cohort study. BMC Med. 2019;17(1):94.

  35. Gilmour H, Ramage-Morin PL. Association of frailty and pre-frailty with increased risk of mortality among older Canadians. Health Rep. 2021;32(4):15–26.

    PubMed  Google Scholar 

  36. Bartley MM, Geda YE, Christianson TJH, Shane Pankratz V, Roberts RO, Petersen RC. Frailty and mortality outcomes in cognitively Normal older people: sex differences in a population-based study. J Am Geriatr Soc. 2016;64(1):132–7.

    Article  PubMed  PubMed Central  Google Scholar 

  37. Farooqi MAM, Gerstein H, Yusuf S, Leong DP. Accumulation of deficits as a key risk factor for cardiovascular morbidity and mortality: a pooled analysis of 154 000 individuals. J Am Heart Assoc. 2020;9(3).

  38. Adabag S, Vo TN, Langsetmo L, Schousboe JT, Cawthon PM, Stone KL, et al. Frailty as a risk factor for cardiovascular versus noncardiovascular mortality in older men: results from the MrOS sleep (outcomes of sleep disorders in older men) study. J Am Heart Assoc. 2018;7(10):e008974.

  39. Papachristou E, Wannamethee SG, Lennon LT, Papacosta O, Whincup PH, Iliffe S, et al. Ability of self-reported frailty components to predict incident disability, falls, and all-cause mortality: results from a population-based study of older British men. J Am Med Dir Assoc. 2017;18(2):152–7.

    Article  PubMed  PubMed Central  Google Scholar 

  40. Ravindrarajah R, Lee DM, Pye SR, Gielen E, Boonen S, Vanderschueren D, et al. The ability of three different models of frailty to predict all-cause mortality: results from the European male aging study (EMAS). Arch Gerontol Geriatr. 2013;57(3):360–8.

    Article  PubMed  Google Scholar 

  41. Ensrud KE, Ewing SK, Taylor BC, Fink HA, Stone KL, Cauley JA, et al. Frailty and risk of falls, fracture, and mortality in older women: the study of osteoporotic fractures. J Gerontol - A Biol Sci Med Sci. 2007;62(7):744–51.

    Article  PubMed  Google Scholar 

  42. Woods NF, LaCroix AZ, Gray SL, Aragaki A, Cochrane BB, Brunner RL, et al. Frailty: emergence and consequences in women aged 65 and older in the Women's Health Initiative observational study. J Am Geriatr Soc. 2005;53(8):1321–30.

    Article  PubMed  Google Scholar 

  43. Susanto M, Hubbard RE, Gardiner PA. Validity and responsiveness of the FRAIL scale in middle-aged women. J Am Med Dir Assoc. 2018;19(1):65–9.

    Article  PubMed  Google Scholar 

  44. Lee H, Chong J, Jung HW, Baek JY, Lee E, Jang IY. Association of the frail scale with geriatric syndromes and health-related outcomes in korean older adults. Ann Geriatr Med Res. 2021;25(2):79–85.

    Article  PubMed  PubMed Central  Google Scholar 

  45. Castellana F, Lampignano L, Bortone I, Zupo R, Lozupone M, Griseta C, et al. Physical frailty, multimorbidity, and all-cause mortality in an older population from southern Italy: results from the Salus in Apulia study. J Am Med Dir Assoc. 2021;22(3):598–605.

    Article  PubMed  Google Scholar 

  46. Hoogendijk EO, Stenholm S, Ferrucci L, Bandinelli S, Inzitari M, Cesari M. Operationalization of a frailty index among older adults in the InCHIANTI study: predictive ability for all-cause and cardiovascular disease mortality. Aging Clin Exp Res. 2020;32(6):1025–34.

    Article  PubMed  PubMed Central  Google Scholar 

  47. Yuki A, Otsuka R, Tange C, Nishita Y, Tomida M, Ando F, et al. Physical frailty and mortality risk in Japanese older adults. Geriatr Gerontol Int. 2018;18(7):1085–92.

    Article  PubMed  Google Scholar 

  48. Higueras-Fresnillo S, Cabanas-Sánchez V, Lopez-Garcia E, Esteban-Cornejo I, Banegas JR, Sadarangani KP, et al. Physical activity and association between frailty and all-cause and cardiovascular mortality in older adults: population-based prospective cohort study. J Am Geriatr Soc. 2018;66(11):2097–103.

    Article  PubMed  Google Scholar 

  49. Crow RS, Lohman MC, Titus AJ, Bruce ML, Mackenzie TA, Bartels SJ, et al. Mortality risk along the frailty Spectrum: data from the National Health and nutrition examination survey 1999 to 2004. J Am Geriatr Soc. 2018;66(3):496–502.

    Article  PubMed  PubMed Central  Google Scholar 

  50. Jiang M, Foebel AD, Kuja-Halkola R, Karlsson I, Pedersen NL, Hägg S, et al. Frailty index as a predictor of all-cause and cause-specific mortality in a Swedish population-based cohort. Aging. 2017;9(12):2629–46.

    Article  PubMed  PubMed Central  Google Scholar 

  51. Baek W, Min A. Frailty index and gender-specific mortality in Korean adults: findings from the Korean longitudinal study of aging (2006-2018). J Adv Nurs. 2022.

  52. Zhang L, Ji T, Sun F, Li Y, Tang Z, Ma L. A simplified frailty index predicts mortality in older adults in Beijing. Risk Manag Healthc Policy. 2021;14:4867–73.

    Article  PubMed  PubMed Central  Google Scholar 

  53. Wang HY, Lv X, Du J, Kong G, Zhang L. Age- and gender-specific prevalence of frailty and its outcomes in the Longevous population: the Chinese longitudinal healthy longevity study. Front Med. 2021;8.

  54. Shi J, Tao Y, Meng L, Zhou B, Duan C, Xi H, et al. Frailty status among the elderly of different genders and the death risk: a follow-up study. Front Med. 2021;8.

  55. Barker FJ, Davies JI, Gomez-Olive FX, Kahn K, Matthews FE, Payne CF, et al. Developing and evaluating a frailty index for older south Africans-findings from the HAALSI study. Age Ageing. 2021;50(6):2167–73.

    Article  PubMed  PubMed Central  Google Scholar 

  56. Wuorela M, Lavonius S, Salminen M, Vahlberg T, Viitanen M, Viikari L. Self-rated health and objective health status as predictors of all-cause mortality among older people: a prospective study with a 5-, 10-, and 27-year follow-up. BMC Geriatr. 2020;20(1):120.

    Article  PubMed  PubMed Central  Google Scholar 

  57. Salminen M, Viljanen A, Eloranta S, Viikari P, Wuorela M, Vahlberg T, et al. Frailty and mortality: an 18-year follow-up study among Finnish community-dwelling older people. Aging Clin Exp Res. 2020;32(10):2013–9.

    Article  PubMed  Google Scholar 

  58. Wang MC, Li TC, Li CI, Liu CS, Lin WY, Lin CH, et al. Frailty, transition in frailty status and all-cause mortality in older adults of a Taichung community-based population. BMC Geriatr. 2019;19(1):26.

    Article  PubMed  PubMed Central  Google Scholar 

  59. Shi GP, Ma T, Zhu YS, Wang ZD, Chu XF, Wang Y, et al. Frailty phenotype, frailty index and risk of mortality in Chinese elderly population- Rugao longevity and ageing study. Arch Gerontol Geriatr. 2019;80:115–9.

    Article  PubMed  Google Scholar 

  60. Keeble E, Parker SG, Arora S, Neuburger J, Duncan R, Kingston A, et al. Frailty, hospital use and mortality in the older population: findings from the Newcastle 85+ study. Age Ageing. 2019;48(6):797–802.

    Article  PubMed  Google Scholar 

  61. Jacobsen KK, Jepsen R, Lembeck MA, Nilsson C, Holm E. Associations between the SHARE frailty phenotype and common frailty characteristics: evidence from a large Danish population study. BMJ Open. 2019;9(10).

  62. Zucchelli A, Vetrano DL, Marengoni A, Grande G, Romanelli G, Calderón-Larrañaga A, et al. Frailty predicts short-term survival even in older adults without multimorbidity. Eur J Intern Med. 2018;56:53–6.

    Article  PubMed  Google Scholar 

  63. Lee Y, Kim J, Chon D, Lee KE, Kim JH, Myeong S, et al. The effects of frailty and cognitive impairment on 3-year mortality in older adults. Maturitas. 2018;107:50–5.

    Article  PubMed  Google Scholar 

  64. Langholz PL, Strand BH, Cook S, Hopstock LA. Frailty phenotype and its association with all-cause mortality in community-dwelling Norwegian women and men aged 70 years and older: the Tromsø study 2001-2016. Geriatr Gerontol Int. 2018;18(8):1200–5.

    Article  PubMed  Google Scholar 

  65. Schoufour JD, Erler NS, Jaspers L, Kiefte-de Jong JC, Voortman T, Ziere G, et al. Design of a frailty index among community living middle-aged and older people: the rotterdam study. Maturitas. 2017;97:14–20.

    Article  PubMed  Google Scholar 

  66. Pereira AA, Borim FSA, Neri AL. Absence of association between frailty index and survival in elderly Brazilians: the FIBRA study. Cadernos de Saude Publica. 2017;33(5):e00194115.

    Article  PubMed  Google Scholar 

  67. Hoogendijk EO, Theou O, Rockwood K, Onwuteaka-Philipsen BD, Deeg DJH, Huisman M. Development and validation of a frailty index in the longitudinal aging study Amsterdam. Aging Clin Exp Res. 2017;29(5):927–33.

    Article  PubMed  Google Scholar 

  68. Turusheva A, Frolova E, Korystina E, Zelenukha D, Tadjibaev P, Gurina N, et al. Do commonly used frailty models predict mortality, loss of autonomy and mental decline in older adults in northwestern Russia? A prospective cohort study. BMC Geriatr. 2016;16:98.

    Article  PubMed  PubMed Central  Google Scholar 

  69. Lin SY, Lee WJ, Chou MY, Peng LN, Chiou ST, Chen LK. Frailty index predicts all-cause mortality for middle-aged and older Taiwanese: implications for active-aging programs. Plos One. 2016;11(8).

  70. Hyde Z, Flicker L, Smith K, Atkinson D, Fenner S, Skeaf L, et al. Prevalence and incidence of frailty in Aboriginal Australians, and associations with mortality and disability. Maturitas. 2016;87:89–94.

    Article  PubMed  Google Scholar 

  71. de León D, González E, Gutiérrez Hermosillo H, Martinez Beltran JA, Chavez JHM, Palacios Corona R, et al. Validation of the FRAIL scale in Mexican elderly: results from the Mexican health and aging study. Aging Clin Exp Res. 2016;28(5):901–8.

    Article  Google Scholar 

  72. Jotheeswaran AT, Bryce R, Prina M, Acosta D, Ferri CP, Guerra M, et al. Frailty and the prediction of dependence and mortality in low- and middle-income countries: a 10/66 population-based cohort study. BMC Med. 2015;13(1).

  73. Kulmala J, Nykänen I, Hartikainen S. Frailty as a predictor of all-cause mortality in older men and women. Geriatr Gerontol Int. 2014;14(4):899–905.

    Article  PubMed  Google Scholar 

  74. Garre-Olmo J, Calvó-Perxas L, López-Pousa S, De Gracia BM, Vilalta-Franch J. Prevalence of frailty phenotypes and risk of mortality in a community-dwelling elderly cohort. Age Ageing. 2013;42(1):46–51.

    Article  PubMed  Google Scholar 

  75. Abizanda P, Romero L, Sánchez-Jurado PM, Martínez-Reig M, Gómez-Arnedo L, Alfonso SA. Frailty and mortality, disability and mobility loss in a Spanish cohort of older adults: the FRADEA study. Maturitas. 2013;74(1):54–60.

    Article  PubMed  Google Scholar 

  76. Rockwood K, Song X, Mitnitski A. Changes in relative fitness and frailty across the adult lifespan: evidence from the Canadian National Population Health Survey. CMAJ. 2011;183(8):E487–E94.

    Article  PubMed  PubMed Central  Google Scholar 

  77. Rockwood K, Mitnitski A. Frailty defined by deficit accumulation and geriatric medicine defined by frailty. Clin Geriatr Med. 2011;27(1):17–26.

    Article  PubMed  Google Scholar 

  78. Graham JE, Snih SA, Berges IM, Ray LA, Markides KS, Ottenbacher KJ. Frailty and 10-year mortality in community-living mexican american older adults. Gerontology. 2009;55(6):644–51.

    Article  PubMed  PubMed Central  Google Scholar 

  79. Hao Q, Song X, Yang M, Dong B, Rockwood K. Understanding risk in the oldest old: frailty and the metabolic syndrome in a Chinese community sample aged 90+ years. J Nutr Health Aging. 2016;20(1):82–8.

    Article  CAS  PubMed  Google Scholar 

  80. Theou O, Rockwood MRH, Mitnitski A, Rockwood K. Disability and co-morbidity in relation to frailty: how much do they overlap? Arch Gerontol Geriatr. 2012;55(2):e1–8.

    Article  PubMed  Google Scholar 

  81. Jacobs JM, Cohen A, Ein-Mor E, Maaravi Y, Stessman J. Frailty, cognitive impairment and mortality among the oldest old. J Nutr Health Aging. 2011;15(8):678–82.

    Article  CAS  PubMed  Google Scholar 

  82. Lucicesare A, Hubbard RE, Fallah N, Forti P, Searle SD, Mitnitski A, et al. Comparison of two frailty measures in the conselice study of brain ageing. J Nutr Health Aging. 2010;14(4):278–81.

    Article  CAS  PubMed  Google Scholar 

  83. Searle SD, Mitnitski A, Gahbauer EA, Gill TM, Rockwood K. A standard procedure for creating a frailty index. BMC Geriatr. 2008;8:24.

  84. Srinonprasert V, Chalermsri C, Aekplakorn W. Frailty index to predict all-cause mortality in Thai community-dwelling older population: a result from a National Health Examination Survey cohort. Arch Gerontol Geriatr. 2018;77:124–8.

    Article  CAS  PubMed  Google Scholar 

  85. Avila-Funes JA, Helmer C, Amieva H, Barberger-Gateau P, Le Goff M, Ritchie K, et al. Frailty among community-dwelling elderly people in France: the three-city study. J Gerontol A Biol Sci Med Sci. 2008;63(10):1089–96.

    Article  PubMed  Google Scholar 

  86. Pranata R, Henrina J, Lim MA, Lawrensia S, Yonas E, Vania R, et al. Clinical frailty scale and mortality in COVID-19: a systematic review and dose-response meta-analysis: clinical frailty scale in COVID-19. Arch Gerontol Geriatr. 2021;93:104324.

  87. Dumitrascu F, Branje KE, Hladkowicz ES, Lalu M, McIsaac DI. Association of frailty with outcomes in individuals with COVID-19: a living review and meta-analysis. J Am Geriatr Soc. 2021;69(9):2419–29.

    Article  PubMed  PubMed Central  Google Scholar 

  88. Gobbens RJ, van Assen MA, Luijkx KG, Wijnen-Sponselee MT, Schols JM. The Tilburg frailty Indicator: psychometric properties. J Am Med Dir Assoc. 2010;11(5):344–55.

    Article  PubMed  Google Scholar 

  89. Rockwood K, Song X, MacKnight C, Bergman H, Hogan DB, McDowell I, et al. A global clinical measure of fitness and frailty in elderly people. Can Med Assoc J. 2005;173(5):489–95.

    Article  Google Scholar 

  90. Jones DM, Song X, Rockwood K. Operationalizing a frailty index from a standardized comprehensive geriatric assessment. J Am Geriatr Soc. 2004;52(11):1929–33.

    Article  PubMed  Google Scholar 

  91. Xue QL, Tian J, Walston JD, Chaves PHM, Newman AB, Bandeen-Roche K. Discrepancy in frailty identification: move beyond predictive validity. J Gerontol A Biol Sci Med Sci. 2020;75(2):387–93.

    Article  PubMed  Google Scholar 

  92. Puts MT, Lips P, Deeg DJ. Sex differences in the risk of frailty for mortality independent of disability and chronic diseases. J Am Geriatr Soc. 2005;53(1):40–7.

    Article  PubMed  Google Scholar 

  93. Fan Y, Huang J, Zhou Y, Gu X, Gong D. Does the impact of frailty on all-cause mortality in older persons differ between women and men? A meta-analysis. J Am Med Dir Assoc. 2021;22(3):606.e1–6.

    Article  Google Scholar 

  94. Liu ZY, Wei YZ, Wei LQ, Jiang XY, Wang XF, Shi Y, et al. Frailty transitions and types of death in Chinese older adults: a population-based cohort study. Clin Interv Aging. 2018;13:947–56.

    Article  PubMed  PubMed Central  Google Scholar 

  95. Morley JE, Vellas B, Abellan van Kan G, Anker SD, Bauer JM, Bernabei R, et al. Frailty consensus: a call to action. J Am Med Dir Assoc. 2013;14(6):392–7.

    Article  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

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Funding

This work was supported by the Sichuan Provincial Health Commission (Grant 19PJ015).

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Authors and Affiliations

  1. Geriatric Diseases Institute of Chengdu, Department of Geriatrics, Chengdu Fifth People’s Hospital, Chengdu, 611137, China

    Yang Peng, Lijuan Guan & Lihua Zhou

  2. The Second Clinical Medical College, Affiliated Fifth People’s Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, 611137, China

    Yang Peng, Lijuan Guan & Lihua Zhou

  3. Department of Hepatobiliary Surgery, the Second Affiliated Hospital of Chongqing Medical University, Chongqing, China

    Guo-Chao Zhong

  4. Department of Laboratory Medicine, Sichuan Provincial People’s Hospital, University of Electronic Science and Technology of China, Chengdu, China

    Xiaoli Zhou

  5. Chinese Academy of Sciences Sichuan Translational Medicine Research Hospital, Chengdu, China

    Xiaoli Zhou

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Contributions

Y.P. conceived the study idea. Y.P., G.C.Z., X.Z. and L.Z. performed literature search, study selection, data extraction and quality assessment. Y.P., G.C.Z., X.Z., L.G. and L.Z. analyzed data. Y.P. participated in manuscript drafting and revision. G.C.Z., X.Z., L.G. and L.Z. edited and revised the manuscript. All authors approved the final version to be published. Y.P. is the study guarantor who takes full responsibility for the work as a whole, including (if applicable) the study design, access to data, and the decision to submit and publish the manuscript.

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Supplementary Information

Additional file 1: Supplementary Table 1.

Search strategy.

Additional file 2: Supplementary Table2.

The list of studies excluded after full-text review.

Additional file 3: Supplementary Table 3.

The results of quality assessment for the included studies.

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Peng, Y., Zhong, GC., Zhou, X. et al. Frailty and risks of all-cause and cause-specific death in community-dwelling adults: a systematic review and meta-analysis. BMC Geriatr 22, 725 (2022). https://doi.org/10.1186/s12877-022-03404-w

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BMC Geriatrics

ISSN: 1471-2318

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