Clinical Frailty Scale and Biomarkers for Assessing Frailty in Elder Inpatients in China

Abstract

Objective

This study aimed to assess the feasibility of the Clinical Frailty Scale (CFS) and clinical biomarkers in assessing the frailty in elder inpatients in China.

Design

The study was a cross-sectional study.

Setting and Participants

The study included 642 elder inpatients (295 females and 347 males) aged ≥65 years, from the Department of Geriatrics of Zhejiang Hospital between January 2018 and December 2019.

Measurements

All participants underwent a comprehensive geriatric assessment and blood tests. Univariate and multivariate logistic regression was used to analyze the association between risk factors and frailty.

Results

The average age of the participants was 82.72±8.06 years (range: 65–95 years) and the prevalence of frailty was 39.1% according to the CFS. Frail participants showed significantly lower short physical performance battery (SPPB), basic activities of daily living (ADL) and instrumental activities of daily living (IADL) scores (all p<0.001), and lower hemoglobin, total protein and albumin levels (all P<0.05) than nonfrail participants. Frail participants had higher CRP, D-dimer and fibrinogen levels than nonfrail participants (all p<0.05). Univariate logistic regression analysis showed a significant association between frailty and age, comorbidity, polypharmacy, fall history, SPPB, ADL, and IADL scores, D-dimer, fibrinogen, hemoglobin, total protein and albumin levels (all P<0.05). Multivariate logistic regression analysis indicated that age (odds ratio (OR), 95% confidence interval (CI)= 1.151(1.042–1.272), P=0.006), SPPB scores (OR, 95% CI=0.901(0.601–1.350), P<0.001), and D-dimer (OR, 95% CI=4.857(2.182–6.983), P<0.001), fibrinogen (OR, 95% CI=2.665(0.977–4.254), P<0.001), hemoglobin (OR, 95% CI=0.837(0.725–0.963), P= 0.044), and albumin (OR, 95% CI=0.860 (0.776–1.188), P<0.001) levels were independently associated with frailty in all participants.

Conclusion

Frailty in elder inpatients in China is characterized by older age, a lower SPPB scores, higher D-dimer and fibrinogen levels and lower hemoglobin and albumin levels. Functional decline and malnutrition may be the targets of frailty interventions.

Introduction

The Chinese population is aging rapidly and, at the end of 2019, it exceeded 1.4 billion, of which 12.6% were older than 65 years of age, i.e., approximately 176 million (1). The number of hospitalized elder individuals is increasing year by year. Cao F et al reported that the average annual growth rate was 27.48% in China in the past ten years (2). Many factors affect the length of stay, cost, prognosis, mortality, etc. of hospitalized elder patients. Frailty is one of the important factors (3., 4., 5., 6., 7.).

Frailty is an age-related decline in multisystem function that causes extreme vulnerability to stressors (8–0). The prevalence of frailty in different studies is inconsistent, but there is a consensus that it increases with age (11, 12). To reduce adverse outcomes of hospitalization, elder inpatients need to be screened for frailty and given an opportunity for intervention to prevent the progression of frailty. However, there is no gold standard to assess frailty, although some methods have been proposed, such as the frailty phenotype (8), the frailty index (FI) (13, 14), the Fatigue, Resistance, Ambulation, Illnesses, & Loss of Weight (FRAIL) scale (15), and the Clinical Frailty Scale (CFS) (9). The CFS, which was developed by Rockwood et al. (9), is commonly used in the clinic and has some advantages in assessing frailty, including ease of implementation, lack of requirements for complex questionnaires, special facilities or many actions by patients. The CFS is widely used in other countries, but it is not used much in elder patients in China, especially not elder Chinese inpatients.

Frailty biomarkers can identify frailty early and enable optimization of treatment and intervention measures for hospitalized patients in a timely manner. Due to the ambiguity of the definition of frailty, different methods of assessing frailty and complex pathophysiology make the identification of frailty biomarkers challenging and affect the accuracy, specificity and sensitivity of frailty biomarkers (16, 17). The current popular biomarkers include inflammatory markers (interleukin (IL)-6, C reactive protein (CRP), tumor necrosis factor alpha (TNFα), etc.), hormone markers (testosterone, vitamin D, etc.), coagulation markers (D-dimer, fibrinogen), and nourishment markers (albumin). The most commonly used methods to assess frailty and study frailty biomarkers are the frailty phenotype and FI, and the CFS is rarely used to study frailty biomarkers (16).

The present study had two objectives: 1) to assess the frailty status of elder Chinese inpatients using the CFS and 2) to study frailty biomarkers of elder Chinese inpatients using the CFS. First, we compared the different characteristics of frail and nonfrail inpatients with the CFS. Then, we used univariate and multivariate logistic regression to investigate the association between biomarkers and frailty in elder inpatients. We hypothesized that high D-dimer levels, high fibrinogen levels, low hemoglobin levels, low albumin levels, and low short physical performance battery (SPPB) scores are high-risk factors for frailty in elder Chinese inpatients.

Materials and methods

Study Subjects

This study was a cross-sectional study. Participants were patients who were hospitalized in the Geriatric Department of Zhejiang Hospital in China from January 2018 to December 2019. The inclusion criteria were age ≥65 years and the ability to communicate. The participants with the following characteristics were excluded from this study: significant cognitive deficit defined as a Mini-Mental-State-Examination (MMSE) score ≤14, poor compliance with the evaluation procedures, or refusal to sign the informed consent form. All participants underwent a comprehensive geriatric assessment and blood collection by well-trained nurses. The study was conducted in accordance with the Declaration of Helsinki and was approved by the Clinical Research Ethics Committee of Zhejiang Hospital. All participants gave written informed consent.

Comprehensive Geriatric Assessment

The comprehensive geriatric assessment was performed by well-trained nurses. It included demographic information (age, gender) and clinical information (hearing, eyesight, fall history, comorbidity, polypharmacy, smoking history, drinking history, basic activities of daily living (ADL) (18), instrumental activities of daily living (IADL) (19), SPPB (20), weight, height, and body mass index (BMI)). Comorbidity was defined as the simultaneous presence of five or more diseases in a patient. Polypharmacy was defined as taking five or more oral prescriptions. ADL comprised the basic actions that involved caring for oneself and one's body, including personal care, mobility and eating. IADL are the skills and abilities needed to perform certain day-to-day tasks associated with an independent lifestyle. Weight (kg) was measured by a standard electronic scale, and height (m) was measured by a fixed graduation ruler on the wall. BMI was calculated by weight/height².

SPPB Measurements

The SPPB is a series of tests of physical performance used in elder persons to assess extremity function and mobility and consists of 3 tests: the balance test, the gait speed test and the chair stand test (20). The score for each test is 0 to 4, so the total score for the three tests is 0 to 12. Balance tests include side-by-side, semi-tandem and tandem balance tests. Participants were required to walk a distance of 4 m at their usual pace. The nurse recorded the total walking time, and gait speed was obtained by dividing 4 meters by the walking time and expressed in meters per second (m/s). Finally, participants were asked to rise from a chair and return to a seated position five times as quickly as possible, and the nurse also recorded the total time.

Frailty Measurements

After the comprehensive geriatric assessment, we used the CFS to assess frailty. The CFS was administered by well-trained nurses. There are three evaluation nurses on our team who underwent systematic evaluation training at Center for Geriatrics and Gerontology, Taipei Veterans General Hospital and they have evaluated more than 1,200 elder patients in total. The CFS scores were between 1 and 7 (1, very fit; 2, well; 3, well, with treated comorbid disease; 4,apparently vulnerable; 5, mildly frail; 6, moderately frail; and 7, severely frail.). A CFS score ≥ 5 was considered frail, and a CFS score ≤ 4 was considered nonfrail (9).

Blood Tests

The blood biomarkers included hallmarks of inflammation (leukocyte, CRP), electrolytes (potassium, sodium, chlorine, calcium, phosphorus, magnesium), coagulation markers (D-dimer, prothrombin time, activated partial prothrombin time, thrombin time, fibrinogen, platelets), nourishment markers (hemoglobin, total protein, albumin), homocysteine and bilirubin.

Statistical Analyses

Statistical analysis was performed using PASW Statistics (version 21.0, SPSS Inc., Chicago, IL). Categorical variables are presented as frequencies and percentiles. Continuous variables are described as the means and standard deviation (SD). Student's t-test (for normally distributed continuous data, such as age, height, weight, blood biomarkers, SPPB, ADL, and IADL) and the chi-square test (for covariate variables, such as sex, comorbidities, polypharmacy, hearing loss, eyesight loss, fall history, smoking history, drinking history) were used to compare the differences between the nonfrail group and the frail group. Furthermore, we used univariate logistic regression to analyze the association between frailty and risk factors in the total sample; then, the factors with P < 0.05 in the univariate analysis were entered into the multivariable logistic regression models to determine the independent risk factors associated with frailty. There are two pseudo R-squareds in the model: Cox & Snell and Nagelkerke. The results of logistic regression models are presented as odds ratios (ORs) and 95% confidence intervals (95% CIs). P<0.05 was considered statistically significant.

Results

From January 2018 to December 2019, a total of 936 patients were admitted to the Department of Geriatrics of Zhejiang

Hospital. A total of 294 patients were excluded from the study due to age <65 years (n =105), refusal to participate (n=75), MMSE score below 14 (n=69), and incompletion of the study (n=45). The study was eventually completed by 642 patients (Figure 1). The average age of the participants was 82.72±8.06 years (range: 65–95 years). Fifty-four percent of the participants were male.

Figure 1.

Flowchart of the patient inclusion

According to the CFS results, 251 of 642 participants (39.1%) were frail. There was no significant difference in gender, height, weight, BMI, bilirubin, potassium, sodium, chlorine, calcium, phosphorus, magnesium, homocysteine, prothrombin time, activated partial prothrombin time, thrombin time, leukocytes, or platelets between the frail and nonfrail groups (P>0.05). There was a significant difference in age, comorbidity, polypharmacy, hearing loss, eyesight loss and fall history between the two groups (p<0.001). Frail participants showed significantly lower SPPB, ADL and IADL scores (all p<0.001) and lower hemoglobin, total protein, and albumin levels (all P<0.05) than nonfrail participants. Frail participants had higher CRP, D-dimer, and fibrinogen levels than nonfrail participants (P<0.05) (Table 1). The breakdown of the CFS scores in the total sample is shown in Figure 2.

Table 1.

Participants' characteristics by frailty subgroups

Characteristics	Total Sample (n=642)	Nonfrail (n=391)	Frail (n=251)	P-Value
Age (mean ± SD)	82.72±8.06	78.28±7.176	86.40±6.96	<0.001
Gender, n (%)				0.911
Male	347(54.0%)	213(54.5%)	134(53.4%)
Female	295(46.0%)	178(45.5%)	117(46.6%)
Comorbidity, n(%)				<0.001
No	210(32.7%)	140(35.8%)	70(27.9%)
Yes	432(67.3)	251(64.2%)	181(72.1%)
Polypharmacy, n(%)				<0.001
No	315(49.0%)	229(58.6%)	86(34.3%)
Yes	327(51.0%)	162(41.4%)	165(65.7%)
Hearing, n(%)				<0.001
Normal	314(48.9%)	243(62.2%)	71 (28.8%)
Loss	328(51.1%)	148(37.8%)	180(71.2%)
Eyesight, n(%)				<0.001
Normal	293(45.7%)	196(50.1%)	97(38.7%)
Loss	349(54.3%))	195(49.9%)	154(61.3%)
Fall history, n(%)				<0.001
No	512(79.7%)	354(90.5%)	158(62.9%)
Yes	130(20.3%)	37(9.5%)	93(37.1%)
Height, (mean ± SD)	161.35±9.68	161.25±9.61	161.45±9.90	0.937
Weight, (mean ± SD)	60.49±12.28	62.62±12.32	58.51±12.10	0.198
BMI,(mean ± SD)	23.18±4.05	23.95±3.50	22.45±4.44	0.153
SPPB, (mean ± SD)	6.63±3.81	9.38±2.29	4.34±3.28	<0.001
ADL, (mean ± SD)	87.19±23.70	99.83±0.93	76.71±28.13	<0.001
IADL, (mean ± SD)	4.59±2.68	6.83±1.31	2.74±2.02	<0.001
CRP, (mean ± SD)	6.14±12.17	2.89±3.67	8.74±15.61	0.038
Bilirubin, (mean ± SD)	12.78±7.18	13.58±5.90	12.12±8.11	0.422
Potassium, (mean ± SD)	3.97±0.40	4.02±0.37	3.92±0.43	0.322
Sodium, (mean ± SD)	141.31±3.29	141.32±2.98	141.29±3.57	0.967
Chlorine, (mean ± SD)	106.16±3.80	106.39±3.10	105.97±4.34	0.664
Calcium, (mean ± SD)	2.28±0.16	2.29±0.11	2.28±0.20	0.797
Phosphorus, (mean ± SD)	1.12±0.16	1.15±0.17	1.10±0.16	0.164
Magnesium, (mean ± SD)	0.97±1.10	0.84±0.07	1.07±1.49	0.421
Homocysteine, (mean ± SD)	18.35±13.30	15.64±4.46	20.62±17.39	0.154
D-dimer, (mean ± SD)	0.70±0.54	0.43±0.22	0.91±0.62	<0.001
Prothrombin time, (mean ± SD)	13.67±1.87	13.77±2.59	13.58±0.95	0.690
Activated partial prothrombin time	37.65±4.79	37.03±4.62	38.16±4.93	0.353
Thrombin time, (mean ± SD)	16.01±1.31	16.04±0.81	15.99±1.63	0.866
Fibrinogen, (mean ± SD)	3.63±0.80	3.38±0.61	3.83±0.88	<0.001
Leukocyte, (mean ± SD)	5.58±1.59	5.18±1.26	5.92±1.77	0.061
Hemoglobin, (mean ± SD)	120.94±19.55	128.00±17.54	115.09±19.42	0.007
Platelets, (mean ± SD)	187.50±59.43	187.97±57.77	187.11±61.61	0.955
Total protein, (mean ± SD)	65.24±5.84	66.84±5.59	63.91±5.78	0.044
Albumin, (mean ± SD)	38.92±3.67	39.99±3.74	38.03±3.42	0.032

Notes: Data in bold indicates P<0.05. Abbreviations: SD, standard deviation; ADL, basic activities of daily living, IADL, instrumental activities of daily living, SPPB, short physical performance battery, BMI, body mass index.

Figure 2.

The breakdown of CFS scores in the total sample

The univariate logistic regression analysis indicated that, for all participants, age, comorbidity, polypharmacy, fall history, SPPB, ADL, IADL scores and D-dimer, fibrinogen, hemoglobin, total protein and albumin levels showed significant associations with frailty (all P<0.05). The risk factors associated with frailty according to the univariate logistic regression analysis are shown in Table 2.

Table 2.

Risk factors associated with frailty by univariate logistic regression analysis

Risk factors	Frailty OR (95% CI)	P-value
Age	1.189(1.082–1.306)	<0.001
Gender	1.891(0.675–5.297)	0.225
Comorbidity	1.244(0.463–3.347)	0.012
Polypharmacy	4.259(1.488–12.192)	0.007
Fall	1.683(1.324–2.256)	<0.001
SPPB	0.582(0.454–0.747)	<0.001
ADL	0.691(0.492–0.971)	0.033
IADL	0.234(0.112–0.486)	<0.001
CRP	1.091(0.977–1.219)	0.120
D-dimer	4.544(2.270–7.143)	<0.001
Fibrinogen	2.236(1.072–4.665)	<0.001
Hemoglobin	0.861(0.832–0.992)	<0.001
Total protein	0.912(0.831–1.001)	0.025
Albumin	0.857(0.741–0.991)	<0.001

Notes: Data in bold indicates P<0.05, Abbreviations: OR=Odds Ratio; CI=Confidence Interval. ADL, basic activities of daily living, IADL, instrumental activities of daily living, SPPB, short physical performance battery.

All covariates with a P<0.05 in the univariate logistic regression analysis were entered into the multivariable model for further analysis. We found that age, SPPB scores, and D-dimer, fibrinogen, hemoglobin and albumin levels were independent risk factors for frailty (Table 3).

Table 3.

Risk factors associated with frailty by multivariable logistic regression

Risk factors	Adjusted model OR (95% CI)	P-value
Age	1.151(1.042–1.272)	0.006
Comorbidity	1.323(1.933–1.150)	0.067
Polypharmacy	3.826(2.505–4.816)	0.186
Fall	1.381(1.424–1.859)	0.479
SPPB	0.901(0.601–1.350)	<0.001
ADL	0.776(0.474–1.270)	0.126
IADL	0.321(0.152–0.179)	0.073
D-dimer	4.857(2.182–6.983)	<0.001
Fibrinogen	2.665(0.977–4.254)	<0.001
Hemoglobin	0.837(0.725–0.963)	0.044
Total protein	0.945(0.829–1.076)	0.393
Albumin	0.860(0.776–1.188)	<0.001

Notes: Data in bold indicates P<0.05. All covariates with a P value of less than 0.05 on univariable analysis were entered into the multivariable model.There are two Pseudo R-Squared, Cox & Snell R-Squared =0.726 and Nagelkerke R-Squared=0.838 in the model. Abbreviations: OR=Odds Ratio; CI=Confidence Interval. ADL, basic activities of daily living, IADL, instrumental activities of daily living, SPPB, short physical performance battery.

Discussion

The main findings of the present study were that frail elder inpatients in China had the characteristics of older age, lower SPPB scores, higher D-dimer and fibrinogen levels, and lower hemoglobin and albumin levels.

The present study showed that the prevalence of frailty of all inpatients was 39.1%, according to the CFS. Due to different methods of assessing frailty, inclusion criteria, nationalities, sexes, ages, etc., the prevalence of frailty may vary widely. Some studies reported that the prevalence of frailty in hospitalized elder patients was between 24.7% and 80% (12, 21, 22). In a review, 21 studies reported frailty prevalence rates of 4.0–59.1% in community-dwelling individuals (11). However, few studies have used the CFS to study the prevalence of frailty in elder hospitalized patients in China. Liang YD et al used the CFS to assess frailty in elder inpatients among different wards and found that the frailty prevalence was 36.2% (23), similar to the results of the present study. Furthermore, in line with the present study, frailty is strongly associated with increasing age, which has also been confirmed in previous studies (11, 12, 23). The SPPB is a test of walking speed, balance and standing from a seated position and is a commonly used instrument to assess functional capacity. Some studies used the SPPB as a method of frailty assessment (24, 25). In the present study, frail elder inpatients had lower SPPB scores than nonfrail inpatients, meaning that hospitalized frail elder patients had a more significant decline in function, which was consistent with the CFS results and previous studies (26, 27, 28).

In the present study, a high D-dimer level was associated with an almost five-fold increased risk of developing frailty, and a high fibrinogen level was associated with an almost three-fold increased risk of developing frailty in elder Chinese inpatients. Several previous studies also supported the association between high D-dimer levels and frailty (29, 30, 31, 32) and the association between frailty and fibrinogen (31). However, Reiner AP et al compared 900 frail and nonfrail postmenopausal women but did not find an association between fibrinogen and incident frailty (29). Sanchis J et al studied biomarkers that are useful for the identification of frailty in elder patients after ACS and did not find an association between fibrinogen and incident frailty (30). It should be pointed out that most of these previous studies used the frailty phenotype or FI as a tool for assessing frailty, while the present study used the CFS. Other factors, such as research objectives, underlying disease, and sex, may also contribute to this difference.

Several studies have suggested that levels of D-dimer, fibrinogen and other markers of activated coagulation increase with advancing age (33., 34., 35.). These markers are also associated with functional decline (36, 37). Therefore, this may be a possible pathophysiological connection among age, activated coagulation systems, functional decline and the occurrence of frailty.

Anemia is defined clinically as a low level of hemoglobin, which is a special protein in red blood cells that enables transportation of oxygen. Anemia means reduced oxygen-carrying capacity, and this can lead to weakness, low physical activity and slow walking speed, which are the specific components of frailty. Consistent with the present study, low levels of hemoglobin (30, 38) and albumin (39, 40) have been shown to correlate with the incidence of frailty. Several studies found that low albumin was associated with a risk of reduced muscle mass, muscle strength and gait speed in elder individuals (41., 42., 43.). Low hemoglobin and albumin levels are often considered markers of malnutrition and are mostly associated with functional decline (44., 45., 46., 47.). Some studies also found that malnutrition may be a symptom of frailty or a risk factor for frailty (48., 49., 50., 51., 52.).

Therefore, clinical biomarkers of frailty and the CFS suggest a close relationship between functional decline and frailty. Functional decline and malnutrition may be the targets of frailty interventions.

Limitations

There were some limitations in the present study. First, this was a cross-sectional study, and the observed associations could not establish a causal nexus between the studied variables and frailty. Second, our study was an observational study, and subjects were from one hospital, which may limit the potential for extrapolating the study results. Third, we did not use comprehensive assessment methods of frailty (i.e., frailty phenotype and the FI) as a “gold standard” to evaluate the accuracy and sensitivity of the CFS in assessing frailty in elder inpatients in China. Further prospective studies are needed to explore the association between frailty and biomarkers in elder inpatients in China.

Conclusion

In this study, we used the CFS to assess frailty status and frailty biomarkers in elder inpatients in China. We found that higher D-dimer and fibrinogen levels, lower hemoglobin albumin levels and lower SPPB scores are high-risk factors for frailty in elder inpatients in China. Functional decline and malnutrition may be the targets of frailty interventions.

Funding

This study was funded by Zhejiang Medical Science and Technology Project (2019KY261,2019KY004).

Conflicts of interest

The authors declare that they have no conflict of interest.

Ethical approval

The study complies with the current laws of the country in which it was performed. The study was conducted in accordance with the Declaration of Helsinki and was approved by the clinical research ethics committee of Zhejiang Hospital (reference number: 2018-C-67). All participants gave written informed consent.

Author Contributions

Xu LY, Chen XJ: study conception and design. Xu LY, Zhang J, Liu ZX, Yang YH, Shen SS, Zeng XK, Hong XF: data collection and analysis. The first draft of the manuscript was written by Xu LY, Chen XJ and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.