|Year : 2020 | Volume
| Issue : 3 | Page : 116-123
Frailty prevalence and agreement between assessment tools in elderly patients of Western India
Navin Kumar Yadav, Dharmendra Kumar, Vivek Aggarwal
Department of Internal Medicine, AFMC, Pune, Maharashtra, India
|Date of Submission||15-Jun-2020|
|Date of Decision||30-Jul-2020|
|Date of Acceptance||25-Aug-2020|
|Date of Web Publication||23-Feb-2021|
Navin Kumar Yadav
Clinical Tutor, Department of Internal Medicine, AFMC, Pune, Maharashtra
Source of Support: None, Conflict of Interest: None
Background: Frailty is a common clinical syndrome in the elderly with increased risk for poor health outcomes manifesting as falls, disabilities, hospitalization, and death. In this study, we measured the prevalence of frailty based on two commonly used tools, i.e., Fried frailty phenotype (FFP) and Edmonton frailty scale (EFS). Furthermore, the agreement between these tools was determined. Materials and Methods: In this cross-sectional study, 296 patients with age 60 years or more visiting geriatric outpatient department at a tertiary care center in Western India were assessed for frailty using FFP and EFS. The study groups' association is evaluated with the Fisher's test, Student's “t-test”, and Pearson's chi-squared test. Results: A total of 296 patients were assessed with the mean age group of 68.47 ± 5.92 years. There were 151 (51.1%) males and 145 (48.9%) females with the majority of patients from the age group of 60–70 years (68.8%). Total 68 patients (22.9%; males: 55.8%, females: 44.2%) and 50 patients (17%; males and females 50% each) were frail as per FFP and EFS, respectively. There was a statistically significant agreement between FFP and EFS (k = 0.602, P < 0.05). Conclusion: There is a significant agreement between frailty prevalence measured by FFP and EFS. In elderly population, measurement of frailty using these simple tools can be effective in screening and subsequent early interventions to reduce ageing-related mortality and morbidities.
Keywords: Ageing, Edmonton frailty scale, frailty, Fried frailty phenotype
|How to cite this article:|
Yadav NK, Kumar D, Aggarwal V. Frailty prevalence and agreement between assessment tools in elderly patients of Western India. J Indian Acad Geriatr 2020;16:116-23
|How to cite this URL:|
Yadav NK, Kumar D, Aggarwal V. Frailty prevalence and agreement between assessment tools in elderly patients of Western India. J Indian Acad Geriatr [serial online] 2020 [cited 2022 Sep 29];16:116-23. Available from: http://www.jiag.com/text.asp?2020/16/3/116/309992
| Introduction|| |
The geriatric age group population is on the rise globally and expected to be doubled by 2050. As per the 2011 census, India has 104 million people with age 60 years or more. With the increasing elderly population healthcare system will be overburdened with various geriatric syndromes and morbidities-mortality associated with them. Frailty is defined as a clinically recognizable biological syndrome, characterized by decreased reserve capacity to maintain homeostasis resulting from cumulative age-related decline across multiple physiologic systems during a lifetime leading to increased vulnerability to stressors and adverse outcomes. It is expressed as increased vulnerability to such an extent that trivial stress events catalyze homeostasis reserve degradation, leading to a disproportionate rise in health-related adverse outcomes.,,
Various frailty tools and models are being practiced for assessment and grading of frailty. Fried et al. described frailty as a clinical entity with three out of five phenotypic criteria indicating compromised energy, which include low handgrip strength, low energy, slowed walking speed, low physical activity, and/or unintentional weight loss. Other frailty assessment tools commonly used are the Edmonton frailty scale (EFS), Rockwood's deficit accumulation index, clinical frailty scale, vulnerable elders survey-13, FRAIL scale, etc.
Frailty is a complex interplay among ageing, genetic and environmental factors leading to gradual, cumulative cellular and molecular damage resulting in sarcopenia which manifests as reduced functional reserves and disabilities. Proposed mechanisms causing frailty include inflammation, reduced metabolism, environmental factors, hormonal dysregulation, loss of proteostasis, senescence and stem cell exhaustion, DNA damage, and epigenetic alteration. There is an association of frailty with raised inflammatory markers, like C-reactive protein and interleukin-6. Loss of tissue regeneration and the accumulation of senescent cells are characteristics of ageing and contribute to frailty. Anabolic hormones such as insulin-like growth factor-1 and androgens have been implicated in the process of ageing. Other factors such as psychological, nutrition, and poly-pharmacy diseases also contribute to frailty.
The prevalence of frailty varies based on ethnicity, geographical distribution, income, comorbidities and type of tool used.,,,,, In western countries prevalence in elderly above 65 years of age ranges from 4% to 16%., In another meta-analysis conducted in developing countries, it was noted that frailty prevalence varied from 17% to 33% and greater in hospitalized patients. Similar findings were seen in a survey done by Rodriguez et al. in India, China, and Latin America. The overall prevalence of frailty was 15.4% with the lowest in China (5.4%–9.1%) and 12.6%–21.5% in other regions. A West Bengal study showed that frailty was at around 38.8% in elderly >60 years of age and frailty was associated with age, female sex, economic dependency, illiteracy, and >2 chronic comorbidities. An Indian study done in Pune found the prevalence of frailty and prefrailty to be 26% and 63.6%, respectively.
Several landmark trials/studies have developed evidence-based frailty assessment models and tools. These tools have augmented epidemiological studies to validate the association of frailty with adverse health outcomes. In this proposed research work, the patients with age 60 years and more were included, and the frailty was assessed based on two commonly used tools, i.e., Fried frailty phenotype (FFP) and EFS. Furthermore, the agreement for frailty between FFP and EFS was determined. This model can augment an easy and early prediction of the frailty. Introducing such frailty models in clinical practice and various modes of interventions, to delay its progression will have a significant overall impact on reducing morbidity-mortality of the geriatric population.
- Primary objective: To determine the prevalence of frailty in the elderly Indian population of age >60 years visiting the medical outpatient department (OPD) of a tertiary care hospital in the Western India by using FFP
- Secondary objective: To find an agreement for frailty between FFP and EFS.
| Materials and Methods|| |
Place of study
Department of Internal Medicine, medical OPD of tertiary care and teaching hospital in the western India.
Type of study
A cross-sectional study.
Elderly patients of age >60 years visiting medical OPD of tertiary care and teaching hospital in Western India.
- The sample size is calculated for the primary objective to estimate 98% Confidence Interval for the prevalence of frailty by FFP among the elderly population with 5% absolute error of margin (Type 1 error α) by using the formula:
n = Z2 π (1 - π)/d2
- The sample size works out to be 296 assuming that prevalence of frailty to be 26% by FFP.
- Elderly patients of age ≥60 years visiting medical OPD of tertiary care and teaching hospital in Western India
- The ambulant patients who can perform activities of daily livings independently.
- Patients with underlying hepatic or renal dysfunction
- Patients with old stroke with neurological deficit
- History of hospitalization in the last 3 months
- Patients with fracture or rheumatologic disorder-related disabilities.
Elderly patients with age ≥60 years attending Medical OPD of tertiary care and teaching hospital were subjected to [Figure 1]:
- Inclusion and exclusion criteria
- Laboratory investigations: Serum creatinine, liver function test (AST/ALT), fasting lipid profile, and fasting blood sugar were tested with 3 ml of blood
- Handgrip strength test: by using Camry 200 Lbs/90 kg digital hand dynamometer
- Short physical performance battery (SPPB) was performed on each patient as per standard pro forma depicted in [Figure 2]. [Table 1] depicts the classification of frailty based on SPPB score.
- FFP and EFS were calculated as per their respective methodology
- For this cross-sectional study, requisite data were collected from a single interview and examination after taking informed consent of eligible patients.
- Ethical considerations:
- Written and informed consent was taken
- Patient identities were not disclosed, and no active intervention was performed
- IEC clearance was obtained.
The quantitative data are expressed as mean and standard deviation. The study groups were compared with the help of unpaired t-test based on normality test, and the qualitative data are presented as frequency and percentage table. The study groups' association is assessed with the Fisher test, Student's t-test, and Pearson's Chi-squared test. P < 0.05 is taken as significant. The appropriate statistical software, including but not restricted to MS Excel, IBM SPSS Statistics for Windows, version 20 (IBM Corp., Armonk, N.Y., USA) were used for statistical analysis.
| Results|| |
The demographic distribution of 296 participants is summarised as per [Table 2]. The mean age was 68.47 years (SD ± 5.92), body mass index 24.43 kg/m2 (SD ± 3.42) with slight male preponderance (51.1%) as compared to females (48.9%). Majority of the patients (68.8%) were in the age group of 60–70 years. Increasing age was associated with a higher prevalence of frailty as evidenced by P < 0.05. The most common comorbidity was hypertension (71.6%), followed by Type 2 diabetes mellitus (T2DM) (38.8%). Low income was associated with a significant increase in frailty prevalence. 139 (46.9%) and 12 (4.1%) male patients had weak and normal handgrip strength, respectively, while 25 (8.4%) and 120 (40.6%) female patients had weak and normal handgrip strength, respectively. There were a significantly higher number of male patients with weak handgrip strength than female patients, as per the Chi-square test (P< 0.05).
According to the SPPB score, 125 (42.2%) patients were non-frail, 82 (27.7%) patients were pre-frail, and 89 (30.1%) patients were frail. SPPB is a good tool to assess frailty as evidenced by the statistically significant P value of <0.05. As per FFP, 104 (35.2%) patients were nonfrail, 124 (41.9%) patients were pre-frail and 68 (22.9%) patients were frail. Prefrail populations were statistically significant in females with a P = 0.034 while there was no significant gender difference between robust and frail population. According to EFS, 187 (63.1%) patients were non-frail, 59 (19.9%) patients were vulnerable, 31 (10.5%) patients were mild frail, 16 (5.4%) patients were moderate frail, and 3 (1.1%) patients were severe frail. [Table 4] summarizes the frailty assessment of our sample population using FFP and EFS.
|Table 4: Distribution of samples based on Fried frailty phenotype and Edmonton frailty scale|
Click here to view
As evident from [Table 5], there was a fair agreement there was a fair agreement between FFP and EFS for determining which patients were pre-frail (k = 0.387, P < 0.05) and substantial agreement between FFP and EFS for determining which patients were frail (k = 0.602, P < 0.05) [Table 3].
|Table 5: Agreement for frailty between Fried frailty phenotype and Edmonton frailty scale|
Click here to view
| Discussion|| |
The population of the geriatric age group is continuously increasing. With the increasing elderly population healthcare system will be overburdened with various geriatric syndromes and morbidities-mortality associated with them. Frailty is associated with a high risk of adverse outcomes associated with such syndromes. Various frailty tools and models are being practiced for the assessment and grading of frailty. The prevalence of frailty varies based on ethnicity, geographical distribution, income, comorbidities-morbidities, and type of tool used.
This is a cross-sectional study with the primary objective to determine the prevalence of frailty in the elderly Indian population of age ≥60 years visiting Medical OPD of tertiary care hospital in the Western India by using FFP. FFP and EFS are the two most commonly used tools to assess frailty in the elderly population. This study also analyzed agreement for frailty between FFP and EFS.
The calculated sample size of this study was 296. The study group's mean age was 68.47 ± 5.92 years with a major contribution by 60–70 years age group (n = 204, 68.8%). The analysis showed a statistically significant association of increasing age with the prevalence of frailty which is a consistent finding in most of the studies. This study had the male and female population of 51.1% and 48.9%, respectively, and there was no statistically significant difference between the prevalence of frailty based on gender. However, a study by Zhang et al. showed that frailty prevalence is higher in females. Similarly, a meta-analysis by Gordon et al. also showed female sex preponderance to high frailty prevalence.
Co-morbidity contributes to the prevalence of frailty.,, Common comorbidities found in this study group were hypertension (71.6%), T2DM (38.8%), and coronary artery disease (8.1%), which corroborates with studies done worldwide. In this study also frailty was associated with multiple comorbidities.
Weak handgrip strength is strongly associated with frailty. A total of 164 individuals had weak handgrip with contributions by males and females being 136 (46.9%) and 25 (8.4%). The finding was statistically significant (P < 0.05) with a higher number of male patients having weaker handgrip strength than females. In a study by Dudzinska-Griszek et al., 70% of the sample size had weaker handgrip strength. In our study, 55% of patient had weak handgrip.
Marital status in the study group consisted of 234 (79%) married, 51 (17%) widowed, and 11 (4%) singles. 50% of married individuals were frail as compared to 18% of widowed/single group. This finding was statistically not significant. This finding did not corroborate with single living status as a risk factor for frailty which is the case in several studies (185). However, the finding may be confounded by the joint family structure in the society. Low socioeconomic status was associated with higher frailty prevalence. This study also suggested the statistically significant prevalence of frailty in the lower economic strata population.
SPPB is a reliable assessment tool for frailty. In a meta-analysis, poor SPPB score (<10) is associated with increased all-cause mortality irrespective of the age of the study group, geographical area, or ethnicity., In this study, 89 (30.1%) of the sample size were frail as per the SPPB score, which was statistically significant (P < 0.05). SPPB should be included in routine clinical practice for geriatric frailty assessment. Follow-up of elderly patients with SPPB score can be utilized for early detection of frailty.
The study's primary objective, i.e., the prevalence of frailty and pre-frailty in the elderly with age 60 years or more, was 22.9% and 41.9%, respectively, using FFP. In a meta-analysis, the prevalence of frailty amongst the elderly population of low and middle-income countries varied between 3.9% and 51%. In the same study prevalence of pre-frailty ranged between 13.4% and 71.6%. An Indian study done in Pune found the prevalence of frailty and pre-frailty to be 26% and 63.6%, respectively. Similarly, a meta-analysis involving 46 observational studies across 28 countries to estimate the global incidence of frailty was found to be 43.4%. EFS categorizes population into non-frail, vulnerable, mild frail, moderate frail, and severe frail groups, in this study, results were 63.1%, 19.9%, 10.5%, and 5.4% and 1.1% respectively. A Canadian study for the prognostic value of EFS in the elderly with acute coronary syndrome showed significant all-cause mortality with EFS > 7.
This study also analyzed agreement for frailty between FFP and EFS. There was a fair agreement between FFP and EFS for determining pre-frailty with Cohen kappa co-efficient of k = 0.387, P < 0.05. Similarly, there was substantial agreement between these tools for determining frail patients with k = 0.602, P < 0.05. A study in Vietnam also showed significant FFP and EFS association on the impact of frailty on prolonged hospitalization and mortality in the elderly.,,
To the best of our knowledge, this is a unique study in India which highlighted agreement for frailty in the elderly between FFP and EFS. The study showed that the factors significantly associated with frailty are more advanced age, comorbidities-morbidities, low socioeconomic strata, diabetes mellitus, weaker handgrip strength, and low SPPB score. As compared with various studies globally, the gender-specific association was not noted in this study. This study demonstrated substantial agreement for frailty between FFP and EFS. These findings can be utilized in transforming clinical practice changes pertaining to geriatric health in the country. Simple assessment tools like SPPB and handgrip strength measurement can significantly impact the early detection of frail or pre-frail population. This will be beneficial in timely active interventions that would delay the progression or reverse pre-frailty. The study's strengths are nearly matched groups, use of validated frailty assessment tools, and statistically significant agreement between the most widely used two frailty assessment tools. This study's limitations include lack of long-term follow-up of frailty progression, majority of patients were from low-income group, family structure of sample population were not included apart from marital status and effect of any intervention on frailty was not studied.
Financial support and sponsorship
Conflicts of interest
There are no conflicts of interest.
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[Figure 1], [Figure 2]
[Table 1], [Table 2], [Table 3], [Table 4], [Table 5]