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Age and Ageing Advance Access originally published online on January 31, 2008
Age and Ageing 2008 37(2):161-166; doi:10.1093/ageing/afm195
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Copyright © The Author 2008. Published by Oxford University Press on behalf of the British Geriatrics Society.

Development of an easy prognostic score for frailty outcomes in the aged

Giovanni Ravaglia1,, Paola Forti1, Anna Lucicesare1, Nicoletta Pisacane1, Elisa Rietti1 and Christopher Patterson2

1 Department of Internal Medicine, Cardioangiology and Hepatology, University of Bologna, Italy
2 Department of Medicine, McMaster University, Canada

Address correspondence to: Giovanni Ravaglia. Tel: +39-051-6364310; Fax: +39-051-340877. Email: ravaglia{at}med.unibo.it


   Abstract
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 Abstract
Introduction
 Subjects and methods
 Results
 Discussion
 Key points
 Funding
 Competing interests
 Supplementary Data
 References
 
Background: identification of frailty is recommended in geriatric practice. However, there is a lack of frailty scores combining easy-to-collect predictors from multiple domains.

Objective: to develop a frailty score including only self-reported information and easy-to-perform standardised measurements recommended in routine geriatric practice.

Design: prospective population-based study.

Setting/Participants: included 1,007 Italian subjects aged 65 and over.

Measurements: seventeen baseline possible mortality predictors from several domains, 4-year risk of mortality and other adverse health outcomes associated with frailty [fractures, hospitalisation, and new and worsening activities of daily living (ADL) disability].

Methods: a multivariate Cox model was used to identify the best sub-group of independent predictors and to develop a mortality prognostic score, defined as the number of adverse predictors present. Logistic regression was used to verify whether the score also predicted risk of other frailty outcomes in the cohort survivors.

Results: nine independent mortality predictors were identified. Among subjects with score ≥3, each one point increase in the score was associated with a doubling in mortality risk and, among survivors, with an increased risk of all the other adverse health outcomes.

Conclusions: nine easy-to-collect predictors may identify aged people at increased risk of adverse health outcomes associated with frailty.

Keywords: frailty, ageing, mortality, fractures, disability, elderly


   Introduction
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 Introduction
Subjects and methods
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 Discussion
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Frailty has been conceptualised as a physiologic syndrome characterised by decreased reserve and diminished resistance to stressors, resulting from cumulative decline across multiple physiologic systems during ageing, and placing older people at risk for death and other adverse health outcomes [1–3]. A consensus on a standardised tool for identification of frail elderly in clinical practice, however, is still lacking.

A phenotype of physical frailty was proposed as a constellation of characteristics (weakness, slowness, poor endurance, weight loss and physical inactivity) related to physical fitness [1]. In contrast with this approach, there is a growing agreement that frailty is multi-dimensional and its clinical measure should include socio-demographic, biomedical, functional, affective and cognitive components in addition to physical features [2–5]. The inclusion of disability and comorbidity is also recommended because, although conceptually different entities than frailty [1], their high frequency in the aged and their inter-relationship with frailty outcomes make the distinction an artificial one [3, 6, 7].

In this study we investigated predictors related to socio-demographic status, lifestyle, comorbidity, physical function, disability, nutrition, mood and cognitive status of an elderly cohort, in order to develop a prognostic score for adverse health outcomes associated with frailty. For each domain, we used the simplest available standardised measures recommended for routine use in geriatric practice.


   Subjects and methods
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 Subjects and methods
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Study population
The Conselice Study of Brain Ageing (CSBA) is an Italian population-based prospective survey [8]. Briefly, in 1999–2000, 1,016 (75%) of the 1,353 individuals aged 65 years and older residing in the Conselice municipality (Emilia Romagna region, Northern Italy) underwent a standardised personal interview and medical evaluation for collection of data about nutritional, functional, and cognitive and affective status. Data on vital status at follow-up examination (2003–2004) were collected for the whole baseline cohort. Official death records were available for all deceased participants. Follow-up data about other incident adverse health outcomes related to frailty (fractures, hospitalisation, onset of new and worsening of pre-existent disability) were collected among survivors. The choice of these outcomes was based on data availability and previous literature [1–3]. Informed consent for collection and use of information was obtained from the subjects or their relatives, as approved by our Institutional Review Board.

Potential predictor variables
Variables from six domains were considered as potential predictors of mortality: socio-demographic, lifestyle, medical status, physical function, nutrition, and mood and cognitive status [1–3]. For purposes of simplicity, all variables were dichotomised using, whenever available, internationally standardised cut-offs. Socio-demographic predictors included age ≥80 years (this cut-off was chosen as the easiest way to summarise the exponential relationship found between mortality and age), male gender, education ≤3 years (the lowest level of mandatory education in the old Italian schooling system), and living alone. Lifestyle predictors included ex/current smoking and physical inactivity, defined as lack of adherence to the current exercise recommendation for older people (<4 h/week of moderate intensity activity, e.g. brisk walking) [9]. Medical predictors included comorbidity and sensory deficits (blindness or deafness). Comorbidity was defined both as daily use of three or more drugs [10] and the presence of two or more of the following chronic medical conditions: hypertension, cardiovascular disease (history of myocardial infarct and congestive heart failure), cerebrovascular disease (history of stroke or transient ischemic attack), diabetes, chronic pulmonary disease, cancer and dementia. Except for dementia (diagnosed ex novo [8]), all diagnoses were based on medical history as provided by the participants. Nutritional predictors included calf circumference <31 cm [11] and body mass index (BMI) <25 [12] (calculated as weight in kilograms divided by the square of the height in metres). Obesity measured as BMI ≥30 kg/m2 was not included as a predictor because, in preliminary analyses, mortality did not differ between obese subjects (29 deaths out of 306, 9.5%) and those with BMI ≥ 25 to <30 kg/m2 (56 out of 471, 11.9%, P = 0.293). This approach has been used in other studies [12]; allows BMI to be dichotomised for easier application; and agrees with previous findings that high BMI is not a good mortality predictor in older people [13].

Functional status predictors included poor physical performance, defined as Tinetti gait and balance test score ≤ 24 [14], and two measures of disability: difficulty with any of the basic activities of daily living (ADL) (bathing, dressing, toileting, transferring, continence and feeding) [15] and difficulty with any of the following four, not gender-dependent, instrumental activities of daily living (IADL): using the telephone, taking medicine, travelling and managing money [16]. Mood and cognitive status predictors included abnormal cognition defined as Mini Mental State Examination (MMSE) score <24 [17], depressive symptoms defined as Geriatric Depression Scale (GDS) score ≥10 [18]), and pessimism about one's health (subjects were asked if they felt ‘their health was worse than others’).

Statistical methods
Univariate associations between predictors and mortality were assessed using hazard ratios (HR) and the corresponding [95% confidence interval (CI)] from an unadjusted Cox proportional-hazards model. All predictors were entered into a multivariable Cox model using mortality as the dependent variable. Backward and forward procedures (P<0.05 to retain/include) were used to identify the best sub-set of independent predictors. The same procedure was applied to each of the six variable categories and to the variables obtained merging the predictors identified by each sub-model. All procedures yielded the same model with nine variables. A risk scoring system was developed, assigning one point to each present predictor (the HRs of all selected predictors had similar magnitude) and summing the points assigned to each participant. Cox regression was used to assess the association between the score and mortality.

Logistic regression was used to assess the association between the score of CSBA survivors and their 4-year risk of developing the following adverse outcomes: hospitalisation, fractures (excluded those due to cancer or road accidents), worsening ADL disability (increase ≥1 unit in the number of ADL difficulties with respect to baseline), and new ADL disability (development of any ADL difficulty among participants reporting none at baseline).

Admission to nursing homes was not investigated as a possible predictor/outcome because only 30 CSBA participants were living in nursing home at baseline (88% of the eligible institutionalised population) and only 19 new cases of institutionalisation were recorded at follow-up.


   Results
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 Abstract
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 Subjects and methods
 Results
Discussion
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 Supplementary Data
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The study included 1,007 of the 1,016 baseline participants (eight subjects were excluded because of missing data and one because of death by a car accident). Mean age was 74.7 ± 7.1 year (range 65–97). Detailed demographic information is provided in Appendix 2 (available on the journal website http://www.ageing.oxfordjournals.org).

During 3.8 ± 1.00 years of follow-up, 147 participants died (14.6%). At univariate analysis, mortality was associated with all predictors except for gender, smoking, and living alone (Table 1). The final multivariable-adjusted model included nine predictors: age ≥ 80 [HR = 1.93 (1.29–2.88)], male gender [HR = 1.92 (1.33–2.86)], physical inactivity [HR = 2.26 (1.47–3.49)], use of three or more drugs [HR = 1.52 (1.08–2.14)], sensory deficits [HR = 2.07 (1.21–3.54)], calf circumference <31 cm [HR = 1.91 (1.33–2.75)], IADL disability [HR = 1.89 (1.20–2.96)], gait and balance test ≤ 24 [HR = 1.77 (1.16–2.69)], and pessimism about one's health [1.70 (1.17–2.48)]. Excluding the point related to gender, score was higher in women than in men (2.06 ± 1.9 versus 1.6 ± 1.5, P < 0.001). Table 2 shows 4-year mortality risk according to individual scores. Subjects scoring 0–2 were pooled into the reference group because of their similar mortality rates (score 0: n = 140, deaths 4.3%; score 1: n = 252, deaths 5.2%; score 2: n = 226, deaths 5.5%). Compared to the reference group, mortality risk for score ≥3 (37.6% of the cohort) increased in an exponential dose-response with the number of present risk factors. For each one point increase in the score, the corresponding HR for mortality was 1.99 (1.82–2.18), P for trend <0.001. Kaplan-Meier curves for survival of the study participants according to their prognostic scores are shown in Appendix 1 (available on the journal website http://www.ageing.oxfordjournals.org).


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  		Table 1. Univariate analysis of potential predictors for 4-year mortality

 


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  		Table 2. Four-year risk of mortality according to individual prognostic scores

 
Among CSBA survivors who underwent follow-up, 749 had data about incident fractures, 706 about incident hospitalisation, and 746 about worsening or new disability, with 584 of them reporting no baseline ADL difficulty. Survivors were younger than deceased participants (73.4 ± 6.3 years versus 78.4 ± 7.8 years, P < 0.001) but did not differ by gender (men 45.4% versus 42.2% women, P = 0.381). As shown in Table 3, the mortality score was associated with increased risk of adverse outcomes in survivors. For each one point increase, the corresponding odds ratio (OR) and 95% CI was as follows: 1.40 (1.12–1.73) for fractures (P for trend = 0.003); 1.48 (1.26–1.77) for hospitalisation (P for trend <0.001); 1.84 (1.57–2.16) for worsening disability (P for trend = 0.001); 2.21 (1.73–2.83) for new disability (P for trend = 0.001). Results did not change after additional adjustment for the number of baseline ADL difficulties (data not shown). Exclusion of subjects living in nursing homes did not affect results.


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  		Table 3. Four-year risk of adverse health outcomes other than death according to individual prognostic scores

 

   Discussion
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 Abstract
 Introduction
 Subjects and methods
 Results
 Discussion
Key points
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 Supplementary Data
 References
 
We developed a prognostic score of nine simple predictors able to stratify an aged population into risk groups for 4-year mortality. The score was also associated with 4-year risk for other adverse health outcomes known to be associated with frailty (fractures, hospitalisation, new and worsening disability). All the included predictors have been individually associated with frailty [2, 4, 19].

The major strength of our score is the attempt to conciliate a multi-dimensional approach to frailty with the inclusion of easy-to-collect information and standardised measurements. The physical phenotype assesses physical performance with grip strength and walking speed [1] which, although simple to perform, lack standardised norms. The reference values of the physical phenotype, which were provided by the authors, were calculated from their own study cohort [1] and may not be applicable to other populations. By contrast, the gait and balance test used in this investigation has a standardised cut-off [14]. A similar advantage comes from our defining physical inactivity as lack of adherence to current exercise recommendations for older people [9], while the physical phenotype requires quantification of the kilocalories spent exercising, and, again, uses reference values from the development cohort itself [1]. Moreover, the physical phenotype focuses on measures of physical fitness only [1], while our score takes a broader view of frailty, also including disability and comorbidity, which are thought to be more sensitive markers of frailty than physical features alone [4, 6, 7]. This multi-dimensional approach is deemed to be more consistent with the principles of geriatric practice [2–5].

Other frailty scores intended as sums of multi-domain impairments were previously proposed [19–24]. However, many of these scores may be problematic to use in clinical practice because they include too many items [19]), require unwieldy instrumental measurements like spirometry [20], are focused on subjects with already remarkable degrees of functional impairment [21, 22], or rely on clinical judgement only [23, 24]. Multi-domain tools like the Functional Autonomy Measurement System (SMAF) [24] and the Vulnerable Elders Survey (VES-13) [25] are currently in use for identification of older people at risk of functional decline. However, they focus on disability, and the only additional domains taken into account are self-rated health for VES-13 [25] and clinical judgement of mental status for SMAF [26].

Although seemingly tautological, the inclusion of age in our score is an acknowledgement that much is still unknown about ageing physiology. It mainly represents a statistical way to account, with one single variable, for the variability related to this unmeasured quantity. Male gender was not a significant predictor at univariate analysis but its association with mortality was unmasked in the multivariate model, when taking into account predictors other than gender. It can be hypothesised that, in univariate analysis, women's lower mortality risk was counteracted by their higher average number of other adverse conditions acting as confounders. Indeed, ageing women are known to accumulate more deficits than men of similar age and yet have a lower mortality risk [12, 27]. Of the two measures of physical disability included in the study, difficulty with IADL, but not with ADL, was an independent predictor of mortality. This may reflect the fact that the ADL index, taken as predictor of functional decline, is weaker than IADL because it captures disability only at the extreme end of the disabling process [28]. Moreover, IADL loss may have both motor and cognitive origin, explaining why no measure of cognitive function was included in the final model. No specific number of medications has actually been established for the definition of polypharmacy, which has been operationalised as assumption of any number of drugs from two to ten [10]. We choose three as a cut-off because only 12% of the CSBA cohort was taking five or more drugs. This may limit the reliability of the predictor in other populations. However, our results are important because they confirm that, in aged persons, a simple count of the drugs taken daily by the subject can be used as a predictor of adverse health outcomes, without need for identification of specific medical conditions [1].

Calf circumference is a good clinical indicator of sarcopenia [12], which is an acknowledged core feature of frailty [1], and this may explain why it is a better mortality predictor than BMI. Finally, the inclusion of pessimism about one's health in the score supports the hypothesis of frailty having a psychological component [2].

This study has several limitations. First, due to the lack of a validation sample, specificity and sensitivity data, and information about its generalisation to other populations, our score must be considered to be still in a developmental stage and cannot be recommended for detection of frailty in clinical practice. Second, all predictors were dichotomised for easier application and important information may have been lost. Moreover, the cut-offs chosen for some predictors (age, drugs, calf circumference) might not apply to different populations. Third, important frailty outcomes like deliriums, falls and placement in nursing homes were not taken into account because of data unavailability or too small number of participants developing a specific condition. The small number of current smokers is also the probable reason why a major mortality risk factor like smoking habit failed to be included in the final model. Fourth, we cannot exclude that education may be a significant independent predictor in populations with a higher educational background. Finally, we could not compare our score with the predictive power of a mortality clinical index validated in over-50-year-old people [12], because some information about functional status (self report for the ability to walk several blocks and to push/pull heavy objects) was not available in the CSBA database. The index for over-50-year-old people, however, did not incorporate parameters of physical activity or objective measures of physical performance which, in older people, are both acknowledged components of the frailty concept [1].

In conclusion, in this older Italian population-based cohort, a mortality score calculated from nine easily collectible predictors was associated with risk of other adverse health outcomes previously associated with frailty [1–3]. This score is still in development and, in its present form, cannot be considered suitable for use in geriatric practice. However, our results support further investigation of frailty scores including easy-to-collect indicators for settings in which a complex and time-consuming examination might be impractical.


   Key points
Top
 Abstract
 Introduction
 Subjects and methods
 Results
 Discussion
 Key points
Funding
 Competing interests
 Supplementary Data
 References
 

  • Frail elderly should be identified in clinical practice because of their increased risk of death and other adverse health outcomes, but simple multi-dimensional scoring systems for frailty are lacking.
  • Using data from a population-based cohort, a frailty score was developed including nine independent predictors of mortality: age ≥80 years, male gender, low physical activity, comorbidity, sensory deficits, calf circumference <31 cm, IADL dependence, gait and performance test score ≤24, and pessimism about one's health.
  • The score also predicted risk of new admission to hospital, incident fractures, and incident new and worsening disability.
  • The study supports further exploration of easy-to-collect indicators of frailty.


   Funding
Top
 Abstract
 Introduction
 Subjects and methods
 Results
 Discussion
 Key points
 Funding
Competing interests
 Supplementary Data
 References
 
The study was supported by grants from the Italian Ministry of University and Scientific Research (basic-oriented research funds).


   Competing interests
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 Abstract
 Introduction
 Subjects and methods
 Results
 Discussion
 Key points
 Funding
 Competing interests
Supplementary Data
 References
 
None declared.


   Supplementary Data
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 Introduction
 Subjects and methods
 Results
 Discussion
 Key points
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 Supplementary Data
References
 
Supplementary data for this article are available online at http://ageing.oxfordjournals.org.


   References
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 Abstract
 Introduction
 Subjects and methods
 Results
 Discussion
 Key points
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Received 12 April 2007; accepted in revised form 24 September 2007.


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