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Derivation of a frailty index from the interRAI acute care instrument.

Hubbard RE, Peel NM, Samanta M, Gray LC, Fries BE, Mitnitski A, Rockwood K - BMC Geriatr (2015)

Bottom Line: In logistic regression analysis including age, gender and FI-AC as covariates, each 0.1 increase in the FI-AC increased the likelihood of inpatient mortality twofold (OR: 2.05 [95% CI 1.70-2.48]).This could optimise clinical utility and minimise costs.The variables used to derive the FI-AC are common to all interRAI instruments, and could be used to precisely measure frailty across the spectrum of health care.

View Article: PubMed Central - PubMed

Affiliation: Centre for Research in Geriatric Medicine, The University of Queensland, Brisbane, QLD, Australia. r.hubbard1@uq.edu.au.

ABSTRACT

Background: A better understanding of the health status of older inpatients could underpin the delivery of more individualised, appropriate health care.

Methods: 1418 patients aged ≥ 70 years admitted to 11 hospitals in Australia were evaluated at admission using the interRAI assessment system for Acute Care. This instrument surveys a large number of domains, including cognition, communication, mood and behaviour, activities of daily living, continence, nutrition, skin condition, falls, and medical diagnosis.

Results: Variables across multiple domains were selected as health deficits. Dichotomous data were coded as symptom absent (0 deficit) or present (1 deficit). Ordinal scales were recoded as 0, 0.5 or 1 deficit based on face validity and the distribution of data. Individual deficit scores were summed and divided by the total number considered (56) to yield a Frailty index (FI-AC) with theoretical range 0-1. The index was normally distributed, with a mean score of 0.32 (±0.14), interquartile range 0.22 to 0.41. The 99% limit to deficit accumulation was 0.69, below the theoretical maximum of 1.0. In logistic regression analysis including age, gender and FI-AC as covariates, each 0.1 increase in the FI-AC increased the likelihood of inpatient mortality twofold (OR: 2.05 [95% CI 1.70-2.48]).

Conclusions: Quantification of frailty status at hospital admission can be incorporated into an existing assessment system, which serves other clinical and administrative purposes. This could optimise clinical utility and minimise costs. The variables used to derive the FI-AC are common to all interRAI instruments, and could be used to precisely measure frailty across the spectrum of health care.

No MeSH data available.


Related in: MedlinePlus

Average Frailty index for 20 samples. Legend: The Frailty index was created 1000 times, each time randomly picking 80% of the variables of the index. Twenty randomly (out of 1000) selected experimental and best fit regression lines of the average Frailty index are shown here.
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Fig3: Average Frailty index for 20 samples. Legend: The Frailty index was created 1000 times, each time randomly picking 80% of the variables of the index. Twenty randomly (out of 1000) selected experimental and best fit regression lines of the average Frailty index are shown here.

Mentions: Random sampling of the FI-AC (Figure 3) showed that when 80% variables were sampled, the difference in slopes was negligible, which indicated there was little sensitivity as to which variables were included in FI-AC construction. The differences in the intercepts of the fitted lines also illustrated the variance in the slope of the FI-AC.Figure 3


Derivation of a frailty index from the interRAI acute care instrument.

Hubbard RE, Peel NM, Samanta M, Gray LC, Fries BE, Mitnitski A, Rockwood K - BMC Geriatr (2015)

Average Frailty index for 20 samples. Legend: The Frailty index was created 1000 times, each time randomly picking 80% of the variables of the index. Twenty randomly (out of 1000) selected experimental and best fit regression lines of the average Frailty index are shown here.
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4373301&req=5

Fig3: Average Frailty index for 20 samples. Legend: The Frailty index was created 1000 times, each time randomly picking 80% of the variables of the index. Twenty randomly (out of 1000) selected experimental and best fit regression lines of the average Frailty index are shown here.
Mentions: Random sampling of the FI-AC (Figure 3) showed that when 80% variables were sampled, the difference in slopes was negligible, which indicated there was little sensitivity as to which variables were included in FI-AC construction. The differences in the intercepts of the fitted lines also illustrated the variance in the slope of the FI-AC.Figure 3

Bottom Line: In logistic regression analysis including age, gender and FI-AC as covariates, each 0.1 increase in the FI-AC increased the likelihood of inpatient mortality twofold (OR: 2.05 [95% CI 1.70-2.48]).This could optimise clinical utility and minimise costs.The variables used to derive the FI-AC are common to all interRAI instruments, and could be used to precisely measure frailty across the spectrum of health care.

View Article: PubMed Central - PubMed

Affiliation: Centre for Research in Geriatric Medicine, The University of Queensland, Brisbane, QLD, Australia. r.hubbard1@uq.edu.au.

ABSTRACT

Background: A better understanding of the health status of older inpatients could underpin the delivery of more individualised, appropriate health care.

Methods: 1418 patients aged ≥ 70 years admitted to 11 hospitals in Australia were evaluated at admission using the interRAI assessment system for Acute Care. This instrument surveys a large number of domains, including cognition, communication, mood and behaviour, activities of daily living, continence, nutrition, skin condition, falls, and medical diagnosis.

Results: Variables across multiple domains were selected as health deficits. Dichotomous data were coded as symptom absent (0 deficit) or present (1 deficit). Ordinal scales were recoded as 0, 0.5 or 1 deficit based on face validity and the distribution of data. Individual deficit scores were summed and divided by the total number considered (56) to yield a Frailty index (FI-AC) with theoretical range 0-1. The index was normally distributed, with a mean score of 0.32 (±0.14), interquartile range 0.22 to 0.41. The 99% limit to deficit accumulation was 0.69, below the theoretical maximum of 1.0. In logistic regression analysis including age, gender and FI-AC as covariates, each 0.1 increase in the FI-AC increased the likelihood of inpatient mortality twofold (OR: 2.05 [95% CI 1.70-2.48]).

Conclusions: Quantification of frailty status at hospital admission can be incorporated into an existing assessment system, which serves other clinical and administrative purposes. This could optimise clinical utility and minimise costs. The variables used to derive the FI-AC are common to all interRAI instruments, and could be used to precisely measure frailty across the spectrum of health care.

No MeSH data available.


Related in: MedlinePlus