The interRAI assessment system for Acute Care (interRAI AC) tool has been specifically developed for use in the acute setting, to support the comprehensive geriatric assessment of older inpatients [
17,
18]. Trained nurse assessors gather information about the patient’s physical, cognitive and psycho-social functioning, based on observations of patients during their first 24 hours in the ward. All available sources of information, including the patient, medical/nursing/allied health staff, the medical record and family members, are utilized to complete the interRAI AC instrument. The information is systematically interpreted, providing clinical profiles and generating problem lists and suggestions for care planning and facilitating the centralization of data [
19]. The clinical items have been shown to provide a valid measure of function in frail patients [
20] and have very good inter-rater reliability, with kappa scores for median observed agreement of 0.89, and greater than 0.80 for 83% of items [
21].
Coding of individual variables
All binary variables were recoded, using the established convention that ‘0’ indicated the absence of the deficit and ‘1’ the presence of a deficit. For ordinal and continuous variables, coding was based on face validity using clinical judgement and according to distribution of the data [
22]. Consensus on coding was reached in discussion and correspondence among authors. For example, the Cognition section of the interRAI-AC interrogates acute change in mental status from the person’s usual functioning. This is defined as restlessness, lethargy, being difficult to arouse or displaying altered environmental perception [
26]. The assessor is advised that accurate assessment requires conversations with staff or others who have direct knowledge of the person’s behaviour over the past 24 hours. The interRAI coding of no = 0; yes = 1 was directly translated to the absence or presence of a deficit. For some cognitive domains, such as being easily distracted, the interRAI-AC distinguishes between “Behaviour not present” = 0, “Behaviour present, consistent with usual functioning” = 1 and “Behaviour present, different from usual functioning” = 2. For these variables, the presence of the behaviour, regardless of its duration, was coded as 1 deficit.
For variables that included three potential responses (e.g. ‘No pain” = 0, “Present but not exhibited in the last 24 hours” = 1, “Exhibited in the last 24 hours” = 2), the intermediate value was coded as 0.5. Evidence from population-based longitudinal studies of community dwellers (N = 41,527) suggests that grading the severity of a deficit rather than dichotomising does not improve the performance of the index in predicting mortality [
27]. However, as this has yet to be confirmed in an inpatient population, we chose to exploit the granularity of available data.
Within the interRAI-AC, measures of functional status are based on the patient’s self-care performance over the preceding 24 hours. These are recorded by the nurse assessor on a scale from 0 through 6, with a response of 8 if the activity did not occur. These ordinal scales do not manifest clear gradations in relation to deficit cut-points, hence recoding of variables was based on clinical judgement. The extremes of the scales were straightforward: the interRAI value of 0 = “independent” translated directly to 0 = absence of the deficit, while 6 = “total dependence” was coded as 1 = full expression of the deficit. An interRAI score of 1 = “independent, set up help only” is used when an article or device is provided or placed within reach. Examples include providing a wash basin and grooming articles for personal hygiene or handing the person a cane for walking. Since no physical assistance or supervision is provided, and placing an article in reach may be an artefact of the hospital environment, scores of 1 were also coded as no deficit. For each functional domain, interRAI convention is to score 2 if “supervision” is required for an activity. The requirement for a nurse to remain nearby to watch over a patient while they use the toilet, for example, reflects a step-wise increase in care needs compared to set-up help only. “Supervision” was therefore coded as 0.5.
Scores on the interRAI of 3 through 5 represent increasing levels of dependency:
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3 = limited assistance – guided manoeuvring of limbs, physical guidance without taking weight
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4 = extensive assistance – weight-bearing support (including lifting limbs) by one helper where person still performs 50% or more of subtasks
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5 = maximal assistance – weight-bearing support (including lifting limbs) by two or more helpers or weight-bearing support for more than 50% of subtasks
While it is axiomatic that patients needing maximal assistance are more reliant than those needing limited assistance, the gradation from 2 to 3 represents the transition to dependence upon another person to complete a functional task. Since it is, arguably, this transition that impacts discharge planning more than the intensity of required support, we assigned 1 deficit for scores of 3, 4 or 5.
To complete coding of functional domains, in view of the association between failure to complete performance-based measures and adverse outcomes [
26], an interRAI score of 8 was also coded as 1 full deficit.
The coding of deficits within the domains of communication and vision, mood and behaviour, continence, pain, oral and nutritional status and skin was based on both clinical judgement and on the distribution of the data. For example, in the assessment of hearing, we considered coding only “severe difficulty” or “no hearing” as a full deficit. However, the prevalence of a deficit so defined would have been low, at only 2.9% of the sample. “Moderate difficulty” (corresponding to “problem hearing normal conversation; requires quiet setting to hear well”) applied to 8.3% of our patients and retains strong face validity as an age-associated problem with an adverse effect on health status. Hence, the coding for hearing was 0 = adequate: no deficit, 1 = minimal difficulty: 0.5 deficit; ≥ 2: 1 deficit. Congruent with the principle applied to functional domains, failure to complete cognitive assessments is associated with adverse outcomes [
28] and patients who could not or would not respond regarding their interest in things, anxiety or sadness were allocated a full deficit for each question.
Each disease diagnosis (whether 1 = primary diagnosis; 2 = disease present, receiving active treatment or 3 = disease present, monitored but no active treatment) was counted as one deficit. At the time of data collection, the interRAI-AC was structured to allow the documentation of up to 10 disease diagnoses and this maximal number was recorded for 126 patients (8.9% of the cohort), with a normal distribution around a mean and median value of 6. Analysis of interRAI Home Care data (N = 351) in which up to 31 diagnoses can be recorded revealed a normal distribution with a mean of 6 and a range from 0 (0.6%) up to 16 (0.3%). For the patients with 10 or more disease diagnoses, the mean was 11.7. Since the interRAI-AC subgroup recorded as having 10 diagnoses was likely to include patients with between 10 and 15 diagnoses, the denominator accounted for up to 15 disease diagnoses and those at the ceiling of 10 were recoded as 12. The interRAI-AC has subsequently been amended to enable capture of up to 15 diagnoses. Whether more precise quantification for those with multiple morbidities has a clinically meaningful impact on FI-AC scores is the focus of ongoing enquiries.
In older people, the association between body mass index (BMI) and adverse outcomes shows a U-shaped curve [
29] and increasing numbers of medications predict both disability and mortality [
30]. BMI, calculated from height (cm) and weight (kg), was therefore coded as 0 deficit for those with a BMI of 20 – 30, 1 deficit if BMI < 20 or >30. Having observed that patients were prescribed between 0 and 25 regular medications per day, we allocated up to 4 deficits for increasing polypharmacy (≤4 medications = 0 deficit; 5 – 9 medications = 1 deficit; 10 – 14 medications = 2 deficits; 15 – 19 medications = 3 deficits; ≥ 20 medications = 4 deficits).
Analysis
We investigated the distribution of FI-AC, its relationship with age and variance in the slope of FI-AC by conducting various statistical analyses. A histogram was used to confirm the approximate normality of the FI-AC. A fitted linear regression model was used to identify the relationship between average FI-AC and age. Due to the small numbers of patients aged ≥ 100 years (N = 2), centenarians were excluded from this model. The rate of accumulation of deficits was calculated by evaluating the slope of the line. To observe the upper limit of the FI-AC, the 99th percentiles were also plotted against age and a linear line fitted to these data. A flattening of this line (i.e. a parallel line to age-axis) would suggest the rate of deficit accumulation reaches a limit with increasing frailty.
A sampling procedure was adopted to extract the information about variability of the slope of FI-AC and to evaluate the impact of a given variable on the FI-AC. We randomly selected 80% of the variables without replacement and calculated FI-AC for each sample. The average FI-AC was plotted against age and the slope and intercept recorded. This procedure was repeated 1000 times to generate 1000 samples which were then evaluated.