110 Original article
Predicting outcome in a postacute stroke rehabilitation programme Peter J. van Bragta, Berbke T. van Ginnekenc, Tessa Westendorpa, Majanka H. Heijenbrok-Kala,b, Markus P. Wijffelsa and Gerard M. Ribbersa,b This study aims to evaluate and predict outcome as part of routine quality assessment of an inpatient stroke rehabilitation programme. By relating functional outcome to patient characteristics, including variables from the quality of life domain, we aim to find a set of variables that can be useful for prognosis, stratification and programme improvement. Data were collected, before and after rehabilitation, from a prospective quality registration database. Included were 250 patients in inpatient stroke rehabilitation after sustaining a first or recurrent ischemic or haemorrhagic stroke. Functional status was measured with the Barthel Index and the Academic Medical Centre Linear Disability Score. Health-related quality of life (HrQoL) was measured with the COOP/WONCA and the Nottingham Health Profile. Significant improvements were found on all outcome measures. A lower functional admission score, older age, more severe stroke, more pain and more negative emotional reactions on admission were found to be independent predictors of a lower outcome score, explaining 39.5% of its variance. Subjective (HrQoL)
factors such as negative emotion and pain have an adverse effect on outcome of stroke rehabilitation, in addition to stroke severity, age and functional status at admission. These factors need to be taken into account in screening, clinical decision making and treatment design. International Journal of Rehabilitation Research c 2014 Wolters Kluwer Health | Lippincott 37:110–117 Williams & Wilkins.
Introduction
et al., 1996; Kelly et al., 2003; Ng et al., 2005), but the clinical relevance of this finding is questioned (Bagg et al., 2002; Denti et al., 2008). Type and severity of stroke, comorbidity and urine incontinence are other known predictors (Kwakkel et al., 1996; Paolucci et al., 1996; Ween et al., 1996; Di Libero et al., 2001; Kelly et al., 2003; Meijer et al., 2003a, 2003b; Karatepe et al., 2008). A consistent finding is the strong association between lower functional status (activities of daily living, motor and cognitive abilities) at admission and lower outcome at discharge and at follow-up (Cifu and Stewart, 1999; Inouye et al., 2000; Bottemiller et al., 2006).
Stroke represents the most prevalent disabling condition requiring rehabilitation service. Stroke accounts for 2–4% of total healthcare expenditure in developed countries (Rosamond et al., 2008). Since stroke predominantly affects older people, the number of people suffering a stroke will rise as the global population ages. Likely the demand for rehabilitation services will increase (Edwards et al., 2010). Stroke rehabilitation is resource intensive and there is an urgent need to optimize healthcare resource efficiency for the care of disability due to stroke. In this context predicting outcome of stroke rehabilitation is a promising line of research. Finding sets of predictive variables will offer clinicians the possibility of better treatment prognosis and planning, and will help to aim treatment at the most relevant determinants. Prediction of outcome of stroke rehabilitation programmes has been the subject of many studies. Despite a substantial heterogeneity in design, methodology and population in earlier prediction studies (Kwakkel et al., 1996; Cifu and Stewart, 1999; Meijer et al., 2003a), a core set of relevant predictors can be identified. Higher age is a negative prognostic factor (Alexander, 1994; Kwakkel The study was conducted in Rijndam Rehabilitation Centre, Rotterdam, The Netherlands. c 2014 Wolters Kluwer Health | Lippincott Williams & Wilkins 0342-5282
International Journal of Rehabilitation Research 2014, 37:110–117 Keywords: prediction of outcome, prognosis, rehabilitation, stroke a
Rotterdam Neurorehabilitation Research, Rijndam Rehabilitation Centre, Erasmus MC, Department of Rehabilitation, Rotterdam and cMaartenskliniek, Research, Development & Education, Woerden, The Netherlands b
Correspondence to Peter J. van Bragt, MA, Rotterdam Neurorehabilitation Research, Rijndam Rehabilitation Centre, PO Box 23181, 3001 KD Rotterdam, The Netherlands Tel: + 31 10 2412412; fax: + 31 70 2412400; e-mail: [email protected] Received 24 May 2013 Accepted 17 October 2013
However, relationships are far from simple. Some studies show a curvilinear effect, with better gains for intermediate levels of functioning at admission. Psychometric properties of outcome measures such as ceiling effects and interaction effects between individual determinants often hinder the interpretation of recovery scores (Johnston et al., 2003; Bode et al., 2004). Environmental factors (e.g. the presence of a caregiver or social support) may also determine outcome and the discharge destination (Meijer et al., 2004). Health-related quality of life (HrQoL) variables refer to subjective health experience (illness) rather than more objective disease characteristics and functional abilities. DOI: 10.1097/MRR.0000000000000041
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Predicting outcome in a postacute stroke van Bragt et al. 111
HrQoL encompasses physical as well as social and mental aspects of life, commonly described in dimensions such as vitality, pain, emotional status, mobility and social role. Thus far, HrQoL variables have mostly been studied to evaluate the long-term impact of stroke or the effectiveness of rehabilitation in the long run (King, 1996; Wyller et al., 1997; Hopman and Verner, 2003; Haacke et al., 2006). The prognostic value of these more subjective variables has not been studied extensively. However, we can assume that the way the patient experiences his condition is not only an outcome but also a predictor, affecting the rehabilitation process itself. In this study, we explored data from a prospective quality registration database from an inpatient stroke rehabilitation programme. We related clinical and demographical patient variables, measured at time of admission, to functional outcome, measured at discharge. In addition to common predictors such as age, severity of stroke and disability, we also explored the predictive value of subjective variables of the HrQoL domain. We aim to find predictors that can help identify patients with a high risk of poor rehabilitation outcome. This may be relevant in clinical decision making, resource allocation and treatment design.
Patients and methods Patients and treatment
Data from patients admitted to the inpatient stroke rehabilitation ward of a large rehabilitation centre in the Netherlands between 2005 and 2007 were prospectively entered in a quality registration database. For this study we analysed the data from all patients who were at least 18 years of age on admission and were diagnosed with a first or recurrent ischemic or haemorrhagic stroke. From each patient with multiple admissions only the last admission was included. Excluded were patients with insufficient mastery of the Dutch language, with aphasia too severe to reliably complete the questionnaires used, and with subarachnoid or traumatic intracranial haemorrhage. Patients all received an intensive rehabilitation programme according to Dutch stroke treatment guidelines (Dutch Institute for Healthcare Improvement, 2008). After approval of the Medical Ethics Committee, all patients gave informed consent that their clinical data were to be entered in an anonymized quality registration database. Measurements
Severity of stroke was assessed using the Bamford classification (lacunar, partial anterior circulation, total anterior circulation and posterior circulation) resulting in three categories of severity: very severe (total anterior circulation), severe (partial anterior or posterior circulation) and nonsevere (lacunar) (Bamford et al., 1991). Side of stroke (left, right or other) and type of stroke (haemorrhagic or ischemic) were also registered. The presence of neglect was determined by the Star
Cancelation Test (Wilson et al., 1987; Halligan et al., 1989), aphasia by the Token Test (De Renzi and Vignolo, 1962) and apraxia by clinical judgement. The presence of comorbidity was assessed using an adaption of the Cumulative Illness Rating Scale (Miller et al., 1992). The Cumulative Illness Rating Scale classifies comorbidity in seven to 13 domains scored on a five-point scale. In this study the disorders were scored on a dichotomous scale (presence of disorder yes/no). Functional outcome was measured by the Academic Medical Centre Linear Disability Score (ALDS), the Barthel Index (BI) and the Modified Rankin Scale (MRS). For the domain of HrQoL the Nottingham Health Profile (NHP) and the Dartmouth COOP Functional Health Assessment Charts of the World Organization of General Practice/Family Physicians (COOP/WONCA) were used. The ALDS is a generic item bank, used to measure functional status by the ability to perform daily activities (de Haan et al., 2002; Lindeboom et al., 2004; Weisscher et al., 2007). Each item from the ALDS item bank describes an activity of daily life and the item score reflects the patient’s ability to perform that activity at the present time (yes, no). The item bank is based on item response theory modelling (Rasch, 1993) with all items hierarchically calibrated along the degree of difficulty to perform each activity (see Appendix). Scores are between 0 (lowest) and 100 (highest). The ALDS instrument has a number of advantages in comparison with classical instruments (such as the BI). The instrument is very flexible. It can be easily adjusted, for example, reducing the number of items to make short forms, or adding items with a higher level of difficulty to deal with ceiling effects, and still the scores remain comparable between different ALDS versions (de Haan et al., 2002; Holman et al., 2005; Weisscher et al., 2007). The ALDS has been validated for several populations, including patients with (acute) stroke (Holman et al., 2005). In our study, a shorter-form ALDS was used by selecting a set of 30 items from the item bank (Appendix). Selection of items was performed by three (stroke) rehabilitation specialists largely based on the current and targeted levels of functioning of stroke patients. The ALDS was scored by a trained nurse based on observations of the functional ability of the patient. The BI is a scale used to measure performance in basic activities of daily living and mobility. It uses ten variables on a mixed three-point and four-point scale. A higher number is associated with a greater likelihood of being able to live at home with a degree of independence following discharge from hospital (Collin et al., 1988). The BI was scored by a trained nurse based on observations of the patient. The MRS assesses the degree of disability including the dependency on help from others. Degree of disability is
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112 International Journal of Rehabilitation Research 2014, Vol 37 No 2
scored on a six-point scale (no disability–severe disability) (van Swieten et al., 1988). The MRS was completed by a trained nurse. The NHP (part 1) assesses perceived physical, emotional and social health problems. The NHP consists of 38 dichotomous (yes/no) propositions in six domains: energy level, pain, sleep, mobility/physical ability, social isolation and emotional reaction. Questions within a domain are weighted, and the sum of weighted scores within a domain adds up to 100. A higher score means having more health problems in that domain (Hunt et al., 1981). The NHP was self-reported by the patient during a face-toface interview with the trained nurse. The COOP/WONCA measures HrQoL on six domains: physical fitness, emotional condition, daily activities, social activities, change in health condition, and overall health and pain. Each domain consists of one question, referring to the experience of the patient at the given time, using a five-point response scale with each point illustrated by a simple pictogram. Each item is rated on this five-point scale ranging from 1 (no problems) to 5 (severe problems). The reference period is 2 weeks. The six items/domains must be interpreted separately (van Weel, 1993). The COOP/WONCA was self-reported by the patient during a face-to-face interview with the trained nurse. Demographic and clinical data were assessed by a physician (severity of stroke and comorbidity), clinical psychologist (neglect), speech therapist (aphasia) or occupational therapist (apraxia). All questionnaires were administered by a trained nurse at time of admission and discharge from the inpatient rehabilitation clinic.
Statistical analysis
Patients with missing data on the ALDS at discharge, the outcome variable of interest, were excluded from the analyses. Descriptive statistics were calculated for all patient and clinical characteristics. Means and SDs of functional outcome scores at admission and discharge were calculated. Differences between admission and discharge scores were analysed using paired t-tests. Linear regression analyses were performed to study the effect of the variables that were measured at admission on functional outcome at discharge, measured with the ALDS. Potential predictors included patient and stroke characteristics (age, sex, time after onset of stroke, severity of stroke, side and type of stroke, presence of aphasia, apraxia and neglect, number of comorbidities, employment and marital status), functional status at admission (ALDS, BI, MRS, presence of urine incontinence or bowel incontinence) and self-reported HrQoL at admission (NHP and COOP/WONCA). From the domain of
the therapy process, only the variable length of stay was included. To prevent loss of power, any missing value in the predictor variables was replaced with the mean of that variable. Domain scores from the COOP/WONCA were dichotomized using a cut-off score of 3 or more (moderate/severe vs. no/minor problems). The same cutoff score was used for the MRS (moderate/severe vs. no/ minor disability). Only significant predictors from univariable analyses were entered in the multivariable regression model using forward variable selection. Significance levels of P less than 0.05 for entry and P more than 0.10 for removal of variables were used in the multivariable regression analyses. Significant regression coefficients and the variance explained (adjusted R2) are presented. All analyses were performed using SPSS version 19.0 (SPSS Inc., IBM Company, Portsmouth, UK).
Results Patient population
In total, 293 patients with stroke were registered in the database. Of these patients, 250 (85%) had complete data on the ALDS discharge score and were included in the analyses. Baseline characteristics are summarized in Table 1. In several cases, the COOP/WONCA, NHP or BI had missing items, precluding calculation of domain scores and sum scores. The proportion of missing data was 7.2% for the ALDS admission score, 20% for items of the BI, 0–6.4% for the domains of the COOP/WONCA score, and 2–16% for the domains of the NHP score, all measured at admission. From the 43 patients who were excluded because of missing data on the ALDS, many also had missing data on the NHP, COOP/WONCA, BI, MRS and several patient characteristics. The excluded patients (n = 43) did not differ from the included patients (n = 250) regarding age, sex, type of stroke, time after stroke or length of stay in rehabilitation. However, the excluded group of patients with missing outcome data contained significantly more patients with severe stroke (P < 0.006), left-sided stroke (P < 0.034), aphasia (P < 0.000), apraxia (P < 0.000) and severe disability measured with the MRS (P < 0.027).
Rehabilitation outcome
Table 2 shows significant improvements in both the functional ability measures and the quality of life measures during inpatient rehabilitation. The ALDS improved 49% and the BI improved 50%. All outcomes on the subscales of the NHP improved in the range from 27% (sleep) to 58% (mobility). The outcomes on the subscales of the COOP/WONCA improved by 14% (pain) to 31% (daily activities).
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Predicting outcome in a postacute stroke van Bragt et al. 113
Table 1
Patient characteristics (N = 250)
Table 3 Multivariable linear regression model predicting Academic Medical Centre Linear Disability Score discharge score (N = 250) n (%)
Sex Male Female Stroke severity Very serious (TACI) Serious (PACI–POCI) Not serious (LACI) Stroke type Haemorrhagic Ischemic Aphasia Neglect Apraxia Comorbidity Modified Rankin Scale No/mild disability, 0–2 Moderate/severe disability, 3–5
157 (63) 93 (37) 20 (9) 134 (62) 63 (29) 49 172 39 63 12 198
(22) (78) (18) (29) (6) (89)
Constant Barthel Index admission score ALDS admission score Age
0.171 – 0.021
Stroke severity
– 0.662
NHP pain
– 0.012
NHP emotional reaction
– 0.008
Total R2 20 230 Mean 58.6 12.5 50.3 20.5 62.7
Age Barthel Iindex ALDS admission score Time after onset of stroke (days) Length of stay in rehabilitation (days)
(8) (92) (SD) (11.7) (5.5) (27.6) (12.0) (31.4)
ALDS, Academic Medical Centre Linear Disability Score; LACI, lacunar anterior circulation infarct; PACI, partial anterior circulation infarct; POCI, posterior circulation infarct; TACI, total anterior circulation infarct.
Table 2
b 1.796 0.092
95% CI
Pvalue
R2 change
0.046–0.137
0.000
0.294
0.063–0.279 – 0.034 to – 0.008 – 1.217 to – 0.108 – 0.019 to – 0.004 – 0.016 to – 0.001
0.002 0.001
0.021 0.021
0.019
0.013
0.004
0.035
0.028
0.012 0.395
ALDS, Academic Medical Centre Linear Disability Score; CI, confidence interval; NHP, Nottingham Health Profile.
who were younger, with less severe stroke, with a higher BI and ALDS admission scores, who reported less pain or less negative emotional reactions measured with the NHP on admission showed higher functional outcome scores at discharge (Table 3). The prediction model, consisting of six variables, explained 39.5% of the total variance in outcome.
Improvement scores from admission to discharge
Instrument/scale ALDS Barthel Index NHP Mobility Pain Sleep Energy Social isolation Emotional reaction COOP/WONCA Fysical Fitness Emotional Condition Daily Activities Social Activities Overall Health Pain
Admission mean (SD)
Discharge mean (SD)
P
48.37 (28.02) 11.91 (5.72)
73.47 (19.45) 18.15 (4.14)
0.000 0.000
48.38 13.29 27.61 34.99 20.14 18.00
(29.32) (20.53) (31.41) (35.11) (22.79) (21.63)
20.73 7.03 20.13 18.93 9.05 9.30
(23.91) (14.58) (26.60) (29.30) (16.39) (18.50)
0.000 0.000 0.000 0.000 0.000 0.000
4.13 2.41 3.02 2.47 3.51 2.36
(1.11) (1.20) (1.30) (1.39) (0.92) (1.27)
3.08 1.75 2.10 1.74 2.77 2.03
(1.09) (1.03) (1.05) (1.09) (0.95) (1.09)
0.000 0.000 0.000 0.000 0.000 0.000
ALDS, Academic Medical Centre Linear Disability Score; COOP/WONCA, Dartmouth COOP Functional Health Assessment Charts/Wonca-revision; NHP, Nottingham Health Profile.
Prediction of functional outcome
Univariable regression analyses showed that age, severity of stroke, presence of neglect, bladder or bowel incontinence, time after onset, length of stay in rehabilitation, admission scores of BI, ALDS, MRS, all domains of NHP, and the domains of daily activities, and general health of the COOP/WONCA were significantly associated with functional outcome as measured with the ALDS at discharge. Multivariable linear regression analysis showed that the admission score of BI and ALDS, age and severity of stroke were independent predictors of functional outcome. In addition to these variables, the NHP pain and NHP emotion score significantly contributed to the prediction of functional outcome. Patients
Discussion Our study showed significant improvements in functional outcomes and quality of life. Further, severe stroke, older age, lower functional status, more negative emotional reactions and more pain at admission were associated with lower functional outcomes. The prognostic value of severity of stroke, age and functional status at admission has been reported earlier (Paolucci et al., 1996; Ween et al., 1996; Cifu and Stewart, 1999; Inouye et al., 2000; Di Libero et al., 2001; Johnston et al., 2003; Kelly et al., 2003; Ng et al., 2005; Bottemiller et al., 2006). These factors probably have a strong limiting, range-setting effect, codetermining the maximum gains that can be reached in inpatient rehabilitation in general. Independent of these well-documented predictors, pain and negative emotional reactions are factors with considerable added predictive value in our model. Pain is a common complication after stroke. Studies show percentages from 30 up to 50% of generalized pain and shoulder pain (Langhorne et al., 2000; Sackley et al., 2008). Little is known about the prognostic value of pain for the outcome of stroke rehabilitation. Our study indicates that, for our patient population, pain may have a negative impact on rehabilitation outcome independently of age, severity and functional status. The factor ‘negative emotional reactions’ as measured by the NHP can be seen as a combination of depression (feeling depressed and down, worrying, feeling life not worth living) and emotional instability (temper, losing control, feeling on edge). Depressive reactions and even
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114 International Journal of Rehabilitation Research 2014, Vol 37 No 2
major depression are common complications of stroke with prevalences of 20% and higher in different populations and settings (Sinyor et al., 1986; Herrmann et al., 1998; van de Weg et al., 1999; Cassidy et al., 2004). Other factors that are conceptually related to the NHP domain of negative emotional reactions are emotional incontinence, distress and anger proneness. Like depression, these conditions are relatively common sequelae to stroke, having adverse effects on rehabilitation outcomes and quality of life (Kim and Choi-Kwon, 2000; Kim et al., 2002; West et al., 2010). Negative emotion in all its forms may be a strictly biological, physiological side effect of stroke, but it may also be a very personal, psychological response to the experience of stroke and the awareness of its consequences. Either way, we may assume that these emotions have a negative influence on the rehabilitation process, a process that strongly depends on personal motivation, stamina and optimism. The results of our study point to the occurrence of such a hampering effect, again separate from age, severity of stroke or disability. Factors such as pain and negative emotion seem to have an unfavourable impact not only during the inpatient rehabilitation process, but also in the chronic phase. Results of a recent qualitative study (Lennon et al., 2013) indicate that, among other factors, pain and negative emotions (e.g. lack of confidence) are barriers to healthylifestyle participation after stroke. The results show that, by applying predictive designs, results of routine outcome monitoring can be helpful in clinical decision making and improving rehabilitation programmes. Our study suggests that it might be useful to stratify patients on the basis of limiting factors such as age, lower functional status and severity of stroke. Programme adjustments should be considered for higher-risk patients. The therapy process could possibly be improved by paying more attention to hampering factors such as pain and negative emotional reactions. To a certain extent these factors can and should be managed, pharmacologically or psychologically. This requires proper screening and diagnosis, but also specific, supplementary treatment strategies. A number of limitations of this study should be mentioned. Outcome was predicted mainly from patient characteristics at the time of admission and was not related to variations in interventions or process variables such as type and number of therapies and therapy intensity. Furthermore, our study does not differentiate first from recurrent strokes, although it is assumed that the clinical status for these groups will be different. In future studies, it is essential to examine the effect of these factors on functional outcome; linking outcome to process and patient characteristics will provide us with more directions for improvement (Mant, 2001). Another limitation of this study is the completeness of the database. Missing data in some variables may cause a loss
of more than 50% of the original data if only complete cases are included in the analyses. This reduction in sample size translates into reduced statistical power. Therefore, we used mean substitution for handling the missing data, a procedure that is easily available in the linear regression analysis options in SPSS. The advantage of this method is that the estimate of the mean for the variable is not affected. However, the procedure is based on the assumption that the missing values are missing completely at random, which may not be the case. Furthermore, the estimate of the SD and variance may be reduced (Saunders et al., 2006). Another limitation is the absence of follow-up data. A decline in quality of life after discharge from subacute stroke rehabilitation is a well-known problem (Hopman and Verner, 2003). Finally, obviously conclusions based on outcomes of a single programme cannot be generalized without further comparison. Further studies with comparable design on databases from other rehabilitation clinics can possibly validate our conclusions.
Conclusion The results of this study suggest that pain and negative emotion have an adverse effect on rehabilitation outcome in stroke patients in addition to age, severity of stroke and functional status. Future research is needed to determine whether rehabilitation outcome of stroke patients could improve if these factors were closely monitored and effectively treated during rehabilitation.
Acknowledgements Conflicts of interest
There are no conflicts of interest.
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Appendix Table A1
Table A1
Academic Medical Centre Linear Disability Score item bank
Item Are you able toy 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65
Item description
Item difficulty parameter
Linear transformed ALDS
Bike for at least 2 h? Vacuum a flight of stairs? Carry a bag of shopping upstairs? Clean a bathroom? Vacuum a room and move light furniture? Fetch groceries for 3–4 days? Go for a walk in the woods? Travel by local bus or tram? Walk for more than 15 min? Carry a tray? Walk up a hill or high bridge? Go shopping for clothes? Cut your toenails? Fill in an official form Go to a party (small circle)? Stand for 10 min? Go to a restaurant? Sweep the floor? Hang and take in a load of washing? Vacuum without moving any furniture? Move a bed or table? Use a washing machine? Reach into a high cupboard? Walk up a flight of stairs? Go to the bank or post office? Walk down a flight of stairs? Go to the general practitioner? Go for a short walk (15 min)? Use a dustpan and brush? Write a letter? change the sheets on a bed? Cross the road? open and close a window? Fetch a few things from the shop? Polish shoes? Have a shower (and wash your hair)? Fold up the washing? Dust? Put on/take off lace-up shoes? Clean a toilet? Make a bed? Cut your fingernails? Reach under a table? Heat tinned food? Make eggs or beans on toast? Reach into a low cupboard? Move between 2 low chairs? Pick something up from the floor? Clean a bathroom sink? Put the washing up away? Read a newspaper? Get in and out of a car? Make porridge? Clear the table after a meal? Peel and core an apple? Prepare breakfast or lunch? Clean the kitchen surfaces? Put a chair up to the table Eat a meal at the table? Wash up? Put on/take off socks and slip-on shoes? Sit up (from lying) in bed? Get a book off a shelf? Answer the telephone? Hang clothes up in a cupboard?
– 3.057 – 2.653 – 2.140 – 1.959 – 1.879 – 1.633 – 1.504 – 1.230 – 0.818 – 0.808 – 0.781 – 0.723 – 0.655 – 0.614 – 0.560 – 0.525 – 0.481 – 0.450 – 0.445 – 0.347 – 0.304 – 0.234 – 0.234 – 0.192 – 0.130 – 0.020 0.020 0.071 0.083 0.175 0.209 0.224 0.240 0.291 0.342 0.657 0.698 0.702 0.759 0.779 0.842 0.901 0.918 0.922 1.022 1.092 1.144 1.151 1.180 1.263 1.278 1.339 1.369 1.471 1.498 1.517 1.765 1.777 1.788 1.863 1.930 1.948 2.106 2.148 2.192
89 87 85 84 84 82 81 78 74 74 73 73 72 71 70 70 69 69 69 67 66 65 65 65 64 62 61 60 60 58 58 58 58 56 56 50 50 50 49 48 47 46 46 46 44 43 42 42 42 40 40 39 39 37 37 36 32 32 32 31 30 30 28 27 27
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Predicting outcome in a postacute stroke van Bragt et al. 117
Table A1 (continued) Item 66 67 68 69 70 71 72 73 74 75 76 77
Item description Make a bowl of cereal? Make coffee or tea? Put long trousers on? Sit on the edge of a bed from lying down? Move between 2 dining chairs? Wash and dry your lower body? Put on/take off a coat? Wash/dry your face and hands? Get out of bed into a chair? Go to the toilet? Wash your lower body (at sink)? Put on and take off a T-shirt?
Item difficulty parameter 2.280 2.348 2.376 2.674 2.722 2.777 2.859 2.969 2.987 3.077 3.235 3.494
Linear transformed ALDS 25 25 24 21 20 20 19 18 18 17 15 11
Items used in this study are given in bold. ALDS, Academic Medical Centre Linear Disability Score.
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Predicting outcome in a postacute stroke rehabilitation programme Peter J. van Bragta, Berbke T. van Ginnekenc, Tessa Westendorpa, Majanka H. Heijenbrok-Kala,b, Markus P. Wijffelsa and Gerard M. Ribbersa,b This study aims to evaluate and predict outcome as part of routine quality assessment of an inpatient stroke rehabilitation programme. By relating functional outcome to patient characteristics, including variables from the quality of life domain, we aim to find a set of variables that can be useful for prognosis, stratification and programme improvement. Data were collected, before and after rehabilitation, from a prospective quality registration database. Included were 250 patients in inpatient stroke rehabilitation after sustaining a first or recurrent ischemic or haemorrhagic stroke. Functional status was measured with the Barthel Index and the Academic Medical Centre Linear Disability Score. Health-related quality of life (HrQoL) was measured with the COOP/WONCA and the Nottingham Health Profile. Significant improvements were found on all outcome measures. A lower functional admission score, older age, more severe stroke, more pain and more negative emotional reactions on admission were found to be independent predictors of a lower outcome score, explaining 39.5% of its variance. Subjective (HrQoL)
factors such as negative emotion and pain have an adverse effect on outcome of stroke rehabilitation, in addition to stroke severity, age and functional status at admission. These factors need to be taken into account in screening, clinical decision making and treatment design. International Journal of Rehabilitation Research c 2014 Wolters Kluwer Health | Lippincott 37:110–117 Williams & Wilkins.
Introduction
et al., 1996; Kelly et al., 2003; Ng et al., 2005), but the clinical relevance of this finding is questioned (Bagg et al., 2002; Denti et al., 2008). Type and severity of stroke, comorbidity and urine incontinence are other known predictors (Kwakkel et al., 1996; Paolucci et al., 1996; Ween et al., 1996; Di Libero et al., 2001; Kelly et al., 2003; Meijer et al., 2003a, 2003b; Karatepe et al., 2008). A consistent finding is the strong association between lower functional status (activities of daily living, motor and cognitive abilities) at admission and lower outcome at discharge and at follow-up (Cifu and Stewart, 1999; Inouye et al., 2000; Bottemiller et al., 2006).
Stroke represents the most prevalent disabling condition requiring rehabilitation service. Stroke accounts for 2–4% of total healthcare expenditure in developed countries (Rosamond et al., 2008). Since stroke predominantly affects older people, the number of people suffering a stroke will rise as the global population ages. Likely the demand for rehabilitation services will increase (Edwards et al., 2010). Stroke rehabilitation is resource intensive and there is an urgent need to optimize healthcare resource efficiency for the care of disability due to stroke. In this context predicting outcome of stroke rehabilitation is a promising line of research. Finding sets of predictive variables will offer clinicians the possibility of better treatment prognosis and planning, and will help to aim treatment at the most relevant determinants. Prediction of outcome of stroke rehabilitation programmes has been the subject of many studies. Despite a substantial heterogeneity in design, methodology and population in earlier prediction studies (Kwakkel et al., 1996; Cifu and Stewart, 1999; Meijer et al., 2003a), a core set of relevant predictors can be identified. Higher age is a negative prognostic factor (Alexander, 1994; Kwakkel The study was conducted in Rijndam Rehabilitation Centre, Rotterdam, The Netherlands. c 2014 Wolters Kluwer Health | Lippincott Williams & Wilkins 0342-5282
International Journal of Rehabilitation Research 2014, 37:110–117 Keywords: prediction of outcome, prognosis, rehabilitation, stroke a
Rotterdam Neurorehabilitation Research, Rijndam Rehabilitation Centre, Erasmus MC, Department of Rehabilitation, Rotterdam and cMaartenskliniek, Research, Development & Education, Woerden, The Netherlands b
Correspondence to Peter J. van Bragt, MA, Rotterdam Neurorehabilitation Research, Rijndam Rehabilitation Centre, PO Box 23181, 3001 KD Rotterdam, The Netherlands Tel: + 31 10 2412412; fax: + 31 70 2412400; e-mail: [email protected] Received 24 May 2013 Accepted 17 October 2013
However, relationships are far from simple. Some studies show a curvilinear effect, with better gains for intermediate levels of functioning at admission. Psychometric properties of outcome measures such as ceiling effects and interaction effects between individual determinants often hinder the interpretation of recovery scores (Johnston et al., 2003; Bode et al., 2004). Environmental factors (e.g. the presence of a caregiver or social support) may also determine outcome and the discharge destination (Meijer et al., 2004). Health-related quality of life (HrQoL) variables refer to subjective health experience (illness) rather than more objective disease characteristics and functional abilities. DOI: 10.1097/MRR.0000000000000041
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Predicting outcome in a postacute stroke van Bragt et al. 111
HrQoL encompasses physical as well as social and mental aspects of life, commonly described in dimensions such as vitality, pain, emotional status, mobility and social role. Thus far, HrQoL variables have mostly been studied to evaluate the long-term impact of stroke or the effectiveness of rehabilitation in the long run (King, 1996; Wyller et al., 1997; Hopman and Verner, 2003; Haacke et al., 2006). The prognostic value of these more subjective variables has not been studied extensively. However, we can assume that the way the patient experiences his condition is not only an outcome but also a predictor, affecting the rehabilitation process itself. In this study, we explored data from a prospective quality registration database from an inpatient stroke rehabilitation programme. We related clinical and demographical patient variables, measured at time of admission, to functional outcome, measured at discharge. In addition to common predictors such as age, severity of stroke and disability, we also explored the predictive value of subjective variables of the HrQoL domain. We aim to find predictors that can help identify patients with a high risk of poor rehabilitation outcome. This may be relevant in clinical decision making, resource allocation and treatment design.
Patients and methods Patients and treatment
Data from patients admitted to the inpatient stroke rehabilitation ward of a large rehabilitation centre in the Netherlands between 2005 and 2007 were prospectively entered in a quality registration database. For this study we analysed the data from all patients who were at least 18 years of age on admission and were diagnosed with a first or recurrent ischemic or haemorrhagic stroke. From each patient with multiple admissions only the last admission was included. Excluded were patients with insufficient mastery of the Dutch language, with aphasia too severe to reliably complete the questionnaires used, and with subarachnoid or traumatic intracranial haemorrhage. Patients all received an intensive rehabilitation programme according to Dutch stroke treatment guidelines (Dutch Institute for Healthcare Improvement, 2008). After approval of the Medical Ethics Committee, all patients gave informed consent that their clinical data were to be entered in an anonymized quality registration database. Measurements
Severity of stroke was assessed using the Bamford classification (lacunar, partial anterior circulation, total anterior circulation and posterior circulation) resulting in three categories of severity: very severe (total anterior circulation), severe (partial anterior or posterior circulation) and nonsevere (lacunar) (Bamford et al., 1991). Side of stroke (left, right or other) and type of stroke (haemorrhagic or ischemic) were also registered. The presence of neglect was determined by the Star
Cancelation Test (Wilson et al., 1987; Halligan et al., 1989), aphasia by the Token Test (De Renzi and Vignolo, 1962) and apraxia by clinical judgement. The presence of comorbidity was assessed using an adaption of the Cumulative Illness Rating Scale (Miller et al., 1992). The Cumulative Illness Rating Scale classifies comorbidity in seven to 13 domains scored on a five-point scale. In this study the disorders were scored on a dichotomous scale (presence of disorder yes/no). Functional outcome was measured by the Academic Medical Centre Linear Disability Score (ALDS), the Barthel Index (BI) and the Modified Rankin Scale (MRS). For the domain of HrQoL the Nottingham Health Profile (NHP) and the Dartmouth COOP Functional Health Assessment Charts of the World Organization of General Practice/Family Physicians (COOP/WONCA) were used. The ALDS is a generic item bank, used to measure functional status by the ability to perform daily activities (de Haan et al., 2002; Lindeboom et al., 2004; Weisscher et al., 2007). Each item from the ALDS item bank describes an activity of daily life and the item score reflects the patient’s ability to perform that activity at the present time (yes, no). The item bank is based on item response theory modelling (Rasch, 1993) with all items hierarchically calibrated along the degree of difficulty to perform each activity (see Appendix). Scores are between 0 (lowest) and 100 (highest). The ALDS instrument has a number of advantages in comparison with classical instruments (such as the BI). The instrument is very flexible. It can be easily adjusted, for example, reducing the number of items to make short forms, or adding items with a higher level of difficulty to deal with ceiling effects, and still the scores remain comparable between different ALDS versions (de Haan et al., 2002; Holman et al., 2005; Weisscher et al., 2007). The ALDS has been validated for several populations, including patients with (acute) stroke (Holman et al., 2005). In our study, a shorter-form ALDS was used by selecting a set of 30 items from the item bank (Appendix). Selection of items was performed by three (stroke) rehabilitation specialists largely based on the current and targeted levels of functioning of stroke patients. The ALDS was scored by a trained nurse based on observations of the functional ability of the patient. The BI is a scale used to measure performance in basic activities of daily living and mobility. It uses ten variables on a mixed three-point and four-point scale. A higher number is associated with a greater likelihood of being able to live at home with a degree of independence following discharge from hospital (Collin et al., 1988). The BI was scored by a trained nurse based on observations of the patient. The MRS assesses the degree of disability including the dependency on help from others. Degree of disability is
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112 International Journal of Rehabilitation Research 2014, Vol 37 No 2
scored on a six-point scale (no disability–severe disability) (van Swieten et al., 1988). The MRS was completed by a trained nurse. The NHP (part 1) assesses perceived physical, emotional and social health problems. The NHP consists of 38 dichotomous (yes/no) propositions in six domains: energy level, pain, sleep, mobility/physical ability, social isolation and emotional reaction. Questions within a domain are weighted, and the sum of weighted scores within a domain adds up to 100. A higher score means having more health problems in that domain (Hunt et al., 1981). The NHP was self-reported by the patient during a face-toface interview with the trained nurse. The COOP/WONCA measures HrQoL on six domains: physical fitness, emotional condition, daily activities, social activities, change in health condition, and overall health and pain. Each domain consists of one question, referring to the experience of the patient at the given time, using a five-point response scale with each point illustrated by a simple pictogram. Each item is rated on this five-point scale ranging from 1 (no problems) to 5 (severe problems). The reference period is 2 weeks. The six items/domains must be interpreted separately (van Weel, 1993). The COOP/WONCA was self-reported by the patient during a face-to-face interview with the trained nurse. Demographic and clinical data were assessed by a physician (severity of stroke and comorbidity), clinical psychologist (neglect), speech therapist (aphasia) or occupational therapist (apraxia). All questionnaires were administered by a trained nurse at time of admission and discharge from the inpatient rehabilitation clinic.
Statistical analysis
Patients with missing data on the ALDS at discharge, the outcome variable of interest, were excluded from the analyses. Descriptive statistics were calculated for all patient and clinical characteristics. Means and SDs of functional outcome scores at admission and discharge were calculated. Differences between admission and discharge scores were analysed using paired t-tests. Linear regression analyses were performed to study the effect of the variables that were measured at admission on functional outcome at discharge, measured with the ALDS. Potential predictors included patient and stroke characteristics (age, sex, time after onset of stroke, severity of stroke, side and type of stroke, presence of aphasia, apraxia and neglect, number of comorbidities, employment and marital status), functional status at admission (ALDS, BI, MRS, presence of urine incontinence or bowel incontinence) and self-reported HrQoL at admission (NHP and COOP/WONCA). From the domain of
the therapy process, only the variable length of stay was included. To prevent loss of power, any missing value in the predictor variables was replaced with the mean of that variable. Domain scores from the COOP/WONCA were dichotomized using a cut-off score of 3 or more (moderate/severe vs. no/minor problems). The same cutoff score was used for the MRS (moderate/severe vs. no/ minor disability). Only significant predictors from univariable analyses were entered in the multivariable regression model using forward variable selection. Significance levels of P less than 0.05 for entry and P more than 0.10 for removal of variables were used in the multivariable regression analyses. Significant regression coefficients and the variance explained (adjusted R2) are presented. All analyses were performed using SPSS version 19.0 (SPSS Inc., IBM Company, Portsmouth, UK).
Results Patient population
In total, 293 patients with stroke were registered in the database. Of these patients, 250 (85%) had complete data on the ALDS discharge score and were included in the analyses. Baseline characteristics are summarized in Table 1. In several cases, the COOP/WONCA, NHP or BI had missing items, precluding calculation of domain scores and sum scores. The proportion of missing data was 7.2% for the ALDS admission score, 20% for items of the BI, 0–6.4% for the domains of the COOP/WONCA score, and 2–16% for the domains of the NHP score, all measured at admission. From the 43 patients who were excluded because of missing data on the ALDS, many also had missing data on the NHP, COOP/WONCA, BI, MRS and several patient characteristics. The excluded patients (n = 43) did not differ from the included patients (n = 250) regarding age, sex, type of stroke, time after stroke or length of stay in rehabilitation. However, the excluded group of patients with missing outcome data contained significantly more patients with severe stroke (P < 0.006), left-sided stroke (P < 0.034), aphasia (P < 0.000), apraxia (P < 0.000) and severe disability measured with the MRS (P < 0.027).
Rehabilitation outcome
Table 2 shows significant improvements in both the functional ability measures and the quality of life measures during inpatient rehabilitation. The ALDS improved 49% and the BI improved 50%. All outcomes on the subscales of the NHP improved in the range from 27% (sleep) to 58% (mobility). The outcomes on the subscales of the COOP/WONCA improved by 14% (pain) to 31% (daily activities).
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Predicting outcome in a postacute stroke van Bragt et al. 113
Table 1
Patient characteristics (N = 250)
Table 3 Multivariable linear regression model predicting Academic Medical Centre Linear Disability Score discharge score (N = 250) n (%)
Sex Male Female Stroke severity Very serious (TACI) Serious (PACI–POCI) Not serious (LACI) Stroke type Haemorrhagic Ischemic Aphasia Neglect Apraxia Comorbidity Modified Rankin Scale No/mild disability, 0–2 Moderate/severe disability, 3–5
157 (63) 93 (37) 20 (9) 134 (62) 63 (29) 49 172 39 63 12 198
(22) (78) (18) (29) (6) (89)
Constant Barthel Index admission score ALDS admission score Age
0.171 – 0.021
Stroke severity
– 0.662
NHP pain
– 0.012
NHP emotional reaction
– 0.008
Total R2 20 230 Mean 58.6 12.5 50.3 20.5 62.7
Age Barthel Iindex ALDS admission score Time after onset of stroke (days) Length of stay in rehabilitation (days)
(8) (92) (SD) (11.7) (5.5) (27.6) (12.0) (31.4)
ALDS, Academic Medical Centre Linear Disability Score; LACI, lacunar anterior circulation infarct; PACI, partial anterior circulation infarct; POCI, posterior circulation infarct; TACI, total anterior circulation infarct.
Table 2
b 1.796 0.092
95% CI
Pvalue
R2 change
0.046–0.137
0.000
0.294
0.063–0.279 – 0.034 to – 0.008 – 1.217 to – 0.108 – 0.019 to – 0.004 – 0.016 to – 0.001
0.002 0.001
0.021 0.021
0.019
0.013
0.004
0.035
0.028
0.012 0.395
ALDS, Academic Medical Centre Linear Disability Score; CI, confidence interval; NHP, Nottingham Health Profile.
who were younger, with less severe stroke, with a higher BI and ALDS admission scores, who reported less pain or less negative emotional reactions measured with the NHP on admission showed higher functional outcome scores at discharge (Table 3). The prediction model, consisting of six variables, explained 39.5% of the total variance in outcome.
Improvement scores from admission to discharge
Instrument/scale ALDS Barthel Index NHP Mobility Pain Sleep Energy Social isolation Emotional reaction COOP/WONCA Fysical Fitness Emotional Condition Daily Activities Social Activities Overall Health Pain
Admission mean (SD)
Discharge mean (SD)
P
48.37 (28.02) 11.91 (5.72)
73.47 (19.45) 18.15 (4.14)
0.000 0.000
48.38 13.29 27.61 34.99 20.14 18.00
(29.32) (20.53) (31.41) (35.11) (22.79) (21.63)
20.73 7.03 20.13 18.93 9.05 9.30
(23.91) (14.58) (26.60) (29.30) (16.39) (18.50)
0.000 0.000 0.000 0.000 0.000 0.000
4.13 2.41 3.02 2.47 3.51 2.36
(1.11) (1.20) (1.30) (1.39) (0.92) (1.27)
3.08 1.75 2.10 1.74 2.77 2.03
(1.09) (1.03) (1.05) (1.09) (0.95) (1.09)
0.000 0.000 0.000 0.000 0.000 0.000
ALDS, Academic Medical Centre Linear Disability Score; COOP/WONCA, Dartmouth COOP Functional Health Assessment Charts/Wonca-revision; NHP, Nottingham Health Profile.
Prediction of functional outcome
Univariable regression analyses showed that age, severity of stroke, presence of neglect, bladder or bowel incontinence, time after onset, length of stay in rehabilitation, admission scores of BI, ALDS, MRS, all domains of NHP, and the domains of daily activities, and general health of the COOP/WONCA were significantly associated with functional outcome as measured with the ALDS at discharge. Multivariable linear regression analysis showed that the admission score of BI and ALDS, age and severity of stroke were independent predictors of functional outcome. In addition to these variables, the NHP pain and NHP emotion score significantly contributed to the prediction of functional outcome. Patients
Discussion Our study showed significant improvements in functional outcomes and quality of life. Further, severe stroke, older age, lower functional status, more negative emotional reactions and more pain at admission were associated with lower functional outcomes. The prognostic value of severity of stroke, age and functional status at admission has been reported earlier (Paolucci et al., 1996; Ween et al., 1996; Cifu and Stewart, 1999; Inouye et al., 2000; Di Libero et al., 2001; Johnston et al., 2003; Kelly et al., 2003; Ng et al., 2005; Bottemiller et al., 2006). These factors probably have a strong limiting, range-setting effect, codetermining the maximum gains that can be reached in inpatient rehabilitation in general. Independent of these well-documented predictors, pain and negative emotional reactions are factors with considerable added predictive value in our model. Pain is a common complication after stroke. Studies show percentages from 30 up to 50% of generalized pain and shoulder pain (Langhorne et al., 2000; Sackley et al., 2008). Little is known about the prognostic value of pain for the outcome of stroke rehabilitation. Our study indicates that, for our patient population, pain may have a negative impact on rehabilitation outcome independently of age, severity and functional status. The factor ‘negative emotional reactions’ as measured by the NHP can be seen as a combination of depression (feeling depressed and down, worrying, feeling life not worth living) and emotional instability (temper, losing control, feeling on edge). Depressive reactions and even
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114 International Journal of Rehabilitation Research 2014, Vol 37 No 2
major depression are common complications of stroke with prevalences of 20% and higher in different populations and settings (Sinyor et al., 1986; Herrmann et al., 1998; van de Weg et al., 1999; Cassidy et al., 2004). Other factors that are conceptually related to the NHP domain of negative emotional reactions are emotional incontinence, distress and anger proneness. Like depression, these conditions are relatively common sequelae to stroke, having adverse effects on rehabilitation outcomes and quality of life (Kim and Choi-Kwon, 2000; Kim et al., 2002; West et al., 2010). Negative emotion in all its forms may be a strictly biological, physiological side effect of stroke, but it may also be a very personal, psychological response to the experience of stroke and the awareness of its consequences. Either way, we may assume that these emotions have a negative influence on the rehabilitation process, a process that strongly depends on personal motivation, stamina and optimism. The results of our study point to the occurrence of such a hampering effect, again separate from age, severity of stroke or disability. Factors such as pain and negative emotion seem to have an unfavourable impact not only during the inpatient rehabilitation process, but also in the chronic phase. Results of a recent qualitative study (Lennon et al., 2013) indicate that, among other factors, pain and negative emotions (e.g. lack of confidence) are barriers to healthylifestyle participation after stroke. The results show that, by applying predictive designs, results of routine outcome monitoring can be helpful in clinical decision making and improving rehabilitation programmes. Our study suggests that it might be useful to stratify patients on the basis of limiting factors such as age, lower functional status and severity of stroke. Programme adjustments should be considered for higher-risk patients. The therapy process could possibly be improved by paying more attention to hampering factors such as pain and negative emotional reactions. To a certain extent these factors can and should be managed, pharmacologically or psychologically. This requires proper screening and diagnosis, but also specific, supplementary treatment strategies. A number of limitations of this study should be mentioned. Outcome was predicted mainly from patient characteristics at the time of admission and was not related to variations in interventions or process variables such as type and number of therapies and therapy intensity. Furthermore, our study does not differentiate first from recurrent strokes, although it is assumed that the clinical status for these groups will be different. In future studies, it is essential to examine the effect of these factors on functional outcome; linking outcome to process and patient characteristics will provide us with more directions for improvement (Mant, 2001). Another limitation of this study is the completeness of the database. Missing data in some variables may cause a loss
of more than 50% of the original data if only complete cases are included in the analyses. This reduction in sample size translates into reduced statistical power. Therefore, we used mean substitution for handling the missing data, a procedure that is easily available in the linear regression analysis options in SPSS. The advantage of this method is that the estimate of the mean for the variable is not affected. However, the procedure is based on the assumption that the missing values are missing completely at random, which may not be the case. Furthermore, the estimate of the SD and variance may be reduced (Saunders et al., 2006). Another limitation is the absence of follow-up data. A decline in quality of life after discharge from subacute stroke rehabilitation is a well-known problem (Hopman and Verner, 2003). Finally, obviously conclusions based on outcomes of a single programme cannot be generalized without further comparison. Further studies with comparable design on databases from other rehabilitation clinics can possibly validate our conclusions.
Conclusion The results of this study suggest that pain and negative emotion have an adverse effect on rehabilitation outcome in stroke patients in addition to age, severity of stroke and functional status. Future research is needed to determine whether rehabilitation outcome of stroke patients could improve if these factors were closely monitored and effectively treated during rehabilitation.
Acknowledgements Conflicts of interest
There are no conflicts of interest.
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Appendix Table A1
Table A1
Academic Medical Centre Linear Disability Score item bank
Item Are you able toy 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65
Item description
Item difficulty parameter
Linear transformed ALDS
Bike for at least 2 h? Vacuum a flight of stairs? Carry a bag of shopping upstairs? Clean a bathroom? Vacuum a room and move light furniture? Fetch groceries for 3–4 days? Go for a walk in the woods? Travel by local bus or tram? Walk for more than 15 min? Carry a tray? Walk up a hill or high bridge? Go shopping for clothes? Cut your toenails? Fill in an official form Go to a party (small circle)? Stand for 10 min? Go to a restaurant? Sweep the floor? Hang and take in a load of washing? Vacuum without moving any furniture? Move a bed or table? Use a washing machine? Reach into a high cupboard? Walk up a flight of stairs? Go to the bank or post office? Walk down a flight of stairs? Go to the general practitioner? Go for a short walk (15 min)? Use a dustpan and brush? Write a letter? change the sheets on a bed? Cross the road? open and close a window? Fetch a few things from the shop? Polish shoes? Have a shower (and wash your hair)? Fold up the washing? Dust? Put on/take off lace-up shoes? Clean a toilet? Make a bed? Cut your fingernails? Reach under a table? Heat tinned food? Make eggs or beans on toast? Reach into a low cupboard? Move between 2 low chairs? Pick something up from the floor? Clean a bathroom sink? Put the washing up away? Read a newspaper? Get in and out of a car? Make porridge? Clear the table after a meal? Peel and core an apple? Prepare breakfast or lunch? Clean the kitchen surfaces? Put a chair up to the table Eat a meal at the table? Wash up? Put on/take off socks and slip-on shoes? Sit up (from lying) in bed? Get a book off a shelf? Answer the telephone? Hang clothes up in a cupboard?
– 3.057 – 2.653 – 2.140 – 1.959 – 1.879 – 1.633 – 1.504 – 1.230 – 0.818 – 0.808 – 0.781 – 0.723 – 0.655 – 0.614 – 0.560 – 0.525 – 0.481 – 0.450 – 0.445 – 0.347 – 0.304 – 0.234 – 0.234 – 0.192 – 0.130 – 0.020 0.020 0.071 0.083 0.175 0.209 0.224 0.240 0.291 0.342 0.657 0.698 0.702 0.759 0.779 0.842 0.901 0.918 0.922 1.022 1.092 1.144 1.151 1.180 1.263 1.278 1.339 1.369 1.471 1.498 1.517 1.765 1.777 1.788 1.863 1.930 1.948 2.106 2.148 2.192
89 87 85 84 84 82 81 78 74 74 73 73 72 71 70 70 69 69 69 67 66 65 65 65 64 62 61 60 60 58 58 58 58 56 56 50 50 50 49 48 47 46 46 46 44 43 42 42 42 40 40 39 39 37 37 36 32 32 32 31 30 30 28 27 27
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Predicting outcome in a postacute stroke van Bragt et al. 117
Table A1 (continued) Item 66 67 68 69 70 71 72 73 74 75 76 77
Item description Make a bowl of cereal? Make coffee or tea? Put long trousers on? Sit on the edge of a bed from lying down? Move between 2 dining chairs? Wash and dry your lower body? Put on/take off a coat? Wash/dry your face and hands? Get out of bed into a chair? Go to the toilet? Wash your lower body (at sink)? Put on and take off a T-shirt?
Item difficulty parameter 2.280 2.348 2.376 2.674 2.722 2.777 2.859 2.969 2.987 3.077 3.235 3.494
Linear transformed ALDS 25 25 24 21 20 20 19 18 18 17 15 11
Items used in this study are given in bold. ALDS, Academic Medical Centre Linear Disability Score.
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