- Email: [email protected]
Predicting failure to improve during rehabilitation for older patients using routinely collected clinical data
European Geriatric Medicine 4 (2013) 324–328
Available online at
www.sciencedirect.com
Research paper
Predicting failure to improve during rehabilitation for older patients using routinely collected clinical data S.C. Tan a, L. Ramage a,b, M.E.T. McMurdo a, M.D. Witham a,* a b
Ageing and Health, University of Dundee, Medical Research Institute, Ninewells Hospital, Dundee DD1 9SY, United Kingdom Department of Medicine for the Elderly, NHS Tayside, Royal Victoria Hospital, Dundee DD1 9SY, United Kingdom
A R T I C L E I N F O
A B S T R A C T
Article history: Received 27 March 2013 Accepted 10 June 2013 Available online 5 July 2013
Objective: Effective use of rehabilitation facilities for older people requires that those selected to undergo rehabilitation are best placed to benefit. We tested whether routinely collected clinical factors could predict deterioration or failure to improve during inpatient rehabilitation. Methods: Analysis of prospectively collected routine clinical data from adults aged 65 and over, admitted to an inpatient rehabilitation between 1st January 1999 and 31st December 2008. Measures analysed were changed in 20-point Barthel score and indices of function including nutrition, swallow, communication and mental health between admission and discharge. Cut-off values for admission Barthel score were used to test which groups of patients would fail to improve further; admission indices of function, comorbidity and demographic information were combined in multivariable analyses to test which factors independently predicted failure to improve, deterioration or death during inpatient rehabilitation. Results: Three thousand five hundred and seventy-two patients were included in the analyses, mean age 81.6 (SD 7.6) years. The mean admission Barthel score was 10.2 (SD 3.8). There was no admission Barthel score above which patients failed to improve either their Barthel score or other indices of function. In multivariate analyses, combinations of age, sex, admission Barthel score and other admission indices of function independently predicted inpatient death, death or deterioration, and death, deterioration or failure to improve on admission Barthel. The classification accuracy for all models was low at 70% or less. Conclusions: Barthel scores and indices of function do not accurately predict which older patients will fail to benefit from inpatient rehabilitation. ß 2013 Elsevier Masson SAS and European Union Geriatric Medicine Society. All rights reserved.
Keywords: Rehabilitation Aged Outcome assessment
1. Introduction Rehabilitation services for older people are a core component of healthcare for older people, and are effective at improving physical function, reducing the need for nursing home admission and reducing mortality [1]. The rising number of very old, frail people, both in developed countries and in the developing world [2], means that rehabilitation services continue to be in great demand at a time of financial constraint. Ensuring efficient and effective use of such services is therefore important, so that all those who could potentially benefit from rehabilitation receive the opportunity to do so. Efforts to find simple predictors of those who are likely to benefit from rehabilitation have proved elusive to date [3]. A number of approaches have been tried, including combinations of age and comorbidity, functional measures, and frailty indices
* Corresponding author. Tel.: +01382 383086; fax: +01382 644972. E-mail address: [email protected] (M.D. Witham).
[4–8], however these admission characteristics lack sufficient discrimination to accurately select patients that will fail to benefit from inpatient rehabilitation. Such discrimination is important to ensure that no older person is denied the chance to undergo rehabilitation when they might benefit, whilst allowing those who will not benefit to be signposted effectively to other services. New approaches are therefore required – firstly in assessing a wider range of potential predictor variables, and secondly in focussing on particular groups of patients that may fail to benefit from rehabilitation. One of these groups comprises those with high levels of function at entry to rehabilitation; the other group, which may be important to identify, is the group that deteriorate or fail to improve once admitted to rehabilitation. We used a large dataset of routinely collected clinical data from a single rehabilitation centre to examine: whether those with high functional levels pre-admission could still benefit from rehabilitation, and; what baseline factors predicted failure to improve during inpatient rehabilitation in our cohort.
1878-7649/$ – see front matter ß 2013 Elsevier Masson SAS and European Union Geriatric Medicine Society. All rights reserved. http://dx.doi.org/10.1016/j.eurger.2013.06.009
S.C. Tan et al. / European Geriatric Medicine 4 (2013) 324–328
2. Methods 2.1. Design and patient population We analysed routinely collected clinical data from a single rehabilitation unit for older adults (those aged 65 and over with occasional exceptions). Clinical teams within the Dundee Medicine for the Elderly unit have collected data on admission reason, discharge place, medications and diagnoses, along with functional measures on admission and discharge, for all patients admitted for inpatient rehabilitation between 1st January 1999 and 31st December 2008. The characteristics of the cohort have been described in detail previously [9]. Caldicott Guardian (local data protection officer) approval was obtained to perform these analyses. Tayside Research Ethics Service has previously confirmed that formal ethics board approval was not needed for analyses using this database of routinely collected clinical data. 2.2. Measures 20-point Barthel scores [10] were measured at admission to rehabilitation and prior to discharge. Higher Barthel scores denote greater independence, measured as a composite score encompassing domains of mobility, transfers, grooming, bathing, bowel and bladder function. Data on a range of other domains of physical and psychosocial function not recorded by the Barthel index were also available at admission and discharge. Cognitive impairment, mental illness, swallowing and feeding difficulties, dietary and fluid intake, expression, understanding, weight and medication management were recorded on a 3-point scale, where 3 indicated no impairment; 2, mild impairment; 1, moderate impairment; and 0, severe impairment. We also included data on admission age, sex, length of stay, discharge medications, main discharge diagnosis and place of discharge. Information on date of death was collected from the Scottish Morbidity Record data held on routine hospital clinical systems. Data for discharge information were censored at 1st June 2009 and mortality data were censored at 31st August 2010. 2.3. Analysis Data analysis was undertaken using SPSS version 18 (SPSS, Chicago, IL, USA). Patients without admission Barthel scores were excluded from the analysis; only those with admission and discharge Barthel scores were included in analyses of change in function. Baseline data were compared using Student’s t test for normally distributed continuous variables and Pearson’s Chi2 test for categorical variables. Descriptive statistics were generated to describe numbers improving and deteriorating for different
325
thresholds of admission Barthel score. A cut-off of 14 days was selected to describe numbers with a short stay in inpatient rehabilitation as local practice is not to accept patients with a predicted inpatient rehabilitation stay of less than 14 days; such patients are directed to intermediate care or home-based rehabilitation services. We defined ‘‘improvement’’ as greater than a 1-point increase in Barthel score between admission and discharge, ‘‘no improvement’’ as one point or less change in Barthel score, and ‘‘deterioration’’ as more than one point reduction in Barthel score between admission and discharge. As the Barthel score is ordinal in nature, defining a minimum clinically important change is difficult, and data on this metric for the Barthel score in non-stroke patients are not readily available. Previous analysis in this cohort has however shown that a 1-point difference in discharge Barthel score equates to a 5% reduction in post-discharge mortality [9]. The c-statistic (for area under the receiver-operator characteristic curve) was calculated for ability of different admission Barthel scores to predict inpatient death, failure to improve during rehabilitation, length of stay of less than 14 days and improvement in at least one non-Barthel domain. Both very high and very low Barthel scores might theoretically be associated with failure to improve rehabilitation outcomes, hence C-statistics were calculated for the whole range of admission Barthel scores, and also for the subset of patients with admission Barthel greater than, or equal to 14. Binary logistic analysis was performed to assess factors predicting failure to improve, defined as any outcome other than at least a 1-point increase in Barthel score between admission and discharge. Because medication and diagnoses were recorded only on those discharged alive from rehabilitation, two sets of models were constructed. The first set included all patients with admission data, including those who died during inpatient stay. Age, sex, admission Barthel and functional scores, admission for stroke or fracture were included in these models. Regression models were run as forward conditional models, P < 0.05 to enter. A second set of models was constructed, excluding those who died during inpatient stay, but also including selected discharge medication data shown previously to be associated with better rehabilitation outcomes in this cohort (statins and allopurinol). For each model, all independent associates of failure to improve were entered into a discriminant model to calculate the percentage of cases for which failure to improve would be correctly predicted from baseline variables. 3. Results Table 1 gives details of admission characteristics for the study population. Of the 4449 patients on the database, 877 were
Table 1 Baseline characteristics by outcome.
*
Category
Total (n = 3572)
Improved (n = 2270)
No improvement (n = 757)
Age Male sex (%) Admitted after stroke (%) Admitted after fracture (%) Admission Barthel score (SD) Medication management score (SD) Weight management score (SD) Dietary intake score (SD) Fluid intake score (SD) Swallowing score (SD) Expression score (SD) Understanding score (SD) Cognition score (SD) Mental health score (SD)
81.6 1443 298 216 10.2 1.1 2.0 2.2 2.1 2.7 2.5 2.6 2.4 2.4
81.1 899 183 143 10.2 1.2 2.1 2.3 2.1 2.8 2.6 2.6 2.5 2.4
82.0 315 68 43 11.1 1.0 2.0 2.2 2.1 2.7 2.3 2.4 2.3 2.3
(7.6) (40.4) (8.3) (6.0) (3.8) (1.2) (1.2) (0.8) (0.6) (0.6) (0.7) (0.6) (0.7) (0.7)
(7.6) (39.6) (8.1) (6.3) (3.1) (1.2) (1.1) (0.7) (0.5) (0.5) (0.7) (0.6) (0.6) (0.6)
P < 0.05. **P < 0.01 compared to group showing improvement during rehabilitation.
(7.6)** (41.6) (9.0) (5.7) (5.2)** (1.2)** (1.1) (0.8)* (0.6) (0.6)** (0.8)** (0.7)** (0.8)** (0.7)**
Deteriorated (n = 136) 82.4 54 11 10 8.9 0.8 2.1 2.2 2.0 2.8 2.3 2.4 2.1 2.2
(7.4)* (39.7) (8.1) (7.4) (3.3)** (1.2)** (1.1) (0.8) (0.6)** (0.6) (0.8)** (0.7)** (0.8)** (0.7)**
Inpatient death (n = 409) 83.6 175 36 20 8.2 1.2 1.6 1.6 1.8 2.5 2.2 2.3 2.2 2.1
(6.8)** (42.8) (8.8) (4.9) (3.8)** (1.2) (1.3)** (0.9)** (0.7)** (0.8)** (0.9)** (0.8)** (0.9)** (0.8)**
S.C. Tan et al. / European Geriatric Medicine 4 (2013) 324–328
326
Table 2 Effect of employing different thresholds for admission Barthel score on rehabilitation outcome. Admission Barthel score
n (%) n (%) staying < 14 days in inpatient rehabilitation n (%) with deterioration or no improvement n (%) inpatient deaths n (%) with > 1-point improvement in additional domainsa a
14
15
16
17
18
19
c-statistic – all patients (95 % CI)
c-statistic – admission Barthel 14 (95 % CI)
569 (15.9) 189 (33.2)
437 (12.2) 159 (36.4)
340 (9.5) 140 (41.2)
248 (6.9) 106 (42.7)
158 (4.4) 75 (47.5)
87 (2.4) 46 (52.9)
– 0.74 (0.71 to 0.77)
– 0.63 (0.58 to 0.67)
253 (44.4)
234 (53.5)
211 (62.1)
178 (71.7)
128 (81.0)
86 (98.9)
0.49 (0.46 to 0.51)
0.81 (0.78 to 0.85)
13 (2.3) 184 (32.3)
10 (2.3) 135 (30.9)
9 (2.6) 103 (30.3)
4 (1.6) 78 (31.5)
2 (1.3) 46 (29.1)
1 (1.1) 25 (28.7)
0.66 (0.62 to 0.70) 0.53 (0.51 to 0.55)
0.53 (0.39 to 0.67) 0.54 (0.49 to 0.59)
Medication, diet and fluid intake, cognition, communication, swallowing, mental health, weight, expression, understanding.
Table 3 Independent predictors of failure to improve in rehabilitation (inpatient deaths included). Inpatient death
Age Female sex Admission Barthel score Medication use score Weight management score Dietary intake score Fluid intake score Swallowing score Expression score Understanding score Cognition score % classified correctly
Death or deterioration
Death, deterioration or no improvement
Exp (B) (95 % CI)
P
Exp (B) (95 % CI)
P
Exp (B) (95 % CI)
P
1.05 0.68 0.87 1.18 0.86 0.55 – – – – – 70
< 0.001 0.021 < 0.001 0.029 0.026 < 0.001 NS NS NS NS NS
1.03 – 0.88 – – 0.72 0.67 1.24 – – – 64
0.001 NS < 0.001 NS NS < 0.001 0.002 0.049 NS NS NS
1.02 – 1.08 0.89 0.93 0.86 – – 0.76 0.80 0.79 61
< 0.001 NS < 0.001 0.002 0.044 0.003 NS NS < 0.001 0.015 0.001
(1.02 (0.50 (0.83 (1.02 (0.76 (0.45
to to to to to to
1.07) 0.94) 0.91) 1.37) 0.98) 0.66)
excluded due to missing data (admission Barthel and functional scores). Three thousand five hundred and seventy-two patients were included in this analysis. The mean age was 81.6 years (range 58 to 102), 1443 (40.4%) were male and the median length of stay was 35 days. The mean admission Barthel score was 10.2 and the mean discharge Barthel score was 14.0. Baseline data for the included patients are given in Table 1. Excluded patients were slightly older (mean age 82.5, P < 0.001 vs included patients), less likely to be male (34.1%, P = 0.001), had a shorter median length of stay (19 days, P < 0.001) but had a higher death rate during inpatient stay (24% vs 6%, P < 0.001). Table 2 applies different cut-offs for admission Barthel score to test what proportion of patients above each cut-off are too fit to realise further gains from rehabilitation. Table 3 gives the result of binary logistic regression modelling to examine independent predictors of death, death or deterioration, and death, deterioration or failure to improve during rehabilitation. The results show that variable combinations of factors are associated with inpatient death, death or deterioration, or death plus deterioration plus failure to improve. The overall ability of any combination of baseline factors to predict a poor outcome from rehabilitation was low, with over 30% misclassification in all models tested. The Supplementary table gives results from binary logistic regression analyses excluding inpatient deaths; these models include statin medication at discharge, but show a similarly high misclassification rate of 37%. 4. Discussion Two main findings stand out from this analysis. One is that even at high admission Barthel scores (14 and above), a significant minority of patients gain functional improvement from inpatient
(1.01 to 1.05) (0.85 to 0.91)
(0.61 to 0.85) (0.54 to 0.87) (1.00 to 1.54)
(1.01 to 1.03) (1.05 (0.82 (0.87 (0.77
to to to to
1.10) 0.96) 1.00) 0.95)
(0.65 to 0.88) (0.67 to 0.96) (0.69 to 0.91)
rehabilitation, and a significant minority of relatively highfunctioning patients are not discharged within 14 days, reinforcing that there is a perceived benefit to rehabilitation of this relatively high-functioning group. These findings are mirrored by the modest areas under the receiver-operator characteristic curves; there is no value of the Barthel index that gives a good combination of sensitivity and specificity for predicting failure to benefit across a range of outcomes tested. The second key finding is that existing factors including age, sex, comorbidity, medication use and baseline function are poor at predicting poor outcome (death, deterioration or failure to improve) during inpatient rehabilitation. Patients with high initial Barthel scores are less likely to show improvement in Barthel score after rehabilitation, but this is likely to be due to ceiling effects of the Barthel score. A third of patients with an admission Barthel score of 19 or more still gained improvement in other (non-Barthel) domains of function, suggesting that high Barthel score is too crude a measure to use in denying rehabilitation services to patients. The significant numbers of patients with high admission Barthel and a length of stay of more than 14 days may reflect a need for complex discharge planning, a slower pace of assessment and therapy in an inpatient setting, or a lack of home-based rehabilitation alternatives. Not every patient with a high Barthel score will require rehabilitation in an inpatient setting; the use of home-based or community-based services for these groups may be more appropriate. Previous research in this area has focused on factors predicting rehabilitation outcome across whole rehabilitation cohorts, examining admission factors such as frailty score [7], comorbidity and dependency [5,11,12], and cognition [13,14]. Whilst considerable effort has been devoted to prediction of rehabilitation outcome after specific diseases, particularly stroke [15,16], less effort has been put into prediction of outcomes for generic
S.C. Tan et al. / European Geriatric Medicine 4 (2013) 324–328
rehabilitation of older people. Whilst some previous studies have focussed on risk markers for deterioration or failure to improve, little work has been performed to examine the outcomes of patients with initially high admission Barthel score. Analysis of predictors of functional improvement in previous studies have suggested that age, admission function (e.g. Barthel score [4]), depression, cognition, frailty, comorbidity and medication use [3,17–19] are associated with rehabilitation outcome; a previous study using the current cohort also found that statin therapy was associated with better outcomes [20]. Previous studies have also failed to find a combination of factors that predicts failure to benefit from rehabilitation with sufficiently high accuracy [6,21]; despite the use of sophisticated neural network prediction approaches, a proportion of patients with apparently adverse scores still improve sufficiently to achieve good rehabilitation outcomes. The addition of biomarkers provides another way of potentially enhancing the predictive ability of existing information; although this approach has not been tested specifically in an older rehabilitation population, interleukin-6 and insulin-like growth factor 1 added only a small amount of value in predicting functional decline in a cohort of older hospitalised patients with hip fracture, infection or heart failure [22]. 4.1. Study limitations We have included a relatively wide range of outcomes measured across several domains of health by implementing Barthel Scores and some additional important domains which are not included in the Barthel score. This has allowed us to include a wider range of factors than in some previous studies. The size of the dataset, collection over several years, the use of a general rehabilitation population, rather than a disease-specific population, and the routinely collected nature of the data may increase the generalisability of our findings, although the single centre nature of our study, set in a country with overwhelmingly Caucasian population does limit the extent to which the results translate to other models of rehabilitation care, wider healthcare, or populations with different ethnicity, social structure and culture. The use of routinely collected data also limits the detail in which we can explore causal factors. The indicators that are available are relatively crude measures of function; more precise scales (e.g. mini-mental state examination for cognition, grip strength, walk distance for muscle function) would perhaps increase the predictive ability of our models. Large-scale collection of such data requires either dedicated large-scale research cohorts (which may not reflect clinical practice), or a very well resourced clinical service able to collect these extra measurements and record them in a systematic way. Finally, a proportion of our cohort had missing data; these excluded that patients are likely to have had missing data due to intercurrent illness, as evidenced by their high death rate. The analysed cohort is thus likely to have been somewhat fitter than the overall population undergoing rehabilitation. 5. Conclusion The use of an extended set of outcomes measures, combined with data on age, comorbid disease and medication use, did not allow prediction of failure to improve with rehabilitation in this large, routinely collected dataset. A proportion of patients with high admission Barthel scores still derived benefit from rehabilitation. The decision to select older patients for generic rehabilitation should continue therefore to be based on clinical judgement by a skilled multidisciplinary team, rather than by scoring systems.
327
Disclosure of interest The authors declare that they have no conflicts of interest concerning this article. Acknowledgements None. Ethical Statement regarding ‘‘Predicting failure to improve during rehabilitation for older patients using routinely collected clinical data’’: The data used in this study was collected as part of routine clinical care, rather than specifically as part of a research study. Research ethics committee approval for this study was therefore not required; the authors all had access to the data as part of their clinical roles. Funding: No additional funding was required for this research. Appendix A. Supplementary data Supplementary data associated with this article can be found, in the online version, at http://www.sciencedirect.com References [1] Bachmann S, Finger C, Huss A, Egger M, Stuck AE, Clough-Gorr KM. Inpatient rehabilitation specifically designed for geriatric patients: systematic review and meta-analysis of randomised controlled trials. BMJ 2010;340 [c1718]. [2] Christensen K, Doblhammer G, Rau R, Vaupel JW. Ageing populations: the challenges ahead. Lancet 2009;374:1196–208. [3] Campbell SE, Seymour DG, Primrose WR. A systematic literature review of factors affecting outcome in older medical patients admitted to hospital. Age Ageing 2004;33:110–5. [4] Elphick HL, Mankad K, Madan S, Parker C, Liddle BJ. The determinants of successful in-hospital rehabilitation in people aged 90 years and older. Gerontology 2007;53:116–20. [5] Naughton BJ, Saltzman S, Priore R, Reedy K, Mylotte JM. Using admission characteristics to predict return to the community from a post-acute geriatric evaluation and management unit. J Am Geriatr Soc 1999;47: 1100–4. [6] Miyamoto H, Hagihara A, Nobutomo K. Predicting the discharge destination of rehabilitation patients using a signal detection approach. J Rehabil Med 2008;40:261–8. [7] Singh I, Gallacher J, Davis K, Johansen A, Eeles E, Hubbard RE. Predictors of adverse outcomes on an acute geriatric rehabilitation ward. Age Ageing 2012; 41:242–6. [8] Heinemann AW, Linacre JM, Wright BD, Hamilton BB, Granger C. Prediction of rehabilitation outcomes with disability measures. Arch Phys Med Rehabil 1994;75:133–43. [9] Witham MD, Ramage L, Burns SL, Gillespie ND, Hanslip J, Laidlaw S, et al. Trends in function and postdischarge mortality in a medicine for the elderly rehabilitation center over a 10-year period. Arch Phys Med Rehabil 2011;92: 1288–92. [10] Wade DT, Collin C, The Barthel ADL. Index: a standard measure of physical disability? Int Disabil Stud 1998;10:64–7. [11] Valderrama-Gama E, Damian J, Guallar E, Rodriguez-Manas L. Previous disability as a predictor of outcome in a geriatric rehabilitation unit. J Gerontol A Biol Sci Med Sci 1998;53:M405–9. [12] Fusco D, Bochicchio GB, Onder G, Barillaro C, Bernabei R, Landi F. Predictors of rehabilitation outcome among frail elderly patients living in the community. J Am Med Dir Assoc 2009;10:335–41. [13] Luk JK, Chiu PK, Chu LW. Rehabilitation of older Chinese patients with different cognitive functions: how do they differ in outcome? Arch Phys Med Rehabil 2008;89:1714–9. [14] Heruti RJ, Lusky A, Barell V, Ohry A, Adunsky A. Cognitive status at admission: does it affect the rehabilitation outcome of elderly patients with hip fracture? Arch Phys Med Rehabil 1999;80:432–6. [15] Agarwal V, McRae MP, Bhardwaj A, Teasell RW. A model to aid in the prediction of discharge location for stroke rehabilitation patients. Arch Phys Med Rehabil 2003;84:1703–9. [16] Chumney D, Nollinger K, Shesko K, Skop K, Spencer M, Newton RA. Ability of Functional Independence Measure to accurately predict functional outcome of stroke-specific population: systematic review. J Rehabil Res Dev 2010;47: 17–29. [17] Campbell SE, Seymour DG, Primrose WR, Lynch JE, Dunstan E, Espallargues M, et al. A multi-centre European study of factors affecting the discharge
328
S.C. Tan et al. / European Geriatric Medicine 4 (2013) 324–328
destination of older people admitted to hospital: analysis of in-hospital data from the ACMEplus project. Age Ageing 2005;34:467–75. [18] de Saint-Hubert M, Schoevaerdts D, Cornette P, D’Hoore W, Boland B, Swine C. Predicting functional adverse outcomes in hospitalized older patients: a systematic review of screening tools. J Nutr Health Aging 2010;14:394–9. [19] Hoogerduijn JG, Schuurmans MJ, Duijnstee MS, de Rooij SE, Grypdonck MF. A systematic review of predictors and screening instruments to identify older hospitalized patients at risk for functional decline. J Clin Nurs 2007; 16:46–57.
[20] Lynch JE, Henderson NR, Ramage L, McMurdo ME, Witham MD. Association between statin medication use and improved outcomes during inpatient rehabilitation in older people. Age Ageing 2012;41:260–2. [21] El Solh AA, Saltzman SK, Ramadan FH, Naughton BJ. Validity of an artificial neural network in predicting discharge destination from a postacute geriatric rehabilitation unit. Arch Phys Med Rehabil 2000;81:1388–93. [22] de Saint-Hubert M, Jamart J, Morrhaye G, Martens HJ, Geenen V, Duy Vo TK, et al. Serum IL-6 and IGF-1 improve clinical prediction of functional decline after hospitalization in older patients. Aging Clin Exp Res 2011;23:106–11.
Available online at
www.sciencedirect.com
Research paper
Predicting failure to improve during rehabilitation for older patients using routinely collected clinical data S.C. Tan a, L. Ramage a,b, M.E.T. McMurdo a, M.D. Witham a,* a b
Ageing and Health, University of Dundee, Medical Research Institute, Ninewells Hospital, Dundee DD1 9SY, United Kingdom Department of Medicine for the Elderly, NHS Tayside, Royal Victoria Hospital, Dundee DD1 9SY, United Kingdom
A R T I C L E I N F O
A B S T R A C T
Article history: Received 27 March 2013 Accepted 10 June 2013 Available online 5 July 2013
Objective: Effective use of rehabilitation facilities for older people requires that those selected to undergo rehabilitation are best placed to benefit. We tested whether routinely collected clinical factors could predict deterioration or failure to improve during inpatient rehabilitation. Methods: Analysis of prospectively collected routine clinical data from adults aged 65 and over, admitted to an inpatient rehabilitation between 1st January 1999 and 31st December 2008. Measures analysed were changed in 20-point Barthel score and indices of function including nutrition, swallow, communication and mental health between admission and discharge. Cut-off values for admission Barthel score were used to test which groups of patients would fail to improve further; admission indices of function, comorbidity and demographic information were combined in multivariable analyses to test which factors independently predicted failure to improve, deterioration or death during inpatient rehabilitation. Results: Three thousand five hundred and seventy-two patients were included in the analyses, mean age 81.6 (SD 7.6) years. The mean admission Barthel score was 10.2 (SD 3.8). There was no admission Barthel score above which patients failed to improve either their Barthel score or other indices of function. In multivariate analyses, combinations of age, sex, admission Barthel score and other admission indices of function independently predicted inpatient death, death or deterioration, and death, deterioration or failure to improve on admission Barthel. The classification accuracy for all models was low at 70% or less. Conclusions: Barthel scores and indices of function do not accurately predict which older patients will fail to benefit from inpatient rehabilitation. ß 2013 Elsevier Masson SAS and European Union Geriatric Medicine Society. All rights reserved.
Keywords: Rehabilitation Aged Outcome assessment
1. Introduction Rehabilitation services for older people are a core component of healthcare for older people, and are effective at improving physical function, reducing the need for nursing home admission and reducing mortality [1]. The rising number of very old, frail people, both in developed countries and in the developing world [2], means that rehabilitation services continue to be in great demand at a time of financial constraint. Ensuring efficient and effective use of such services is therefore important, so that all those who could potentially benefit from rehabilitation receive the opportunity to do so. Efforts to find simple predictors of those who are likely to benefit from rehabilitation have proved elusive to date [3]. A number of approaches have been tried, including combinations of age and comorbidity, functional measures, and frailty indices
* Corresponding author. Tel.: +01382 383086; fax: +01382 644972. E-mail address: [email protected] (M.D. Witham).
[4–8], however these admission characteristics lack sufficient discrimination to accurately select patients that will fail to benefit from inpatient rehabilitation. Such discrimination is important to ensure that no older person is denied the chance to undergo rehabilitation when they might benefit, whilst allowing those who will not benefit to be signposted effectively to other services. New approaches are therefore required – firstly in assessing a wider range of potential predictor variables, and secondly in focussing on particular groups of patients that may fail to benefit from rehabilitation. One of these groups comprises those with high levels of function at entry to rehabilitation; the other group, which may be important to identify, is the group that deteriorate or fail to improve once admitted to rehabilitation. We used a large dataset of routinely collected clinical data from a single rehabilitation centre to examine: whether those with high functional levels pre-admission could still benefit from rehabilitation, and; what baseline factors predicted failure to improve during inpatient rehabilitation in our cohort.
1878-7649/$ – see front matter ß 2013 Elsevier Masson SAS and European Union Geriatric Medicine Society. All rights reserved. http://dx.doi.org/10.1016/j.eurger.2013.06.009
S.C. Tan et al. / European Geriatric Medicine 4 (2013) 324–328
2. Methods 2.1. Design and patient population We analysed routinely collected clinical data from a single rehabilitation unit for older adults (those aged 65 and over with occasional exceptions). Clinical teams within the Dundee Medicine for the Elderly unit have collected data on admission reason, discharge place, medications and diagnoses, along with functional measures on admission and discharge, for all patients admitted for inpatient rehabilitation between 1st January 1999 and 31st December 2008. The characteristics of the cohort have been described in detail previously [9]. Caldicott Guardian (local data protection officer) approval was obtained to perform these analyses. Tayside Research Ethics Service has previously confirmed that formal ethics board approval was not needed for analyses using this database of routinely collected clinical data. 2.2. Measures 20-point Barthel scores [10] were measured at admission to rehabilitation and prior to discharge. Higher Barthel scores denote greater independence, measured as a composite score encompassing domains of mobility, transfers, grooming, bathing, bowel and bladder function. Data on a range of other domains of physical and psychosocial function not recorded by the Barthel index were also available at admission and discharge. Cognitive impairment, mental illness, swallowing and feeding difficulties, dietary and fluid intake, expression, understanding, weight and medication management were recorded on a 3-point scale, where 3 indicated no impairment; 2, mild impairment; 1, moderate impairment; and 0, severe impairment. We also included data on admission age, sex, length of stay, discharge medications, main discharge diagnosis and place of discharge. Information on date of death was collected from the Scottish Morbidity Record data held on routine hospital clinical systems. Data for discharge information were censored at 1st June 2009 and mortality data were censored at 31st August 2010. 2.3. Analysis Data analysis was undertaken using SPSS version 18 (SPSS, Chicago, IL, USA). Patients without admission Barthel scores were excluded from the analysis; only those with admission and discharge Barthel scores were included in analyses of change in function. Baseline data were compared using Student’s t test for normally distributed continuous variables and Pearson’s Chi2 test for categorical variables. Descriptive statistics were generated to describe numbers improving and deteriorating for different
325
thresholds of admission Barthel score. A cut-off of 14 days was selected to describe numbers with a short stay in inpatient rehabilitation as local practice is not to accept patients with a predicted inpatient rehabilitation stay of less than 14 days; such patients are directed to intermediate care or home-based rehabilitation services. We defined ‘‘improvement’’ as greater than a 1-point increase in Barthel score between admission and discharge, ‘‘no improvement’’ as one point or less change in Barthel score, and ‘‘deterioration’’ as more than one point reduction in Barthel score between admission and discharge. As the Barthel score is ordinal in nature, defining a minimum clinically important change is difficult, and data on this metric for the Barthel score in non-stroke patients are not readily available. Previous analysis in this cohort has however shown that a 1-point difference in discharge Barthel score equates to a 5% reduction in post-discharge mortality [9]. The c-statistic (for area under the receiver-operator characteristic curve) was calculated for ability of different admission Barthel scores to predict inpatient death, failure to improve during rehabilitation, length of stay of less than 14 days and improvement in at least one non-Barthel domain. Both very high and very low Barthel scores might theoretically be associated with failure to improve rehabilitation outcomes, hence C-statistics were calculated for the whole range of admission Barthel scores, and also for the subset of patients with admission Barthel greater than, or equal to 14. Binary logistic analysis was performed to assess factors predicting failure to improve, defined as any outcome other than at least a 1-point increase in Barthel score between admission and discharge. Because medication and diagnoses were recorded only on those discharged alive from rehabilitation, two sets of models were constructed. The first set included all patients with admission data, including those who died during inpatient stay. Age, sex, admission Barthel and functional scores, admission for stroke or fracture were included in these models. Regression models were run as forward conditional models, P < 0.05 to enter. A second set of models was constructed, excluding those who died during inpatient stay, but also including selected discharge medication data shown previously to be associated with better rehabilitation outcomes in this cohort (statins and allopurinol). For each model, all independent associates of failure to improve were entered into a discriminant model to calculate the percentage of cases for which failure to improve would be correctly predicted from baseline variables. 3. Results Table 1 gives details of admission characteristics for the study population. Of the 4449 patients on the database, 877 were
Table 1 Baseline characteristics by outcome.
*
Category
Total (n = 3572)
Improved (n = 2270)
No improvement (n = 757)
Age Male sex (%) Admitted after stroke (%) Admitted after fracture (%) Admission Barthel score (SD) Medication management score (SD) Weight management score (SD) Dietary intake score (SD) Fluid intake score (SD) Swallowing score (SD) Expression score (SD) Understanding score (SD) Cognition score (SD) Mental health score (SD)
81.6 1443 298 216 10.2 1.1 2.0 2.2 2.1 2.7 2.5 2.6 2.4 2.4
81.1 899 183 143 10.2 1.2 2.1 2.3 2.1 2.8 2.6 2.6 2.5 2.4
82.0 315 68 43 11.1 1.0 2.0 2.2 2.1 2.7 2.3 2.4 2.3 2.3
(7.6) (40.4) (8.3) (6.0) (3.8) (1.2) (1.2) (0.8) (0.6) (0.6) (0.7) (0.6) (0.7) (0.7)
(7.6) (39.6) (8.1) (6.3) (3.1) (1.2) (1.1) (0.7) (0.5) (0.5) (0.7) (0.6) (0.6) (0.6)
P < 0.05. **P < 0.01 compared to group showing improvement during rehabilitation.
(7.6)** (41.6) (9.0) (5.7) (5.2)** (1.2)** (1.1) (0.8)* (0.6) (0.6)** (0.8)** (0.7)** (0.8)** (0.7)**
Deteriorated (n = 136) 82.4 54 11 10 8.9 0.8 2.1 2.2 2.0 2.8 2.3 2.4 2.1 2.2
(7.4)* (39.7) (8.1) (7.4) (3.3)** (1.2)** (1.1) (0.8) (0.6)** (0.6) (0.8)** (0.7)** (0.8)** (0.7)**
Inpatient death (n = 409) 83.6 175 36 20 8.2 1.2 1.6 1.6 1.8 2.5 2.2 2.3 2.2 2.1
(6.8)** (42.8) (8.8) (4.9) (3.8)** (1.2) (1.3)** (0.9)** (0.7)** (0.8)** (0.9)** (0.8)** (0.9)** (0.8)**
S.C. Tan et al. / European Geriatric Medicine 4 (2013) 324–328
326
Table 2 Effect of employing different thresholds for admission Barthel score on rehabilitation outcome. Admission Barthel score
n (%) n (%) staying < 14 days in inpatient rehabilitation n (%) with deterioration or no improvement n (%) inpatient deaths n (%) with > 1-point improvement in additional domainsa a
14
15
16
17
18
19
c-statistic – all patients (95 % CI)
c-statistic – admission Barthel 14 (95 % CI)
569 (15.9) 189 (33.2)
437 (12.2) 159 (36.4)
340 (9.5) 140 (41.2)
248 (6.9) 106 (42.7)
158 (4.4) 75 (47.5)
87 (2.4) 46 (52.9)
– 0.74 (0.71 to 0.77)
– 0.63 (0.58 to 0.67)
253 (44.4)
234 (53.5)
211 (62.1)
178 (71.7)
128 (81.0)
86 (98.9)
0.49 (0.46 to 0.51)
0.81 (0.78 to 0.85)
13 (2.3) 184 (32.3)
10 (2.3) 135 (30.9)
9 (2.6) 103 (30.3)
4 (1.6) 78 (31.5)
2 (1.3) 46 (29.1)
1 (1.1) 25 (28.7)
0.66 (0.62 to 0.70) 0.53 (0.51 to 0.55)
0.53 (0.39 to 0.67) 0.54 (0.49 to 0.59)
Medication, diet and fluid intake, cognition, communication, swallowing, mental health, weight, expression, understanding.
Table 3 Independent predictors of failure to improve in rehabilitation (inpatient deaths included). Inpatient death
Age Female sex Admission Barthel score Medication use score Weight management score Dietary intake score Fluid intake score Swallowing score Expression score Understanding score Cognition score % classified correctly
Death or deterioration
Death, deterioration or no improvement
Exp (B) (95 % CI)
P
Exp (B) (95 % CI)
P
Exp (B) (95 % CI)
P
1.05 0.68 0.87 1.18 0.86 0.55 – – – – – 70
< 0.001 0.021 < 0.001 0.029 0.026 < 0.001 NS NS NS NS NS
1.03 – 0.88 – – 0.72 0.67 1.24 – – – 64
0.001 NS < 0.001 NS NS < 0.001 0.002 0.049 NS NS NS
1.02 – 1.08 0.89 0.93 0.86 – – 0.76 0.80 0.79 61
< 0.001 NS < 0.001 0.002 0.044 0.003 NS NS < 0.001 0.015 0.001
(1.02 (0.50 (0.83 (1.02 (0.76 (0.45
to to to to to to
1.07) 0.94) 0.91) 1.37) 0.98) 0.66)
excluded due to missing data (admission Barthel and functional scores). Three thousand five hundred and seventy-two patients were included in this analysis. The mean age was 81.6 years (range 58 to 102), 1443 (40.4%) were male and the median length of stay was 35 days. The mean admission Barthel score was 10.2 and the mean discharge Barthel score was 14.0. Baseline data for the included patients are given in Table 1. Excluded patients were slightly older (mean age 82.5, P < 0.001 vs included patients), less likely to be male (34.1%, P = 0.001), had a shorter median length of stay (19 days, P < 0.001) but had a higher death rate during inpatient stay (24% vs 6%, P < 0.001). Table 2 applies different cut-offs for admission Barthel score to test what proportion of patients above each cut-off are too fit to realise further gains from rehabilitation. Table 3 gives the result of binary logistic regression modelling to examine independent predictors of death, death or deterioration, and death, deterioration or failure to improve during rehabilitation. The results show that variable combinations of factors are associated with inpatient death, death or deterioration, or death plus deterioration plus failure to improve. The overall ability of any combination of baseline factors to predict a poor outcome from rehabilitation was low, with over 30% misclassification in all models tested. The Supplementary table gives results from binary logistic regression analyses excluding inpatient deaths; these models include statin medication at discharge, but show a similarly high misclassification rate of 37%. 4. Discussion Two main findings stand out from this analysis. One is that even at high admission Barthel scores (14 and above), a significant minority of patients gain functional improvement from inpatient
(1.01 to 1.05) (0.85 to 0.91)
(0.61 to 0.85) (0.54 to 0.87) (1.00 to 1.54)
(1.01 to 1.03) (1.05 (0.82 (0.87 (0.77
to to to to
1.10) 0.96) 1.00) 0.95)
(0.65 to 0.88) (0.67 to 0.96) (0.69 to 0.91)
rehabilitation, and a significant minority of relatively highfunctioning patients are not discharged within 14 days, reinforcing that there is a perceived benefit to rehabilitation of this relatively high-functioning group. These findings are mirrored by the modest areas under the receiver-operator characteristic curves; there is no value of the Barthel index that gives a good combination of sensitivity and specificity for predicting failure to benefit across a range of outcomes tested. The second key finding is that existing factors including age, sex, comorbidity, medication use and baseline function are poor at predicting poor outcome (death, deterioration or failure to improve) during inpatient rehabilitation. Patients with high initial Barthel scores are less likely to show improvement in Barthel score after rehabilitation, but this is likely to be due to ceiling effects of the Barthel score. A third of patients with an admission Barthel score of 19 or more still gained improvement in other (non-Barthel) domains of function, suggesting that high Barthel score is too crude a measure to use in denying rehabilitation services to patients. The significant numbers of patients with high admission Barthel and a length of stay of more than 14 days may reflect a need for complex discharge planning, a slower pace of assessment and therapy in an inpatient setting, or a lack of home-based rehabilitation alternatives. Not every patient with a high Barthel score will require rehabilitation in an inpatient setting; the use of home-based or community-based services for these groups may be more appropriate. Previous research in this area has focused on factors predicting rehabilitation outcome across whole rehabilitation cohorts, examining admission factors such as frailty score [7], comorbidity and dependency [5,11,12], and cognition [13,14]. Whilst considerable effort has been devoted to prediction of rehabilitation outcome after specific diseases, particularly stroke [15,16], less effort has been put into prediction of outcomes for generic
S.C. Tan et al. / European Geriatric Medicine 4 (2013) 324–328
rehabilitation of older people. Whilst some previous studies have focussed on risk markers for deterioration or failure to improve, little work has been performed to examine the outcomes of patients with initially high admission Barthel score. Analysis of predictors of functional improvement in previous studies have suggested that age, admission function (e.g. Barthel score [4]), depression, cognition, frailty, comorbidity and medication use [3,17–19] are associated with rehabilitation outcome; a previous study using the current cohort also found that statin therapy was associated with better outcomes [20]. Previous studies have also failed to find a combination of factors that predicts failure to benefit from rehabilitation with sufficiently high accuracy [6,21]; despite the use of sophisticated neural network prediction approaches, a proportion of patients with apparently adverse scores still improve sufficiently to achieve good rehabilitation outcomes. The addition of biomarkers provides another way of potentially enhancing the predictive ability of existing information; although this approach has not been tested specifically in an older rehabilitation population, interleukin-6 and insulin-like growth factor 1 added only a small amount of value in predicting functional decline in a cohort of older hospitalised patients with hip fracture, infection or heart failure [22]. 4.1. Study limitations We have included a relatively wide range of outcomes measured across several domains of health by implementing Barthel Scores and some additional important domains which are not included in the Barthel score. This has allowed us to include a wider range of factors than in some previous studies. The size of the dataset, collection over several years, the use of a general rehabilitation population, rather than a disease-specific population, and the routinely collected nature of the data may increase the generalisability of our findings, although the single centre nature of our study, set in a country with overwhelmingly Caucasian population does limit the extent to which the results translate to other models of rehabilitation care, wider healthcare, or populations with different ethnicity, social structure and culture. The use of routinely collected data also limits the detail in which we can explore causal factors. The indicators that are available are relatively crude measures of function; more precise scales (e.g. mini-mental state examination for cognition, grip strength, walk distance for muscle function) would perhaps increase the predictive ability of our models. Large-scale collection of such data requires either dedicated large-scale research cohorts (which may not reflect clinical practice), or a very well resourced clinical service able to collect these extra measurements and record them in a systematic way. Finally, a proportion of our cohort had missing data; these excluded that patients are likely to have had missing data due to intercurrent illness, as evidenced by their high death rate. The analysed cohort is thus likely to have been somewhat fitter than the overall population undergoing rehabilitation. 5. Conclusion The use of an extended set of outcomes measures, combined with data on age, comorbid disease and medication use, did not allow prediction of failure to improve with rehabilitation in this large, routinely collected dataset. A proportion of patients with high admission Barthel scores still derived benefit from rehabilitation. The decision to select older patients for generic rehabilitation should continue therefore to be based on clinical judgement by a skilled multidisciplinary team, rather than by scoring systems.
327
Disclosure of interest The authors declare that they have no conflicts of interest concerning this article. Acknowledgements None. Ethical Statement regarding ‘‘Predicting failure to improve during rehabilitation for older patients using routinely collected clinical data’’: The data used in this study was collected as part of routine clinical care, rather than specifically as part of a research study. Research ethics committee approval for this study was therefore not required; the authors all had access to the data as part of their clinical roles. Funding: No additional funding was required for this research. Appendix A. Supplementary data Supplementary data associated with this article can be found, in the online version, at http://www.sciencedirect.com References [1] Bachmann S, Finger C, Huss A, Egger M, Stuck AE, Clough-Gorr KM. Inpatient rehabilitation specifically designed for geriatric patients: systematic review and meta-analysis of randomised controlled trials. BMJ 2010;340 [c1718]. [2] Christensen K, Doblhammer G, Rau R, Vaupel JW. Ageing populations: the challenges ahead. Lancet 2009;374:1196–208. [3] Campbell SE, Seymour DG, Primrose WR. A systematic literature review of factors affecting outcome in older medical patients admitted to hospital. Age Ageing 2004;33:110–5. [4] Elphick HL, Mankad K, Madan S, Parker C, Liddle BJ. The determinants of successful in-hospital rehabilitation in people aged 90 years and older. Gerontology 2007;53:116–20. [5] Naughton BJ, Saltzman S, Priore R, Reedy K, Mylotte JM. Using admission characteristics to predict return to the community from a post-acute geriatric evaluation and management unit. J Am Geriatr Soc 1999;47: 1100–4. [6] Miyamoto H, Hagihara A, Nobutomo K. Predicting the discharge destination of rehabilitation patients using a signal detection approach. J Rehabil Med 2008;40:261–8. [7] Singh I, Gallacher J, Davis K, Johansen A, Eeles E, Hubbard RE. Predictors of adverse outcomes on an acute geriatric rehabilitation ward. Age Ageing 2012; 41:242–6. [8] Heinemann AW, Linacre JM, Wright BD, Hamilton BB, Granger C. Prediction of rehabilitation outcomes with disability measures. Arch Phys Med Rehabil 1994;75:133–43. [9] Witham MD, Ramage L, Burns SL, Gillespie ND, Hanslip J, Laidlaw S, et al. Trends in function and postdischarge mortality in a medicine for the elderly rehabilitation center over a 10-year period. Arch Phys Med Rehabil 2011;92: 1288–92. [10] Wade DT, Collin C, The Barthel ADL. Index: a standard measure of physical disability? Int Disabil Stud 1998;10:64–7. [11] Valderrama-Gama E, Damian J, Guallar E, Rodriguez-Manas L. Previous disability as a predictor of outcome in a geriatric rehabilitation unit. J Gerontol A Biol Sci Med Sci 1998;53:M405–9. [12] Fusco D, Bochicchio GB, Onder G, Barillaro C, Bernabei R, Landi F. Predictors of rehabilitation outcome among frail elderly patients living in the community. J Am Med Dir Assoc 2009;10:335–41. [13] Luk JK, Chiu PK, Chu LW. Rehabilitation of older Chinese patients with different cognitive functions: how do they differ in outcome? Arch Phys Med Rehabil 2008;89:1714–9. [14] Heruti RJ, Lusky A, Barell V, Ohry A, Adunsky A. Cognitive status at admission: does it affect the rehabilitation outcome of elderly patients with hip fracture? Arch Phys Med Rehabil 1999;80:432–6. [15] Agarwal V, McRae MP, Bhardwaj A, Teasell RW. A model to aid in the prediction of discharge location for stroke rehabilitation patients. Arch Phys Med Rehabil 2003;84:1703–9. [16] Chumney D, Nollinger K, Shesko K, Skop K, Spencer M, Newton RA. Ability of Functional Independence Measure to accurately predict functional outcome of stroke-specific population: systematic review. J Rehabil Res Dev 2010;47: 17–29. [17] Campbell SE, Seymour DG, Primrose WR, Lynch JE, Dunstan E, Espallargues M, et al. A multi-centre European study of factors affecting the discharge
328
S.C. Tan et al. / European Geriatric Medicine 4 (2013) 324–328
destination of older people admitted to hospital: analysis of in-hospital data from the ACMEplus project. Age Ageing 2005;34:467–75. [18] de Saint-Hubert M, Schoevaerdts D, Cornette P, D’Hoore W, Boland B, Swine C. Predicting functional adverse outcomes in hospitalized older patients: a systematic review of screening tools. J Nutr Health Aging 2010;14:394–9. [19] Hoogerduijn JG, Schuurmans MJ, Duijnstee MS, de Rooij SE, Grypdonck MF. A systematic review of predictors and screening instruments to identify older hospitalized patients at risk for functional decline. J Clin Nurs 2007; 16:46–57.
[20] Lynch JE, Henderson NR, Ramage L, McMurdo ME, Witham MD. Association between statin medication use and improved outcomes during inpatient rehabilitation in older people. Age Ageing 2012;41:260–2. [21] El Solh AA, Saltzman SK, Ramadan FH, Naughton BJ. Validity of an artificial neural network in predicting discharge destination from a postacute geriatric rehabilitation unit. Arch Phys Med Rehabil 2000;81:1388–93. [22] de Saint-Hubert M, Jamart J, Morrhaye G, Martens HJ, Geenen V, Duy Vo TK, et al. Serum IL-6 and IGF-1 improve clinical prediction of functional decline after hospitalization in older patients. Aging Clin Exp Res 2011;23:106–11.