https://orcid.org/0000-0001-9954-1445
ABSTRACT
Background
Previous studies have investigated the predictors for home discharge without considering stroke severity.
Objectives
To develop a practical assessment tool that predicts home discharge for moderate stroke patients after subacute rehabilitation therapy in the tertiary hospitals.
Methods
Stroke patients with National Institutes of Health Stroke Scale scores of 6 to 13 were included in this prospective cohort study. Various demographic, clinical, and functional factors were analyzed as potential predictive factors. A weighted scoring model was developed through the following three-step process: 1) selection of the factors by logistic regression analyses, 2) development of a weighted scoring model, and 3) validation of the generalizability of the model.
Results
The home discharge rate was 51% (n = 372), and the overall mean length of stay of hospitalization was 32.5 days. 1) The Cognitive Functional Independence Measure, 2) the Functional Ambulation Categories, 3) the modified Charlson Comorbidity Index, and 4) marital status were independent predictors of home discharge. The coefficient value for marital status was adjusted to 1 in the scoring system, and the values of the other parameters were proportionally converted to the nearest integer. Possible total scores ranged from 0 to 13 in the model, with a higher score indicating a higher probability of home discharge. With a cutoff point of 7, this model showed 87.0% sensitivity and 86.2% specificity (area under the curve = 0.90).
Conclusions
This novel assessment tool can be useful in predicting home discharge after subacute rehabilitation of moderate stroke patients.
Introduction
Discharge planning for patients is a vital topic in acute stroke rehabilitation. Appropriate discharge destination planning can improve clinical outcomes and decrease the financial burden on patients, as well as enhance the use of healthcare resources.Citation1 The institutions that serve as discharge locations after acute stroke rehabilitation vary in each country, but in the Republic of Korea, the patients are generally discharged to their homes or to hospitals that can provide rehabilitation services. In particular, returning home means that a patient has made a successful functional recovery after inpatient stroke rehabilitation, and the patients can live with his or her family again without imposing a caregiving burden.Citation2–Citation4 Therefore, the factors contributing to home discharge are important for establishing rehabilitation therapeutic strategies.
Various factors have been studied previously as potential predictors of home discharge. These factors include demographic variables; socioeconomic status; and clinical characteristics such as Functional Independence Measure (FIM) score, lesion location, stroke type, bladder or bowel incontinence, dysphagia, cognitive impairment, aphasia, poor balance, unilateral neglect, and comorbidities.Citation1,Citation2,Citation4,Citation5 It has also been reported that the decision to discharge patients with stroke either to their home or to other rehabilitation facilities could depend on socioeconomic factors, such as marital status, living situation, and caregiver availability.Citation1,Citation4,Citation6 In addition, several assessment models have been designed on the basis of these well-known factors to facilitate the identification of individuals with the greatest likelihood of returning home after acute stroke.Citation1,Citation2,Citation7–Citation11 Among the indicators in these assessment tools, the degree of dependence in activities of daily living (ADL) as a result of neurological impairment has been confirmed as the most powerful factor in determining patients’ poststroke discharge destination.Citation1,Citation2,Citation7,Citation8,Citation12
However, the predictive models suggested in past studies were limited in that they targeted stroke patients in general, regardless of stroke severity. The degree of neurological impairment or poststroke disability is heavily weighted in previous models. Therefore, these tools may be insufficient to predict the discharge destination of moderate stroke patients, as opposed to mild or severe stroke patients. In a previous study that investigated discharge destination after stroke, approximately half of moderate stroke patients were discharged home, while the other half were discharged to rehabilitation clinics.Citation13 Other studies have reported that the home discharge rate after moderate stroke varies depending on the study even when the severity is comparable.Citation3,Citation14
Therefore, the purpose of this study was to identify the factors determining the home discharge of moderate stroke patients, and to develop a practical assessment tool to predict home discharge using the identified factors.
Materials and methods
Patient selection
For this multicenter prospective longitudinal cohort study, data derived from the Korean Stroke Cohort for Functioning and Rehabilitation (KOSCO) were used.Citation15 The study objectives, design, and risks/benefits were explained to the patients, and written informed consent was obtained upon hospitalization. This study was designed according to the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) Statement.
The patients were stratified by stroke severity as measured by the National Institutes of Health Stroke Scale (NIHSS), which is the gold standard for rating neurological impairments in stroke care.Citation13 In this study, patients were categorized into 3 groups according to stroke severity at discharge after stroke rehabilitation. The groups were defined as in a previous study: mild, NIHSS score ≤ 5; moderate, NIHSS score from 6 to 13; and severe NIHSS score >13.Citation13
A total of 1,565 first-time stroke patients were enrolled over 3 consecutive years starting in August 2012 and were discharged after acute stroke rehabilitation (). Of these patients, 814 patients with mild or severe stroke evaluated on the 7th day were excluded. Among the remaining 751 patients, 19 were excluded because of stroke recurrence, newly detected comorbidities such as cancer or cardiac disease, or death. Ultimately, 732 patients with moderate disability were selected for this study.
Figure 1. Patient selection flowchart.
Clinical assessments
The primary outcome of this study was patient discharge destination after acute stroke rehabilitation in tertiary hospitals. In Korea, patients receive follow-up inpatient rehabilitation therapies provided by rehabilitation hospitals if they do not return home. Various demographic and clinical factors were analyzed as potential predictive factors for home discharge. Age, gender, marital status, education level, smoking habits, alcohol consumption, and family history were investigated as sociodemographic variables. Marital status was classified based on whether the patient was currently living with his or her spouse; and education level was divided into two groups according to whether the patient graduated from high school. The clinical characteristics encompassed stroke risk factors, body mass index (BMI), and the level of comorbidity as measured by the modified Charlson Comorbidity Index (mCCI) for stroke outcome studies, as suggested by Goldstein et al.Citation16 That study reported that stroke patients with mCCI scores over 2 points showed poor outcomes.Citation16 The stroke type, lesion side, and NIHSS score on the 7th day after stroke were assessed, and acute stroke treatments were also explored using medical chart review.
Functional assessments
Comprehensive functional evaluations were performed at discharge through in-person assessments. All evaluations were conducted by research assistants who were sufficiently trained and rigorously tested. Functional status encompasses ADL, neurologic impairments, cognitive function, motor function, swallowing function, language, ambulatory function, and depressive mood and was extensively evaluated using the following standardized tools: the FIM, the NIHSS, the Korean version of the Mini-Mental State Examination (K-MMSE), the Fugl-Meyer Assessment (FMA), the American Speech-Language-Hearing Association National Outcome Measurement System swallowing scale (ASHA NOMS), the Korean version of the Frenchay Aphasia Screening Test (K-FAST), the Functional Ambulation Categories (FAC), and the Korean version of the Geriatric Depression Scale-Short Form (SGDS-K).
The FIM was divided into its motor and cognitive subscales for analysis. Motor FIM scores range from 13 to 91 points, where a higher score indicates that the patient can live more independently. Motor FIM scores were classified as low (13–38), medium (39–50), or high (51–91) using cutoffs defined by previous research.Citation1 Cognitive FIM scores range from 5 to 35 points, encompassing five components that are necessary for proper social interaction: comprehension, expression, social interaction, problem solving and memory.Citation17 A higher score means that the patient has better cognitive and social abilities. Similar to motor scores, cognitive FIM scores were divided into 3 categories: low (5–20), medium (21–29), and high (30–35).Citation1
Other functional indicators were analyzed after being dichotomized according to the degree of dependence or with consideration of planned rehabilitation interventions based on previous studies. For example, cognitive function was analyzed based on a cutoff K-MMSE score of 24 points.Citation18 FMA motor scores were divided into two levels of severity: mild (≥80) and moderate to severe (<80).Citation19 ASHA NOMS scores were also analyzed by dividing patients into two groups, corresponding to those with normal (7 points) and abnormal (≤6 points) swallowing function.Citation20 A K-FAST score less than 25 points was regarded as abnormal,Citation21 and FAC scores were analyzed after being dichotomized according to the degree of dependence. Patients who scored over 10 points on the SGDS-K were diagnosed with depression.Citation22
Statistical analysis
Sample size was calculated using G*Power 3.1.7. The purpose of this study is to investigate various indicators that are significantly different between the patients discharged to their homes and patients discharged to rehabilitation hospitals. If the effect size d = 0.5, α error = 0.05, and power (1-β) = 0.8, a minimum of 51 patients in each group are required.
A weighted scoring system to screen home discharge in patients with moderate disability after stroke rehabilitation was developed through the following three-step process: 1) selection of the factors by logistic regression analyses, 2) development of the weighted scoring system, and 3) validation of the generalizability.
First, we performed univariate logistic regression analysis to investigate the clinical and functional factors correlated with home discharge. Variables that were positively associated with home discharge (p < .10) were entered into the initial multivariate logistic regression model. All candidate predictors that correlated with home discharge in univariable logistic regression analysis were included in the multivariable logistic regression analysis. The calibration was analyzed using the Hosmer-Lemeshow goodness-of-fit test.
In order to develop a scoring system with the selected variables, the smallest coefficient value (b1) in the logistic regression model was adjusted to 1 in the scoring system, and then the coefficient values for the other variables were proportionally converted to the nearest integers. The score was defined as the sum of the individual weighted scores for all variables in the final model. The area under the curve (AUC) was estimated for the discriminatory ability of the scoring system. The cutoff point for identifying home discharge patients was determined based on clinical relevance and an acceptable false-positive rate. To validate the generalizability of our scoring system, we fitted the model to an additional 50 stroke patients who met the same inclusion and exclusion criteria as the original cohort. These patients were recruited separately from the cohort used to develop the tools.
Results
Patient characteristics
Of the 732 patients with moderate stroke severity, 372 patients (51%) returned home after acute stroke rehabilitation (). The mean period from admission to discharge was 32.5 ± 4.8 days, which means that a one-month poststroke functional evaluation was performed in this study. The mean patient age was 68.7 ± 13.4 years old, and 57% of the patients were male. In total, 74% of the patients had suffered an ischemic stroke, and the mean NIHSS score on the 7th day was 14.2 ± 5.8.
Table 1. Clinical characteristics and univariate analysis of moderate stroke patients.
Logistic regression analysis
Regarding demographic and clinical characteristics, patients who were living with a spouse (odds ratio (OR) = 1.93), had a low mCCI (OR = 4.80), had suffered an ischemic stroke (OR = 2.83), or had a right-sided cerebral lesion (OR = 3.32) had a significantly increased probability of home discharge. Regarding functional status at discharge, motor FIM (OR = 2.39, middle; OR = 5.05, high), cognitive FIM (OR = 4.80, middle; OR = 15.28, high), ASHA NOMS (OR = 2.34), and FAC (OR = 11.36) scores were significantly related to home discharge.
Multivariate analysis and assessment model
After multivariate logistic regression with backward selection, 1) marital status (OR = 1.84), 2) mCCI (OR = 3.21), 3) cognitive FIM (OR = 3.21, middle; OR = 8.43, high), and 4) FAC (OR = 6.80) were identified as independent predictors. The Hosmer–Lemeshow chi-squared test result was 8.5 (degree of freedom = 8; p = .405) for the model.
The coefficient value for marital status (B = 0.61) was modified to 1 in the scoring system, and the values of the other parameters were proportionally converted to the nearest integer (). The total score ranged from 0 to 13 in the model, with a higher score indicating a higher probability of home discharge. The mean score of the participants in this model was 8.1 ± 4.3 points. The AUC of the model was 0.90 (95% CI 0.87–0.93) and showed a sensitivity of 87.0% and specificity of 86.2% when the cutoff point was 7 ().
Table 2. Multivariate model for home discharge and weighted scoring system for moderate stroke patients.
Figure 2. The receiver operating characteristic curve after applying the model in this study to (A) development data set (AUC = 0.87; 95% CI = 0.84–0.90) and (B) the external data set (AUC = 0.86; 95% CI = 0.80–0.92).
External validation with additional data from 50 moderately disabled stroke patients was performed to confirm the generalizability of this model. These patients who constituted the validation cohort had similar clinical and functional characteristics and home discharge rates (52%, n = 26) to those of the training cohort (Supplementary Table 1). Applying this model to the validation cohort, the receiver operating characteristic curve of the total score is shown in (AUC = 0.86; 95% CI = 0.80–0.92). At the cutoff point of 7, the sensitivity and specificity were 83.5% and 83.3%, respectively.
Discussion
The purpose of this study was to develop a screening tool that could predict the home discharge of moderate stroke patients after acute stroke rehabilitation. As a result, 1) sociocognitive function, 2) marital status, 3) ambulatory function, and 4) comorbidity were important determinants for returning home. The screening model developed by these determinant factors has high sensitivity and specificity.
The most significant feature of this model is that it was developed for moderate stroke patients. Because previous studies related to a predictive model for discharge destination were developed using the entire stroke patient population as the training cohort regardless of their functional status, the sensitivity and specificity were relatively low in moderate stroke patients. Analysis after the separation of the patients who needed practical discharge planning in the clinical setting revealed that cognitive FIM was the most important factor. Although this finding is partially consistent with the results of previous discharge prediction models,Citation1,Citation2,Citation5,Citation11 which is substantially different from previous results showing that motor FIM or motor impairment was the most important factor.Citation23–Citation25
It is noteworthy that moderate stroke patients were more sensitive to functional domains for proper communication with family members and social interaction rather than physical disabilities. Several hypotheses could account for this finding. First, the ultimate goal of stroke rehabilitation is to promote the quality of life (QOL),Citation26 and the QOL of stroke survivors was lower than that of healthy elderly people living in the community.Citation27 Park et al.Citation28 reported that poststroke cognitive impairment even without dementia critically affects QOL in stroke survivors. Therefore, patients with low cognitive FIM may be less likely to be discharged home because they want to enhance their QOL through additional inpatient rehabilitation. Another cause is the caregiver burden when the patient stays at home after discharge. In the Republic of Korea, it is often the responsibility of families, especially offspring, to care for stroke patients at home.Citation29 Patients with low cognitive FIM are often difficult to discharge home by themselves or to their families because cognitive and mood impairments impose a heavier caregiver burden than physical disabilities.Citation29 Therefore, a rehabilitation strategy for sociocognitive enhancement can contribute to increasing the possibility of home discharge for patients with moderate disabilities.
The patient’s ambulatory ability was also considered when determining the discharge pathway in this model. Ambulatory function is crucial for stroke survivors to perform ADL and social activities,Citation30 and patients requiring continuous or intermittent support of another person to walk greatly increases the burden on caregivers.Citation31 Previous studies emphasized the importance of mobility in predicting discharge destination, which is consistent with the results of this study. For example, Tinl et al.Citation32 reported that the Mobility Scale for Acute Stroke, consisting of six basic mobility activities, showed higher sensitivity and specificity for predicting home discharge than the FIM score. Another study using the Motor Assessment Scale demonstrated that gait function among the various functional abilities is the key factor for returning home after acute stroke.Citation33 In particular, Rabadi and BlauCitation34 found that ambulatory speed was the most important among the several factors composing gait function. In this study, patients with relatively fast walking speeds (greater than 0.15 m/s) were more likely to be discharged home, whereas patients with slow walking speeds of 0.15 m/s or less were more likely to be discharged to a subacute facility. Therefore, gait function is also important in stroke patients with moderate neurologic impairments when determining whether to discharge them home, and rehabilitation that enhances walking function will help them return home.
Marital status is also an important factor for home discharge in our predictive model. It is well known that social factors such as marital status/living with a spouse have considerable influence on discharge destinations.Citation1,Citation2,Citation5,Citation35 In particular, the presence of spouses has been reported to be an even more important factor than the level of functional disability in determining the discharge destination for severely disabled patients with low FIM scores,Citation36 and elderly patients.Citation6 Because the spouses generally act as the main caregiver, patients with greater functional disability are less likely to go home due to the increased caregiving burden.Citation4,Citation37 Stroke survivors with moderate neurologic impairments still need to perform ADL, participate in social communication, and engage in outpatient rehabilitation therapies after returning home. From this point of view, marital status can be one of the determinants for home discharge in our model.
The mCCI was a significant indicator for determining home discharge in our model. Although we used a modified version of the Charlson Comorbidity Index specifically for predicting stroke outcomes, previous studies indicated that there was an association between comorbidity and discharge destination.Citation38 In addition, it is known that preexisting comorbidities are negatively associated with functional rehabilitation outcomes in stroke patients.Citation39 Another study reported that the mCCI on admission was an independent indicator of unfavorable functional outcome.Citation40 Therefore, it can be indirectly inferred that patients with greater levels of comorbidity are more likely to be discharged to rehabilitation institutions than to their homes due to poor functional recovery.
This study has the limitation of not performing validation divided into a data set and validation set. The generalizability can be limited because the validation process was performed based on the previous study.Citation41 Therefore, it is necessary to develop assessment tools by dividing development data and verification data in the future.
In summary, strong sociocognitive function, favorable gait function, living with spouses, and fewer comorbidities increased the possibility of home discharge in moderate stroke patients. The predictive model developed with these indicators showed high sensitivity and specificity for predicting home discharge after acute stroke rehabilitation. This model can help improve the reliability of discharge plans in practice after acute stroke rehabilitation.
Supplemental Material
Download MS Word (14.4 KB)Supplemental material
The supplemental data for this article can be accessed publisher’s website
Additional information
Funding
Related Research Data
References
- Itaya T, Murakami Y, Ota A, Nomura E, Fukushima T, Nishigaki M. Assessment model to identify patients with stroke with a high possibility of discharge to home: a retrospective cohort study. Stroke. 2017;48(10):2812–2818. doi:10.1161/STROKEAHA.117.018075.
- Frank M, Conzelmann M, Engelter S. Prediction of discharge destination after neurological rehabilitation in stroke patients. Eur Neurol. 2010;63(4):227–233. doi:10.1159/000279491.
- Fonarow GC, Liang L, Thomas L, et al. Assessment of home-time after acute ischemic stroke in medicare beneficiaries. Stroke. 2016;47(3):836–842. doi:10.1161/STROKEAHA.115.011599.
- Pereira S, Foley N, Salter K, et al. Discharge destination of individuals with severe stroke undergoing rehabilitation: a predictive model. Disabil Rehabil. 2014;36(9):727–731. doi:10.3109/09638288.2014.902510.
- Mees M, Klein J, Yperzeele L, Vanacker P, Cras P. Predicting discharge destination after stroke: A systematic review. Clin Neurol Neurosurg. 2016;142:15–21. doi:10.1016/j.clineuro.2016.01.004.
- Dutrieux RD, van Eijk M, van Mierlo ML, van Heugten CM, Visser-Meily JM, Achterberg WP. Discharge home after acute stroke: differences between older and younger patients. J Rehabil Med. 2016;48(1):14–18. doi:10.2340/16501977-2045.
- Ouellette DS, Timple C, Kaplan SE, Rosenberg SS, Rosario ER. Predicting discharge destination with admission outcome scores in stroke patients. NeuroRehabilitation. 2015;37(2):173–179. doi:10.3233/NRE-151250.
- Roberts PS, Mix J, Rupp K, et al. Using functional status in the acute hospital to predict discharge destination for stroke patients. Am J Phys Med Rehabil. 2016;95(6):416–424. doi:10.1097/PHM.0000000000000412.
- Ward I, Pivko S, Brooks G, Parkin K. Validity of the stroke rehabilitation assessment of movement scale in acute rehabilitation: a comparison with the functional independence measure and stroke impact scale-16. Pm R. 2011;3(11):1013–1021. doi:10.1016/j.pmrj.2011.08.537.
- Ekstrand E, Ringsberg KA, Pessah-Rasmussen H. The physiotherapy clinical outcome variables scale predicts length of hospital stay, discharge destination and future home facility in the acute comprehensive stroke unit. J Rehabil Med. 2008;40(7):524–528. doi:10.2340/16501977-0210.
- Hu HY, Chi WC, Chang KH, et al. The World Health Organization disability assessment schedule 2.0 can predict the institutionalization of patients with stroke. Eur J Phys Rehabil Med. 2017;53(6):856–862. doi:10.23736/S1973-9087.17.04615-9.
- Pinedo S, Erazo P, Tejada P, et al. Rehabilitation efficiency and destination on discharge after stroke. Eur J Phys Rehabil Med. 2014;50(3):323–333.
- Schlegel D, Kolb SJ, Luciano JM, et al. Utility of the NIH stroke scale as a predictor of hospital disposition. Stroke. 2003;34(1):134–137. doi:10.1161/01.STR.0000048217.44714.02.
- Yu AYX, Rogers E, Wang M, et al. Population-based study of home-time by stroke type and correlation with modified Rankin score. Neurology. 2017;89(19):1970–1976. doi:10.1212/WNL.0000000000004631.
- Chang WH, Sohn MK, Lee J, et al. Korean stroke cohort for functioning and rehabilitation (KOSCO): study rationale and protocol of a multi-centre prospective cohort study. BMC Neurol. 2015;15:42. doi:10.1186/s12883-015-0293-5.
- Goldstein LB, Samsa GP, Matchar DB, Horner RD. Charlson index comorbidity adjustment for ischemic stroke outcome studies. Stroke. 2004;35(8):1941–1945. doi:10.1161/01.STR.0000135225.80898.1c.
- Gialanella B, Ferlucci C. Functional outcome after stroke in patients with aphasia and neglect: assessment by the motor and cognitive functional independence measure instrument. Cerebrovasc Dis. 2010;30(5):440–447. doi:10.1159/000317080.
- Dong Y, Sharma VK, Chan BP, et al. The Montreal Cognitive Assessment (MoCA) is superior to the Mini-Mental State Examination (MMSE) for the detection of vascular cognitive impairment after acute stroke. J Neurol Sci. 2010;299(1–2):15–18. doi:10.1016/j.jns.2010.08.051.
- Duncan PW, Goldstein LB, Matchar D, Divine GW, Feussner J. Measurement of motor recovery after stroke. Outcome assessment and sample size requirements. Stroke. 1992;23(8):1084–1089. doi:10.1161/01.STR.23.8.1084.
- Kim J, Oh B-M, Lee G, Seung AL, Chun S, Han T. Clinical factors associated with severity of post-stroke dysphagia. Brain Neurorehabil. 2011;4:116. doi:10.12786/bn.2011.4.2.116.
- Bom PS, Mi HY, Wan HJ, Hoyoung Y, Woo PK, Kichun N. Standardization of Korean version of frenchay aphasia screening test in normal adults. Ann Rehabil Med. 2009;33:436–440.
- Park JH, Kim KW, Kim MH, et al. A nationwide survey on the prevalence and risk factors of late life depression in South Korea. J Affect Disord. 2012;138(1–2):34–40. doi:10.1016/j.jad.2011.12.038.
- Saji N, Kimura K, Ohsaka G, et al. Functional independence measure scores predict level of long-term care required by patients after stroke: a multicenter retrospective cohort study. Disabil Rehabil. 2015;37(4):331–337. doi:10.3109/09638288.2014.918195.
- Koyama T, Sako Y, Konta M, Domen K. Poststroke discharge destination: functional independence and sociodemographic factors in urban Japan. J Stroke Cerebrovasc Dis. 2011;20(3):202–207. doi:10.1016/j.jstrokecerebrovasdis.2009.11.020.
- Thorpe ER, Garrett KB, Smith AM, Reneker JC, Phillips RS. Outcome measure scores predict discharge destination in patients with acute and subacute stroke: a systematic review and series of meta-analyses. J Neurol Phys Ther. 2018;42(1):2–11. doi:10.1097/NPT.0000000000000211.
- Levack WM, Weatherall M, Hay-Smith JC, Dean SG, McPherson K, Siegert RJ. Goal setting and strategies to enhance goal pursuit in adult rehabilitation: summary of a cochrane systematic review and meta-analysis. Eur J Phys Rehabil Med. 2016;52:400–416.
- Sturm JW, Donnan GA, Dewey HM, et al. Quality of life after stroke: the North East Melbourne Stroke Incidence Study (NEMESIS). Stroke. 2004;35(10):2340–2345. doi:10.1161/01.STR.0000141977.18520.3b.
- Park JH, Kim BJ, Bae HJ, et al. Impact of post-stroke cognitive impairment with no dementia on health-related quality of life. J Stroke. 2013;15(1):49–56. doi:10.5853/jos.2013.15.1.49.
- Choi-Kwon S, Kim HS, Kwon SU, Kim JS. Factors affecting the burden on caregivers of stroke survivors in South Korea. Arch Phys Med Rehabil. 2005;86(5):1043–1048. doi:10.1016/j.apmr.2004.09.013.
- Béjot Y, Blanc C, Delpont B, et al. Increasing early ambulation disability in spontaneous intracerebral hemorrhage survivors. Neurology. 2018;90(23):e2017–e2024.
- Oliva-Moreno J, Pena-Longobardo LM, Mar J, et al. Determinants of informal care, burden, and risk of burnout in caregivers of stroke survivors: the CONOCES study. Stroke. 2018;49(1):140–146. doi:10.1161/STROKEAHA.117.017575.
- Tinl ML, Kale MK, Doshi S, Guarino AJ, Beninato M. The mobility scale for acute stroke predicts discharge destination after acute hospitalization. J Rehabil Med. 2014;46(3):219–224. doi:10.2340/16501977-1269.
- Brauer SG, Bew PG, Kuys SS, Lynch MR, Morrison G. Prediction of discharge destination after stroke using the motor assessment scale on admission: a prospective, multisite study. Arch Phys Med Rehabil. 2008;89(6):1061–1065. doi:10.1016/j.apmr.2007.10.042.
- Rabadi MH, Blau A. Admission ambulation velocity predicts length of stay and discharge disposition following stroke in an acute rehabilitation hospital. Neurorehabil Neural Repair. 2005;19(1):20–26. doi:10.1177/1545968304272762.
- Maeshima S, Okamoto S, Okazaki H, et al. Potential factors, including activities of daily living, influencing home discharge for patients with putaminal haemorrhage. BMC Neurol. 2016;16:16. doi:10.1186/s12883-016-0539-x.
- Nguyen TA, Page A, Aggarwal A, Henke P. Social determinants of discharge destination for patients after stroke with low admission FIM instrument scores. Arch Phys Med Rehabil. 2007;88(6):740–744. doi:10.1016/j.apmr.2007.03.011.
- Tanwir S, Montgomery K, Chari V, Nesathurai S. Stroke rehabilitation: availability of a family member as caregiver and discharge destination. Eur J Phys Rehabil Med. 2014;50:355–362.
- Van der Cruyssen K, Vereeck L, Saeys W, Remmen R. Prognostic factors for discharge destination after acute stroke: a comprehensive literature review. Disabil Rehabil. 2015;37(14):1214–1227. doi:10.3109/09638288.2014.961655.
- Kabboord AD, van Eijk M, Fiocco M, van Balen R, Achterberg WP. Assessment of comorbidity burden and its association with functional rehabilitation outcome after stroke or hip fracture: a systematic review and meta-analysis. J Am Med Dir Assoc. 2016;17(11):1066.e1013-1066.e1021. doi:10.1016/j.jamda.2016.07.028.
- Turhan N, Atalay A, Muderrisoglu H. Predictors of functional outcome in first-ever ischemic stroke: a special interest to ischemic subtypes, comorbidity and age. NeuroRehabilitation. 2009;24(4):321–326. doi:10.3233/NRE-2009-0485.
- Lee EJ, Kim JH, Park ES, et al. A novel weighted scoring system for estimating the risk of rapid growth in untreated intracranial meningiomas. J Neurosurg. 2017;127(5):971–980. doi:10.3171/2016.9.JNS161669.