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Japanese Journal of Comprehensive Rehabilitation Science (2020)

Original Article

Predictive factors of home discharge in elderly stroke patients
hospitalized in a convalescent rehabilitation ward
Ken Sato, MD1
Department of Rehabilitation Medicine, Kameda Medical Center, Kamogawa, Chiba, Japan

1

ABSTRACT
Sato K: Predictive factors of home discharge in elderly
stroke patients hospitalized in a convalescentrehabilitation ward. Jpn J Compr Rehabil Sci 2020;
11: 43‐48.
Purpose: To identify predictive factors of homedischarge in elderly stroke patients hospitalized in ourconvalescent rehabilitation ward based on markers
determined during hospitalization and to evaluate theirpredictive capability.
Methods: Parameters measured at hospitalization in
179 elderly stroke patients aged ≥65 years hospitalized
between April 1, 2015 and July 1, 2018 were comparedafter dividing the patients into two groups dependingon whether they were discharged home. In addition,
multivariate analysis was performed using whether
home discharge was possible as the objective variable
to prepare a prediction formula and receiver operating
characteristic (ROC) curves.
Results: A total of 150 patients (84%) were discharged
home. Differences in acute phase hospitalization duration,
number of cohabiting people, National Institutes of
Health Stroke Scale at the time of onset, functional
independence measure (FIM) at hospitalization, foodtype, and nutritional evaluation were significant between
the two groups. Multivariate analysis revealed significant
differences f or a cute p hase h ospitalization duration,
number of cohabiting people, and FIM at hospitalization.
The area under the curve for the ROC curve was 0.891.
Conclusions: The results indicated that the
combination of acute phase hospitalization duration,
number of cohabiting people, and FIM at hospitalization
could be used as a predictive factor for home discharge
in elderly stroke patients hospitalized in a convalescentrehabilitation ward.
Correspondence: Ken Sato, MD
Department of Rehabilitation Medicine, Kameda Medical
Center, 929 Higashi-cho, Kamogawa, Chiba 296‐8602,
Japan.
E-mail: sato.ken@kameda.jp
Accepted: February 25, 2020.
Conflicts of interest: The author have no conflict of interest
directly relevant to the content of this article.
©Kaifukuki Rehabilitation Ward Association 2020
doi.org/10.11336/jjcrs.11.43

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Key words: elderly individuals, stroke, home discharge
predictive factors, convalescent rehabilitation
Introduction
Stroke causes various symptoms that are prone toremain as sequelae. Therefore, it is known to be
associated with a high risk of causing elderly
individuals to require long-term caregiving.
The age at onset of both ischemic and hemorrhagic
strokes in Japan is increasing with the increase in the
aging population. Both survival and functional
prognoses upon discharge have been reported to
become poorer with increasing age [1]. According to
the 2018 Annual Report on the Aging Society releasedby the Cabinet Office, stroke is the second mostcommon condition that requires long-term caregiving
after dementia, and is more likely than dementia inmen [2].
Regarding the post-onset functional improvement,
the 2015 Stroke Treatment Guidelines established by
the Japan Stroke Society suggest the importance ofconvalescent rehabilitation, indicating that “it isrecommended to perform convalescent rehabilitation
following acute phase rehabilitation (Grade B)” [3].
However, the maximum hospitalization period coveredby health insurance is limited in convalescentrehabilitation wards (hereafter “rehabilitation wards”).
When an elderly patient is hospitalized, in addition to
being discharged home, they may also be admitted toa long-term care facility covered by long-term careinsurance. Therefore, a decision should be made upondischarge from the rehabilitation ward as to whether
the patient will be discharged home or admitted to afacility. However, various factors make prediction ofwhere patients will be ultimately discharged extremely
difficult. Several previous reports within and outside
Japan have investigated the factors that affect thehome discharge of stroke patients [4‐8]. However,studies attempting to predict outcomes of elderly
patients from the convalescent ward are extremely
limited. Moreover, no studies described the developmentof a prediction formula based on identified related
factors and evaluated its predictive capability.
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In this study, markers measured before stroke onset
until admission to the rehabilitation ward were used to
identify factors related to home discharge, and thepredictive capability of the formula developed basedon these results was evaluated.
Methods
This study comprised elderly stroke patients aged
≥65 years hospitalized in our rehabilitation ward
between April 1, 2015 and July 1, 2018. Data of 179
patients were retrospectively investigated, after
excluding those who met the following exclusion
criteria: <14 days spent in the rehabilitation ward (11patients), subarachnoid hemorrhage (24 patients),
missing data (6 patients), transferred to an acute phaseward (14 patients), and hospitalized from a place otherthan home (20 patients). Patients were divided into
two groups depending on whether they were discharged
home (hereafter “home discharge cases”) or not(hereafter “non-home discharge cases”). Univariate
analysis was performed to compare the two groups.
The evaluation parameters were attributes, nutritionalparameters, and rehabilitation-related parameters. The
following detailed parameters were also compared(see section 1 below). Moreover, multivariate analysis
was performed to determine whether the patient wasdischarged home as the objective variable and identify
parameters with significant differences. A prediction
formula was developed based on the identified
parameters. A receiver operating characteristic (ROC)
curve was also prepared based on the obtained
variables, and the area under the curve (AUC) was
calculated.
1. Evaluation parameter details
1.1 Attributes
Age, sex, disease type, recurrence, acute phase
hospitalization duration, number of cohabiting people,
and degree of daily independence before the onset ofstroke were compared between the two groups.
Disease type was classified as either cerebral infarction
or cerebral hemorrhage using the main disease nameas a reference. Hemorrhagic infarction cases included
cerebral infarction cases. Cohabiting people were
defined as those sharing the same living space and did
not include those living in a separate space within thesame premises. The matter of whether cohabiting
people were blood relatives was not considered. If thepatient lived alone, the number of cohabiting people
was described as “1.” Pre-hospitalization activities ofdaily living (ADL) were evaluated as the degree ofdaily independence.
1.2 Nutritional and rehabilitation-related parameters
A previous report by Nishioka et al. [9] comparing agroup with good nutritional status and one with poor
nutritional status indicated that the proportion of thosedischarged home was 81.6% for the former and 44.6%
for the latter group, revealing a statistically significant
difference. Therefore, nutritional parameters were
included as items for comparison in this study.
Detailed parameters for comparison were weightand body mass index (BMI) upon hospitalization,
nutritional evaluation, and food type. BMI was
calculated as weight (kg)/height (m) × height (m).
Food type was evaluated as non-oral, swallowing
training, or normal meals. Nutritional intake route
was defined as “non-oral cases” if a meal wasadministered by a non-oral route even once per day
in the rehabilitation ward. Nutritional state was
evaluated using the Mini Nutritional AssessmentⓇShort Form (MNAⓇ-SF) and Geriatric Nutritional
Risk Index (GNRI). GNRI was calculated as 14.89 ×serum albumin (g/dL) + 41.7 × current weight (kg)/ideal weight (kg). Ideal weight was calculated using
Lorentz’s formula. Current weight/ideal weight of>1was treated as “1” for the calculations.
Severity at onset was evaluated using the National
Institutes of Health Stroke Scale (NIHSS). Functional
independence measure (FIM) upon hospitalization
was compared based on motor and cognitive items
when comparing the two groups. For multivariate
analysis, multi-collinearity was observed when motorand dementia parameters were considered as separate
variables. Therefore, only FIM upon hospitalization
was used, which comprised the total score.
2. Statistical analysis
When comparing the home discharge cases andnon-home discharge cases, we performed univariate
analysis using Fisher’s exact test, t test, or Mann–
Whitney U test considering whether the target forcomparison was a continuous or nominal variable andwhether a Gaussian distribution was conformed to.
Binomial logistic regression analysis was performed
using items for which a significant difference wasnoted on univariate analysis as explanatory variables,
and whether cases were discharged home as theobjective variable (1 = home, 0 = other than home).
Items that exhibited a significant difference on logistic
regression analysis were then used to develop a
prediction formula. The ROC curve was created basedon the objective variables obtained from the prediction
formula, and the AUC was calculated. We calculated
the cutoff value to be the closest point to the upper
left corner of the ROC curve; next, using a 2 × 2
contingency table, we determined the sensitivity,
specificity, positive predictive value, and negative
predictive value. The level of statistical significancewas set at <0.05. The statistical software used was
EZR version 1.37.
Results
Table 1 presents the attributes of the subjects. The

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overall mean age was 76.6 ± 7.2 years. In terms of
disease type, several patients suffered from cerebral
infarction, and recurrence cases accounted for 25%of the patients. The median number of cohabiting people
was 2 (including the patient themselves), and acute phase
hospitalization duration was approximately 3 weeks. Prehospitalization ADL was almost independent in daily
lifestyle for >80% of the patients, indicating that the
population was able to go on outings alone.
Of the 179 subjects, 150 (84%) were discharged home.
Comparison of the home discharge cases and non-homedischarge cases indicated significant differences (p <0.05) for acute phase hospitalization duration and numberof cohabiting people. The acute phase hospitalization
duration was approximately 8 days longer for the nonhome discharge cases than for the home discharge cases.
The number of cohabiting people also tended to be lower
for the non-home discharge cases than for the homedischarge cases. Although mean age and the proportionof recurrent cases both tended to be higher for the non-home discharge cases than for the home dischargecases, no significant differences were observed between
the two groups. No significant differences wereobserved between the two groups in terms of sex,
disease type, or pre-onset degree of daily independence.
Nutritional evaluation revealed significant differences
for the MNAⓇ-SF, GNRI, and food type (p < 0.01,
Table 2). It was confirmed that MNAⓇ-SF and GNRI
scores were low in the non-home discharge cases,
indicating a tendency for a high proportion of cases
being determined as undernourished or at high risk ofsuch undernutrition. Regarding food type, approximately
80% of the home discharge cases were consuming“normal meals.” In contrast, just 40% of non-homedischarge cases were consuming “normal meals” andlarge proportions of these cases were consuming
dysphagia meals or non-oral meals. In terms ofrehabilitation-related items, significant differenceswere noted between the groups for NIHSS at the timeof onset and FIM at hospitalization. The non-home

Table 1. Univariate analysis of the attributes of home discharge cases and non-home discharge cases (n = 179).

Age (years)
Sex (male cases)Disease type (history of cerebral
infarction)
Recurrence casesAcute phase hospitalization
duration (days)Number of co-habituating
people (people)
Pre-onset degree of daily
independence (“Rank J” cases)

Overall (n = 179)

Home dischargecases: 150 (84%)

Non-home dischargecases: 29 (16%)

p Value76.6±7.2
106 (59%)76.2±7.3
89 (59%)78.6±6.4
17 (59%)

0.103
1.000

130 (73%)111 (74%)

19 (66%)0.367

44 (25%)34 (23%)

11 (38%)0.102

23 (17‐29.5)

22 (17‐28)  30 (21‐44)

0.001>  2 (2‐3)  2 (2‐3)

2 (1‐2)0.037

170 (84%)

125 (83%)

25 (83%)0.810

Values are shown as number of cases (proportion: %), mean ± SD or median values (interquartile range).
SD, Standard Deviation.
Table 2. Univariate analysis of nutritional parameters and rehabilitation-related parameters.
Home discharge cases
Weight at hospitalization (kg)56 (46.7‐64.9)
BMI at hospitalization (kg/m2)
22.2 (19.7‐25.1)
MNAⓇ-SF at hospitalization (score)8 (6‐10)GNRI at hospitalization (score)
95.8 (89.2‐102.1)
Food type
119 (79%), 26 (17%), 5 (3%)
  (normal, dysphagia, non-oral)
NIHSS at onset (score)7 (4‐11)
FIM at hospitalization (score)77 (60‐92)
Motor items (score)51 (39‐64)
Cognitive items (score)26 (20‐32)

Non-home discharge cases

p Value51 (46.7‐58.2)
20.2 (19.2‐22.6)
5 (3‐8)86.8 (82.5‐95.6)

0.086
0.051
0.001>
0.001>

12 (41%), 8 (28%), 9 (31%)

0.001>

18.5 (7.25‐24.8)
34 (25‐55)
19 (15‐41)
13 (7.0‐20)

0.001>
0.001>
0.001>
0.001>

Values are shown as number of cases (proportion: %), or median values (interquartile range).
BMI, Body Mass Index; MNAⓇ-SF, Mini Nutritional AssessmentⓇShort Form; GNRI, Geriatric Nutritional Risk
Index; NIHSS, National Institutes of Health Stroke Scale; FIM, Functional Independence Measure.
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Sato K et al.: Predictive Factors of Convalescent Phase Home Discharge in Elderly Stroke Patients

Table 3. Multiple logistic regression analysis.
Odd’s ratio

95% confidence intervalp Value

0.955
1.050
0.943
1.640
0.906
0.990
0.985

0.886‐1.030
1.020‐1.090
0.901‐0.988
1.030‐2.620
0.347‐2.370
0.769‐1.270
0.914‐1.060

0.236
0.001
0.013
0.037
0.841
0.940
0.682

NIHSS at onset (score)
FIM at hospitalization (score)
Acute phase hospitalization duration (days)
Number of co-habituating people (people)
Food type
MNAⓇ-SF (score)
GNRI (score)

MNAⓇ-SF, Mini Nutritional AssessmentⓇShort Form; GNRI, Geriatric Nutritional Risk Index; NIHSS, National
Institutes of Health Stroke Scale; FIM, Functional Independence Measure.
Table 4. 2 × 2 contingency table based on prediction
formula and cutoff values.
Home
Non-home
discharge
discharge(n = 150)(n = 29)
Less than 1.122(positive)
1.122 or more(negative)

113

3

37

26

Cut off value 1.122
75.3%
89.7%

0.891
95% confidence interval: 0.837～0.944

Prediction formula: Objective variable = 1/1 + e-(0.05531
× FIM at hospitalization (score) + 0.51761 × number ofco-habituating family members (people) − 0.06119 ×
acute phase hospitalization duration (days) − 1.08616)
discharge cases tended to exhibit high NIHSS at thetime of onset and low FIM at hospitalization. It wasalso confirmed that both motor and cognitive items on
the FIM were lower in the non-home discharge cases(p < 0.01).
Multiple logistic regression analysis revealed
significant differences for acute phase hospitalization
duration, number of cohabiting people, and FIM
at hospitalization (p < 0.05, Table 3). The prediction
formula developed based on these items was objective
variable = 1/1 + e− (0.05531 × FIM at hospitalization(points) + 0.51761 × number of cohabiting family (people)− 0.06119 × acute phase hospitalization duration (days)− 1.08616). When an ROC curve was created based on
the calculated objective variable, the AUC was found tobe 0.891 (95% confidence interval: 0.837–0.944, Figure1). The optimal cutoff value was 1.122, sensitivity was
75.3%, and specificity was 89.7%. The positive andnegative predictive values were 97.4% and 41.3%,
respectively (Table 4).
Discussion
This study targeted elderly stroke patients aged ≥65
years. We identified factors influencing discharge tohome based on indices obtained while subjects were
hospitalized at a rehabilitation ward. Next, we used a
prediction formula created by combining acute phase

Figure 1. ROC curve.
hospitalization duration, number of cohabiting people,
and FIM at hospitalization to evaluate the ability of theformula to predict home discharge using the ROC
curve.
As stated above, no previous studies have investigatedconvalescent ward outcome prediction for elderly
patients. Moreover, no previous studies have used the
ROC curve to evaluate predictive ability, as our study
did. Therefore, although we were unable to discuss how
useful and accurate our prediction formula and its
predictive ability are, the AUC of 0.891 indicates that it
offers a reasonably high level of precision (in general,higher precision is indicated by closer proximity to the
AUC of the ROC curve to 0.9‐1.0).
The results of previous studies investigating thehome discharge of stroke patients have indicated thatsocial factors including age, NIHSS, ADL at
hospitalization and discharge (degree of paralysis andlevel of required nursing care), whether oral nutritionis possible, and family aspects as well as rehabilitationintervention units are factors that contribute to homedischarge for stroke patients [4, 10‐14]. For example,
according to a report by Iwai et al. who analyzed thestroke data of 482 cases registered with the Japanese
Association of Rehabilitation Medicine, predictive
factors for home discharge were age, FIM at

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hospitalization, and long-term caregiving ability(evaluated on a scale from 1‐5) [14]. In their study, no
limitations were placed on age of stroke patient, and it
was reported that age was a direct predictive factor fordischarge to home. However, in the current study, as
univariate analysis did not indicate any significant
difference for age, age was not used as a factor for
multivariate analysis. This lack of a significant
difference could be attributed to the fact that our
subjects were limited to individuals aged ≥65 years.
This suggested that factors enabling home discharge
differ between elderly and non-elderly individuals. As
was also the case for the aforementioned study, ourresults for multivariate analysis confirmed that FIM at
hospitalization was an independent predictive factor.
Degree of severity according to NIHSS and severity ofparalysis according to Brunnstrom Stage have been
confirmed to affect decreased ADL in previous studies,and the effects of FIM at hospitalization could be
confirmed with the evaluation in the current study.
Long-term caregiving ability was cited as the third
factor in the aforementioned study. It was confirmed in
the current study that the number of cohabiting people
had similar implications for long-term caregiving
ability, and we confirmed that it was an importantfactor in enabling home discharge. However, as a
cohabiting person was defined as a person sharing thesame living space in the current study and thefrequency and amount of time spent interacting withthe patient was not taken into consideration, it must be
noted that the number of cohabiting people is notexactly the same as long-term caregiving ability, as
described in previous studies. As a wide range offactors, including physical elements such as the
caregiver’s state of health, cognitive function, andADL and financial elements such as economic power
are involved in long-term caregiving ability, studies
investigating predictive factors for home discharge
must carefully discuss whether long-term caregiving
ability should be taken into consideration as anindependent evaluation parameter. In general, because
factors themselves need to be simplified and userfriendliness is required when investigating practicalprediction indices, the number of cohabiting people,
which was used in the current study, might be a more
useful index for evaluation than long-term caregiving
ability. In recent years, as types of cohabiting are
becoming more diverse and some cohabiting people
take on the role of long-term caregiver despite notbeing related to the patient by blood, it would be better
to use the number of cohabiting people rather than thenumber of cohabiting family members as an evaluation
parameter.
It was also demonstrated in the current study that
acute phase hospitalization duration was a usefulpredictive factor for home discharge. Although it is
impossible to determine the factors associated withprolonged duration of acute phase hospitalization

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among stroke patients based on the results of thecurrent study, a study by Okabayashi et al. found thatswallowing function-related items such as severity
during onset, fasting duration, and respiratory tract
infection complications correlated with acute phase
hospitalization duration [15]. However, in the current
study, we confirmed that even when adjusted for
NIHSS at the time of onset, food type at hospitalization,and nutritional status, acute phase hospitalization
duration produced a significant difference according tomultiple logistic regression analysis. Urakawa et al.
found that complications and exacerbation ofsymptoms of the underlying disease that occurred
during the acute phase hospitalization period werealso related to the acute phase hospitalization duration[16]; these factors prolonged the time until ambulation,
thereby increasing the number of days spent in hospitalduring the acute phase. Although the question of what
disease the complications in this study specificallyindicated was not clarified, previous studies have citedischemic heart disease, pneumonia, urinary tract
infections, and deep vein thrombosis as complicationsof stroke [17‐20]. However, evaluation of each ofthese individual complications as predictive factors
for home discharge could result in an overly
complicated analysis. Moreover, as diseases other thanthe complications cited above could also be thecomplications of stroke, it would not be feasible toinclude complications occurring during acute phase
hospitalization as predictive factors for homedischarge. In this context, the current study isinteresting because it demonstrated that the index ofacute phase hospitalization duration comprehensively
including the effects of these complications is usefulas a predictive factor for home discharge. It is anobjective and simple evaluation parameter that can beeasily counted as the number of days.
The current study had several limitations. First, theonly social factor that we could investigate was thenumber of cohabiting people. Other social factors,
including regional characteristics such as homeownership rates, specific housing environment, annual
household income, use of public assistance, and degreeof intervention with long-term care insurance, willneed to be considered in future studies. Because prehospitalization cognitive function and newly occurring
higher brain dysfunction can strongly affect long-term
caregiving, the question of how these indices influencehome discharge is an important topic for future
research. Furthermore, in the current study, we did notconsider evaluation parameters that arise in the
rehabilitation ward such as complications that occur
during hospitalization in the rehabilitation ward andwaiting period until hospitalization based on the bed
occupancy status in the rehabilitation ward. However,these indices must be evaluated to understand how
operations in the rehabilitation ward affect homedischarge.

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Sato K et al.: Predictive Factors of Convalescent Phase Home Discharge in Elderly Stroke Patients

Conclusions
Our results suggested that acute phase hospitalization
duration, number of cohabiting people, and FIM at
hospitalization should be combined to form a predictive
factor for the home discharge of elderly stroke patients
hospitalized in a convalescent rehabilitation ward.

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