Open Access

Development and evaluation of a formula for predicting introduction of medication self-management in stroke patients in the Kaifukuki rehabilitation ward

  • Hisato Fujihara1, 2Email author,
  • Mari Kogo2, 3,
  • Isao Saito2,
  • Nobuyuki Kawate4,
  • Masazumi Mizuma4,
  • Hiroko Suzuki1,
  • Jun-ichiro Murayama2, 5 and
  • Tadanori Sasaki2, 5
Journal of Pharmaceutical Health Care and SciencesThe official journal of the Japanese Society of Pharmaceutical Health Care and Sciences20173:2

DOI: 10.1186/s40780-016-0070-7

Received: 1 October 2016

Accepted: 19 December 2016

Published: 10 January 2017

Abstract

Background

Medication self-management in stroke patients is important to prevent further progression of disease and incidence of side effects. The purpose of this study was to create a formula for predicting medication self-management introduction in stroke patients using functional independence measure items and patient data, including medication-related information.

Methods

This was a retrospective analysis of 104 patients (cerebral infarction, cerebral hemorrhage, subarachnoid hemorrhage) discharged from the Kaifukuki rehabilitation ward at Showa University Fujigaoka Rehabilitation Hospital from January to December 2012. Multivariate analysis was performed to develop a formula for predicting achievement of medication self-management.

Results

Of the 104 patients, 39 (37.5%) achieved medication self-management. In the logistic regression analysis, number of drugs, age, walk/wheelchair mobility FIM, and memory FIM were extracted as significant factors independently contributing to achievement of medication self-management (p < 0.05). The prediction formula was [4.404 − 0.229 × number of drugs at admission + 0.470 × walk/wheelchair mobility FIM at admission + 0.416 × memory FIM at admission − 0.112 × age].

Conclusions

In the future, this formula may be used as an index to predict success of medication self-management in stroke patients.

Keywords

Stroke patient Rehabilitation Multivariate analysis Predictive factor Medication self-management FIM

Background

Stroke patients admitted to a Kaifukuki rehabilitation ward often develop cognitive impairment. Therefore, there is a pressing need to improve patients’ abilities to self-manage their social life and activities. Previous reports have shown that establishing goals during the early phase of hospitalization hastens effective rehabilitation [13].

However, the medication self-management has not been appropriately evaluated for stroke patients, although it is important to prevent further disease progression and incidence of side effects.

Pharmacists who work in a Kaifukuki rehabilitation ward must support introduction of safe medication self-management and prevent medication errors in stroke patients. In addition, the mission of hospital pharmacists is to help patients achieve optimal medication self-management during hospitalization. However, decisions regarding whether to introduce medication self-management for stroke patients should be based on optimal objective indicators.

There are many studies reporting timing of discharge for stroke patients using admission data [415]. These reports use the functional independence measure (FIM), which objectively quantifies activities of daily living and is widely used in the rehabilitation ward as an evaluation criterion. Moreover, many studies used FIM items at admission to predict achievement of medication self-management in stroke patients [1618]. However, there is currently no objective index for determining the likelihood of achieving medication self-management, including those measuring medication-taking behavior, such as number of drugs or number of doses per day.

The purpose of this study was to create a formula to predict if medication self-management would be effective for stroke patients using FIM items and patient data, including medication-related information.

Methods

Patients

The subjects included 104 patients (cerebral infarction, cerebral hemorrhage, subarachnoid hemorrhage) discharged from the Kaifukuki rehabilitation ward in Showa University Fujigaoka Rehabilitation Hospital from January to December 2012. A retrospective cohort study was conducted using data from the medical charts of the subjects. Subjects were excluded if they had a medication error during hospitalization after achievement of medication self-management. This study was approved by the ethics committee of Showa University Fujigaoka Hospital (approval no. 2012105).

Clinical parameters

We collected data from the medical charts, including age, sex, post-onset rehabilitation hospital day, type of disease (cerebral infarction, cerebral hemorrhage, subarachnoid hemorrhage), number of drugs, number of doses per day, number of doses to be taken once only, one-dose packages, and FIM item score as scored by nurses at the inpatient ward.

Standards to introduce medication self-management

Patients need to achieve all eight items shown in Table 1, and medical staff (physicians, pharmacists, nurses and occupational therapists) discuss and judge if safe medication self-management is applicable.
Table 1

Eight items that are necessary to introduce drug action

□ Do you know purpose of drugs?

YES/NO

□ Can you count the number of drugs?

YES/NO

□ Do you know when to take drugs?

YES/NO

□ Can you remember when to took drugs?

YES/NO

□ Can you bring drugs to the mouth?

YES/NO

□ Can you swallow drugs?

YES/NO

□ Could you management daily medication by yourself?

YES/NO

□ Could you continue taking drugs?

YES/NO

Endpoint

The endpoint of this survey was achievement of medication self-management at discharge.

Univariate analysis

We compared each variable between two groups: those achieving self-management and those who did not.

Comparison of changes in the number of drugs and number of doses at admission, introduction of medication self-management, and discharge

To exclude the influence of changes in medicine in the hospital, we compared the number of drugs and number of doses between admission and discharge. In addition, we compared the number of drugs and number of doses per day between admission and at the start of medication self-management.

Multivariate analysis and creation of a prediction formula

Parameters that were significantly different in the univariate analysis were entered in the multivariate analysis. Significant independent variables contributing to medication self-management were extracted using stepwise selection methods. In addition, we composed a formula to predict medication self-management by using extracted items along with the regression coefficient. The prediction formula was y = aX1 + bX2 + cX3, where y is the objective variable; X1, X 2, and X3 are the explanatory variables; and a, b, and c are regression coefficients. We used backward stepwise multiple regression analysis to select the variables.

Evaluation of the validity of the prediction formula

We evaluated the validity of the formula by using the degrees of freedom adjusted R2 statistic, lack of fit (LOF), and area under the receiver operating characteristic (ROC) curve, which provides an index indicating the association of the sensitivity and the specificity.

Statistical analysis

To examine between-group differences, the t-test was used for continuous variables, Fisher’s exact test was used for categorical variables, and the Wilcoxon rank sum test was used for the FIM item score. A value of p < 0.05 was considered statistically significant. JMP® v. 9.0 (SAS Institute Inc., Cary, NC, USA) was used for the statistical analyses.

Results

Patients characteristics

Table 2 shows the characteristics of all patients. The average age was 70.0 ± 12.3 years; 65 (62.5%) were men, and 39 (37.5%) were women. Of the 104 patients, 39 (37.5%) achieved medication self-management, and 65 (62.5%) patients did not.
Table 2

Characteristics of the patients : host-related factors (n = 104)

Variable

n (%) or Mean ± SD

Age (years)

70.0 ± 12.3

Sex

 Male

65 (62.5)

 Female

39 (37.5)

Post-onset rehabilitation hospital day (days)

23.5 ± 9.4

Diagnosis

 Cerebral infarction

65 (62.5)

 Cerebral hemorrhage

34 (32.7)

 Subarachnoid hemorrhage

5 (4.8)

Univariate analysis

Table 3 lists the results of the univariate analysis. Age, post-onset rehabilitation hospital day, number of drugs, and number of doses per day were statistically different between the self-management group and the non-self-management group (p < 0.05). All FIM items score were significantly different between the two groups (Table 3, p < 0.05).
Table 3

Comparison of variables between medication self-management and medication non-self-management groups

 

SM

(n = 39)

Non-SM

(n = 65)

P value

n, Mean ± SD

n, Mean ± SD

Characteristics of the patients

Age (years)

62.2 ± 11.6

74.7 ± 10.1

<0.001

Sex

  

0.536

 Male

26

39

 

 Female

13

26

 

Post-onset rehabilitation hospital day (days)

23.5 ± 9.4

29.7 ± 20.5

0.040

Medication-related item

number of drug

4.4 ± 2.3

6.5 ± 3.3

<0.001

number of doses per day

2.3 ± 1.4

3.0 ± 1.2

0.008

number of dose of medicine to be taken only once

0.5 ± 1.1

0.7 ± 0.8

0.759

one-dose packages/not one-dose packages

33/6

61/4

0.170

FIM item

Eating

6.2 ± 1.3

4.8 ± 2.0

<0.001

Grooming

5.7 ± 1.3

3.8 ± 2.0

<0.001

Bathing

4.7 ± 1.7

2.9 ± 1.9

<0.001

Dressing upper body

4.9 ± 1.6

3.2 ± 1.9

<0.001

Dressing under body

4.8 ± 1.7

2.9 ± 1.9

<0.001

Toileting

5.6 ± 1.7

3.3 ± 2.3

<0.001

Bladder

6.0 ± 1.8

4.0 ± 2.6

<0.001

Bowel

5.7 ± 2.1

4.0 ± 2.5

<0.001

Bed chair transfer

5.7 ± 1.4

3.7 ± 1.7

<0.001

Toilet transfer

5.6 ± 1.5

3.5 ± 1.8

<0.001

Tub shower transfer

4.7 ± 1.5

3.1 ± 1.7

<0.001

Walk/wheelchair mobility

5.3 ± 1.9

2.7 ± 1.9

<0.001

Stairs

2.3 ± 2.3

1.2 ± 0.9

0.002

Comprehension

6.1 ± 1.2

4.6 ± 2.0

<0.001

Expression

6.0 ± 1.5

4.8 ± 2.1

0.003

Social interaction

6.7 ± 1.0

5.2 ± 2.2

<0.001

Problem solving

5.7 ± 1.4

3.5 ± 2.1

<0.001

Memory

5.9 ± 1.4

3.8 ± 2.0

<0.001

SM medication self-management group

Comparison of changes in the number of drugs and number of doses from admission to discharge

There was no significant difference between admission and discharge in the number of drugs and number of doses. In the medication self-management group, there was no significant difference between admission and the introduction of medication self-management (Table 4).
Table 4

Comparison of changes in the number of drugs and number of doses from admission to discharge

 

Admission

Self-management introduced taking the drug

P value

Discharge

P value

Mean ± SD

Mean ± SD

Mean ± SD

SM

(n = 39)

Number of drugs

4.4 ± 2.3

4.8 ± 2.4

0.147

5.0 ± 2.6

0.068

Number of doses per day

2.3 ± 1.4

2.3 ± 1.2

0.744

2.2 ± 1.1

0.680

non-SM

(n = 65)

Number of drugs

6.4 ± 3.3

-

-

6.9 ± 2.7

0.084

Number of doses per day

3.0 ± 1.2

-

-

3.1 ± 1.2

0.494

SM medication self-management group

Multivariate analysis and formation of a prediction formula

In the logistic regression analysis, number of drugs, age, walk/wheelchair mobility FIM, and memory FIM were extracted as significant factors independently contributing to achievement of medication self-management in stroke patients (p < 0.05). Table 5 lists odds ratios and 95% confidence intervals. We created the prediction formula by extracting four factors and using the regression coefficient.
Table 5

Results of stepwise multiple regression analyses

Factor

Regression coefficient

Odds ratio

95% confidence interval

P value

Number of drugs

−0.229

0.795

0.629–0.969

0.035

Walk/wheelchair mobility FIM

0.470

1.600

1.186–2.251

0.004

Memory FIM

0.416

1.517

1.073–2.225

0.023

Age

−0.112

0.894

0.837–0.944

0.0002

Intercept

4.404

  

0.045

The formula was
$$ \begin{array}{l}\Big[4.404-0.229\times \mathrm{number}\;\mathrm{of}\;\mathrm{drugs}+0.470\times \mathrm{walk}/\mathrm{wheelchair}\;\mathrm{mobility}\ \mathrm{F}\mathrm{I}\mathrm{M}+0.416\\ {}\times \mathrm{memory}\;\mathrm{F}\mathrm{I}\mathrm{M}-0.112\times \mathrm{age}\Big].\end{array} $$

Evaluation of the validity of prediction formula

In testing the validity of the prediction formula, the R2 value was 0.49, and the P-value of LOF was 0.987. The area under the ROC curve was 0.926. Thus, our prediction model showed high accuracy.

Discussion

We created a formula to predict the likelihood of achievement of medication self-management for stroke patients using patient data, including items from the FIM item as well as drug-related information. It is often emphasized that pharmacists working in the Kaifukuki rehabilitation ward have an important role to support stroke patients to achieve medication self-management during hospitalization. In the current study, we confirmed the internal validity of a new prediction formula that may function as an appropriate index to predict whether patients will achieve medication self-management at discharge. Pharmacists will be able to use this formula to help provide appropriate instruction to stroke patients.

The multivariate analysis revealed that the number of drugs at admission greatly influenced medication self-management. There are some studies including each FIM item, but there is no report including drug-related information [1618]. Accordingly, when pharmacists introduce medication self-management for stroke patients, it is important to consider the number of drugs at admission.

Moreover, to exclude the influence of changes in drugs occurring during hospitalization, we compared changes in the number of drugs and in the number of doses from admission to discharge. However, there were no differences. Previously, Sato et al. reported that the number of drugs during hospitalization decreased by 0.47 per patient with pharmacist intervention; thus, it may be difficult to further decrease the number of drugs during hospitalization in the Kaifukuki rehabilitation ward [19]. Regardless, based on the current data, it is possible to predict medication self-management using drug-related information data at admission.

This study suggests that the fewer drugs at admission, the more likely a patient is to achieve medication self-management. Some studies report the relation between the number of drugs and medication behavior [20, 21]. For example, Horne et al. reported that adherence is more influenced by the values that the patient places on their medicine than their characteristics [20]. Kamishima et al. reported that medication adherence of stroke patients decreased as the number of drugs increased [21] and that there are three characteristic in patients with poor adherence: 1) those who feel the number of medicines is too much, 2) those who have not received instruction by pharmacists and 3) those who feel anxiety taking medication for a long time [21]. When pharmacists introduce medication self-management for stroke patients, poor adherence is an important problem. Considering the above discussion, decreasing the number of drugs before hospitalization improves medicine self-management. This previous study supports the current results and increases the validity of the prediction formula.

In the current study, age was significantly lower in the self-management group than in the non-self-management group. Aging is associated with poor medication adherence caused by factors such as declining cognitive function and dysphagia [22, 23]. Accordingly, age is an important factor in considering introduction of medication self-management [22, 23].

In addition, the memory FIM item was associated with medicine self-management. This is similar to a previous study on achievement of medication self-management using the FIM [1618]. The memory FIM item evaluates ability to memorize and reproduce linguistic and visual information in everyday life, a skill which is required to take medications correctly. In addition, it is also important to correctly understand pharmacist’s instructions, such as how and when to take a medicine. Accordingly, the FIM score is also an important factor in considering introduction of medication self-management.

This study has some limitations. The prediction formula was created for the patients in our hospital, but it was not subjected to external validation using data from other facilities. Therefore, there is a need to validate the formula if it is to be useful as a benchmark in other facilities. Furthermore, we did not examine the influence of the endpoint based on the rehabilitation program or that of patient education provided by pharmacists. Nonetheless, despite the limitations, the current prediction formula can be an effective tool to determine the likelihood of medication self-management in stroke patients at admission. Additionally, the formula may also help to prevent medication error.

Conclusion

The number of drugs at admission greatly influenced achievement of medication self-management in stroke patients. In addition, the prediction formula developed herein may be useful to predict whether to introduce medication self-management for stroke patients.

Abbreviations

FIM: 

Functional independence measure

LOF: 

Lack of fit

ROC: 

Area under the receiver operating characteristic

Declarations

Acknowledgements

Not applicable.

Funding

There are no funding sources for this report.

Availability of data and materials

Patients information cannot be shared.

Authors’contributions

HF carried out the data management, performed the statistical analysis and drafted the manuscript. HF, MK, JM designed the research. MK, IS, NK, MM, HS, JM, and TS helped to draft the manuscript. All authors read and approved the final manuscript.

Competing interests

The authors declare that they have no competing interests.

Consent for publication

Not applicable.

Ethics approval and consent to participate

This study was approved by the ethics committee of Showa University Fujigaoka Hospital (approval no. 2012105).

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Authors’ Affiliations

(1)
Department of Pharmacy, Showa University Fujigaoka Rehabilitation Hospital
(2)
Department of Hospital Pharmaceutics, School of Pharmacy, Showa University
(3)
Department of Pharmacy, Showa University Fujigaoka Hospital
(4)
Department of Rehabilitation Medicine, School of Medicine, Showa University
(5)
Department of Pharmacy, Showa University Hospital

References

  1. Nakamura R, Nagasaki H, Amakusa B. Shinpan Nosotyu-no Kinohyoka to Yogoyosoku (Assessment and Prediction of functional State in Stroke). Tokyo: Ishiyaku Publishers, INC.; 2011.Google Scholar
  2. Kwakkel G, Wagenaar R, Kollen B, Lankhorst G. Predicting disability in stroke—a critical review of the literature. Age Ageing. 1996;25:479–89.View ArticlePubMedGoogle Scholar
  3. Shinohara Y, Ogawa A, Suzuki N, Katayama Y, Kimura A. Japanese Guidelines for the Management of Stroke 2009. Tokyo: Kyowa Kikaku; 2009. p. 281–2.Google Scholar
  4. Tokunaga M, Sannomiya K, Nakanishi R, Yonemitsu H. The external validity of multiple regression analyses predicting discharge FIM score in patients with stroke hospitalized in Kaifukuki rehabilitation wards – An analysis of the Japan Rehabilitation Database –. Jpn J Compr Rehabil Sci. 2015;6:14–20.Google Scholar
  5. Jeong S, Inoue Y, Kondo K, Matsumoto D, Shiraishi N. Formula for predicting FIM for stroke patients at discharge from an acute ward or convalescent rehabilitation ward. Jpn J Compr Rehabil Sci. 2014;5:19–25.Google Scholar
  6. Sonoda S, Saitoh E, Nagai S, Okuyama Y, Suzuki T, Suzuki M. Stroke outcome prediction using reciprocal number of initial activities of daily living status. J Stroke Cerebrovasc Dis. 2005;14:8–11.View ArticlePubMedGoogle Scholar
  7. Iwai N, Aoyagi Y. Discharge index and prediction for stroke patients in the post-acute stage. Jpn J Compr Rehabil Sci. 2012;3:37–41.Google Scholar
  8. Inouye M. Predicting models of outcome stratified by age after first stroke rehabilitation in Japan. Am J Phys Med Rehabil. 2001;80:586–91.View ArticlePubMedGoogle Scholar
  9. Liu M, Domen K, Chino N. Comorbidity measures for stroke outcome research. Arch Phys Med Rehabil. 1997;78:166–72.View ArticlePubMedGoogle Scholar
  10. Sonoda S, Saitoh E, Domen K, Chino N. Prognostication of stroke patients using SIAS and FIM. In Functional Evaluation of Stroke Patients (ed by Chino N, Melvin JL). Springer-Verlag, Tokyo; 1996. p.103–14.
  11. Jeong S, Kondo K, Shiraishi N, Inoue Y. An evaluation of post-stroke rehabilitation in Japan. Clin Audit. 2010;2:59–66.View ArticleGoogle Scholar
  12. Tsuji T, Liu M, Sonoda S, Domen K, Chino N. The stroke impairment assessment set. Arch Phys Med Rehabil. 2000;81:863–8.View ArticlePubMedGoogle Scholar
  13. Mutai H, Furukawa T, Araki K, Misawa K, Hanihara T. Factors associated with functional recovery and home discharge in stroke patients admitted to a convalescent rehabilitation ward. Geriatr Gerontol Int. 2012;12:215–22.View ArticlePubMedGoogle Scholar
  14. Tokunaga M, Fukunaga K, Sannomiya K, Imada Y, Hamasaki H, Noguchi D, et al. The difference between measured Nichijo-seikatsu-kino-hyokahyo (NSKH) score and predicted NSKH score derived from ADL is related to FIM gain. Jpn J Compr Rehabil Sci. 2013;4:61–6.Google Scholar
  15. Hirano Y, Okura Y, Takeuchi M. The influence of ADL severity at admission on ADL at discharge in convalescent stroke rehabilitation. Tohoku Rigaku-ryoho Kagaku. 2011;23:32–7.Google Scholar
  16. Ochiai K, Aoki M, Yazawa T, Komuro R, Kobayashi E, Kaneko S. The Step by Step Self-Management System of Medication for the Patients Suffering from the Aftereffects of Cerebrovascular Disease. Acta Scientiarvm Valettvdinis Universitatis Praefectvralis Ibarakiensis: ASVPI. 2004;9:21–35.Google Scholar
  17. Yamabe T, Tokunaga M, Furusato K, Miyamoto M, Ikeda Y, Kozono M, Ueda H, Harada S. Nousothu no Fukuyakujikokanri he muketa Tasyokusyu ni yoru Kainyu no Kouka: FIM no Ninchikoumoku wo Kaishikijyun to shite. Brain Nursing. 2007;23:89–95.Google Scholar
  18. Furusato K, Tokunaga M, Kuwata T, Yamabe T, Miyamoto M, Ueda H, Harada S. Fukuyakujikokanri ga Jiritsu suru Nosotyu no Yosokusiki: FIM no Undo 3 Komoku to Ninchi 2 Komoku wo mochiite. Brain Nursing. 2007;23:103–8.Google Scholar
  19. Sato T, Sato K, Sato A. Medication Reduction in a Convalescent Rehabilitation Ward. Nippon Ronen Igakkai Zasshi. 2010;47:440–4.View ArticlePubMedGoogle Scholar
  20. Horne R, Weinman J, Hankins M. The beliefs about medicines questionnaire: The development and evaluation of a new method for assessing the cognitive representation of medication. Psycol Health. 1999;14:1–24.View ArticleGoogle Scholar
  21. Kamishima S, Noji A, Katakura Y, Maruyama T. Factor Related to Adherence to a Medication Regimen in Out-patients Being Treated for Stroke. J Jpn Acad Nurs Sci. 2008;28:21–30.View ArticleGoogle Scholar
  22. Kato S. Yakuzaishi kara mita Koureisya no Fukuyakushien: Genjyo to Kadai. Geriat Med. 2008;46:761–3.Google Scholar
  23. Osterberg L, Blaschke T. Adherence to Medication. N Engl J Med. 2005;353:487–97.View ArticlePubMedGoogle Scholar

Copyright

© The Author(s). 2017

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