144
Views
5
CrossRef citations to date
0
Altmetric
Original Research

Home-Time as a Surrogate Measure for Functional Outcome After Stroke: A Validation Study

ORCID Icon, , ORCID Icon, , , ORCID Icon, & show all
Pages 617-624 | Published online: 16 Jun 2020
 

Abstract

Purpose

Home-time has been found to correlate well with modified Rankin Scale (mRS) scores in patients with stroke. This study aimed to determine its correlations in patients with different types of stroke at various time points after stroke in a non-Western population.

Methods

This study used linked data from multi-center stroke registry databases and a nationwide claims database of health insurance. Functional outcomes as measured with the modified Rankin Scale were obtained from the registry databases and home-time was derived from the claims database. Spearman correlation coefficients were used to assess the correlation between home-time and mRS scores. The area under the receiver operating characteristic curve (AUC) was used to evaluate the performance of home-time in predicting good functional outcome.

Results

This study included 7959 patients hospitalized for stroke or transient ischemic attack (TIA), for whom mRS scores were available in 6809, 6694, and 4330 patients at 90, 180, and 365 days, respectively. Home-time was highly correlated with mRS scores at the three time-points in patients with ischemic (Spearman’s rho −0.69 to −0.83) or hemorrhagic (Spearman’s rho −0.86 to −0.88) stroke, but the correlation was only weak to moderate in those with TIA (Spearman’s rho −0.32 to −0.58). Home-time predicted good functional outcome with excellent discrimination in patients with ischemic (AUCs >0.8) or hemorrhagic (AUCs >0.9) stroke but less so in those with TIA (AUCs >0.7).

Conclusion

Home-time was highly correlated with mRS scores and showed excellent discrimination in predicting good functional outcome in patients with ischemic or hemorrhagic stroke. Home-time could serve as a valid surrogate measure for functional outcome after stroke.

Acknowledgments

This research was supported in part by the Ministry of Science and Technology, Taiwan (grant number MOST 107-2320-B-006-035). We are grateful to the Health Data Science Center, National Cheng Kung University Hospital for providing administrative and technical support. We would like to thank Ms. Li-Ying Sung for English language editing.

Disclosure

The authors declare they have no conflicts of interest with respect to this research study.