Predictive Model for Quality of Life in Patients 1 Year after First Stroke

Sung Reul Kim, Sung Hee Yoo, Hye Young Kim, Gye Gyoung Kim

Research output: Contribution to journalArticlepeer-review

Abstract

Background Because the recurrence rate of stroke increases 1 year after its initial occurrence, it is important to predict quality of life at this stage, although most functions are likely recovered. Objectives We aimed to construct and verify a model of quality of life in patients 1 year post stroke by considering their characteristics at the time of the stroke based on the Wilson and Cleary model and previous literature. Methods Participants comprised 288 patients who had experienced their first ischemic stroke and were enrolled in 3 regional stroke centers in South Korea. Data were analyzed using path analysis to identify a model of patients' quality of life 1 year post stroke. Results Age, National Institutes of Health Stroke Scale score, the modified Rankin Scale score, and anxiety had a direct effect on quality of life, whereas type D personality and social support had an indirect effect. Type D personality, age, National Institutes of Health Stroke Scale score, social support, the modified Rankin Scale score, and anxiety explained 32.9% of the total variance in quality of life. Conclusions When predicting quality of life in a patient 1 year after a stroke, it is important to consider variables such as type D personality, age, National Institutes of Health Stroke Scale score, social support, the modified Rankin Scale score, and anxiety at the time of the first stroke. Interventions to improve the quality of life of patients with stroke should consider these factors.

Original languageEnglish
Pages (from-to)E60-E70
JournalJournal of Cardiovascular Nursing
Volume36
Issue number5
DOIs
Publication statusPublished - 2021

Keywords

  • anxiety
  • quality of life
  • social support
  • stroke
  • type D personality

ASJC Scopus subject areas

  • Cardiology and Cardiovascular Medicine
  • Advanced and Specialised Nursing

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