Department of Physical Medicine and Rehabilitation, Innlandet Hospital Trust, Ottestad, Norway.
Department of Psychology, University of Oslo, Oslo, Norway.
PLoS One. 2020 Apr 15;15(4):e0231709. doi: 10.1371/journal.pone.0231709. eCollection 2020.
Post-stroke fatigue (PSF) is a common symptom affecting 23-75% of stroke survivors. It is associated with increased risk of institutionalization and death, and it is of many patients considered among the worst symptoms to cope with after stroke. Longitudinal studies focusing on trajectories of fatigue may contribute to understanding patients' experience of fatigue over time and its associated factors, yet only a few have been conducted to date.
To explore whether subgroups of stroke survivors with distinct trajectories of fatigue in the first 18 months post stroke could be identified and whether these subgroups differ regarding sociodemographic, medical and/or symptom-related characteristics.
115 patients with first-ever stroke admitted to Oslo University Hospital or Buskerud Hospital were recruited and data was collected prospectively during the acute phase and at 6, 12 and 18 months post stroke. Data on fatigue (both pre- and post-stroke), sociodemographic, medical and symptom-related characteristics were collected through structured interviews, standardized questionnaires and from the patients' medical records. Growth mixture modeling (GMM) was used to identify latent classes, i.e., subgroups of patients, based on their Fatigue Severity Scales (FSS) scores at the four time points. Differences in sociodemographic, medical, and symptom-related characteristics between the latent classes were evaluated using univariate and multivariable ordinal regression analyses.
Using GMM, three latent classes of fatigue trajectories over 18 months were identified, characterized by differing levels of fatigue: low, moderate and high. The mean FSS score for each class remained relatively stable across all four time points. In the univariate analyses, age <75, pre-stroke fatigue, multiple comorbidities, current depression, disturbed sleep and some ADL impairment were associated with higher fatigue trajectories. In the multivariable analyses, pre-stroke fatigue (OR 4.92, 95% CI 1.84-13.2), multiple comorbidities (OR 4,52,95% CI 1.85-11.1) and not working (OR 4.61, 95% CI 1.36-15,7) were the strongest predictor of higher fatigue trajectories The findings of this study may be helpful for clinicians in identifying patients at risk of developing chronic fatigue after stroke.
中风后疲劳（PSF）是一种常见的症状，影响 23-75%的中风幸存者。它与更高的住院和死亡风险相关，许多患者认为这是中风后最糟糕的症状之一。关注疲劳轨迹的纵向研究可能有助于了解患者随着时间的推移对疲劳的体验及其相关因素，但迄今为止，只有少数研究进行了研究。
探讨是否可以识别出中风后 18 个月内疲劳轨迹不同的中风幸存者亚组，以及这些亚组在社会人口统计学、医学和/或症状相关特征方面是否存在差异。
招募了 115 名首次中风后入住奥斯陆大学医院或布斯克吕德医院的患者，并在急性发作期间以及中风后 6、12 和 18 个月前瞻性收集数据。通过结构化访谈、标准化问卷以及患者病历收集疲劳（包括中风前和中风后）、社会人口统计学、医学和症状相关特征的数据。使用增长混合模型（GMM）根据患者四个时间点的疲劳严重程度量表（FSS）评分识别潜在类别，即患者亚组。使用单变量和多变量有序回归分析评估潜在类别之间在社会人口统计学、医学和症状相关特征方面的差异。
使用 GMM 确定了 18 个月内疲劳轨迹的三个潜在类别，其特征是疲劳程度不同：低、中、高。每个类别的平均 FSS 评分在所有四个时间点都相对稳定。在单变量分析中，年龄<75 岁、中风前疲劳、多种合并症、当前抑郁、睡眠障碍和一些 ADL 受损与更高的疲劳轨迹相关。在多变量分析中，中风前疲劳（OR 4.92，95%CI 1.84-13.2）、多种合并症（OR 4，52，95%CI 1.85-11.1）和不工作（OR 4.61，95%CI 1.36-15.7）是更高疲劳轨迹的最强预测因素。本研究的结果可能有助于临床医生识别出中风后易发生慢性疲劳的患者。
