新诊断卒中患者卒中后抑郁的轨迹和预测因素：前瞻性纵向研究

IF 2 4区医学 Q3 NEUROSCIENCES

Journal of Stroke & Cerebrovascular Diseases Pub Date : 2024-10-16 DOI:10.1016/j.jstrokecerebrovasdis.2024.108092

Yanjin Huang Ph.D. , Jiachun You MSN(c) , Qi Wang MSN , Wen Wen Ph.D.(c) , Changrong Yuan Ph.D., FAAN

{"title":"新诊断卒中患者卒中后抑郁的轨迹和预测因素：前瞻性纵向研究","authors":"Yanjin Huang Ph.D. , Jiachun You MSN(c) , Qi Wang MSN , Wen Wen Ph.D.(c) , Changrong Yuan Ph.D., FAAN","doi":"10.1016/j.jstrokecerebrovasdis.2024.108092","DOIUrl":null,"url":null,"abstract":"<div><h3>Background</h3><div>Post-stroke depression (PSD) is the most prevalent neuropsychological disorder among stroke patients, affecting approximately one-third of stroke survivors at any one time after a stroke. We identified between-person associations between post-stroke depression trajectories across 3 timepoints and predictors affecting trajectory classification among stroke patients.</div></div><div><h3>Methods</h3><div>This is a prospective longitudinal study using a convenience sample of 119 participants from 2 tertiary hospitals from March 2022 to September 2022. Clinical assessments and data collection were performed at diagnosis (T1), 3 months (T2), and 6 months (T3) after diagnosis. The instruments were Demographic and Disease Information Sheet and PROMIS-Depression 8a. Data were analyzed using SPSS 27.0 for descriptive statistics, logistic regression, and the Mplus program for growth mixture model analysis.</div></div><div><h3>Results</h3><div>Two stroke survivors depression trajectory classes (Class 1, moderate level decreasing- [37.8 %], and Class 2, high level increasing- [62.2%]) were delineated. Class 1 experienced moderate depression post-stroke, with a smooth diminishing pattern at T2 and T3, while Class 2 had a higher baseline depressive score and a significant increase at T2 and T3. The best growth mixture model was Class 2 model (LMR, <em>p</em>=0.010, BLRT, <em>p</em>≤0.01, AIC=2611.934, BIC=2650.842, aBIC=2606.583, Entropy= 0.944). The logistic regression results revealed that Class 2 of depression trajectory had a significant association with a lower score on cognitive function (B=-5.29, 95%CI: -8.80, -1.78, <em>p</em> <0.05) compared with Class 1. The stroke type, marital status, and monthly income were predictors of the Class 2 depression trajectory group among stroke patients. Precisely, ischemic stroke is associated with lower risk of class 2 trajectory.</div></div><div><h3>Conclusion</h3><div>The trajectory of post-stroke depression changes over time. This research has the potential to serve as a foundation for the assessment of high-risk stroke patients, the development of precise management programs, the implementation of risk stratification, and the enhancement of prognosis.</div></div>","PeriodicalId":54368,"journal":{"name":"Journal of Stroke & Cerebrovascular Diseases","volume":"33 12","pages":"Article 108092"},"PeriodicalIF":2.0000,"publicationDate":"2024-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Trajectory and predictors of post-stroke depression among patients with newly diagnosed stroke: A prospective longitudinal study\",\"authors\":\"Yanjin Huang Ph.D. , Jiachun You MSN(c) , Qi Wang MSN , Wen Wen Ph.D.(c) , Changrong Yuan Ph.D., FAAN\",\"doi\":\"10.1016/j.jstrokecerebrovasdis.2024.108092\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><h3>Background</h3><div>Post-stroke depression (PSD) is the most prevalent neuropsychological disorder among stroke patients, affecting approximately one-third of stroke survivors at any one time after a stroke. We identified between-person associations between post-stroke depression trajectories across 3 timepoints and predictors affecting trajectory classification among stroke patients.</div></div><div><h3>Methods</h3><div>This is a prospective longitudinal study using a convenience sample of 119 participants from 2 tertiary hospitals from March 2022 to September 2022. Clinical assessments and data collection were performed at diagnosis (T1), 3 months (T2), and 6 months (T3) after diagnosis. The instruments were Demographic and Disease Information Sheet and PROMIS-Depression 8a. Data were analyzed using SPSS 27.0 for descriptive statistics, logistic regression, and the Mplus program for growth mixture model analysis.</div></div><div><h3>Results</h3><div>Two stroke survivors depression trajectory classes (Class 1, moderate level decreasing- [37.8 %], and Class 2, high level increasing- [62.2%]) were delineated. Class 1 experienced moderate depression post-stroke, with a smooth diminishing pattern at T2 and T3, while Class 2 had a higher baseline depressive score and a significant increase at T2 and T3. The best growth mixture model was Class 2 model (LMR, <em>p</em>=0.010, BLRT, <em>p</em>≤0.01, AIC=2611.934, BIC=2650.842, aBIC=2606.583, Entropy= 0.944). The logistic regression results revealed that Class 2 of depression trajectory had a significant association with a lower score on cognitive function (B=-5.29, 95%CI: -8.80, -1.78, <em>p</em> <0.05) compared with Class 1. The stroke type, marital status, and monthly income were predictors of the Class 2 depression trajectory group among stroke patients. Precisely, ischemic stroke is associated with lower risk of class 2 trajectory.</div></div><div><h3>Conclusion</h3><div>The trajectory of post-stroke depression changes over time. This research has the potential to serve as a foundation for the assessment of high-risk stroke patients, the development of precise management programs, the implementation of risk stratification, and the enhancement of prognosis.</div></div>\",\"PeriodicalId\":54368,\"journal\":{\"name\":\"Journal of Stroke & Cerebrovascular Diseases\",\"volume\":\"33 12\",\"pages\":\"Article 108092\"},\"PeriodicalIF\":2.0000,\"publicationDate\":\"2024-10-16\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Stroke & Cerebrovascular Diseases\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S1052305724005366\",\"RegionNum\":4,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"NEUROSCIENCES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Stroke & Cerebrovascular Diseases","FirstCategoryId":"3","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1052305724005366","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"NEUROSCIENCES","Score":null,"Total":0}

引用次数: 0

摘要

背景：卒中后抑郁（PSD）是卒中患者中最常见的神经心理障碍，约有三分之一的卒中幸存者在卒中后的任何时间都会受到影响。我们确定了中风后抑郁轨迹在 3 个时间点上的人际关联以及影响中风患者轨迹分类的预测因素：这是一项前瞻性纵向研究，从 2022 年 3 月至 2022 年 9 月对两家三甲医院的 119 名参与者进行了方便抽样调查。临床评估和数据收集分别在诊断时（T1）、诊断后 3 个月（T2）和 6 个月（T3）进行。评估工具为人口与疾病信息表和 PROMIS 抑郁 8a。数据分析采用 SPSS 27.0 进行描述性统计、逻辑回归和 Mplus 程序进行生长混合模型分析：结果：划分出两个中风幸存者抑郁轨迹等级（1 级，中度抑郁--[37.8%]；2 级，高度抑郁--[62.2%]）。第 1 类患者在卒中后出现中度抑郁，在 T2 和 T3 阶段呈平稳递减模式，而第 2 类患者的基线抑郁评分较高，在 T2 和 T3 阶段显著增加。最佳增长混合模型为 2 类模型（LMR，p=0.010，BLRT，p≤0.01，AIC=2611.934，BIC=2650.842，aBIC=2606.583，熵=0.944）。逻辑回归结果显示，抑郁轨迹 2 与认知功能得分较低有显著相关性（B=-5.29，95%CI：-8.80，-1.78，P 结论：抑郁轨迹 2 与认知功能得分较低有显著相关性：卒中后抑郁的轨迹会随着时间的推移而改变。这项研究有望为评估高危卒中患者、制定精确的管理方案、实施风险分层和改善预后奠定基础。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

Trajectory and predictors of post-stroke depression among patients with newly diagnosed stroke: A prospective longitudinal study

Background

Post-stroke depression (PSD) is the most prevalent neuropsychological disorder among stroke patients, affecting approximately one-third of stroke survivors at any one time after a stroke. We identified between-person associations between post-stroke depression trajectories across 3 timepoints and predictors affecting trajectory classification among stroke patients.

Methods

This is a prospective longitudinal study using a convenience sample of 119 participants from 2 tertiary hospitals from March 2022 to September 2022. Clinical assessments and data collection were performed at diagnosis (T1), 3 months (T2), and 6 months (T3) after diagnosis. The instruments were Demographic and Disease Information Sheet and PROMIS-Depression 8a. Data were analyzed using SPSS 27.0 for descriptive statistics, logistic regression, and the Mplus program for growth mixture model analysis.

Results

Two stroke survivors depression trajectory classes (Class 1, moderate level decreasing- [37.8 %], and Class 2, high level increasing- [62.2%]) were delineated. Class 1 experienced moderate depression post-stroke, with a smooth diminishing pattern at T2 and T3, while Class 2 had a higher baseline depressive score and a significant increase at T2 and T3. The best growth mixture model was Class 2 model (LMR, p=0.010, BLRT, p≤0.01, AIC=2611.934, BIC=2650.842, aBIC=2606.583, Entropy= 0.944). The logistic regression results revealed that Class 2 of depression trajectory had a significant association with a lower score on cognitive function (B=-5.29, 95%CI: -8.80, -1.78, p <0.05) compared with Class 1. The stroke type, marital status, and monthly income were predictors of the Class 2 depression trajectory group among stroke patients. Precisely, ischemic stroke is associated with lower risk of class 2 trajectory.

Conclusion

The trajectory of post-stroke depression changes over time. This research has the potential to serve as a foundation for the assessment of high-risk stroke patients, the development of precise management programs, the implementation of risk stratification, and the enhancement of prognosis.

求助全文

通过发布文献求助，成功后即可免费获取论文全文。去求助

来源期刊

Journal of Stroke & Cerebrovascular Diseases Medicine-Surgery

CiteScore

5.00

自引率

4.00%

发文量

583

审稿时长

62 days

期刊介绍： The Journal of Stroke & Cerebrovascular Diseases publishes original papers on basic and clinical science related to the fields of stroke and cerebrovascular diseases. The Journal also features review articles, controversies, methods and technical notes, selected case reports and other original articles of special nature. Its editorial mission is to focus on prevention and repair of cerebrovascular disease. Clinical papers emphasize medical and surgical aspects of stroke, clinical trials and design, epidemiology, stroke care delivery systems and outcomes, imaging sciences and rehabilitation of stroke. The Journal will be of special interest to specialists involved in caring for patients with cerebrovascular disease, including neurologists, neurosurgeons and cardiologists.