Guo Lina, Zauszniewski Jaclene A, Zhang Gege, Lei Xiaoyu, Zhang Mengyu, Wei Miao, Ma Keke, Yang Caixia, Liu Yanjin, Guo Yuanli
Department of Neurology, National Advanced Stroke Center, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, People's Republic of China.
Frances Payne Bolton School of Nursing, Case Western Reserve University, Cleveland, OH, USA.
Patient Prefer Adherence. 2024 Mar 5;18:565-577. doi: 10.2147/PPA.S448647. eCollection 2024.
To explore distinct longitudinal trajectories of resourcefulness among initial ischemic stroke patients from diagnosis to 12 months, and to identify whether sociodemographic factors, disease-related factors, self-efficacy, family function, and social support can predict patterns in the trajectories of resourcefulness.
A prospective longitudinal study was conducted. Initial ischemic stroke patients who met inclusion and exclusion criteria were followed up when still in hospital (Preparing for discharge, Baseline, T1), at 1 month (T2), at 3 months (T3), at 6 months (T4), at 9 months (T5) and 12 months (T6) (±1 week) after discharge. General information, National Institute of Health Stroke Scale (NIHSS), Modified Rankin Scale (mRS), General Self-Efficacy Scale (GSES), General Family Functioning Subscale (FAD-GF), and Social Support Rate Scale (SSRS) were used in T1. The Resourcefulness Scale was evaluated at 6 time points. Growth mixture modeling was used to identify trajectory patterns of resourcefulness. Logistic regression was used to identify predictors of resourcefulness trajectories.
Three longitudinal trajectories of resourcefulness were identified and named as the high-stable class (38.9%, n=71), fluctuation class (41.2%, n=75), and low-stable class (19.9%, n=36), respectively. Dwelling areas (6.805, 0.009), education (44.865, 0.000), monthly income (13.063, 0.001), NIHSS scores (44.730, 0.000), mRS scores (51.788, 0.000), Hcy (9.345, 0.002), GSES (56.933, 0.000), FAD-GF (41.305, 0.000) and SSRS (=52.373, 0.000) were found to be statistically significant for distinguishing between different resourcefulness trajectory patterns. Lower education (OR=0.404), higher NIHSS(OR=6.672) scores, and higher mRS(OR=21.418) scores were found to be risk factors for lower resourcefulness, whereas higher education(OR=0.404), GSES(OR=0.276), FAD-GF(OR=0.344), and SSRS(OR=0.358) scores were identified as protective factors enhancing resourcefulness.
This study obtained three patterns of trajectories and identified their predictive factors in initial ischemic stroke. The findings will assist health care professionals in identifying subgroups of patients and when they may be at risk of low resourcefulness and provide timely targeted intervention to promote resourcefulness.
探讨初发缺血性脑卒中患者从诊断到12个月期间心理韧性的不同纵向轨迹，并确定社会人口学因素、疾病相关因素、自我效能感、家庭功能和社会支持是否能够预测心理韧性轨迹模式。
进行一项前瞻性纵向研究。符合纳入和排除标准的初发缺血性脑卒中患者在住院期间（准备出院、基线、T1）、出院后1个月（T2）、3个月（T3）、6个月（T4）、9个月（T5）和12个月（T6）（±1周）进行随访。在T1时收集一般信息、美国国立卫生研究院卒中量表（NIHSS）、改良Rankin量表（mRS）、一般自我效能感量表（GSES）、一般家庭功能子量表（FAD-GF）和社会支持评定量表（SSRS）。在6个时间点评估心理韧性量表。采用生长混合模型确定心理韧性的轨迹模式。采用逻辑回归确定心理韧性轨迹的预测因素。
确定了心理韧性的三种纵向轨迹，分别命名为高稳定组（38.9%，n = 71）、波动组（41.2%，n = 75）和低稳定组（19.9%，n = 36）。发现居住地区（6.805，0.009）、教育程度（44.865，0.000）、月收入（13.063，0.001）、NIHSS评分（44.730，0.000）、mRS评分（51.788，0.000）、同型半胱氨酸（9.345，0.002）、GSES（56.933，0.000）、FAD-GF（41.305，0.000）和SSRS（=52.373，0.000）在区分不同心理韧性轨迹模式方面具有统计学意义。较低的教育程度（OR = 0.404）、较高的NIHSS评分（OR = 6.672）和较高的mRS评分（OR = 21.418）被发现是心理韧性较低的危险因素，而较高的教育程度（OR = 0.404）、GSES（OR = 0.276）、FAD-GF（OR = 0.344）和SSRS（OR = 0.358）评分被确定为增强心理韧性的保护因素。
本研究获得了三种轨迹模式，并确定了初发缺血性脑卒中患者心理韧性轨迹的预测因素。这些发现将有助于医护人员识别患者亚组以及他们可能出现心理韧性较低风险的时间，并提供及时的针对性干预以促进心理韧性。
