The incidence of stroke is increasing year by year, and behavioral control is a direct and effective intervention means to prevent stroke. Proactive health medical model improves the initiative and accessibility of chronic disease prevention and control, while the concept of proactive health behavior in stroke patients remains to be explored.
To explore the level of proactive health cognition and behavior in stroke patients, and construct the conceptual framework of proactive health behavior in stroke patients.
From August to October 2023, a total of 26 inpatients with stroke in the Department of Neurology of the Second Affiliated Hospital of Zhengzhou University were selected as the study objects by means of purposive sampling method. Following the grounded theory methodology of interpretivism, 26 patients with stroke were interviewed by semi-structured method, and the data were analyzed by coding and persistence comparison methods.
The 10 main categories and 4 core categories of the theme "proactive health behavior of stroke patients" were separated out, including 3 intrinsic behaviors of "health motivation, health responsibility and mental health", 1 habitual behavior of "lifestyle management", 3 social behaviors of "active compliance with doctors, social relations and information seeking", and 3 intervention conditions of "consciousness awakenings, self-control and resource availability". And establish the conceptual framework.
The conceptual framework of proactive health behavior in stroke patients includes intrinsic behavior, habitual behavior, social behavior and intervention conditions. This framework may be helpful for the further development of assessment tools and the formulation of personalized intervention measures, and has guiding significance for promoting the research and practice of proactive health behavior in stroke patients.
Systemic inflammatory response index (SIRI) is an emerging biomarker associated with ischemic stroke (IS) , but its correlation with recurrent IS remains unclear.
To investigate the correlation between SIRI and one-year recurrence of IS.
Patients diagnosed with IS and hospitalized in the First Affiliated Hospital of Nanchang University, the Second Affiliated Hospital of Nanchang University, the Second Hospital of Nanchang, and the Third Hospital of Nanchang between March 2019 and March 2021 were enrolled into the cohort. All patients were followed up for one year. Relevant clinical information within 48 hours of admission was collected. The recurrence of IS was recorded during the 1-year follow-up. The correlation between SIRI and one-year recurrence of IS was examined using Cox regression model, restricted cubic splines (RCS) , and subgroup analysis.
A total of 1 023 eligible patients were enrolled in the cohort, including 107 (10.46%) experiencing a recurrence of IS during the one-year follow-up period. After adjusting for confounders, multivariable Cox regression analysis showed that an elevated SIRI was a risk factor for IS recurrence (HR=1.06, 95%CI=1.01-1.10, P<0.05) . Categorized into quartiles, patients in the highest quartile (fourth quartile, Q4 subgroup, n=256) of SIRI exhibited a significantly higher risk of IS recurrence compared to those in the lowest quartile (first quartile, Q1 subgroup, n=256) (HR=1.80, 95%CI=1.08-3.00, P<0.05) . RCS analysis demonstrated a J-shaped dose-response relationship between SIRI and the risk of IS recurrence (PNonlinear=0.025) . Subgroup analyses stratified by gender, age, history of stroke, and the National Institutes of Health Stroke Scale (NIHSS) score at admission were performed. A significant correlation was identified between SIRI and NIHSS score (P<0.001) . Specifically, for patients with an NIHSS score of 0-1 point, an elevated SIRI was significantly correlated with an increased risk of IS recurrence (HR=1.25, 95%CI=1.04-1.51, P=0.020) . For those with an NIHSS score of 5-15 points, an elevated SIRI was significantly correlated with a higher recurrence risk (HR=1.20, 95%CI=1.12-1.28, P<0.001) . It was indicated that a higher SIRI was significantly correlated with an increased risk of IS recurrence within these score ranges.
A higher SIRI is significantly correlated with an increased risk of IS recurrence. A J-shaped association is observed between SIRI and IS recurrence risk. Notably, in IS patients with NIHSS scores of 0-1 and 5-15, elevated SIRI is significantly correlated with an increased risk of recurrence.
Patients with stroke often experience sleep disturbances and imbalances, which are easily overlooked in clinical treatment. Moreover, there is currently limited research on whether sleep status affects the prognosis of such diseases.
Exploration of factors influencing sleep state changes and prognosis in patients with acute posterior circulation ischemic stroke.
A total of 60 patients with acute posterior circulation ischemic stroke, admitted to Kailuan General Hospital Affiliated to North China University of Science and Technology, from December 2019 to December 2023, were selected as the case group. Based on the modified Rankin Scale (mRS) score at discharge, the case group was divided into a good prognosis subgroup (45 cases) and a poor prognosis subgroup (15 cases) . Additionally, 52 patients without cerebral vascular stenosis and acute ischemic stroke during the same period were selected as the control group. General and clinical data of the patients were collected to compare the circadian sleep-wake rhythms, daytime sleep-wake rhythms, nighttime sleep-wake rhythms, and the distribution of infarcted brain regions between the good prognosis subgroup and the poor prognosis subgroup. Multivariate Logistic regression analysis was used to identify the prognostic factors influencing the outcomes of patients with acute posterior circulation ischemic stroke.
The apnea-hypopnea index (AHI) in the case group was higher than in the control group (P<0.05) . The proportions of patients in the case group with reversed sleep cycles, increased daytime sleep, and difficulty falling asleep were higher than those in the control group, with statistically significant differences (P<0.05) . The case group showed higher total daytime sleep time, wake time after sleep onset, light sleep duration, deep sleep duration, NREM sleep duration, REM sleep duration, REM sleep proportion, and deep sleep proportion compared to the control group, whereas the proportions of NREM sleep and light sleep were lower, all with statistically significant differences (P<0.05) . The case group also exhibited longer total nighttime sleep time, light sleep duration, and NREM sleep duration than the control group, with statistically significant differences (P<0.05) . The proportion of pontine infarction in the poor prognosis subgroup was higher than in the good prognosis subgroup, with a statistically significant difference (P<0.05) . Multivariate Logistic regression analysis showed that daytime deep sleep duration (OR=1.203, 95%CI=1.032-1.401) and pontine infarction (OR=16.497, 95%CI=1.142-238.391) were influencing factors for the prognosis of acute posterior circulation ischemic stroke (P<0.05) .
Patients with acute posterior circulation ischemic stroke exhibit an increased AHI and present with sleep characteristics such as reversed sleep cycles, increased daytime sleep, and difficulty falling asleep at night. Additionally, daytime deep sleep duration and pontine infarction are factors that adversely affect the prognosis of these patients.
Acute ischemic stroke (AIS) is characterized by high rates of disability, mortality, and recurrence, posing a significant burden on patients and society. In the era of big data, predictive models are increasingly used in patient diagnosis, treatment decisions, prognosis management, and healthcare resource allocation, highlighting their growing importance. Machine learning methods have become a crucial tool for predicting the prognosis of AIS patients and have been widely applied. This review explores recent advancements in the study of AIS prognosis prediction, focusing on machine learning methods. It discusses current issues and challenges faced by machine learning models, aiming to provide new insights and references for methods of early assessment and prediction of prognosis outcomes in AIS patients.
