Artificial neural network and decision tree models of post-stroke depression at 3 months after stroke in patients with BMI ≥ 24

Fear of disease progression and resilience parallelly mediated the effect of post-stroke fatigue on post-stroke depression: A cross-sectional study

AIMS

To explore the effect of post-stroke fatigue (PSF) on post-stroke depression (PSD) and examine the mediating effects of fear of disease progression (FOP) and resilience between PSF and PSD.

DESIGN

A cross-sectional study.

METHODS

A total of 315 stroke patients participated in the questionnaire survey between November 2022 and June 2023. Data were collected using the General Information Questionnaire, Fatigue Severity Scale, Fear of Disease Progression Questionnaire-Short Form, Connor-Davidson Resilience Scale-10 Item and Hospital Anxiety and Depression Scale-Depression Subscale. Data were analysed by descriptive analysis, Mann-Whitney U-test, Kruskal-Wallis H-test, Pearson or Spearman correlation, hierarchical regression analysis and mediation analysis.

RESULTS

PSF had a significant positive total effect on PSD (β = .354, 95% CI: .251, .454). Additionally, FOP and resilience played a partial parallel-mediating role in the relationship between PSF and PSD (β = .202, 95% CI: .140, .265), and the total indirect effect accounted for 57.06% of the total effect.

CONCLUSIONS

FOP and resilience parallelly mediated the effect of PSF on PSD, which may provide a novel perspective for healthcare professionals in preventing PSD. Targeted interventions aiming at reducing PSF, lowering FOP levels and enhancing resilience may be possible ways to alleviate PSD.

IMPLICATIONS FOR THE PROFESSION AND PATIENT CARE

Interventions that tail to reducing PSF, lowering FOP levels and enhancing resilience may be considered as possible ways to alleviate PSD.

IMPACT

This study enriched the literature by exploring the effect of PSF on PSD and further examining the mediating effects of FOP and resilience between PSF and PSD. Findings emphasized the important effects of PSF, FOP and resilience on PSD.

REPORTING METHOD

The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) checklist for cross-sectional studies was used to guide reporting.

PATIENT OR PUBLIC CONTRIBUTION

One tertiary hospital assisted participants recruitment.

Drivers of Chronic Pathology Following Ischemic Stroke: A Descriptive Review

Stroke is the third leading cause of death and long-term disability in the world. Considered largely a disease of aging, its global economic and healthcare burden is expected to rise as more people survive into advanced age. With recent advances in acute stroke management, including the expansion of time windows for treatment with intravenous thrombolysis and mechanical thrombectomy, we are likely to see an increase in survival rates. It is therefore critically important to understand the complete pathophysiology of ischemic stroke, both in the acute and subacute stages and during the chronic phase in the months and years following an ischemic event. One of the most clinically relevant aspects of the chronic sequelae of stroke is its extended negative effect on cognition. Cognitive impairment may be related to the deterioration and dysfunctional reorganization of white matter seen at later timepoints after stroke, as well as ongoing progressive neurodegeneration. The vasculature of the brain also undergoes significant insult and remodeling following stroke, undergoing changes which may further contribute to chronic stroke pathology. While inflammation and the immune response are well established drivers of acute stroke pathology, the chronicity and functional role of innate and adaptive immune responses in the post-ischemic brain and in the peripheral environment remain largely uncharacterized. In this review, we summarize the current literature on post-stroke injury progression, its chronic pathological features, and the putative secondary injury mechanisms underlying the development of cognitive impairment and dementia. We present findings from clinical and experimental studies and discuss the long-term effects of ischemic stroke on both brain anatomy and functional outcome. Identifying mechanisms that occur months to years after injury could lead to treatment strategies in the chronic phase of stroke to help mitigate stroke-associated cognitive decline in patients.

Identifying Alzheimer's disease and mild cognitive impairment with atlas-based multi-modal metrics

Introduction

Multi-modal neuroimaging metrics in combination with advanced machine learning techniques have attracted more and more attention for an effective multi-class identification of Alzheimer's disease (AD), mild cognitive impairment (MCI) and health controls (HC) recently.

Methods

In this paper, a total of 180 subjects consisting of 44 AD, 66 MCI and 58 HC subjects were enrolled, and the multi-modalities of the resting-state functional magnetic resonance imaging (rs-fMRI) and the structural MRI (sMRI) for all participants were obtained. Then, four kinds of metrics including the Hurst exponent (HE) metric and bilateral hippocampus seed independently based connectivity metrics generated from fMRI data, and the gray matter volume (GMV) metric obtained from sMRI data, were calculated and extracted in each region of interest (ROI) based on a newly proposed automated anatomical Labeling (AAL3) atlas after data pre-processing. Next, these metrics were selected with a minimal redundancy maximal relevance (MRMR) method and a sequential feature collection (SFC) algorithm, and only a subset of optimal features were retained after this step. Finally, the support vector machine (SVM) based classification methods and artificial neural network (ANN) algorithm were utilized to identify the multi-class of AD, MCI and HC subjects in single modal and multi-modal metrics respectively, and a nested ten-fold cross-validation was utilized to estimate the final classification performance.

Results

The results of the SVM and ANN based methods indicated the best accuracies of 80.36 and 74.40%, respectively, by utilizing all the multi-modal metrics, and the optimal accuracies for AD, MCI and HC were 79.55, 78.79 and 82.76%, respectively, in the SVM based method. In contrast, when using single modal metric, the SVM based method obtained a best accuracy of 72.62% with the HE metric, and the accuracies for AD, MCI and HC subjects were just 56.82, 80.30 and 75.86%, respectively. Moreover, the overlapping abnormal brain regions detected by multi-modal metrics were mainly located at posterior cingulate gyrus, superior frontal gyrus and cuneus.

Conclusion

Taken together, the SVM based method with multi-modal metrics could provide effective diagnostic information for identifying AD, MCI and HC subjects.

Explore the influencing factors and construct random forest models of post-stroke depression at 3 months in males and females

BACKGROUND

Post-stroke depression (PSD) is one of the most common neuropsychiatric complications after stroke. The occurrence, development and prognosis of PSD have long been different between males and females. The main purpose of this study was to explore the influencing factors of PSD at 3 months in males and females, and construct random forest (RF) models to rank the influencing factors.

METHODS

This is a prospective multicenter cohort study (Registration number: ChiCTR-ROC-17013993). Stroke patients hospitalized in the department of Neurology of three hospitals in Wuhan were enrolled from May 2018 to August 2019. Scale assessments were performed 24 hours after admission and 3 months after stroke onset. Binary logistic regression analysis was used for univariate and multivariate (stepwise backward method) analysis, when p was less than 0.05, the difference between groups was considered statistically significant. Lastly, the RF models were constructed according to the results of multivariate regression analysis.

RESULTS

This study found that several baseline variables were associated with PSD at 3 months in males and females. RF model ranked them as stroke severity (OR [odds ratio] =1.17, p < 0.001, 95%CI [confidence interval]:1.11-1.24), neuroticism dimension (OR = 1.06, p = 0.002, 95%CI:1.02-1.10), physical exercise (OR = 0.62, p = 0.007, 95%CI:0.44-0.88), sleeping time < 5 h (OR = 1.91, p = 0.006, 95% CI:1.20-3.04) and atrial fibrillation (OR = 4.18, p = 0.012, 95%CI:1.38-12.68) in males. In females, RF model ranked them as psychological resilience (OR = 0.98, p = 0.015, 95%CI:0.96-1.00), ability of daily living (OR = 0.98, p = 0.001, 95%CI:0.97-0.99), neuroticism dimension (OR = 1.11, p = 0.002, 95%CI:1.04-1.18) and subjective support (OR = 1.11, p < 0.001, 95%CI:1.05-1.78).

CONCLUSION

The study found influencing factors of PSD at 3 months were different in males and females, and construct RF models to rank them according to their importance. This suggests that clinicians should focus their interventions on sex-specific influencing factors in order to improve the prognosis of PSD patients.

TRIAL REGISTRATION

ChiCTR-ROC-17013993.

Association between physical activity levels and depressive symptoms in patients with minor ischemic stroke

OBJECTIVE

Post-stroke depression is associated with stroke recurrence and it is necessary to identify its influencing factors. The study aims to determine whether physical activity during hospitalization, as measured by accelerometer, was associated with depression after discharge in patients with minor ischemic stroke.

MATERIALS AND METHODS

This prospective observational study assessed 76 patients with minor ischemic stroke (aged 71.2 years) admitted to an acute care hospital. Depressive symptoms 3 months after discharge from the hospital was assessed using a questionnaire sent by mail. Baseline was set during hospitalization, and accelerometers were used to measure sedentary behavior, light and moderate-to-vigorous physical activities during hospitalization.

RESULTS

Three months after hospital discharge, 14 patients (18.4%) were placed in the depressive symptom group, with significantly more sedentary behavior (p = 0.021), less light physical activity (p = 0.016) and more depressive symptoms during hospitalization (p = 0.005) than in the non-depressive symptom group. Logistic regression analysis showed that sedentary behavior (odds ratio = 1.130, 95% confidence interval = 1.013‒1.281, p = 0.028) and light-intensity physical activity (odds ratio = 0.853, 95% confidence interval = 0.746‒0.976, p = 0.021) were independent factors for depressive symptoms at three months after discharge. Moderate to vigorous physical activity was not an independent factor.

CONCLUSIONS

Sedentary behavior and light-intensity physical activity during hospitalization were associated with depressive symptoms in patients with minor ischemic stroke after discharge. Reducing sedentary behavior and increasing light-intensity physical activity as part of inpatient rehabilitation may help prevent post-stroke depression.

Blood biomarkers of post-stroke depression after minor stroke at three months in males and females

BACKGROUND

Post-stroke depression (PSD) is one of the most common neuropsychiatric complications after stroke. Studies on the underlying mechanisms and biological markers of sex differences in PSD are of great significance, but there are still few such studies. Therefore, the main objective of this study was to investigate the association of biomarkers with PSD at 3 months after minor stroke in men and women.

METHODS

This was a prospective multicenter cohort study that enrolled 530 patients with minor stroke (males, 415; females, 115). Demographic information and blood samples of patients were collected within 24 h of admission, and followed up at 3 months after stroke onset. PSD was defined as a depressive disorder due to another medical condition with depressive features, major depressive-like episode, or mixed-mood features according to the Diagnostic and Statistical Manual of Mental Disorders, 5th edition (DSM-V). Univariate analysis was performed using the chi-square test, Mann-Whitney U test, or t-test. Partial least-squares discriminant analysis (PLS-DA) was used to distinguish between patients with and without PSD. Factors with variable importance for projection (VIP) > 1.0 were classified as the most important factors in the model segregation.

RESULTS

The PLS-DA model mainly included component 1 and component 2 for males and females. For males, the model could explain 13% and 16.9% of the variables, respectively, and 29.9% of the variables in total; the most meaningful predictors were exercise habit and fibrinogen level. For females, the model could explain 15.7% and 10.5% of the variables, respectively, and 26.2% of the variables in total; the most meaningful predictors in the model were brain-derived neurotrophic factor (BDNF), magnesium and free T3. Fibrinogen was positively correlated with the Hamilton Depression Scale-17 items (HAMD-17) score. BDNF, magnesium, and free T3 levels were negatively correlated with the HAMD-17 score.

CONCLUSIONS

This was a prospective cohort study. The most important markers found to be affecting PSD at 3 months were fibrinogen in males, and free T3, magnesium, and BDNF in females.

TRIAL REGISTRATION

ChiCTR-ROC-17013993 .

Role of social support in poststroke depression: A meta-analysis

Poststroke depression significantly affects health and quality of life of stroke patients. This study evaluates the role of social support in influencing poststroke depression. The literature search was conducted in electronic databases and study selection was based on precise eligibility criteria. The prevalence rates reported by individual studies were pooled. A meta-analysis of standardized mean differences (SMD) in social support between depressed and non-depressed stroke patients was performed. The odds ratios and correlation coefficients showing the relationship between social support and depression were pooled to achieve overall estimates. Twenty-five studies (9431 patients) were included. The prevalence of depression was 36% [95% confidence interval (CI): 28, 45]. Patients with poststroke depression had significantly lower social support in comparison with patients with no or lower levels of depression [SMD in social support scores -0.338 (95% CI: -0.589, -0.087); p = 0.008]. The odds of depression were lower in patients receiving higher levels of social support [OR 0.82 (95% CI: 0.69, 0.95)] but were higher in patients who were receiving weaker social support [OR 5.22 (95% CI: -0.87, 11.31)]. A meta-analysis of correlation coefficients found a significantly inverse correlation between social support and poststroke depression [r -0.336 (95% CI: -0.414, -0.254)]. Poststroke depression has a significant independent inverse association with social support.