Influence of inflammatory status in the acute phase of stroke on post-stroke depression

Development of evaluation index system for functional ability of older patients with stroke based on healthy aging: a modified Delphi study

Background

The prevalence of stroke in the older population is high and it is critical to promote and maintain the functional status of older patients post stroke. Health measures centered on functional ability can scientifically reflect the health status of older individuals. The aim of this study was to develop an evaluation index system for assessing the functional ability of older patients with stroke based on the World Health Organization Healthy Aging Model.

Methods

Key indicators were identified through literature analysis and semi-structured interviews with 10 older patients with stroke. A two-round expert consultation process was conducted to evaluate and revise the indicators. Subsequently, a hierarchical construction model was established using the analytic hierarchy process to determine the weight of each level indicator.

Results

The evaluation index system comprised three first-level, 13 s-level, and 53 third-level indicators. The weights ranged from 0.143-0.429 for first-level indicators, 0.052-0.349 for second-level indicators, and 0.040-0.667 for third-level indicators.

Conclusion

The developed evaluation index system demonstrates reliability for assessing the functional ability of older stroke patients and provides a standardized framework for nursing staff to conduct functional assessment of older stroke patients.

Association between the systemic immunity-inflammation index and stroke: a population-based study from NHANES (2015-2020)

Background The systemic immunity-inflammation index(SII) is a new indicator of composite inflammatory response. Inflammatory response is an important pathological process in stroke. Therefore, this study sought to investigate the association between SII and stroke. Methods We collected data on participants with SII and stroke from the 2015-2020 cycle of National Health and Nutrition Examination Survey (NHANES) for the cross-sectional investigation. Multivariate linear regression models were used to test the association between SII and stroke. Fitted smoothing curves and threshold effect analysis were applied to describe the nonlinear relationship. Results A total of 13,287 participants were included in our study, including 611 (4.598%) participants with stroke. In a multivariate linear regression analysis, we found a significant positive association between SII and stroke, and the odds ratio (OR) [95% CI] of SII associating with prevalence of stroke was [1.02 (1.01, 1.04)] (P < 0.01). In subgroup analysis and interaction experiments, we found that this positive relationship was not significantly correlated among different population settings such as age, gender, race, education level, smoking status, high blood pressure, diabetes and coronary heart disease (P for trend > 0.05). Moreover, we found an nonlinear relationship between SII and stroke with an inflection point of 740 (1,000 cells /µl) by using a two-segment linear regression model. Conclusions This study implies that increased SII levels are linked to stroke. To confirm our findings, more large-scale prospective investigations are needed.

Peripheral inflammation and trajectories of depressive symptomology after ischemic stroke: A prospective cohort study

BACKGROUND

Understanding the association of peripheral inflammation and post-stroke depressive symptomology (PSDS) might provide further insights into the complex etiological mechanism of organic depression. However, studies focusing on the longitudinal patterns of PSDS were limited and it remained unclear whether peripheral inflammation influences the occurrence and development of PSDS.

METHODS

A total of 427 prospectively enrolled and followed ischemic stroke patients were included in the analytical sample. Depressive symptomology was assessed on four occasions during 1 year after ischemic stroke. Peripheral inflammatory proteins on admission and repeated measures of peripheral immune markers in three stages were collected. Latent class growth analysis (LCGA) was employed to delineate group-based trajectories of peripheral immune markers and PSDS. Multinomial regression was performed to investigate the association of peripheral inflammation with PSDS trajectories.

RESULTS

Four distinct trajectories of PSDS were identified: stable-low (n = 237, 55.5 %), high-remitting (n = 120, 28.1 %), late-onset (n = 44, 10.3 %), and high-persistent (n = 26, 6.1 %) PSDS trajectories. The elevation of peripheral fibrinogen on admission increased the risk of high-persistent PSDS in patients with early high PSDS. Additionally, chronic elevation of innate immune levels might not only increase the risk of high-persistent PSDS in patients with early high PSDS but also increase the risk of late-onset PSDS in patients without early high PSDS. The elevation of adaptive immune levels in the convalescence of ischemic stroke may contribute to the remission of early high PSDS.

CONCLUSIONS

Peripheral immunity could influence the development of PSDS, and this influence might have temporal heterogeneity. These results might provide vital clues for the inflammation hypothesis of PSD.

Molecular mechanisms underlying major depressive disorder and post-stroke affective disorders

Two of the most common and incapacitating mental health disorders around the world are major depressive disorder (MDD) and post-stroke depression (PSD). MDD is thought to result from abnormal connectivity between the monoaminergic, glutamatergic, GABAergic, and/or cholinergic pathways. Additional factors include the roles of hormonal, immune, ageing, as well as the influence of cellular, molecular, and epigenetics in the development of mood disorders. This complexity of factors has been anticipated by the Swiss psychiatrists Paul Kielholz and Jules Angst who introduced a multimodal treatment of MDD. Depression is the predominant mood disorder, impacting around one-third of individuals who have experienced a stroke. MDD and PSD share common underlying biological mechanisms related to the disruption of monoaminergic pathways. The major contributor to PSD is the stroke lesion location, which can involve the disruption of the serotoninergic, dopaminergic, glutamatergic, GABAergic, or cholinergic pathways. Additionally, various other disorders such as mania, bipolar disorder, anxiety disorder, and apathy might occur post-stroke, although their prevalence is considerably lower. However, there are differences in the onset of MDD among mood disorders. Some mood disorders develop gradually and can persist for a lifetime, potentially culminating in suicide. In contrast, PSD has a rapid onset because of the severe disruption of neural pathways essential for mood behavior caused by the lesion. However, PSD might also spontaneously resolve several months after a stroke, though it is associated with higher mortality. This review also provides a brief overview of the treatments currently available in medical practice.

Predicting new-onset post-stroke depression from real-world data using machine learning algorithm

Introduction

Post-stroke depression (PSD) is a serious mental disorder after ischemic stroke. Early detection is important for clinical practice. This research aims to develop machine learning models to predict new-onset PSD using real-world data.

Methods

We collected data for ischemic stroke patients from multiple medical institutions in Taiwan between 2001 and 2019. We developed models from 61,460 patients and used 15,366 independent patients to test the models' performance by evaluating their specificities and sensitivities. The predicted targets were whether PSD occurred at 30, 90, 180, and 365 days post-stroke. We ranked the important clinical features in these models.

Results

In the study's database sample, 1.3% of patients were diagnosed with PSD. The average specificity and sensitivity of these four models were 0.83-0.91 and 0.30-0.48, respectively. Ten features were listed as important features related to PSD at different time points, namely old age, high height, low weight post-stroke, higher diastolic blood pressure after stroke, no pre-stroke hypertension but post-stroke hypertension (new-onset hypertension), post-stroke sleep-wake disorders, post-stroke anxiety disorders, post-stroke hemiplegia, and lower blood urea nitrogen during stroke.

Discussion

Machine learning models can provide as potential predictive tools for PSD and important factors are identified to alert clinicians for early detection of depression in high-risk stroke patients.

An explanatory model of depressive symptoms from anxiety, post-traumatic stress, somatic symptoms, and symptom perception: the potential role of inflammatory markers in hospitalized COVID-19 patients

BACKGROUND

The context of the COVID-19 pandemic has harmed the mental health of the population, increasing the incidence of mental health problems such as depression, especially in those who have had COVID-19. Our study puts forward an explanatory model of depressive symptoms based on subjective psychological factors in those hospitalized for COVID-19 with and without biological markers (i.e., inflammatory markers). Therefore, we aim to evaluate the hypotheses proposed in the model to predict the presence of depressive symptoms.

METHOD

We conducted a cross-sectional study, using a simple random sampling. Data from 277 hospitalized patients with COVID-19 in Lima-Peru, were collected to assess mental health variables (i.e., depressive, anxiety, post-traumatic stress, and somatic symptoms), self-perception of COVID-19 related symptoms, and neutrophil/lymphocyte ratio (NLR) such as inflammatory marker. We performed a structural equation modeling analysis to evaluate a predictive model of depressive symptoms.

RESULTS

The results showed a prevalence of depressive symptoms (11.2%), anxiety symptoms (7.9%), somatic symptoms (2.2%), and symptoms of post-traumatic stress (6.1%) in the overall sample. No association was found between the prevalence of these mental health problems among individuals with and without severe inflammatory response. The mental health indicators with the highest prevalence were sleep problems (48%), low energy (47.7%), nervousness (48.77%), worry (47.7%), irritability (43.7%) and back pain (52%) in the overall sample. The model proposed to explain depressive symptoms was able to explain more than 83.7% of the variance and presented good goodness-of-fit indices. Also, a different performance between the proposed model was found between those with and without severe inflammatory response. This difference was mainly found in the relationship between anxiety and post-traumatic stress symptoms, and between the perception of COVID-19 related symptoms and somatic symptoms.

CONCLUSIONS

Results demonstrated that our model of mental health variables may explain depressive symptoms in hospitalized patients of COVID-19 from a third-level hospital in Peru. In the model, perception of symptoms influences somatic symptoms, which impact both anxiety symptoms and symptoms of post-traumatic stress. Thus, anxiety symptoms could directly influence depressive symptoms or through symptoms of post-traumatic stress. Our findings could be useful to decision-makers for the prevention of depression, used to inform the creation of screening tools (i.e., perception of symptoms, somatic and anxiety symptoms) to identify vulnerable patients to depression.

Higher Plasma Fibrinogen Level at Admission Is Associated with Post-Stroke Depression at Discharge

Background: Post-stroke depression (PSD) is a common complication of stroke, which seriously affects the functional outcome of patients. Systemic low-grade inflammation associated with PSD has been shown to occur at several months to years, however, whether these inflammatory markers predicted PSD at an acute stage of stroke is controversial. Method: A total of 625 patients with acute ischemic stroke (219 female, 35.40%) were included in this study. PSD was diagnosed using the 17-item Hamilton depression scale (HAMD) at 7 days following discharge (7−14 days after stroke onset). Multivariable logistic regression analysis was applied to build a prediction model for PSD at discharge. Discrimination and calibration of the model were assessed by C-index, calibration plot. Internal validation was conducted using bootstrapping validation. Results: At discharge of hospitalization, 95 patients (15.20%) were diagnosed with PSD. Multivariable logistic regression suggested that female gender (OR = 2.043, 95% CI = 1.287−3.245, p = 0.002), baseline NIHSS (OR = 1.108, 95% CI = 1.055−1.165, p < 0.001) and fibrinogen (OR = 1.388, 95% CI = 1.129−1.706, p = 0.002) were independent predictors for PSD at discharge. The cut-off of the fibrinogen plasma level was 3.08 g/L. These predictors were included in the nomogram. The model displayed good discrimination, with a C-index of 0.730 (95% CI = 0.683−0.777) and good calibration. Conclusion: Female gender, baseline stroke severity and a higher level of fibrinogen were independently associated with PSD at discharge. A nomogram based on these three predictors can be used to provide an individual, visual prediction of the risk probability of PSD.

Symptomatic plaque enhancement is associated with early-onset post-stroke depression

BACKGROUND

The association between imaging features closely associated with symptomatic intracranial atherosclerotic plaques and early-onset post-stroke depression (PSD) is currently unclear.

MATERIALS AND METHODS

76 ischemic stroke patients who underwent high-resolution vessel wall magnetic resonance imaging (HR-VWI) were divided into PSD and non-PSD groups according to their DSM-V diagnoses and HAMD-17 scores at 14 days after onset. Clinical data and the imaging features associated with symptomatic plaques (including the enhancement index (EI), remodeling index, and plaque surface irregularity) were compared between groups. Multifactorial logistic regression analysis was used to find independent predictors of early-onset PSD. Spearman rank correlation analysis explores the association between clinical data, symptomatic plaque imaging features, and HAMD-17 in patients.

RESULTS

The sample comprised 36 patients with early-onset PSD. The symptomatic plaque EI and infarct volume were significantly higher in depressed patients than in patients without depression (P < 0.05). Multivariate logistic regression showed that symptomatic plaque EI could be used as an independent predictor of early-onset PSD after correcting for the confounding factor of infarct volume (OR = 1.034, 95% CI:1.014-1.055, P = 0.001). In the total sample, symptomatic plaque EI, infarct volume, and HAMD-17 had a significant positive correlation with each other (P < 0.05).

LIMITATIONS

This study focused only on the patients' symptomatic plaques and did not monitor patients' systemic inflammation levels at the time of HR-VWI.

CONCLUSIONS

The degree of symptomatic plaque enhancement is an independent predictive imaging marker of early-onset PSD and can be used the early diagnosis of early-onset PSD.

Elevated Red Blood Cell Distribution Width Levels at Admission Predicts Depression After Acute Ischemic Stroke: A 3-Month Follow-Up Study

PURPOSE

Red blood cell distribution width (RDW) is closely related to inflammatory-related disease markers. The present study aimed to investigate the association between the red blood cell distribution width (RDW) and post-stroke depression (PSD).

PATIENTS AND METHODS

A total of 414 patients with acute ischemic stroke (AIS) admitted to our hospital from June 2018 to July 2021 were consecutively enrolled and received 3 months' follow-up. According to the 17-item Hamilton Depression Scale (HAMD) assessment, they were divided into PSD group and non-PSD group. Diagnosis of PSD was made in accordance with DSM-IV. RDW was recorded within 24 hours of admission.

RESULTS

Among the included 414 patients, 95 (22.95%) patients were diagnosed as having PSD at 3 months after stroke. The results showed significantly higher level of RDW in patients with depression (13.69 (IQR13.24-13.88) vs. 13.56 (IQR 12.67-13.77), P<0.001) at admission than patients without depression. After adjustment for potential confounding factors, the odds ratio of PSD was 5.707 (95% CI, 2.717-11.989) for the highest tertile of RDW compared with the lowest tertile. Moreover, based on the receiver operating characteristic (ROC) curve, the optimal cutoff of RDW levels as an indicator for the prediction of PSD was projected as 13.01, which yielded a sensitivity of 83% and a specificity of 41.0%, with an area under the curve (AUC) of 0.643 (95% CI, 0.585-0.701; P = 0.012).

CONCLUSION

Higher RDW levels at admission were found to be correlated with PSD 3 months after stroke.

Elevated monocyte-to-HDL cholesterol ratio predicts post-stroke depression

OBJECTIVES

Inflammation plays an important role in the development of depression after stroke. Monocyte-to-HDL Cholesterol Ratio (MHR) recently emerged as a novel comprehensive inflammatory indicator in recent years. This study aimed to investigate whether there is a relationship between MHR levels and post-stroke depression (PSD).

METHODS

From February 2019 to September 2021, patients with acute ischemic stroke (AIS) were recruited within 7 days post-stroke from the two centers and blood samples were collected after admission. The 17-item Hamilton Depression Scale (HAMD-17) was used to measure depressive symptoms at 3 months after stroke. Patients were given the DSM-V criteria for diagnosis of PSD.

RESULTS

Of the 411 enrolled patients, 92 (22.38%) patients were diagnosed with PSD at 3-months follow-up. The results also showed significantly higher level of MHR in patients with depression [0.81 (IQR 0.67-0.87) vs. 0.61 (IQR 0.44-0.82), P < 0.001] at admission than patients without depression. Multivariate logistic regression revealed that MHR (OR 6.568, 95% CI: 2.123-14.565, P = 0.015) was an independent risk factor for the depression at 3 months after stroke. After adjustment for potential confounding factors, the odds ratio of PSD was 5.018 (95% CI: 1.694-14.867, P = 0.004) for the highest tertile of MHR compared with the lowest tertile. Based on the ROC curve, the optimal cut-off value of MHR as an indicator for prediction of PSD was projected to be 0.55, which yielded a sensitivity of 87% and a specificity of 68.3%, with the area under the curve at 0.660 (95% CI: 0.683-0.781; P = 0.003).

CONCLUSION

Elevated level of MHR was associated with PSD at 3 months, suggesting that MHR might be a useful Inflammatory markers to predict depression after stroke.