Development and Validation of 3-Month Major Post-Stroke Depression Prediction Nomogram After Acute Ischemic Stroke Onset

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.

Development and validation of a nomogram for predicting depressive symptoms in dentistry patients: A cross-sectional study

Depressive symptoms are frequently occur among dentistry patients, many of whom struggle with dental anxiety and poor oral conditions. Identifying the factors that influence these symptoms can enable dentists to recognize and address mental health concerns more effectively. This study aimed to investigate the factors associated with depressive symptoms in dentistry patients and develop a clinical tool, a nomogram, to assist dentists in predicting these symptoms. Methods: After exclusion of ineligible participants, a total of 1355 patients from the dentistry department were included. The patients were randomly assigned to training and validation sets at a 2:1 ratio. The LASSO regression method was initially employed to select highly influrtial features. This was followed by the application of a multi-factor logistic regression to determine independent factors and construct a nomogram. And it was evaluated by 4 methods and 2 indicators. The nomograms were formulated based on questionnaire data collected from dentistry patients. Nomogram2 incorporated factors such as medical burden, personality traits (extraversion, conscientiousness, and emotional stability), life purpose, and life satisfaction. In the training set, Nomogram2 exhibited a Concordance index (C-index) of 0.805 and an Area Under the Receiver Operating Characteristic Curve (AUC) of 0.805 (95% CI: 0.775-0.835). In the validation set, Nomogram2 demonstrated an Area Under the Receiver Operating Characteristic Curve (AUC) of 0.810 (0.768-0.851) and a Concordance index (C-index) of 0.810. Similarly, Nomogram1 achieved an Area Under the Receiver Operating Characteristic Curve (AUC) of 0.816 (0.788-0.845) and a Concordance index (C-index) of 0.816 in the training set, and an Area Under the Receiver Operating Characteristic Curve (AUC) of 0.824 (95% CI: 0.784-0.864) and a Concordance index (C-index) of 0.824 in the validation set. Net Reclassification Improvement (NRI) and Integrated Discrimination Improvement (IDI) indicated that Nomogram1, which included oral-related factors (oral health and dental anxiety), outperformed Nomogram2. We developed a nomogram to predict depressive symptoms in dentistry patients. Importantly, this nomogram can serve as a valuable psychometric tool for dentists, facilitating the assessment of their patients' mental health and enabling more tailored treatment plans.

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.

Depressive symptom dimensions predict the treatment effect of repetitive transcranial magnetic stimulation for post-stroke depression

OBJECTIVE

Repetitive transcranial magnetic stimulation (rTMS) has attracted considerable attention because of its non-invasiveness, minimal side effects, and treatment efficacy. Despite an adequate duration of rTMS treatment, some patients with post-stroke depression (PSD) do not achieve full symptom response or remission.

METHODS

This was a prospective randomized controlled trial. Participants receiving rTMS were randomly assigned to the ventromedial prefrontal cortex (VMPFC), left dorsolateral prefrontal cortex (DLPFC), or contralateral motor area (M1) groups in a ratio of 1:1:1. Enrollment assessments and data collection were performed in weeks 0, 2, 4, and 8. The impact of depressive symptom dimensions on treatment outcomes were tested using a linear mixed-effects model fitted with maximum likelihood. Univariate analysis of variance (ANOVA) and back-testing were used to analyze the differences between the groups.

RESULTS

In total, 276 patients were included in the analysis. Comparisons across groups showed that 17-item Hamilton Rating Scale for Depression (HAMD-17) scores of the DLPFC group significantly differed from those of the VMPFC and M1 groups at 2, 4, and 8 weeks after treatment (p < 0.05). A higher observed mood score (β = -0.44, 95% confidence interval [CI]: -0.85-0.04, p = 0.030) could predict a greater improvement in depressive symptoms in the DLPFC group. Higher neurovegetative scores (β = 0.60, 95% CI: 0.25-0.96, p = 0.001) could predict less improvement of depressive symptoms in the DLPFC group.

CONCLUSION

Stimulation of the left DLPFC by high-frequency rTMS (HF-rTMS) could significantly improve depressive symptoms in the subacute period of subcortical ischemic stroke, and the dimension of depressive symptoms at admission might predict the treatment effect.

Sociodemographic and clinical characteristics of children with tic disorders and behavioral problems: A real-world study and development of a prediction model

BACKGROUND

Tic disorders (TD) are complex neuropsychiatric disorders frequently associated with a variety of comorbid problems, whose negative effects may exceed those of the tics themselves. In this study, we aimed to explore the sociodemographic and clinical characteristics of children with TD and behavioral problems, and develop a prediction model of behavioral problems based on the predictors under real-world conditions.

METHODS

A hospital-based cross-sectional study was conducted on children with TD. Behavioral problems were surveyed using the Achenbach Child Behavior Checklist (CBCL). Sociodemographic information was collected from face-to-face interviews using an electronic questionnaire administered during the initial ambulatory visit. Clinical data were collected from medical records, and quality control was performed. The sociodemographic and clinical characteristics of patients with and without behavioral problems were statistically compared, and a nomogram prediction model was developed based on multivariate logistic regression analysis. The discriminatory ability and clinical utility of the nomogram were assessed by concordance index (C-index), receiver operating characteristic (ROC) curve, decision curve analysis (DCA) and clinical impact curve (CIC).

RESULTS

A total of 343 TD cases were included in the final analysis, of which 30.32% had behavioral problems. The prediction model showed age 12-16 years, abnormal birth history, parenting pattern of indulgence, parent/close relatives with psychiatric disorders, chronic motor or vocal tic disorder (CTD)/Tourette syndrome (TS) and moderate/severe tic severity were associated with behavioral problems in children with TD. The C-index of the prediction model (nomogram) was 0.763 (95% confidence interval, 0.710 ~ 0.816). The nomogram was feasible for making beneficial clinical decisions, according to the satisfactory results of the DCA and CIC.

CONCLUSIONS

A nomogram prediction model for comorbid behavioral problems in children with TD was established. The prediction model demonstrated a good discriminative ability and predictive performance for beneficial clinical decisions. This model further provides a comprehensive understanding of associated sociodemographic and clinical characteristics by visual graphs and allows clinicians to rapidly identify patients with a higher risk of behavioral problems and tailor necessary interventions to improve clinical outcomes.

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.

A new nomogram including total cerebral small vessel disease burden for individualized prediction of early-onset depression in patients with acute ischemic stroke

Objectives

The present study was designed to evaluate the effects of total cerebral small vessel disease (CSVD) on early-onset depression after acute ischemic stroke (AIS), and to develop a new nomogram including total CSVD burden to predict early-onset post-stroke depression (PSD).

Methods

We continuously enrolled patients with AIS who were hospitalized at the First Affiliated Hospital of Soochow University between October 2017 and June 2019. All patients were assessed for depressive symptoms using the 17-item Hamilton Depression Scale (HAMD-17) at 14 ± 2 days after the onset of AIS. The diagnosis for depression was made according to the American Diagnostic and Statistical Manual of Mental Disorders Version 5 (DSM-5). The demographic and clinical data were collected including total CSVD burden. On the basis of a multivariate logistic model, the independent factors of early-onset PSD were identified and the predictive nomogram was generated. The performance of the nomogram was evaluated by Harrell's concordance index (C-index) and calibration plot.

Results

A total of 346 patients were enrolled. When contrasted to a 0 score of total CSVD burden, the score ≥2 (moderate to severe total CSVD burden) was an independent risk factor for early-onset PSD. Besides, gender, cognitive impairments, baseline Barthel Index (BI), and plasma fibrinogen were independently associated with early-onset PSD. The nomogram based on all these five independent risk factors was developed and validated with an Area Under Curve (AUC) of 0.780. In addition, the calibration plot revealed an adequate fit of the nomogram in predicting the risk of early-onset depression in patients with AIS.

Conclusions

Our study found the total CSVD burden score of 2–4 points was an independent risk factor of early-onset PSD. The proposed nomogram based on total CSVD burden, gender, cognitive impairments, baseline BI, and plasma fibrinogen concentration gave rise to a more accurate and more comprehensive prediction for early-onset PSD.

The etiologies of post-stroke depression: Different between lacunar stroke and non-lacunar stroke

OBJECTIVES

Depression is common after both lacunar stroke and non-lacunar stroke and might be associated with lesion locations as proven by some studies. This study aimed to identify whether lesion location was critical for depression after both lacunar and non-lacunar strokes.

METHODS

A cohort of ischemic stroke patients was assigned to either a lacunar stroke group or a non-lacunar stroke group after a brain MRI scan. Neurological deficits and treatment response was evaluated during hospitalization. The occurrence of depression was evaluated 3 months later. Logistic regressions were used to identify the independent risk factors for depression after lacunar and non-lacunar stroke respectively.

RESULTS

83 of 246 patients with lacunar stroke and 71 of 185 patients with non-lacunar stroke developed depression. Infarctions in the frontal cortex, severe neurological deficits, and a high degree of handicap were identified as the independent risk factors for depression after non-lacunar stroke, while lesion location was not associated with depression after lacunar stroke.

CONCLUSION

The main determinants for depression after lacunar and non-lacunar stroke were different. Lesion location was critical only for depression after non-lacunar stroke.