ArticleBMC psychiatry2022
Explore the influencing factors and construct random forest models of post-stroke depression at 3 months in males and females.
Article in BMC psychiatry, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.
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Who cites it
4 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Accuracy of Machine Learning in Predicting Post-Stroke Depression: A Systematic Review and Meta-Analysis.Brain and behavior · 2025Pooled it
- Cardiac history and post-stroke depression association in Chinese stroke survivors: a cross sectional study.Scientific reports · 2025Article
- Friends' Perspective: Young Adults' Reaction to Disclosure of Chronic Illness.Journal of clinical psychology in medical settings · 2024Article
- Early hospital readmission following stroke: the Florida Stroke Registry.Frontiers in stroke · 2023Article
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11 authors.
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Abstract
backgroundPost-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.
methodsThis 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.
resultsThis 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).
conclusionThe 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 registrationChiCTR-ROC-17013993.
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