BACKGROUND
Post-stroke depression (PSD) is one of the most common neuropsychiatric complications after cerebral infarction, and reported to affect 25%-55% of stroke survivors. PSD profoundly affects rehabilitation, increases mortality and limits quality of life. Notwithstanding its clinical significance, prediction and identification of PSD in advance is a challenge as the pathogenesis of the disease is multifactorial. This study aimed to identify clinical, neuroimaging and biochemical predictive factors for PSD, and a comprehensive predictive model for the early screening of clinical patients was established.
AIM
To identify clinical, neuroimaging, and biochemical predictors of PSD and to develop a comprehensive predictive model for its early screening in patients with acute cerebral infarction.
METHODS
This was a single-center, retrospective cohort study that included 156 patients who were admitted to Department of Neurology with acute cerebral infarction between March 2021 and June 2024. At 2 weeks and 3 months after stroke, depression was evaluated using the 17-item Hamilton Depression Rating Scale and the Patient Health Questionnaire-9. According to 17-item Hamilton Depression Rating Scale scores at 3 months (≥ 7 PSD group; < 7 non-PSD group), patients were classified into PSD (n = 68, 43.6%) and non-PSD (n = 88, 56.4%) groups. Extensive data were collected: Demographic and comorbidities, stroke-related characteristics {including neuroimaging features (infarct volume, location, white matter hyperintensity, brain atrophy) and serum biomarkers [cytokines/inflammatory markers, brain-derived neurotrophic factor (BDNF)], cortisol/homocysteine}. Independent predictors were derived from multivariate logistic regression and a combined predictive model was established and validated through receiver operating characteristic analysis.
RESULTS
A total of 68 of the 156 patients (43.6%) developed PSD. Eight independent predictors were revealed by multivariate logistic regression: Psychiatric history [odds ratio (OR) = 3.85, 95% confidence interval (CI): 2.15-6.89, P < 0.001], infarction in frontal lobe (OR = 3.21, 95%CI: 1.88-5.48, P < 0.001), serum BDNF < 15.8 ng/mL (OR = 2.97, 95%CI: 1.72-5.13, P < 0.001), National Institutes of Health Stroke Scale ≥ 8 (OR = 2.68, 95%CI: 1.58-4.54, P < 0001), female gender(OR = 2.45, 95%CI: 2.52-3.73, P = 0.001), low social support (OR = 2.31, 95%CI: 1.38-3.87, P = 0.002), elevated interleukin-6 > 6.5 pg/mL (OR = 2.18, 95%CI: 1.25-3.81, P = 0.006), and left hemisphere lesion (OR = 1.95, 95%CI: 1.12-3.39, P = 0.018). The area under the curve of the multi-model was 0.89, (95%CI: 0.84-0.94), with a sensitivity of 82.4%, and specificity of 85.2%. The highest individual diagnostic value (area under the curve = 0.83) was held by serum BDNF alone. The PSD cluster had markedly increased interleukin-6 (8.6 ± 2.1 pg/mL vs 4.2 ± 1.3 pg/mL; P < 0.001), tumor necrosis factor-α (16.8 ± 3.5 pg/mL vs 9.5 ± 2.2 pg/mL; P < 0.001) and morning cortisol levels (395.2 ± 98.7 nmol/L vs 318.4 ± 722.3 nmol/L; P < 0.001), alongside lower overall BDNF concentrations (12.5 ± 36 nmol/L vs 22.7 ± 56 ng/mL; P < 0.001).
CONCLUSION
It is a multifactorial neuropsychiatric disorder with predictors identifiable across demographic, neuroanatomical, inflammatory and neurotrophic domains. The proposed predictive model based on both clinical features and serum biomarkers is sufficiently sensitive for diagnosis of early PSD. BDNF may be a particularly good biomarker to help with this. This study lays the scientific foundation for focusing early intervention plans on patients at high risk of cerebral infarction.
Core Tip: Post-stroke depression (PSD) is a common and disabling complication after cerebral infarction, but early prediction of PSD remains challenging. In this cohort of 156 patients, we demonstrated clinical, neuroimaging and biological predictors most notably low serum brain-derived neurotrophic factor and high interleukin-6 levels which independently predict PSD at three months. A combination predictive model showed high diagnostic accuracy. These findings support early multidimensional risk stratification and indicate that easily measurable biomarkers can aid the screening of PSD and improve targeted early treatment in high-risk stroke survivors.
