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The objective of this study was to analyze the latent categories of fatigue in patients with chronic heart failure (CHF), explore their characteristic differences, and identify the associated influencing factors. This cross-sectional study included 289 patients with CHF who were enrolled at 2 tertiary-level hospitals in Shandong, China, from August to December 2023. The convenience sampling method was used to collect data. Furthermore, the level of fatigue, insomnia, anxiety, depression, and social support were evaluated using the Chinese version of the Multidimensional Fatigue Inventory-20, Insomnia Severity Index, Generalized Anxiety Disorder-7, Patient Health Questionnaire-9, and Multidimensional Scale of Perceived Social Support. Latent profile analysis was performed to elucidate the latent categories of fatigue in the patients. In addition, the risk factors associated with the different categories were assessed using multiple logistic regression analyses. The average fatigue score was 62.45 ± 13.55. The potential fatigue profile of CHF was divided into three categories: low fatigue group C1 (18.6%), moderate fatigue group C2 (47.4%), and high fatigue group C3 (34.0%). Multiple logistic regression analysis showed that C3 patients with CHF were mainly characterized by lower ejection fraction ( = 0.01, = 0.008), insomnia ( = 1.19, = 0.005), and anxiety ( = 1.20, = 0.034). C2 patients indicated lower ejection fraction ( = 0.04, = 0.040), and C1 patients had higher social support ( = 0.91, < 0.001; = 0.93, < 0.001). This study indicated that CHF patients had significantly heterogeneous levels of fatigue. Therefore, it is recommended that medical staff could adopt more precise interventions according to different category characteristics to improve the outcomes of patients with CHF.

Objectives We aim to investigate whether cerebral small vessel disease (cSVD) imaging markers correlate with deep medullary vein (DMV) damage in small vessel occlusion acute ischemic stroke (SVO-AIS) patients. Methods The DMV was divided into six segments according to the regional anatomy. The total DMV score (0–18) was calculated based on segmental continuity and visibility. The damage of DMV was grouped according to the quartiles of the total DMV score. Neuroimaging biomarkers of cSVD including white matter hyperintensity (WMH), cerebral microbleed (CMB), perivascular space (PVS), and lacune were identified. The cSVD score were further analyzed. Results We included 229 SVO-AIS patients, the mean age was 63.7 ± 23.1 years, the median NIHSS score was 3 (IQR, 2–6). In the severe DMV burden group (the 4th quartile), the NIHSS score grade (6 (3–9)) was significantly higher than other groups ( p  < 0.01). The grade scores for basal ganglia PVS (BG-PVS) were positively correlated with the degree of DMV ( R  = 0.67, p  < 0.01), rather than centrum semivole PVS (CS-PVS) ( R  = 0.17, p  = 0.1). In multivariate analysis, high CMB burden (adjusted odds ratio [aOR], 25.38; 95% confidence interval [CI], 1.87–345.23) was associated with severe DMV scores. In addition, BG-PVS was related to severe DMV burden in a dose-dependent manner: when BG-PVS score was 3 and 4, the aORs of severe DMV burden were 18.5 and 12.19, respectively. Conclusion The DMV impairment was associated with the severity of cSVD, which suggests that DMV burden may be used for risk stratification in SVO-AIS patients. Clinical relevance statement The DMV damage score, based on the association between small vessel disease and the deep medullary veins impairment, is a potential new imaging biomarker for the prognosis of small vessel occlusion acute ischemic stroke, with clinical management implications. Key Points • The damage to the deep medullary vein may be one mechanism of cerebral small vessel disease. • Severe burden of the basal ganglia perivascular space and cerebral microbleed is closely associated with significant impairment to the deep medullary vein. • The deep medullary vein damage score may reflect a risk of added vascular damage in small vessel occlusion acute ischemic stroke patients.

Aims Our purpose is to assess the role of cerebral small vessel disease (SVD) in prediction models in patients with different subtypes of acute ischemic stroke (AIS). Methods We enrolled 398 small‐vessel occlusion (SVO) and 175 large artery atherosclerosis (LAA) AIS patients. Functional outcomes were assessed using the modified Rankin Scale (mRS) at 90 days. MRI was performed to assess white matter hyperintensity (WMH), perivascular space (PVS), lacune, and cerebral microbleed (CMB). Logistic regression (LR) and machine learning (ML) were used to develop predictive models to assess the influences of SVD on the prognosis. Results In the feature evaluation of SVO‐AIS for different outcomes, the modified total SVD score (Gain: 0.38, 0.28) has the maximum weight, and periventricular WMH (Gain: 0.07, 0.09) was more important than deep WMH (Gain: 0.01, 0.01) in prognosis. In SVO‐AIS, SVD performed better than regular clinical data, which is the opposite of LAA‐AIS. Among all models, eXtreme gradient boosting (XGBoost) method with optimal index (OI) has the best performance to predict excellent outcome in SVO‐AIS. [0.91 (0.84–0.97)]. Conclusions Our results revealed that different SVD markers had distinct prognostic weights in AIS patients, and SVD burden alone may accurately predict the SVO‐AIS patients' prognosis. Characteristics of cerebral small vessel disease (CSVD) in acute ischemic stroke (AIS) patients can affect outcomes at 90 days. Meanwhile, different imaging markers of CSVD have different weights of impact on large artery atherosclerosis and small vessel occlusion subtype AIS.

The interaction effect between collateral circulation and ischemic core size on stroke outcomes has been highlighted in acute ischemic stroke (AIS). However, biomarkers that assess the magnitude of this interaction are still lacking. We aimed to present a new imaging marker, the collateral-core ratio (CCR), to quantify the interaction effect between these factors and evaluate its ability to predict functional outcomes using machine learning (ML) in AIS. Patients with AIS caused by anterior circulation large vessel occlusion (LVO) were recruited from a prospective multicenter study. CCR was calculated as collateral perfusion volume/ischemic core volume. Functional outcomes were assessed using the modified Rankin Scale (mRS) at 90 days. An ML model was built and tested with a tenfold cross-validation using nine clinical and four imaging variables with mRS score 3–6 as unfavorable outcomes. Among 129 patients, CCR was identified as the most important variable. The prediction model incorporating clinical factors, ischemic core volume, collateral perfusion volume, and CCR showed better discriminatory power in predicting unfavorable outcomes than the model without CCR (mean C index 0.853 ± 0.108 versus 0.793 ± 0.133, P  = 0.70; mean net reclassification index 52.7% ± 32.7%, P  < 0.05). When patients were divided into two groups based on their CCR value with a threshold of 0.73, unfavorable outcomes were significantly more prevalent in patients with CCR ≤ 0.73 than in those with CCR > 0.73. CCR is a robust predictor of functional outcomes, as identified by ML, in patients with acute LVO. The prediction model that incorporated CCR improved the model’s ability to identify unfavorable outcomes. ClinicalTrials.gov Identifier: NCT02580097.

NOABSTRACTThe Alberta Stroke Program CT Score (ASPECTS) is a widely used rating system for assessing infarct extent and location. We aimed to investigate the prognostic value of ASPECTS subregions’ involvement in the long-term functional outcomes of acute ischemic stroke (AIS).Consecutive patients with AIS and anterior circulation large-vessel stenosis and occlusion between January 2019 and December 2020 were included. The ASPECTS score and subregion involvement for each patient was assessed using posttreatment magnetic resonance diffusion-weighted imaging. Univariate and multivariable regression analyses were conducted to identify subregions related to 3-month poor functional outcome (modified Rankin Scale scores, 3–6) in the reperfusion and medical therapy cohorts, respectively. In addition, prognostic efficiency between the region-based ASPECTS and ASPECTS score methods were compared using receiver operating characteristic curves and DeLong’s test.A total of 365 patients (median age, 64 years; 70% men) were included, of whom 169 had poor outcomes. In the reperfusion therapy cohort, multivariable regression analyses revealed that the involvement of the left M4 cortical region in left-hemisphere stroke (adjusted odds ratio [aOR] 5.39, 95% confidence interval [CI] 1.53–19.02) and the involvement of the right M3 cortical region in right-hemisphere stroke (aOR 4.21, 95% CI 1.05–16.78) were independently associated with poor functional outcomes. In the medical therapy cohort, left-hemisphere stroke with left M5 cortical region (aOR 2.87, 95% CI 1.08–7.59) and caudate nucleus (aOR 3.14, 95% CI 1.00–9.85) involved and right-hemisphere stroke with right M3 cortical region (aOR 4.15, 95% CI 1.29–8.18) and internal capsule (aOR 3.94, 95% CI 1.22–12.78) affected were related to the increased risks of poststroke disability. In addition, region-based ASPECTS significantly improved the prognostic efficiency compared with the conventional ASPECTS score method.The involvement of specific ASPECTS subregions depending on the affected hemisphere was associated with worse functional outcomes 3 months after stroke, and the critical subregion distribution varied by clinical management. Therefore, region-based ASPECTS could provide additional value in guiding individual decision making and neurological recovery in patients with AIS.