Elevated plasma high-sensitivity C-reactive protein at admission predicts the occurrence of post-stroke fatigue at 6 months after ischaemic stroke

Prevalence, Trajectory, and Predictors of Poststroke Fatigue in Older Adults

OBJECTIVE

To explore the prevalence, trajectories, and predictors of poststroke fatigue in older adults after a first ischemic stroke.

DESIGN

A longitudinal observational cohort study.

SETTING

Two hospitals.

PARTICIPANTS

A total of 381 patients aged ≥65 years with their first ischemic stroke were included. The mean (standard deviation) age was 71.1 (4.27) years, with 96 patients (25.2%) being women and 285 (74.8%) being men.

INTERVENTIONS

Not applicable.

MAIN OUTCOME MEASURES

Patients were assessed using the Fatigue Severity Scale at admission, 3 months, and 12 months. Growth mixture models were used to identify distinct fatigue trajectories, and baseline variables were analyzed to determine their association with these trajectories.

RESULTS

The prevalence of clinical fatigue was 39.11%, 33.33%, and 22.31% at admission, 3 months, and 12 months, respectively. Five distinct fatigue trajectories were identified: persistently low fatigue (class 1, 49.1%), persistently high fatigue (class 2, 21.5%), initial high but early decreasing fatigue (class 3, 15.0%), initial high but late decreasing fatigue (class 4, 8.7%), and increasing-then-decreasing fatigue (class 5, 5.8%). Multinomial logistic regression analysis revealed that several factors were significantly associated with high and persistent fatigue (class 2), including older age, lower social support, decreased physical activity, higher depression and anxiety scores, cognitive impairment, and greater stroke severity.

CONCLUSIONS

These findings indicate significant variability in the progression of fatigue among stroke survivors. Further research is necessary to determine the outcomes linked to these fatigue trajectory subgroups and to identify the most effective treatment strategies tailored to each specific subgroup.

Circulating Neutrophil-to-Lymphocyte Ratio Predicts Stroke-Associated Infection and Poststroke Fatigue Affecting Long-Term Neurological Outcomes in Stroke Patients

Background: Since peripheral leukocytes may contribute to the pathophysiology of stroke, the aim of this study was to elucidate the relationship between leukocytes and stroke outcomes and identify which leukocyte subtypes most accurately predict functional outcomes and poststroke fatigue (PSF) in stroke patients. Methods: A total of 788 ischemic stroke patients within 72 h of onset of disease were admitted in our study. Stroke-associated infection (SAI) and PSF were evaluated according to diagnosis standards by a special neurologist. Analyses were performed using SPSS 23.0 and GraphPad Prism 10.0. Results: Neutrophil-to-lymphocyte ratio (NLR) has discriminative power in predicting stroke outcome, and the area under the curve (AUC) of NLR to distinguish stroke outcomes was 0.689 (95% confidence interval, 0.646-0.732). Positive correlation was found between NLR levels and NIHSS score on admission (r = 0.2786, p < 0.001). Risk model for predicting stroke outcome was constructed using age, NIHSS, previous stroke history, triglycerides, glucose and hemoglobin levels, thrombolysis treatment, and NLR, with an AUC of 0.865. Patients who developed SAI and PSF both had significantly higher NLR levels at admission than those patients not diagnosed with SAI and PSF (p < 0.0001). A risk model was constructed to predict PSF based on parameters including age, NIHSS score, lipoprotein(a) and NLR, and an AUC of 0.751. Conclusions: Higher NLR levels in the acute phase of stroke might indicate a higher incidence of SAI and PSF. Therefore, higher NLR is associated with a poor stroke prognosis.

Machine Learning-Based Model for Prediction of Early Post-Stroke Fatigue in Patients With Stroke: A Longitudinal Study

BackgroundPost-stroke fatigue, as one of the long-lasting physical and mental symptoms accompanying stroke survivors, will seriously affect the daily living ability and quality of life of stroke patients.ObjectiveThe aim of this study was to develop machine learning (ML) algorithms to predict early post-stroke fatigue among patients with stroke.MethodsA longitudinal study of 702 patients with stroke followed for 3 months. Twenty-three clinical features were obtained from medical records and questionnaires before discharge. Early post-stroke fatigue was assessed using the Fatigue Severity Scale. The dataset was randomly divided into a training group (70%) and an internal validation group (30%), applied oversampling, 10-fold cross-validation, and grid search to optimize the hyperparameter. Feature selection using the Least Absolute Shrinkage and Selection Operator (LASSO) regression. Sixteen ML algorithms were performed to predict early post-stroke fatigue in this study. Accuracy, precision, recall, F1 score, area under the receiver operating characteristic curve (AUC), and brier score were used to evaluate the models performance.ResultsAmong the 16 ML algorithms, the Bagging model was the optimal model for predicting early post-stroke fatigue in patients with stroke (AUC = 0.8479, accuracy = 0.7518, precision = 0.5741, recall = 0.7209, F1 score = 0.6392, brier score = 0.1490). The feature selection based on LASSO revealed that risk factors for early post-stroke fatigue in patients with stroke included anxiety, sleep, social support, family care, pain, depression, neural-functional defect, quit/no drinking, balance function, type of stroke, sex, heart disease, smoking, and hemiplegia.ConclusionsIn this study, the Bagging model proved to be effective in predicting early post-stroke fatigue.

Predictive value of serological indices for guiding bundle of care to prevent the occurrence of poststroke fatigue for ischemic stroke survivors

Almost half of ischemic stroke (IS) survivors have poststroke fatigue (PSF) during rehabilitation, which can reduce their rehabilitation compliance and quality of life. The primary link of PSF management is early identification, which can guide bundle of care for prevention. This study aimed to explore the predictive value of serological indicators for guiding bundle of care to prevent the occurrence of PSF among IS survivors. This study was a prospective observational study. A total of 350 patients with IS who were hospitalized in 2 tertiary hospitals in Nanning from October 2022 to September 2023 were selected. The general data of patients and serological indicators within 24 hours of admission were collected. Based on the follow-up results, the patients were divided into the PSF group and the NPSF group. Multivariate logistic regression analysis was used to screen the risk factors affecting the occurrence of PSF, and the receiver operating characteristic curve (ROC curve) method was used to analyze the predictive value of this factor. The incidence of acute-phase PSF among elderly patients with IS was 49.26%. The elevated levels of fasting plasma glucose (FPG) (OR = 1.485, 95% CI: 1.145-1.925, P = .003), total cholesterol (TC) (OR = 1.394, 95% CI: 1.013-1.917, P = .041), C-reactive protein (CRP) (OR = 1.394, 95% CI: 1.013-1.917, P = .041), and homocysteine (Hcy) (OR = 1.370, 95% CI: 1.233-1.524, P < .001) were risk factors of PSF in elderly patients with acute IS (P < .05). FPG (area under the curve = 0.632), TC (area under the curve = 0.621), CRP (area under the curve = 0.889), and Hcy (area under the curve = 0.807) had a good predictive value for acute-phase PSF, and the combination of the 4 indicators could further improve the predictive efficacy (area under the curve = 0.938, sensitivity 86.2%, specificity 90.7%, P < .05). The elevated levels of FPG, TC, CRP, and Hcy could predict the risk of PSF, and the combination of the 4 indicators can effectively improve prediction efficiency and provide a reference for guiding the formulation of bundle nursing programs.

Are central and systemic inflammation associated with fatigue in cerebral small vessel disease?

BACKGROUND

Fatigue is a common symptom in cerebral small vessel disease (SVD), but its pathogenesis is poorly understood. It has been suggested that inflammation may play a role. We determined whether central (neuro) inflammation and peripheral inflammation were associated with fatigue in SVD.

METHODS

Notably, 36 patients with moderate-to-severe SVD underwent neuropsychometric testing, combined positron emission tomography and magnetic resonance imaging (PET-MRI) scan, and blood draw for the analysis of inflammatory blood biomarkers. Microglial signal was taken as a proxy for neuroinflammation, assessed with radioligand 11C-PK11195. Of these, 30 subjects had full PET datasets for analysis. We assessed global 11C-PK11195 binding and hotspots of 11C-PK11195 binding in the normal-appearing white matter, lesioned tissue, and combined total white matter. Peripheral inflammation was assessed with serum C-reactive protein (CRP) and using the Olink cardiovascular III proteomic panel comprising 92 biomarkers of cardiovascular inflammation and endothelial activation. Fatigue was assessed using the fatigue severity scale (FSS), the visual analog fatigue scale, and a subscale of the Geriatric Depression Scale.

RESULTS

Mean (SD) age was 68.7 (11.2) years, and 63.9% were male. Of these, 55.6% showed fatigue on the FSS. Fatigued participants had higher disability scores (p = 0.02), higher total GDS scores (p = 0.02), and more commonly reported a history of depression (p = 0.04). 11C-PK11195 ligand binding in the white matter was not associated with any measure of fatigue. Serum CRP was significantly associated with average fatigue score on FSS (ρ = 0.48, p = 0.004); this association persisted when controlling for age, sex, disability score, and depression (β = 0.49, 95% CI (0.17, 2.26), p = 0.03). Blood biomarkers from the Olink panel showed no association with fatigue.

CONCLUSION

In symptomatic SVD patients, neuroinflammation, assessed with microglial marker 11C-PK11195, was not associated with fatigue. We found some evidence for a role of systematic inflammation, evidenced by an association between fatigue severity and raised CRP, but further studies are required to understand this relationship and inform whether it could be therapeutically modified to reduce fatigue severity.

DATA ACCESS STATEMENT

Data for this study are available from the corresponding author upon reasonable request.

Improving physical movement during stroke rehabilitation: investigating associations between sleep measured by wearable actigraphy technology, fatigue, and key biomarkers

BACKGROUND

Sleep disturbance and fatigue are common in individuals undergoing inpatient rehabilitation following stroke. Understanding the relationships between sleep, fatigue, motor performance, and key biomarkers of inflammation and neuroplasticity could provide valuable insight into stroke recovery, possibly leading to personalized rehabilitation strategies. This study aimed to investigate the influence of sleep quality on motor function following stroke utilizing wearable technology to obtain objective sleep measurements. Additionally, we aimed to determine if there were relationships between sleep, fatigue, and motor function. Lastly, the study aimed to determine if salivary biomarkers of stress, inflammation, and neuroplasticity were associated with motor function or fatigue post-stroke.

METHODS

Eighteen individuals who experienced a stroke and were undergoing inpatient rehabilitation participated in a cross-sectional observational study. Following consent, participants completed questionnaires to assess sleep patterns, fatigue, and quality of life. Objective sleep was measured throughout one night using the wearable Philips Actiwatch. Upper limb motor performance was assessed on the following day and saliva was collected for biomarker analysis. Correlation analyses were performed to assess the relationships between variables.

RESULTS

Participants reported poor sleep quality, frequent awakenings, and difficulties falling asleep following stroke. We identified a significant negative relationship between fatigue severity and both sleep quality (r=-0.539, p = 0.021) and participants experience of awakening from sleep (r=-0.656, p = 0.003). A significant positive relationship was found between grip strength on the non-hemiplegic limb and salivary gene expression of Brain-derived Neurotrophic Factor (r = 0.606, p = 0.028), as well as a significant negative relationship between grip strength on the hemiplegic side and salivary gene expression of C-reactive Protein (r=-0.556, p = 0.048).

CONCLUSION

The findings of this study emphasize the importance of considering sleep quality, fatigue, and biomarkers in stroke rehabilitation to optimize recovery and that interventions may need to be tailored to the individual. Future longitudinal studies are required to explore these relationships over time. Integrating wearable technology for sleep and biomarker analysis can enhance monitoring and prediction of outcomes following stroke, ultimately improving rehabilitation strategies and patient outcomes.

A Machine Learning Approach to Predict Post-stroke Fatigue. The Nor-COAST study

OBJECTIVE

This study aimed to predict fatigue 18 months post-stroke by utilizing comprehensive data from the acute and sub-acute phases after stroke in a machine-learning set-up.

DESIGN

A prospective multicenter cohort-study with 18-month follow-up.

SETTING

Outpatient clinics at 3 university hospitals and 2 local hospitals.

PARTICIPANTS

474 participants with the diagnosis of acute stroke (mean ± SD age; 70.5 (11.3), 59% male; N=474).

INTERVENTIONS

Not applicable.

MAIN OUTCOME MEASURES

The primary outcome, fatigue at 18 months, was assessed using the Fatigue Severity Scale (FSS-7). FSS-7≥5 was defined as fatigue. In total, 45 prediction variables were collected, at initial hospital-stay and 3-month post-stroke.

RESULTS

The best performing model, random forest, predicted 69% of all subjects with fatigue correctly with a sensitivity of 0.69 (95% CI: 0.50, 0.86), a specificity of 0.74 (95% CI: 0.66, 0.83), and an Area under the Receiver Operator Characteristic curve of 0.79 (95% CI: 0.69, 0.87) in new unseen data. The proportion of subjects predicted to suffer from fatigue, who truly suffered from fatigue at 18-months was estimated to 0.41 (95% CI: 0.26, 0.57). The proportion of subjects predicted to be free from fatigue who truly did not have fatigue at 18-months was estimated to 0.90 (95% CI: 0.83, 0.96).

CONCLUSIONS

Our findings indicate that the model has satisfactory ability to predict fatigue in the chronic phase post-stroke and may be applicable in clinical settings.

Relationship between glycated hemoglobin levels at admission and chronic post-stroke fatigue in patients with acute ischemic stroke

BACKGROUND

Chronic Post-Stroke Fatigue (PSF) is a common and persistent complications among ischemic stroke survivors. The serum glycated hemoglobin (HbA1c) level, as it is known has emerged as a critical risk factor for Acute Ischemic Stroke (AIS) and post-stroke cognitive and emotional impairment. However, no studies have been conducted on the link between HbA1c and PSF. Therefore, this study aims to estimate the relationship between HbA1c and PSF in the chronic phase.

METHODS

A longitudinal study was conducted on 559 patients diagnosed with their first AIS episode and admitted to Suining Central Hospital within three days after onset. All patients were examined for serum HbA1c, blood glucose levels and routine blood biochemical indicators at admission. The Fatigue Severity Scale (FSS) was employed to assess fatigue symptoms at six months post-stroke. Multivariate logistic regression and smooth curve fitting were used to analyze the relationship between admission HbA1c, blood glucose levels, discharge blood glucose and PSF, and the predictive value of HbA1c on PSF was assessed using a segmented linear regression model.

RESULTS

189(33.8 %)of the 559 patients included in the study, reported PSF at six-month follow-up. Compared with the non-PSF group, the PSF group displayed significantly higher levels of HbA1c (7.8 ± 3.0 vs 6.5 ± 2.0 %, P < 0.001), admission blood glucose (7.8 ± 3.8 vs 7.1 ± 3.5 mmol/L, P = 0.041), and discharge blood glucose (6.3 ± 1.6 vs 5.8 ± 1.2 mmol/L, P < 0.001). The dose-response relationship among admission HbA1c, blood glucose, discharge blood glucose and PSF showed that HbA1c level is positively and non-linearly related to the risk of PSF. A linear positive correlation is noted between PSF and discharge blood glucose levels, while no significant correlation was observed for the blood glucose levels upon admission.

CONCLUSIONS

Higher HbA1c levels at admission were independently associated with the risk of chronic PSF, the correlation between blood glucose and PSF showed significant variability, HbA1c may serve as a more stable risk factor in predicting the occurrence of chronic PSF and long-term active glycemic management may have a favorable impact on chronic PSF after AIS.

Mechanisms of Post-Stroke Fatigue: A Follow-Up From the Third Stroke Recovery and Rehabilitation Roundtable

BACKGROUND

Post-stroke fatigue (PSF) is a significant and highly prevalent symptom, whose mechanisms are poorly understood. The third Stroke Recovery and Rehabilitation Roundtable paper on PSF focussed primarily on defining and measuring PSF while mechanisms were briefly discussed. This companion paper to the main paper is aimed at elaborating possible mechanisms of PSF.

METHODS

This paper reviews the available evidence that potentially explains the pathophysiology of PSF and draws parallels from fatigue literature in other conditions. We start by proposing a case for phenotyping PSF based on structural, functional, and behavioral characteristics of PSF. This is followed by discussion of a potentially significant role of early inflammation in the development of fatigue, specifically the impact of low-grade inflammation and its long-term systemic effects resulting in PSF. Of the many neurotransmitter systems in the brain, the dopaminergic systems have the most evidence for a role in PSF, along with a role in sensorimotor processing. Sensorimotor neural network dynamics are compromised as highlighted by evidence from both neurostimulation and neuromodulation studies. The double-edged sword effect of exercise on PSF provides further insight into how PSF might emerge and the importance of carefully titrating interventional paradigms.

CONCLUSION

The paper concludes by synthesizing the presented evidence into a unifying model of fatigue which distinguishes between factors that pre-dispose, precipitate, and perpetuate PSF. This framework will help guide new research into the biological mechanisms of PSF which is a necessary prerequisite for developing treatments to mitigate the debilitating effects of post-stroke fatigue.

Post-stroke fatigue: a review of development, prevalence, predisposing factors, measurements, and treatments

Background

Post-stroke fatigue (PSF) is a ubiquitous and overwhelming symptom for most stroke survivors. However, there are no effective management strategies for PSF, which is partly due to our limited understanding.

Objective

In this paper, we review the development, prevalence, predisposing factors, measurements, and treatments of PSF.

Results

PSF is an independent symptom after stroke, with a prevalence ranging from 42 to 53%, which depends on the selection of measurement tools and stroke characteristics. It is affected by biological, physical, and psychological factors, among which inflammation may play a key role.

Conclusion

Numerous but non-specific evaluation measurement tools limit the management of PSF. In clinical practice, it may be beneficial to identify PSF by combining scales and objective indexes, such as walking tests and electromyographic examinations. There are no evidence-based interventions to improve PSF. However, increasing evidence suggests that transcranial direct-current stimulation and mindfulness-based interventions may become promising treatments. Further studies are urgently needed to better understand the etiology of PSF, thereby providing the basis for developing new measurement tools and targeted treatments.

C-Reactive Protein: Pathophysiology, Diagnosis, False Test Results and a Novel Diagnostic Algorithm for Clinicians

The current literature provides a body of evidence on C-Reactive Protein (CRP) and its potential role in inflammation. However, most pieces of evidence are sparse and controversial. This critical state-of-the-art monography provides all the crucial data on the potential biochemical properties of the protein, along with further evidence on its potential pathobiology, both for its pentameric and monomeric forms, including information for its ligands as well as the possible function of autoantibodies against the protein. Furthermore, the current evidence on its potential utility as a biomarker of various diseases is presented, of all cardiovascular, respiratory, hepatobiliary, gastrointestinal, pancreatic, renal, gynecological, andrological, dental, oral, otorhinolaryngological, ophthalmological, dermatological, musculoskeletal, neurological, mental, splenic, thyroid conditions, as well as infections, autoimmune-supposed conditions and neoplasms, including other possible factors that have been linked with elevated concentrations of that protein. Moreover, data on molecular diagnostics on CRP are discussed, and possible etiologies of false test results are highlighted. Additionally, this review evaluates all current pieces of evidence on CRP and systemic inflammation, and highlights future goals. Finally, a novel diagnostic algorithm to carefully assess the CRP level for a precise diagnosis of a medical condition is illustrated.

Reduced ex vivo TNFα synthesis upon whole blood stimulation with endotoxin predicts post-stroke fatigue

OBJECTIVE

Fatigue is a common, debilitating syndrome after stroke. Peripheral inflammation plays a role in the pathogenesis of fatigue of different origin, but its contribution to post-stroke fatigue (PSF) remains unclear. We aimed to determine if there is any association between ex vivo synthesized and circulating cytokines, and risk of PSF.

METHODS

We included 174 patients with ischemic stroke. We stimulated in vitro blood taken on day 3 after stroke with endotoxin. We measured ex vivo released (TNFα, IP-10, IL-1β, IL-6, IL-8, IL-10, IL-12p70) and plasma (TNFα, IL-6, sIL-6R, IL-1Ra) cytokines. We assessed fatigue at month 3 using Fatigue Severity Scale (FSS). We used logistic regression to assess the relationship between cytokines and fatigue scores.

RESULTS

Compared with patients with lower fatigue at month 3 (FSS < 36), patients with higher fatigue (FSS ≥ 36) had lower endotoxin-stimulated TNFα release after 24 h (median: 429 vs 581 pg/mL, P = 0.05). Plasma TNFα tended to be higher in patients who developed fatigue (median: 0.8 vs 0.6 pg/mL, P = 0.06). Other cytokines did not differ between groups. After adjusting for pre-stroke fatigue and depressive symptoms, TNFα release <559.7 pg/mL after 24 h was associated with an increased risk of PSF (OR: 2.61, 95%CI: 1.22-5.57, P = 0.01). Plasma TNFα >0.76 pg/mL was associated with higher risk of PSF in univariable (OR: 2.41, 95%CI: 1.13-5.15, P = 0.02), but not multivariable analysis (OR: 2.41, 95%CI: 0.96-6.00, P = 0.06).

CONCLUSION

Reduced ex vivo TNFα synthesis upon whole blood stimulation with endotoxin in the acute phase of stroke predicted PSF.

Does urinary metabolite signature act as a biomarker of post-stroke depression?

BACKGROUND

It is difficult to conduct the precise diagnosis of post-stroke depression (PSD) in clinical practice due to the complex psychopathology of depressive disorder. Several studies showed that gas chromatography-mass spectrometry (GC-MS)-identified urinary metabolite biomarkers could significantly discriminate PSD from stroke survivors.

METHODS

A systematic review was performed for the keywords of "urinary metabolite" and "PSD" using Medline, Cochrane Library, Embase, Web of Science, PsycINFO, Wanfang, CNKI, CBM, and VIP database from inception to 31 March 2022.

RESULTS

Four related studies were included in the review. Differential urinary metabolites including lactic acid, palmitic acid, azelaic acid, and tyrosine were identified in all the included studies. As a significant deviation in the metabolite biomarker panel, glyceric acid, azelaic acid, phenylalanine, palmitic acid, pseudouridine, and tyrosine were found in at least 2 included studies, which indicated good potential for the differentiation of PSD.

CONCLUSION

The systematic review provided evidence that differential urinary metabolites analyzed by the GC-MS-based approach might be used as a biomarker for the diagnosis and prognosis of PSD.

Analysis of Factors Affecting Post-Stroke Fatigue: An Observational, Cross-Sectional, Retrospective Chart Review Study

Post-stroke fatigue (PSF) is one of the most common emotional and mood disorders in stroke survivors. Several studies have suggested associations between PSF and various factors. However, they describe conflicting results. Therefore, this study aimed to evaluate the factors affecting PSF. We retrospectively reviewed the medical records of 178 hospitalized stroke patients. The collected data were compared between the PSF and control groups. To evaluate the association between factors and PSF, regression analysis was conducted. A total of 96 patients (53.9%) were assigned to the PSF group, and 82 patients were assigned to the control group. Age, neurological deficits, cognitive dysfunction, degree of depression, hs-CRP, and ESR differed significantly between the two groups. For both types of stroke, multiple linear regression analyses showed that degree of depression and degree of inflammation were significantly associated with PSF. Through subgroup analysis, multiple linear regression analyses showed that the degree of depression in ischemic and hemorrhagic stroke and the platelet-to-lymphocyte ratio in hemorrhagic stroke had a significant association with PSF. In conclusion, post-stroke depression and degree of inflammation could be clinically significant predictors of PSF in all types of stroke patients. However, larger, prospective studies are required to obtain more concrete results.

The advances of post-stroke depression: 2021 update

Post-stroke depression (PSD) is one of common and serious sequelae of stroke. Approximately, one in three stroke survivors suffered from depression after stroke. It heavily affected functional rehabilitation, which leaded to poor quality of life. What is worse, it is strongly associated with high mortality. In this review, we aimed to derive a comprehensive and integrated understanding of PSD according to recently published papers and previous classic articles. Based on the considerable number of studies, we found that within 2 years incidence of PSD has a range from 11 to 41%. Many factors contribute to the occurrence of PSD, including the history of depression, stroke severity, lesion location, and so on. Currently, the diagnosis of PSD is mainly based on the DSM guidelines and combined with various depression scales. Unfortunately, we lack a unified mechanism to explain PSD which mechanisms now involve dysregulation of hypothalamic-pituitary-adrenal (HPA) axis, increased inflammatory factors, decreased levels of monoamines, glutamate-mediated excitotoxicity, and abnormal neurotrophic response. At present, both pharmacotherapy and psychological therapies are employed in treating PSD. Although great advance has been made by researchers, there are still a lot of issues need to be addressed. Especially, the mechanism of PSD is not completely clear.

Hypersensitive C-reactive protein-albumin ratio predicts symptomatic intracranial hemorrhage after endovascular therapy in acute ischemic stroke patients

Background

Approximately 10% of patients would develop symptomatic intracranial hemorrhage (sICH) after endovascular therapy. The aim of our study was to explore the ability of hypersensitive C-reactive protein-albumin ratio (HAR) in predicting sICH after endovascular therapy.

Methods

From April 2016 to December 2018, 334 consecutive patients with anterior circulation infarction undergoing endovascular therapy were enrolled in our study. sICH was defined using Heidelberg bleeding classification after endovascular therapy. Multiple regression analysis was used to investigate the potential risk factors of sICH after endovascular therapy. We used receiver operating characteristic curve analysis and nomogram analysis to assess the overall discriminative ability of the HAR in predicting sICH after endovascular therapy.

Results

Among these 334 patients enrolled, 37 (11.1%) patients with anterior circulation infarction were identified with sICH after endovascular therapy. Univariate logistic regression analysis demonstrated that patients with higher levels of HAR may be inclined to develop sICH (odds ratio, 10.994; 95% confidence interval, 4.567–26.463; P = 0.001). This association remained significant even after adjustment for potential confounders. Also, a cutoff value of 0.526× 10^− 3 for HAR was detected in predicting sICH (area under curve, 0.763). Furthermore, nomogram analysis also suggested that HAR was an indicator of sICH (c-index was 0.890, P< 0.001).

Conclusions

This study showed that high levels of HAR could predict sICH after endovascular therapy.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12883-021-02066-2.