Influence of Hemoglobin Concentration on Stroke Recurrence and Composite Vascular Events

Taurine levels and long-term adverse cerebrovascular risk in moyamoya disease: A prognostic perspective study

BACKGROUND

Taurine has been proven to play a significant role in cardiovascular and cerebrovascular diseases, but its relationship with moyamoya disease (MMD) remains unclear. This study aims to investigate the association between serum taurine levels and long-term adverse cerebrovascular events in patients with MMD after revascularization.

METHODS

This study involved 352 patients with MMD, from whom comprehensive clinical data and blood samples were collected. Serum taurine levels were measured using liquid chromatography-tandem mass spectrometry, and the relationship between serum taurine concentration and various blood indices was evaluated. Cerebrovascular adverse events included transient ischemic attack, ischemic stroke, and hemorrhagic stroke. Taurine, analyzed as a continuous variable, was found to predict a cut-off for postoperative cerebrovascular adverse events in MMD patients at 842.52 μmol/L. The impact of serum taurine levels on the risk of cerebrovascular events was analyzed using Kaplan-Meier (KM) curves, and univariate and multivariate Cox regression analyses were performed to identify predictive factors for postoperative prognosis.

RESULTS

Grouping MMD patients by serum taurine levels revealed that higher taurine levels were significantly associated with a lower proportion of hemorrhagic MMD (p = 0.044). Compared with ischemic MMD, patients with hemorrhagic MMD had lower taurine concentrations (p = 0.005). KM curves showed that the incidence of postoperative cerebrovascular adverse events in the high taurine group was significantly lower than in the low taurine group (p = 0.026). Univariate Cox regression analysis indicated that higher taurine concentrations significantly reduced the risk of postoperative cerebrovascular adverse events (Hazard Ratio [HR] = 0.334, 95 % Confidence Interval [CI] = 0.121-0.923, p = 0.035). Furthermore, the multivariate Cox regression model confirmed that taurine level is an independent predictor of long-term adverse cerebrovascular events, with the high concentration group showing a significantly reduced risk.

CONCLUSIONS

Low serum taurine levels are associated with a higher risk of long-term adverse cerebrovascular events following MMD revascularization. This suggests the significant potential of serum taurine as a prognostic biomarker for postoperative outcomes.

CLINICAL TRIAL REGISTRY NUMBER

URL: https://www.chictr.org.cn/. Unique identifier: ChiCTR2200061889.

Predicting the likelihood of readmission in patients with ischemic stroke: An explainable machine learning approach using common data model data

BACKGROUND

Ischemic stroke affects 15 million people worldwide, causing five million deaths annually. Despite declining mortality rates, stroke incidence and readmission risks remain high, highlighting the need for preventing readmission to improve the quality of life of survivors. This study developed a machine-learning model to predict 90-day stroke readmission using electronic medical records converted to the common data model (CDM) from the Regional Accountable Care Hospital in Gangwon state in South Korea.

METHODS

We retrospectively analyzed data from 1,136 patients with ischemic stroke admitted between August 2003 and August 2021 after excluding cases with missing blood test values. Demographics, blood test results, treatments, and comorbidities were used as key features. Six machine learning models and three deep learning models were used to predict 90-day readmission using the synthetic minority over-sampling technique to address class imbalance. Models were evaluated using threefold cross-validation, and SHapley Additive exPlanations (SHAP) values were calculated to interpret feature importance.

RESULTS

Among 1,136 patients, 196 (17.2 %) were readmitted within 90 days. Male patients were significantly more likely to experience readmission (p = 0.02). LightGBM achieved an area under the curve of 0.94, demonstrating that analyzing stroke and stroke-related conditions provides greater predictive accuracy than predicting stroke alone or all-cause readmissions. SHAP analysis highlighted renal and metabolic variables, including creatinine, blood urea nitrogen, calcium, sodium, and potassium, as key predictors of readmission.

CONCLUSION

Machine-learning models using electronic health record-based CDM data demonstrated strong predictive performance for 90-day stroke readmission. These results support personalized post-discharge management and lay the groundwork for future multicenter studies.

Association between hemoglobin level and clinical outcomes in ischemic stroke patients with high-grade carotid artery stenosis

OBJECTIVE

Abnormal hemoglobin levels may influence stroke outcome, while high-grade carotid artery stenosis (CAS) is linked to distal hemodynamic compromise. The relationship between hemoglobin and ischemic stroke (IS) outcome in patients with high-grade CAS remains unclear. We aimed to investigate this association in acute IS patients with high-grade CAS.

METHODS

To compare the characteristics and outcome in acute IS patients with high-grade CAS across different hemoglobin levels, we conducted an observational cohort study from January 2007 to April 2012 and followed for 5 years.

RESULTS

Among 372 enrolled patients, 75 had hemoglobin < 12 g/dL, 153 had 12-14 g/dL, and 144 had > 14 g/dL. Hemoglobin < 12 g/dL was associated with higher rates of congestive heart failure, gout, and chronic kidney disease, but lower rate of hyperlipidemia. Hemoglobin< 12 g/dL had lower levels of white blood cells, total cholesterol, and estimated glomerular filtration rate, but higher levels of high-sensitivity C-reactive protein and potassium. The Cox proportional hazards model revealed that hemoglobin< 12 g/dL was associated with higher risk of all-cause mortality (hazard ratio (HR) 1.99, 95 % confidence interval (CI) 1.20-3.32, P = 0.008) and lower risk of stroke recurrence over 5 years in IS patients with high-grade CAS (HR 0.50, 95 % CI 0.26-0.95; P = 0.033).

CONCLUSIONS

Hemoglobin< 12 g/dL was associated with higher mortality and lower stroke recurrence risk over 5 years in IS patients with high-grade CAS. Further studies are warranted to determine the optimal hemoglobin level for improving outcomes in these patients.

The performance of machine learning for predicting the recurrent stroke: a systematic review and meta-analysis on 24,350 patients

BACKGROUND

Stroke is a leading cause of death and disability worldwide. Approximately one-third of patients with stroke experienced a second stroke. This study investigates the predictive value of machine learning (ML) algorithms for recurrent stroke.

METHOD

This study was prepared according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guideline. PubMed, Scopus, Embase, and Web of Science (WOS) were searched until January 1, 2024. The quality assessment of studies was conducted using the QUADAS-2 tool. The diagnostic meta-analysis was conducted to calculate the pooled sensitivity, specificity, diagnostic accuracy, positive and negative diagnostic likelihood ratio (DLR), diagnostic accuracy, diagnostic odds ratio (DOR), and area under of the curve (AUC) by the MIDAS package in STATA V.17.

RESULTS

Twelve studies, comprising 24,350 individuals, were included. The meta-analysis revealed a sensitivity of 71% (95% CI 0.64-0.78) and a specificity of 88% (95% confidence interval (CI) 0.76-0.95). Positive and negative DLR were 5.93 (95% CI 3.05-11.55) and 0.33 (95% CI 0.28-0.39), respectively. The diagnostic accuracy and DOR was 2.89 (95% CI 2.32-3.46) and 18.04 (95% CI 10.21-31.87), respectively. The summary ROC curve indicated an AUC of 0.82 (95% CI 0.78-0.85).

CONCLUSION

ML demonstrates promise in predicting recurrent strokes, with moderate to high sensitivity and specificity. However, the high heterogeneity observed underscores the need for standardized approaches and further research to enhance the reliability and generalizability of these models. ML-based recurrent stroke prediction can potentially augment clinical decision-making and improve patient outcomes by identifying high-risk patients.

Recurrent Ischemic Stroke and Transient Ischemic Attack: Risk of Single and Multiple Recurrence

Background/Objectives: Efforts have been made toward primary or secondary stroke or transient ischemic attack (TIA) prevention. However, little attention has been paid to recurrent stroke or TIA. This study investigated risk factors for multiple or single recurrent stroke or TIA. Methods: Data from 3646 patients with ischemic stroke or TIA were obtained from the Korea University Ansan Hospital Stroke Center between March 2014 and December 2021, using the prospective institutional database of the Korea University Stroke Registry. The associations between clinical features and recurrent stroke or TIA were assessed using bivariable and multivariable Cox models. Results: Recurrent stroke or TIA was associated with male sex (adjusted hazard ratio (HR) 1.95, 95% confidence interval (CI) 1.42-2.80), hypertension (HR 1.49, 95% CI 1.00-2.23), diabetes mellitus (HR 1.54, 95% CI 1.13-2.13), an etiologic subtype of transient ischemic attack (HR 1.88, 95% CI 1.09-3.16), white matter changes (HR 1.62, 95% CI 1.05-2.38), and cerebral microbleeds (HR 1.79, 95% CI 1.26-2.59). Multiple recurrent stroke or TIA was associated with male sex (HR 3.86, 95% CI 1.94-11.55), diabetes mellitus (HR 2.40, 95% CI 1.31-4.53), and anemia (HR 4,58, 95% CI 2.31-10.44). Conclusions: Given the risk factor profiles for recurrent stroke or TIA, risks differed among patient subgroups and were based on multiple or single recurrences. It may exert an effect as a prognostic indicator in the high risk of recurrences.

Impact of Hemoglobin Levels on Composite Cardiac Arrest or Stroke Outcome in Patients With Respiratory Failure Due to COVID-19

OBJECTIVES

Anemia has been associated with an increased risk of both cardiac arrest and stroke, frequent complications of COVID-19. The effect of hemoglobin level at ICU admission on a composite outcome of cardiac arrest or stroke in an international cohort of COVID-19 patients was investigated.

DESIGN

Retrospective analysis of prospectively collected database.

SETTING

A registry of COVID-19 patients admitted to ICUs at over 370 international sites was reviewed for patients diagnosed with cardiac arrest or stroke up to 30 days after ICU admission. Anemia was defined as: normal (hemoglobin ≥ 12.0 g/dL for women, ≥ 13.5 g/dL for men), mild (hemoglobin 10.0-11.9 g/dL for women, 10.0-13.4 g/dL for men), moderate (hemoglobin ≥ 8.0 and < 10.0 g/dL for women and men), and severe (hemoglobin < 8.0 g/dL for women and men).

PATIENTS

Patients older than 18 years with acute COVID-19 infection in the ICU.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

Of 6926 patients (median age = 59 yr, male = 65%), 760 patients (11.0%) experienced stroke (2.0%) and/or cardiac arrest (9.4%). Cardiac arrest or stroke was more common in patients with low hemoglobin, occurring in 12.8% of patients with normal hemoglobin, 13.3% of patients with mild anemia, and 16.7% of patients with moderate/severe anemia. Time to stroke or cardiac arrest by anemia status was analyzed using Cox proportional hazards regression with death as a competing risk. Covariates selected through clinical knowledge were age, sex, comorbidities (diabetes, hypertension, obesity, and cardiac or neurologic conditions), pandemic era, country income, mechanical ventilation, and extracorporeal membrane oxygenation. Moderate/severe anemia was associated with a higher risk of cardiac arrest or stroke (hazard ratio, 1.32; 95% CI, 1.05-1.67).

CONCLUSIONS

In an international registry of ICU patients with COVID-19, moderate/severe anemia was associated with increased hazard of cardiac arrest or stroke.

Elevated Fibrinogen-to-Albumin Ratio Correlates with Incident Stroke in Cerebral Small Vessel Disease

Purpose

We aimed to explore the association between fibrinogen-to-albumin ratio (FAR) and the risk of incident stroke (IS) in a cohort of cerebral small vessel disease (CSVD) patients.

Patients and Methods

Participants were screened from a prospective CSVD database. Clinical data, hematologic measures and imaging findings were collected. The primary outcome was IS during follow-up, with a secondary outcome of composite vascular events (CVE) including IS, myocardial infarction (MI), and vascular deaths. Univariate and multivariate COX proportional risk models, along with competing risk models, were employed to identify factors associated with outcomes. Restricted cubic spline (RCS) and subgroup analyses were conducted to assess the association between FAR and the risk of IS and CVE in CSVD patients.

Results

In the final analysis of 682 CSVD patients over a median observation period of 34.0 [24.0-53.0] months, there were 33 cases of IS (4.84%, 1.55/100 person-years), 4 incidents of MI (0.59%, 0.19/100 person-years), 15 non-vascular deaths (2.20%, 0.70/100 person-years), and 37 occurrences of CVE (5.43%, 1.74/100 person-years). Multivariate Cox regression analysis revealed a significant positive correlation between elevated FAR and both IS (HR 1.146; 95% CI 1.043-1.259; P=0.004) and CVE (HR 1.156; 95% CI 1.063-1.257; P=0.001) in CSVD patients. Multivariate competing risk model showed the similar results (IS: HR 1.16; 95% CI 1.06-1.27; P=0.001, CVE: HR 1.15; 95% CI 1.05-1.26; P=0.003). RCS analysis indicated a linear relationship between FAR and the risks of both IS (P for non-linearity =0.7016) and CVE (P for non-linearity =0.6475), with an optimal cutoff value of 8.69, particularly in individuals over 60 years of age.

Conclusion

Elevated FAR demonstrated an independent and linear association with IS and the development of CVE in CSVD patients.

Exploring Relationships of Heart Rate Variability, Neurological Function, and Clinical Factors with Mortality and Behavioral Functional Outcome in Patients with Ischemic Stroke

Ischemic stroke is a leading cause of mortality and disability. The relationships of heart rate variability (HRV) and stroke-related factors with mortality and functional outcome are complex and not fully understood. Understanding these relationships is crucial for providing better insights regarding ischemic stroke prognosis. The objective of this study is to examine the relationship between HRV, neurological function, and clinical factors with mortality and 3-month behavioral functional outcome in ischemic stroke. We prospectively collected the HRV data and monitored the behavioral functional outcome of patients with ischemic stroke. The behavioral functional outcome was represented by a modified Rankin Scale (mRS) score. This study population consisted of 58 ischemic stroke patients (56.9% male; mean age 70) with favorable (mRS score ≤ 2) and unfavorable (mRS score ≥ 3) outcome. The analysis indicated that the median of the mean RR interval (RR mean) showed no statistical difference between mortality groups. Conversely, the median of the RR mean had significant association with unfavorable outcome (OR = 0.989, p = 0.007). Lower hemoglobin levels had significant association with unfavorable outcome (OR = 0.411, p = 0.010). Higher National Institute of Health Stroke Scale (NIHSS) score at admission had significant association with unfavorable outcome (OR = 1.396, p = 0.002). In contrast, age, stroke history, NIHSS score at admission, and hemoglobin showed no significant association with mortality in ischemic stroke. These results imply that HRV, as indicated by the median of RR mean, alongside specific clinical factors and neurological function at admission (measured by NIHSS score), may serve as potential prognostic indicators for 3-month behavioral functional outcome in ischemic stroke.

Association of Higher Hemoglobin Level With Significant Carotid Artery Plaque in the General Population

Objective

Serum hemoglobin (Hb) level affects the viscosity of blood. Several studies have reported that Hb level is associated with adverse cardiovascular outcome. However, there is a paucity of evidence on the association between serum Hb level and the risk of subclinical atherosclerosis. Thus, the objective of this study was to investigate the relationship between Hb level and risk of carotid plaque in a health checkup cohort.

Methods

This retrospective study analyzed a total of 3,805 individuals without history of cardiovascular disease (CVD) who underwent carotid ultrasonography (USG) between January 2016 and June 2018. Participants were divided into 4 groups based on Hb quartiles in each of male and female. Carotid plaque score was calculated based on USG reports. Multivariable logistic regression analysis was performed for each index of quartile groups regarding the risk of carotid plaque.

Results

Of 3,805 individuals (mean age, 52.62±10.25 years; 2,674 [70.28%] males), mean Hb level was 15.11±0.75 g/dL in male and 13.35±0.74 g/dL in female. When the Q1 group was compared to the Q4, increasing quartile of Hb was associated with the presence of significant carotid plaque (plaque score ≥3) in male (adjusted odds ratio [OR], 1.538; 95% confidence interval [CI], 1.182-2.001; p=0.001) and female (adjusted OR, 1.749; 95% CI, 1.058-2.676; p=0.01).

Conclusion

A high Hb level is associated with an increased risk of carotid plaques in individuals without history of CVD. This finding may support the need for early screening of CVD in individuals with high Hb levels.

Detrimental effect of anemia after mechanical thrombectomy on functional outcome in patients with ischemic stroke

Background

Anemia can occur due to an aspiration maneuver of blood with thrombi during mechanical thrombectomy (MT) for stroke. However, the association between postoperative anemia and stroke outcomes is unknown.

Methods

In a registry-based hospital cohort, consecutive patients with acute ischemic stroke who underwent MT were retrospectively recruited. Patients were divided into the following three groups according to their hemoglobin (Hb) concentrations within 24 h after MT; no anemia (Hb concentrations ≥13 g/dL for men and ≥ 12 g/dL for women), mild anemia (Hb concentrations of 11-13 g/dL and 10-12 g/dL, respectively), and moderate-to-severe anemia (Hb concentrations <11 g/dL and < 10 g/dL, respectively). A 3-month modified Rankin Scale score of 0-2 indicated a favorable outcome.

Results

Of 470 patients, 166 were classified into the no anemia group, 168 into the mild anemia group, and 136 into the moderate-to-severe anemia group. Patients in the moderate-to-severe anemia group were older and more commonly had congestive heart failure than those in the other groups. Patients in the moderate-to-severe anemia group also had more device passes than those in the other groups (p < 0.001). However, no difference was observed in the rate of final extended thrombolysis in cerebral infarction ≥2b reperfusion or intracranial hemorrhage among the groups. A favorable outcome was less frequently achieved in the moderate-to-severe anemia group than in the no anemia group (adjusted odds ratio, 0.46; 95% confidence interval, 0.26-0.81) independent of the baseline Hb concentration. A restricted cubic spline model with three knots showed that the adjusted odds ratio for a favorable outcome was lower in patients with lower Hb concentrations within 24 h after MT.

Conclusion

Moderate-to-severe anemia within 24 h after MT is independently associated with a reduced likelihood of a favorable outcome.

Clinical trial registration

https://www.clinicaltrials.gov, NCT02251665.

Prediction of plasma volume and total hemoglobin mass with machine learning

Hemoglobin concentration ([Hb]) is used for the clinical diagnosis of anemia, and in sports as a marker of blood doping. [Hb] is however subject to significant variations mainly due to shifts in plasma volume (PV). This study proposes a newly developed model able to accurately predict total hemoglobin mass (Hbmass) and PV from a single complete blood count (CBC) and anthropometric variables in healthy subject. Seven hundred and sixty-nine CBC coupled to measures of Hbmass and PV using a CO-rebreathing method were used with a machine learning tool to calculate an estimation model. The predictive model resulted in a root mean square error of 33.2 g and 35.6 g for Hbmass, and 179 mL and 244 mL for PV, in women and men, respectively. Measured and predicted data were significantly correlated (p < 0.001) with a coefficient of determination (R2 ) ranging from 0.76 to 0.90 for Hbmass and PV, in both women and men. The Bland-Altman bias was on average 0.23 for Hbmass and 4.15 for PV. We herewith present a model with a robust prediction potential for Hbmass and PV. Such model would be relevant in providing complementary data in contexts such as the epidemiology of anemia or the individual monitoring of [Hb] in anti-doping.

Causal Association Between Anemia and Cardiovascular Disease: A 2-Sample Bidirectional Mendelian Randomization Study

Background Although previous observational studies have shown an association between anemia and cardiovascular disease (CVD), the underlying causal relationship between anemia and CVD remains uncertain. Methods and Results We conducted a 2-sample bidirectional Mendelian randomization (MR) study to assess the causal association between anemia and CVD. We extracted summary statistics data for anemia, heart failure (HF), coronary artery disease (CAD), atrial fibrillation, any stroke, and any ischemic stroke (AIS) from relevant published genome-wide association studies. After rigorous quality control steps, independent single-nucleotide polymorphisms for each disease were selected as instrumental variables. Inverse-variance weighting was used as the primary method to estimate the causal association between anemia and CVD in the 2-sample MR analysis. Simultaneously, we performed a series of multiple methods analyses (median weighting, maximum likelihood [MR robust adjusted profile score]), sensitivity analyses (Cochran's Q test and MR-Egger intercept, leave-one-out test [MR pleiotropy residual sum and outlier]), instrumental variable strength evaluations (F statistic), and statistic power estimates to verify the robustness and reliability of our results. Furthermore, the associations between anemia and CVD from different studies, including the UK Biobank and FinnGen studies, were combined by meta-analysis. The MR analysis showed that genetically predicted anemia was significantly associated with HF risk at the Bonferroni-corrected significance level (odds ratio [OR], 1.11 [95% CI, 1.04-1.18]; P=0.002) and was suggestively associated with CAD risk (OR, 1.11 [95% CI, 1.02-1.22]; P=0.020). However, the associations between anemia and atrial fibrillation, any stroke, or AIS were not statistically significant. In the reverse MR analysis, we found that genetic susceptibility to HF, CAD, and AIS was significantly associated with anemia risk. The ORs of HF, CAD, and AIS were 1.64 (95% CI, 1.39-1.94; P=7.60E-09), 1.16 (95% CI, 1.08-1.24; P=2.32E-05), and 1.30 (95% CI, 1.11-1.52; P=0.001), respectively. Genetically predicted atrial fibrillation was suggestively associated with anemia (OR, 1.06 [95% CI, 1.01-1.12]; P=0.015). Sensitivity analyses found weak evidence of horizontal pleiotropy and heterogeneity, which ensured the robustness and reliability of the results. Meta-analysis also showed the statistically significant association between anemia and HF risk. Conclusions Our study supports bidirectional causality between anemia and HF and significant associations between genetic predisposition to CAD and AIS with anemia, which contributes to the clinical management of both diseases.

Prediction of recurrence of ischemic stroke within 1 year of discharge based on machine learning MRI radiomics

Purpose

This study aimed to investigate the value of a machine learning-based magnetic resonance imaging (MRI) radiomics model in predicting the risk of recurrence within 1 year following an acute ischemic stroke (AIS).

Methods

The MRI and clinical data of 612 patients diagnosed with AIS at the Second Affiliated Hospital of Nanchang University from March 1, 2019, to March 5, 2021, were obtained. The patients were divided into recurrence and non-recurrence groups according to whether they had a recurrent stroke within 1 year after discharge. Randomized splitting was used to divide the data into training and validation sets using a ratio of 7:3. Two radiologists used the 3D-slicer software to label the lesions on brain diffusion-weighted (DWI) MRI sequences. Radiomics features were extracted from the annotated images using the pyradiomics software package, and the features were filtered using the Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis. Four machine learning algorithms, logistic regression (LR), Support Vector Classification (SVC), LightGBM, and Random forest (RF), were used to construct a recurrence prediction model. For each algorithm, three models were constructed based on the MRI radiomics features, clinical features, and combined MRI radiomics and clinical features. The sensitivity, specificity, and area under the receiver operating characteristic (ROC) curve (AUC) were used to compare the predictive efficacy of the models.

Results

Twenty features were selected from 1,037 radiomics features extracted from DWI images. The LightGBM model based on data with three different features achieved the best prediction accuracy from all 4 models in the validation set. The LightGBM model based solely on radiomics features achieved a sensitivity, specificity, and AUC of 0.65, 0.671, and 0.647, respectively, and the model based on clinical data achieved a sensitivity, specificity, and AUC of 0.7, 0.799, 0.735, respectively. The sensitivity, specificity, and AUC of the LightGBM model base on both radiomics and clinical features achieved the best performance with a sensitivity, specificity, and AUC of 0.85, 0.805, 0.789, respectively.

Conclusion

The ischemic stroke recurrence prediction model based on LightGBM achieved the best prediction of recurrence within 1 year following an AIS. The combination of MRI radiomics features and clinical data improved the prediction performance of the model.

Prognosis of Individual-Level Mobility and Daily Activities Recovery From Acute Care to Community, Part 2: A Proof-of-Concept Single Group Prospective Cohort Study

OBJECTIVE

To demonstrate a proof-of-concept for prognostic models of post-stroke recovery on activity level outcomes.

DESIGN

Longitudinal cohort with repeated measures from acute care, inpatient rehabilitation, and post-discharge follow-up to 6 months post-stroke.

SETTING

Enrollment from a single Midwest USA inpatient rehabilitation facility with community follow-up.

PARTICIPANTS

One-hundred fifteen persons recovering from stroke admitted to an acute rehabilitation facility (N=115).

INTERVENTIONS

Not applicable.

MAIN OUTCOME MEASURE(S)

Activity Measure for Post-Acute Care Basic Mobility and Daily Activities domains administered as 6 Clicks and patient-reported short forms.

RESULTS

The final Basic Mobility model defined a group-averaged trajectory rising from a baseline (pseudo-intercept) T score of 35.5 (P<.001) to a plateau (asymptote) T score of 56.4 points (P<.001) at a negative exponential rate of -1.49 (P<.001). Individual baseline scores varied by age, acute care tissue plasminogen activator, and acute care length of stay. Individual plateau scores varied by walking speed, acute care tissue plasminogen activator, and lower extremity Motricity Index scores. The final Daily Activities model defined a group-averaged trajectory rising from a baseline T score of 24.5 (P<.001) to a plateau T score of 41.3 points (P<.001) at a negative exponential rate of -1.75 (P<.001). Individual baseline scores varied by acute care length of stay, and plateau scores varied by self-care, upper extremity Motricity Index, and Berg Balance Scale scores.

CONCLUSIONS

As a proof-of-concept, individual activity-level recovery can be predicted as patient-level trajectories generated from electronic medical record data, but models require attention to completeness and accuracy of data elements collected on a fully representative patient sample.

Prognosis of Individual-Level Mobility and Self-Care Stroke Recovery During Inpatient Rehabilitation, Part 1: A Proof-of-Concept Single Group Retrospective Cohort Study

OBJECTIVE

To demonstrate feasibility of generating predictive short-term individual trajectory recovery models after acute stroke by extracting clinical data from an electronic medical record (EMR) system.

DESIGN

Single-group retrospective patient cohort design.

SETTING

Stroke rehabilitation unit at an independent inpatient rehabilitation facility (IRF).

PARTICIPANTS

Cohort of 1408 inpatients with acute ischemic or hemorrhagic stroke with a mean ± SD age of 66 (14.5) years admitted between April 2014 and October 2019 (N=1408).

INTERVENTIONS

Not applicable.

MAIN OUTCOME MEASURES

0-100 Rasch-scaled Functional Independence Measure (FIM) Mobility and Self-Care subscales.

RESULTS

Unconditional models were best-fit on FIM Mobility and Self-Care subscales by spline fixed-effect functions with knots at weeks 1 and 2, and random effects on the baseline (FIM 0-100 Rasch score at IRF admission), initial rate (slope at time zero), and second knot (change in slope pre-to-post week 2) parameters. The final Mobility multivariable model had intercept associations with Private/Other Insurance, Ischemic Stroke, Serum Albumin, Motricity Index Lower Extremity, and FIM Cognition; and initial slope associations with Ischemic Stroke, Private/Other and Medicaid Insurance, and FIM Cognition. The final Self-Care multivariable model had intercept associations with Private/Other Insurance, Ischemic Stroke, Living with One or More persons, Serum Albumin, and FIM Cognition; and initial slope associations with Ischemic Stroke, Private/Other and Medicaid Insurance, and FIM Cognition. Final models explained 52% and 27% of the variance compared with unconditional Mobility and Self-Care models. However, some EMR data elements had apparent coding errors or missing data, and desired elements from acute care were not available. Also, unbalanced outcome data may have biased trajectories.

CONCLUSIONS

We demonstrate the feasibility of developing individual-level prognostic models from EMR data; however, some data elements were poorly defined, subject to error, or missing for some or all cases. Development of prognostic models from EMR will require improvements in EMR data collection and standardization.

Prediction of Hemorrhagic Complication after Thrombolytic Therapy Based on Multimodal Data from Multiple Centers: An Approach to Machine Learning and System Implementation

Hemorrhagic complication (HC) is the most severe complication of intravenous thrombolysis (IVT) in patients with acute ischemic stroke (AIS). This study aimed to build a machine learning (ML) prediction model and an application system for a personalized analysis of the risk of HC in patients undergoing IVT therapy. We included patients from Chongqing, Hainan and other centers, including Computed Tomography (CT) images, demographics, and other data, before the occurrence of HC. After feature engineering, a better feature subset was obtained, which was used to build a machine learning (ML) prediction model (Logistic Regression (LR), Random Forest (RF), Support Vector Machine (SVM), eXtreme Gradient Boosting (XGB)), and then evaluated with relevant indicators. Finally, a prediction model with better performance was obtained. Based on this, an application system was built using the Flask framework. A total of 517 patients were included, of which 332 were in the training cohort, 83 were in the internal validation cohort, and 102 were in the external validation cohort. After evaluation, the performance of the XGB model is better, with an AUC of 0.9454 and ACC of 0.8554 on the internal validation cohort, and 0.9142 and ACC of 0.8431 on the external validation cohort. A total of 18 features were used to construct the model, including hemoglobin and fasting blood sugar. Furthermore, the validity of the model is demonstrated through decision curves. Subsequently, a system prototype is developed to verify the test prediction effect. The clinical decision support system (CDSS) embedded with the XGB model based on clinical data and image features can better carry out personalized analysis of the risk of HC in intravenous injection patients.

The relationship between hemoglobin and triglycerides in moyamoya disease: A cross-sectional study

Hemoglobin (Hb) and lipid metabolism are critical in the pathophysiology of moyamoya disease (MMD), and Hb and triglycerides (TGs) both play roles in the development of cerebrovascular illness. However, there is little evidence of a link between Hb and TGs in patients with MMD. This study aimed to determine the association between Hb and TGs in patients who had recently been diagnosed with MMD. From March 2013 to December 2018, 337 patients clinically diagnosed with MMD were admitted to our hospital. Among these, 235 were selected for analysis in this retrospective, cross-sectional study. Each patient's clinical features were documented. For analysis, we used univariate analysis, smoothed-curve fitting, and multivariable, piecewise linear regression. Overall, the mean±standard deviation patient age was 48.14 ± 11.24 years, 44.68% were men, and the mean Hb concentration was 135.72 ± 18.99 g/L. After controlling for relevant confounders, smoothed-curve fitting revealed a nonlinear association between the Hb and TG concentrations (P = 0.0448). When the Hb concentration was below 141 g/L, multivariate piecewise linear regression analysis revealed a significant association between the Hb and TG concentrations [β: 0.01, 95% confidence interval (CI): 0.00, 0.01; P = 0.0182], although the association disappeared above this threshold (β:-0.00, 95% CI:-0.01, 0.01; P = 0.4429). In individuals newly diagnosed with MMD, there is a significant correlation between Hb and TGs, which may be connected to MMD pathogenesis.

Hemoglobin Concentration and Clinical Outcomes After Acute Ischemic Stroke or Transient Ischemic Attack

Background

Anemia or low hemoglobin can increase the risk of stroke. However, the association between hemoglobin and outcomes after stroke is uncertain. In this study, we aimed to investigate the association between hemoglobin and clinical outcomes, including mortality, poor functional outcome, stroke recurrence, and composite vascular events at 1 year.

Methods and Results

We included the patients diagnosed with acute ischemic stroke or transient ischemic attack from the Third China National Stroke Registry. We used the Cox model for mortality, stroke recurrence, and composite vascular events and the logistic model for the poor functional outcome to examine the relationship between hemoglobin and clinical outcomes. In addition, we used the restricted cubic spline to evaluate the nonlinear relationship. This study included 14 159 patients with acute ischemic stroke or transient ischemic attack. After adjusted for potential cofounders, both anemia and high hemoglobin were associated with the higher risk of mortality (hazard ratio [HR], 1.73; 95% CI, 1.39–2.15; HR, 2.71; 95% CI, 1.95–3.76) and poor functional outcome (odds ratio [OR], 1.36; 95% CI, 1.18–1.57; OR, 1.42; 95% CI, 1.07–1.87). High hemoglobin, but not anemia, increased the risk of stroke recurrence (HR, 1.37; 95% CI, 1.05–1.79) and composite vascular events (HR, 1.41; 95% CI, 1.08–1.83). There was a U‐shaped relationship between hemoglobin and mortality and poor functional outcome.

Conclusions

Abnormal hemoglobin was associated with a higher risk of all‐cause mortality, poor functional outcome, stroke recurrence, and composite vascular events. More well‐designed clinical studies are needed to confirm the relationship between hemoglobin and clinical outcomes after stroke.

Association of Prestroke Glycemic Control With Vascular Events During 1-Year Follow-up

Background and Objectives

We evaluated the association between admission glycated hemoglobin (HbA1c) and subsequent risk of composite vascular events, including stroke, myocardial infarction (MI), and vascular death, in patients with acute ischemic stroke and diabetes.

Methods

Patients who had a TIA or an acute ischemic stroke within 7 days of symptom onset and diabetes were included in a retrospective cohort design using the stroke registry of the Clinical Research Center for Stroke in Korea. The association between admission HbA1c and composite vascular events, including stroke, MI, and vascular death, during 1-year follow-up was estimated using the Fine-Gray model. The risk of composite vascular events according to the ischemic stroke subtype was explored using fractional polynomial and linear-quadratic models.

Results

Of the 18,567 patients, 1,437 developed composite vascular events during follow-up. In multivariable analysis using HbA1c as a categorical variable, the risk significantly increased at a threshold of 6.8%–7.0%. The influence of admission HbA1c level on the risk of composite vascular events was pronounced particularly among those in whom fasting glucose at admission was ≤130 mg/dL. The optimal ranges of HbA1c associated with minimal risks for composite vascular events were lowest for the small vessel occlusion subtype (6.6 [95% confidence internal [CI], 6.3–6.9]) compared to the large artery atherosclerosis (7.3 [95% CI, 6.8–7.9]) or the cardioembolic subtype (7.4 [95% CI, 6.3–8.5]).

Dicussion

In patients with ischemic stroke and diabetes, the risks of composite vascular events were significantly associated with admission HbA1c. The optimal range of admission HbA1c was below 6.8%–7.0% and differed according to the ischemic stroke subtype.

Association between aspirin-induced hemoglobin decline and outcome after acute ischemic stroke in G6PD-deficient patients

Aims

The risk of hemoglobin decline induced by low‐dose aspirin in glucose‐6‐phosphate dehydrogenase (G6PD) deficiency remains unknown, and its influence on stroke outcome remains to be investigated. This study aimed to evaluate the effect of G6PD deficiency on hemoglobin level during aspirin treatment and its association with outcome after acute ischemic stroke.

Methods

In total, 279 patients (40 G6PD‐deficient and 239 G6PD‐normal) with acute ischemic stroke treated with aspirin 100 mg/day from a cohort study were examined. The primary safety endpoint was a hemoglobin decline ≥25 g/L or 25% from baseline within 14 days after aspirin treatment. Poor outcomes were defined as a modified Rankin Scale score ≥2 at 3 months. The χ² test was used to compare stroke outcomes, and multivariate logistic regression analyses were performed to analyze the association between hemoglobin level and outcomes.

Results

The G6PD‐deficient group had lower baseline hemoglobin and tended to develop comorbid pulmonary infection more frequently (p < 0.05). The proportion of patients with hemoglobin decline ≥25 g/L or 25% from baseline (15.0% vs. 3.3%; p = 0.006) and anemia (30.0% vs. 14.6%; p = 0.016) after aspirin treatment was higher in the G6PD‐deficient group, which was accompanied by a more significant bilirubin increase. The rate of poor functional outcomes at 3 months after acute ischemic stroke was higher in the G6PD‐deficient group (Risk ratio = 1.31 [95% confidence interval (CI) = 1.10–1.56]; p = 0.017). Confounder‐adjusted analysis showed that lower hemoglobin levels (odds ratio = 0.98 [95% CI = 0.96–0.99]; adjusted p = 0.009) increased the risk of poor functional outcomes.

Conclusion

Hemoglobin decrease with bilirubin increase after aspirin treatment in patients with G6PD deficiency suggests hemolysis, which may influence stroke prognosis. The risk of hemoglobin decline should be carefully monitored in G6PD‐deficient patients with ischemic stroke taking aspirin.

Anemia Is a Risk Factor for the Development of Ischemic Stroke and Post-Stroke Mortality

BACKGROUND

anemia is known to be a risk factor for developing ischemic stroke in long-term follow-up studies, and it is also known to increase the risk of death in ischemic stroke patients. We aimed to determine the association of anemia with the risk of ischemic stroke and the risk of death after ischemic stroke.

METHODS

The study included patients from National Health Insurance Service cohort, from January 2005 to December 2015. Anemia patients were defined as those with confirmed diagnostic codes and related medications in the sample cohort, and patients under the age of 18 were excluded. To perform a comparative analysis with the control group, twice as many patients were extracted by propensity score matching. The effects of anemia on the development of ischemic stroke were analyzed.

RESULTS

A total of 58,699 patients were newly diagnosed with anemia during the study period. In anemia group, the rate of ischemic stroke occurring within 1 year was 0.550%, and the rate was 0.272% in the control group. The odds ratio of anemia related to ischemic stroke was 1.602 (95% confidence intervals (CI) 1.363-1.883). During the follow-up period, 175 out of 309 (56.6%) died in anemia group, and 130 out of 314 (41.4%) died in control group. The anemia group showed a higher risk of death than the control group (Hazard ratio 1.509, 95% CI 1.197-1.902).

CONCLUSION

Analysis of the nationwide health insurance data revealed that anemia is one of the risk factors for the development of ischemic stroke, and also an independent prognostic factor affecting post-stroke mortality.

The Hemoglobin, Albumin, Lymphocyte, and Platelet (HALP) Score Is Associated With Poor Outcome of Acute Ischemic Stroke

Background: The combined index of hemoglobin, albumin, lymphocyte, and platelet (HALP) is considered a novel score to reflect systemic inflammation and nutritional status. This study aimed to investigate the association between HALP score and poor outcome in patients with acute ischemic stroke (AIS). Methods: Consecutive AIS patients within 24 h after onset were prospectively enrolled. Poor outcome was a combination of a new stroke event (ischemic and hemorrhagic) and all-cause death within 90 days and 1 year. The association between HALP score and poor outcome was analyzed using Cox proportional hazards. Results: A total of 1,337 patients were included. Overall, 60 (4.5%) and 118 (8.8%) patients experienced poor outcome within 90 days and 1 year, respectively. Patients in the highest tertile of HALP score had a lower risk of poor outcome within 90 days and 1 year (hazard ratio: 0.25 and 0.42; 95% confidence intervals: 0.11-0.57 and 0.25-0.69, P for trend <0.01 for all) compared with those in the lowest tertile after adjusting relevant confounding factors. Adding HALP score to the conventional risk factors improved prediction of poor outcome in patients with AIS within 90 days and 1 year (net reclassification index, 48.38 and 28.95%; integrated discrimination improvement, 1.51 and 1.51%; P < 0.05 for all). Conclusions: Increased HALP score was associated with a decreased risk of recurrent stroke and death within 90 days and 1 year after stroke onset, suggesting that HALP score may serve as a powerful indicator for AIS.