A Nomogram for Predicting Stroke Recurrence Among Young Adults

Nomogram established on account of Lasso-logistic regression for predicting hemorrhagic transformation in patients with acute ischemic stroke after endovascular thrombectomy

BACKGROUND

Hemorrhagic transformation (HT) is a common and serious complication in patients with acute ischemic stroke (AIS) after endovascular thrombectomy (EVT). This study was performed to determine the predictive factors associated with HT in stroke patients with EVT and to establish and validate a nomogram that combines with independent predictors to predict the probability of HT after EVT in patients with AIS.

METHODS

All patients were randomly divided into development and validation cohorts at a ratio of 7:3. The least absolute shrinkage and selection operator (LASSO) regression was used to select the optimal factors, and multivariate logistic regression analysis was used to build a clinical prediction model. Calibration plots, decision curve analysis (DCA) and receiver operating characteristic curve (ROC) were generated to assess predictive performance.

RESULTS

LASSO regression analysis showed that Alberta Stroke Program Early CT Scores (ASPECTS), international normalized ratio (INR), uric acid (UA), neutrophils (NEU) were the influencing factors for AIS with HT after EVT. A novel prognostic nomogram model was established to predict the possibility of HT with AIS after EVT. The calibration curve showed that the model had good consistency. The results of ROC analysis showed that the AUC of the prediction model established in this study for predicting HT was 0.797 in the development cohort and 0.786 in the validation cohort.

CONCLUSION

This study proposes a novel and practical nomogram based on ASPECTS, INR, UA, NEU, which can well predict the probability of HT after EVT in patients with AIS.

Development and validation of outcome prediction model for reperfusion therapy in acute ischemic stroke using nomogram and machine learning

OBJECTIVE

To develop logistic regression nomogram and machine learning (ML)-based models to predict 3-month unfavorable functional outcome for acute ischemic stroke (AIS) patients undergoing reperfusion therapy.

METHODS

Patients undergoing reperfusion therapy (intravenous thrombolysis and/or endovascular treatment) were prospectively recruited. Unfavorable outcome was defined as 3-month modified Rankin Scale (mRS) score 3-6. The independent risk factors associated with unfavorable outcome were obtained by regression analysis and included in the prediction model. The performance of nomogram was assessed by the area under the curve (AUC), calibration curve, and decision curve analysis (DCA). ML models were compared with nomogram using AUC; the generalizability of all models was ascertained in an external cohort.

RESULTS

A total of 505 patients were enrolled, with 256 in the model construction, and 249 in the external validation. Five variables were identified as prognostic factors: baseline NIHSS, D-dimer level, random blood glucose (RBG), blood urea nitrogen (BUN), and systolic blood pressure (SBP) before reperfusion. The AUC values of nomogram were 0.865, 0.818, and 0.779 in the training set, test set, and external validation, respectively. The calibration curve and DCA indicated appreciable reliability and good net benefits. The best three ML models were extra trees (ET), CatBoost, and random forest (RF) models; all of them showed favorable discrimination in the training cohort, and confirmed in the test and external sets.

CONCLUSION

Baseline NIHSS, D-dimer, RBG, BUN, and SBP before reperfusion were independent predictors for 3-month unfavorable outcome after reperfusion therapy in AIS patients. Both nomogram and ML models showed good discrimination and generalizability.

M6A-related bioinformatics analysis indicates that LRPPRC is an immune marker for ischemic stroke

Ischemic stroke (IS) is a common cerebrovascular disease whose pathogenesis involves a variety of immune molecules, immune channels and immune processes. 6-methyladenosine (m6A) modification regulates a variety of immune metabolic and immunopathological processes, but the role of m6A in IS is not yet understood. We downloaded the data set GSE58294 from the GEO database and screened for m6A-regulated differential expression genes. The RF algorithm was selected to screen the m6A key regulatory genes. Clinical prediction models were constructed and validated based on m6A key regulatory genes. IS patients were grouped according to the expression of m6A key regulatory genes, and immune markers of IS were identified based on immune infiltration characteristics and correlation. Finally, we performed functional enrichment, protein interaction network analysis and molecular prediction of the immune biomarkers. We identified a total of 7 differentially expressed genes in the dataset, namely METTL3, WTAP, YWHAG, TRA2A, YTHDF3, LRPPRC and HNRNPA2B1. The random forest algorithm indicated that all 7 genes were m6A key regulatory genes of IS, and the credibility of the above key regulatory genes was verified by constructing a clinical prediction model. Based on the expression of key regulatory genes, we divided IS patients into 2 groups. Based on the expression of the gene LRPPRC and the correlation of immune infiltration under different subgroups, LRPPRC was identified as an immune biomarker for IS. GO enrichment analyses indicate that LRPPRC is associated with a variety of cellular functions. Protein interaction network analysis and molecular prediction indicated that LRPPRC correlates with a variety of immune proteins, and LRPPRC may serve as a target for IS drug therapy. Our findings suggest that LRPPRC is an immune marker for IS. Further analysis based on LRPPRC could elucidate its role in the immune microenvironment of IS.

Impact of tobacco smoking on disease-specific outcomes in common neurological disorders: A scoping review

Although the association of smoking with the risk of incident neurological disorders is well established, less is known about the impact of smoking and smoking cessation on outcomes of these conditions. The objective of this scoping review was to synthesize what is known about the impact of smoking and smoking cessation on disease-specific outcomes for seven common neurological disorders. We included 67 studies on the association of smoking and smoking cessation on disease-specific outcomes. For multiple sclerosis, smoking was associated with greater clinical and radiological disease progression, relapses, risk for disease-related death, cognitive decline, and mood symptoms, in addition to reduced treatment effectiveness. For stroke and transient ischemic attack, smoking was associated with greater rates of stroke recurrence, post-stroke cardiovascular outcomes, post-stroke mortality, post-stroke cognitive impairment, and functional impairment. In patients with cognitive impairment and dementia, smoking was associated with faster cognitive decline, and smoking was also associated with greater cognitive decline in Parkinson's disease, but not motor symptom worsening. Patients with amyotrophic lateral sclerosis who smoked faced increased mortality. Last, in patients with cluster headache, smoking was associated with more frequent and longer cluster attack periods. Conversely, for multiple sclerosis and stroke, smoking cessation was associated with improved disease-specific outcomes. In summary, whereas smoking is detrimentally associated with disease-specific outcomes in common neurological conditions, there is growing evidence that smoking cessation may improve outcomes. Effective smoking cessation interventions should be leveraged in the management of common neurological disorders to improve patient outcomes.

Development of a nomogram to predict the incidence of acute kidney injury among ischemic stroke individuals during ICU hospitalization

Background

Limited clinical prediction models exist to assess the likelihood of acute kidney injury (AKI) occurrence in ischemic stroke individuals. In this retrospective study, our aim was to construct a nomogram that utilizes commonly available clinical features to predict the occurrence of AKI during intensive care unit hospitalization among this patient population.

Methods

In this study, the MIMIC-IV database was utilized to investigate potential risk factors associated with the incidence of AKI among ischemic stroke individuals. A predictive nomogram was developed based on these identified risk factors. The discriminative performance of the constructed nomogram was assessed. Calibration analysis was utilized to evaluate the calibration performance of the constructed model, assessing the agreement between predicted probabilities and actual outcomes. Furthermore, decision curve analysis (DCA) was employed to assess the clinical net benefit, taking into account the potential risks and benefits associated with different decision thresholds.

Results

A total of 2089 ischemic stroke individuals were included and randomly allocated into developing (n = 1452) and verification cohorts (n = 637). Risk factors for AKI incidence in ischemic stroke individuals, determined through LASSO and logistic regression. The constructed nomogram had good performance in predicting the occurrence of AKI among ischemic stroke patients and provided significant improvement compared to existing scoring systems. DCA demonstrated satisfactory clinical net benefit of the constructed nomogram in both the validation and development cohorts.

Conclusions

The developed nomogram exhibits robust predictive performance in forecasting AKI occurrence in ischemic stroke individuals.

Age-Period-Cohort Analysis of Long-Term Trends in Ischemic Stroke Mortality in China Caused by Specific Risk Factors from 1990 to 2019

OBJECTIVE

The objective of this study was to study the primary risk factors for the long-term trends of mortality rates in ischemic stroke (IS) in China.

METHODS

Using the Global Burden of Disease Study 2019 (GBD 2019) database, research was conducted on the 11 primary risk factors for the mortality rates of IS in China from 1990 to 2019. This study employed joinpoint regression software and the age-period-cohort method to evaluate the trends of mortality rates divided by age, period, and cohort over time.

RESULTS

From 1990 to 2019, the age-standardized mortality rate (ASMR) caused by a diet high in red meat and high body mass index in China showed an upward trend. ASMR increased first and then decreased due to smoking, diet high in sodium, particulate matter pollution, high fasting plasma glucose, and high systolic blood pressure. Low-density lipoprotein cholesterol (LDL-C), kidney dysfunction, low temperature, and lead exposure remained relatively stable during this period. In the 35-45 age group, the mortality rate of IS due to high LDL-C was up to about 60%, and smoking affected men more than women. Overall, high LDL-C, high systolic blood pressure, and particulate matter pollution were the most common risk factors in patients with IS. The risk of death rose with age. The period and cohort relative risks showed that metabolic risk factors had the greatest impact on the mortality of IS.

CONCLUSION

Metabolic risk factors have become the primary risk factors for the ASMR of IS in China. Relevant authorities should pay attention to their long-term effects on IS. Effective public health policies and interventions should be implemented to reduce the burden of IS.

Development and validation of a nomogram for predicting the risk of poor prognosis in patients with cerebral infarction

Objective

To identify factors related to poor prognosis in patients with cerebral infarction (CI) and to construct and validate a personalized prediction model based on these factors.

Methods

A retrospective analysis was conducted on the clinical and follow-up data of 857 patients with CI who were diagnosed in the neurology department of a tertiary A hospital in Anhui Province, China from April 2020 to March 2022. Based on follow-up data and the Modified Rankin Scale (mRS) score one year after discharge, patients were divided into a good prognosis group (793 cases, mRS ≤2) and a poor prognosis group (64 cases, mRS >2). Multivariate logistic regression analysis was used to identify independent risk factors, which were then used to establish a nomogram model. The predictive performance of the model was evaluated using the area under the receiver operating characteristic curve (ROC, AUC), and the calibration curve was used to evaluate the calibration of the nomogram.

Results

There was a statistically significant difference in the distribution of eight variables between the groups, including post-discharge use of biguanide hypoglycemic drugs, insulin, systolic blood pressure, exercise status, alcohol consumption, smoking status, age, and gender (P < 0.05). Multivariate logistic regression analysis suggested that gender, smoking after discharge, alcohol consumption, lack of exercise, and oral administration of biguanide hypoglycemic drugs are independent risk factors for poor prognosis in patients with CI (P < 0.05). The personalized poor prognosis nomogram constructed based on these five predictive factors showed good discriminative ability and predictive stability, with AUCs of 0.768 (95 % CI: 0.712-0.825) and 0.775 (95 % CI: 0.725-0.836) before and after internal validation, respectively. The calibration curve confirmed the accuracy and consistency of the nomogram (P = 0.956).

Conclusion

Female gender, smoking, alcohol consumption, lack of exercise, and post-discharge use of biguanide hypoglycemic drugs are independent risk factors for poor prognosis in patients with CI. The constructed nomogram shows good predictive efficiency for post-discharge prognosis and can help in clinical decision-making.

Development and external validation of a nomogram predicting overall survival for Gastric adenocarcinoma patients with radical gastrectomy

PURPOSE

The aim of this study was to develop and externally validate a nomogram to accurately predict the overall survival (OS) of patients with gastric adenocarcinoma who underwent radical gastrectomy.

MATERIALS AND METHODS

A total of 3492 patients with gastric adenocarcinoma who underwent radical gastrectomy from 2012 to 2017 were included as the training cohort. Survival analysis was performed via Kaplan Meier method and log-rank test. Independent postoperative prognostic factors in patients with gastric adenocarcinoma were analyzed using univariate and multifactorial COX analysis methods. The prognosis nomogram was established in the training cohort and verified externally in the Surveillance, Epidemiology and End Results (SEER) database.

RESULTS

According to the univariate and multifactorial COX analyses, metastatic lymph node ratio (MLNR) and five other independent prognostic factors (age at surgery, type of gastrectomy, tumor size, T stage, and pathological grade) were included in the prognostic nomogram. The nomogram had better prognostic predictive ability than the American Joint Committee on Cancer (AJCC) TNM staging in both the training (C-index: 0.736 VS. 0.668) and external validation cohort (C-index: 0.712 VS. 0.627). The calibration plots showed that the predicted survival rate was in good agreement with the actual survival rate. And the decision curve analysis (DCA) curves revealed that nomogram showed stronger ability in predicting 1-year, 3-year, and 5-year OS.

CONCLUSION

This study estimated the excellent prognostic predictive power and clinical application potential of the MLNR-based nomogram, which may be used to facilitate postoperative clinical treatment decisions and potentially improve patient survival outcomes.

Normalization weighted combination scores re-evaluate TNM staging of gastric cancer: a retrospective cohort study based on a multicenter database

BACKGROUND

The pathological depth of tumor invasion (pT) and lymph node metastasis (pN) are critical independent prognostic factors for patients with gastric cancer (GC), representing effective methods for evaluating prognosis. In this study, the authors employed a normalization weight combination score to calculate the weight ratio of the pT stage and pN stage. Subsequently, the authors established a novel weighted TN (wTN) staging model based on these T and N weights, evaluating its prognostic capacity.

METHODS

This study utilized a training cohort from A Medical University Cancer Hospital and a validation cohort from the SEER database. Least absolute shrinkage and selection operator (LASSO) and Cox regression were employed to screen clinical characteristics. Multivariate linear regression and cluster analysis calculated the weight ratio of T stage and N stage in the training and validation cohorts, respectively, followed by re-staging. Prognostic value was evaluated using C-index, likelihood ratio, Wald, and Score tests for wTN stage and tumor-node-metastasis (TNM) stage. A nomogram model was developed, and accuracy was assessed using receiver operating characteristic curve (ROC), decision curve analysis (DCA), and restricted cubic spline (RCS) analyses.

RESULTS

LASSO was used for initial screening, selecting eight potential features for Cox analysis. Age, tumor size, metastasis lymph nodes (MLNs), and tumor location were confirmed as independent prognostic factors. wTN was calculated in the training and validation cohorts, and nomograms were established with the independent factors. N stage had a higher weight proportion than T stage in both cohorts (0.625/0.375 in training cohort, 0.556/0.444 in validation cohort). wTN outperformed the 8th TNM stage in C-index, likelihood ratio, Wald, and Score tests in the training cohort, with successful validation in the validation cohort. Stratified analysis of distinct pathological types further demonstrates that wTN staging exhibits superior prognostic performance.

CONCLUSION

The wTN staging model based on T stage and N stage weights has a good prognostic value for GC patients. The same conclusion was obtained in different pathological stratification.

Association between liver fibrosis and stroke recurrence in young patients with ischemic stroke

BACKGROUND AND AIM

Stroke incidence rates are rising among young adults. Liver fibrosis has recently been recognized as a risk factor for cardiovascular events and stroke in the general population. It remains unclear whether liver fibrosis influences the prognosis of stroke. We aimed to evaluate the association between liver fibrosis and stroke recurrence in young stroke patients.

METHODS AND RESULTS

Young adults with first-ever ischemic stroke were enrolled from a prospective stroke registry and were followed up for stroke recurrence. Liver fibrosis was evaluated by Fibrosis-4 (FIB-4) score and was stratified into three categories. Cox regression analysis was performed to assess the relationship between liver fibrosis and stroke recurrence. Over a median follow-up of 3.1 (1.7-4.6) years, 72 (11.6%) recurrent strokes occurred among 621 patients. According to the FIB-4 score, 73 (11.7%) patients had indeterminate fibrosis, while 11 (1.8%) had advanced fibrosis. Univariate Cox analysis revealed that patients with a high FIB-4 score were more likely to experience stroke recurrence than those with a low FIB-4 score (hazard ratio 3.748, 95% confidence interval 1.359-10.332, P = 0.011). After adjusting for potential confounders in the multivariate analysis, FIB-4 score remained an independent risk factor.

CONCLUSIONS

Young stroke patients with advanced liver fibrosis were at a greater risk of stroke recurrence. Evaluating liver fibrosis may provide valuable information for stroke risk stratification, and the FIB-4 score could serve as a useful tool.

Development and validation of a nutrition-related genetic-clinical-radiological nomogram associated with behavioral and psychological symptoms in Alzheimer's disease

BACKGROUND

Few evidence is available in the early prediction models of behavioral and psychological symptoms of dementia (BPSD) in Alzheimer's disease (AD). This study aimed to develop and validate a novel genetic-clinical-radiological nomogram for evaluating BPSD in patients with AD and explore its underlying nutritional mechanism.

METHODS

This retrospective study included 165 patients with AD from the Chinese Imaging, Biomarkers, and Lifestyle (CIBL) cohort between June 1, 2021, and March 31, 2022. Data on demoimagedatas, neuropsychological assessments, single-nucleotide polymorphisms of AD risk genes, and regional brain volumes were collected. A multivariate logistic regression model identified BPSD-associated factors, for subsequently constructing a diagnostic nomogram. This nomogram was internally validated through 1000-bootstrap resampling and externally validated using a time-series split based on the CIBL cohort data between June 1, 2022, and February 1, 2023. Area under receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA) were used to assess the discrimination, calibration, and clinical applicability of the nomogram.

RESULTS

Factors independently associated with BPSD were: CETP rs1800775 (odds ratio [OR] = 4.137, 95% confidence interval [CI]: 1.276-13.415, P = 0.018), decreased Mini Nutritional Assessment score (OR = 0.187, 95% CI: 0.086-0.405, P <0.001), increased caregiver burden inventory score (OR = 8.993, 95% CI: 3.830-21.119, P <0.001), and decreased brain stem volume (OR = 0.006, 95% CI: 0.001-0.191, P = 0.004). These variables were incorporated into the nomogram. The area under the ROC curve was 0.925 (95% CI: 0.884-0.967, P <0.001) in the internal validation and 0.791 (95% CI: 0.686-0.895, P <0.001) in the external validation. The calibration plots showed favorable consistency between the prediction of nomogram and actual observations, and the DCA showed that the model was clinically useful in both validations.

CONCLUSION

A novel nomogram was established and validated based on lipid metabolism-related genes, nutritional status, and brain stem volumes, which may allow patients with AD to benefit from early triage and more intensive monitoring of BPSD.

REGISTRATION

Chictr.org.cn, ChiCTR2100049131.

Developing and Validating a New Model to Predict the Risk of Poor Neurological Status of Acute Ischemic Stroke After Intravenous Thrombolysis

OBJECTIVES

The objective of this study was to develop and validate a predictive model for the risk of poor neurological status in in-hospital patients with acute ischemic stroke (AIS) after intravenous thrombolysis.

METHODS

This 2-center retrospective study included patients with AIS treated at the Advanced Stroke Center of the Second Hospital of Hebei Medical University and Baoding No.1 Central Hospital between January 2018 and January 2020). The neurological function status at day 7 of AIS onset was used as the endpoint of the study, which was evaluated using the National Institute of Health Stroke Scale (NIHSS) score.

RESULTS

A total of 878 patients were included in the study and divided into training (n=652) and validation (n=226) sets. Seven variables were selected as predictors to establish the risk model: age, NIHSS before thrombolysis (NIHSS1), NIHSS 24 hours after thrombolysis (NIHSS3), high-density lipoprotein, antiplatelet, cerebral computed tomography after thrombolysis (CT2), and lower extremity venous color Doppler ultrasound. The risk prediction model achieved good discrimination (the areas under the Receiver Operating Characteristic curve in the training and validation sets were 0.9626 and 0.9413, respectively) and calibration (in the training set Emax=0.072, Eavg=0.01, P =0.528, and in the validation set Emax=0.123, Eavg=0.019, P =0.594, respectively). The decision curve analysis showed that the model could achieve a good net benefit.

CONCLUSIONS

The prediction model obtained in this study showed good discrimination, calibration, and clinical efficacy. This new nomogram can provide a reference for predicting the risk of poor neurological status in patients with acute ischemic stroke after intravenous thrombolysis.

Integrate prediction of machine learning for single ACoA rupture risk: a multicenter retrospective analysis

Background

Statistically, Anterior communicating aneurysm (ACoA) accounts for 30 to 35% of intracranial aneurysms. ACoA, once ruptured, will have an acute onset and cause severe neurological dysfunction and even death. Therefore, clinical analysis of risk factors related to ACoA and the establishment of prediction model are the benefits to the primary prevention of ACoA.

Methods

Among 1,436 cases of single ACoA patients, we screened 1,325 valid cases, classified risk factors of 1,124 cases in the ruptured group and 201 cases in the unruptured group, and assessed the risk factors, respectively, and predicted the risk of single ACoA rupture by using the logistic regression and the machine learning.

Results

In the ruptured group (84.8%) of 1,124 cases and the unruptured group (15.2%) of 201 cases, the multivariable logistic regression (MLR) model shows hemorrhagic stroke history (OR 95%CI, p:0.233 (0.120-0.454),<0.001) and the age stratification of 60-69 years (OR 95%CI, p:0.425 (0.271-0.668),<0.001) has a significant statistic difference. In the RandomForest (RF) model, hemorrhagic stroke history and age are the best predictive factors.

Conclusion

We combined the analysis of MLR, RF, and PCA models to conclude that hemorrhagic stroke history and gender affect single ACoA rupture. The RF model with web dynamic nomogram, allows for real-time personalized analysis based on different patients' conditions, which is a tremendous advantage for the primary prevention of single ACoA rupture.

Clinical trial registration

https://www.chictr.org.cn/showproj.html?proj=178501.

Prediction of recurrent ischaemic stroke using radiomics data and machine learning methods in patients with acute ischaemic stroke: protocol for a multicentre, large sample, prospective observational cohort study in China

INTRODUCTION

Stroke is a leading cause of mortality and disability worldwide. Recurrent strokes result in prolonged hospitalisation and worsened functional outcomes compared with the initial stroke. Thus, it is critical to identify patients who are at high risk of stroke recurrence. This study is positioned to develop and validate a prediction model using radiomics data and machine learning methods to identify the risk of stroke recurrence in patients with acute ischaemic stroke (AIS).

METHODS AND ANALYSIS

A total of 1957 patients with AIS will be needed. Enrolment at participating hospitals will continue until the required sample size is reached, and we will recruit as many participants as possible. Multiple indicators including basic clinical data, image data, laboratory data, CYP2C19 genotype and follow-up data will be assessed at various time points during the registry, including baseline, 24 hours, 7 days, 1 month, 3 months, 6 months, 9 months and 12 months. The primary outcome was stroke recurrence. The secondary outcomes were death events, prognosis scores and adverse events. Imaging images were processed using deep learning algorithms to construct a programme capable of automatically labelling the lesion area and extracting radiomics features. The machine learning algorithms will be applied to integrate cross-scale, multidimensional data for exploratory analysis. Then, an ischaemic stroke recurrence prediction model of the best performance for patients with AIS will be established. Calibration, receiver operating characteristic and decision curve analyses will be evaluated.

ETHICS AND DISSEMINATION

This study has received ethical approval from the Medical Ethics Committee of the Second Affiliated Hospital of Nanchang University (medical research review No.34/2021), and informed consent will be obtained voluntarily. The research findings will be disseminated through publication in journals and presented at conferences.

TRIAL REGISTRATION NUMBER

ChiCTR2200055209.

In-stent restenosis after vertebral artery origin stenosis stenting: a nomogram for risk assessment

OBJECTIVE

To propose a nomogram for individual risk assessment of in-stent restenosis (ISR) after vertebral artery origin stenosis (VAOS) stenting.

METHODS

We included 793 patients with VAOS treated with stenting from October 2006 to May 2013, with a median follow-up of 27.8 months. Cox regression and the least absolute shrinkage and selection operator (LASSO) regression were adopted for variable selection. The nomogram was formulated and validated by concordance indexes (C-indexes) and calibration curves. An in-stent restenosis risk table (ISR-RT) was subsequently generated for risk stratification. Differences between low-, intermediate-, and high-risk levels were shown by Kaplan-Meier curves and compared by log-rank test.

RESULTS

The training and validation set included 594 and 199 patients, with a mean ISR rate of 37.2% and 35.2%, respectively. Stent type (HR=1.64, 95% CI 1.26 to 2.14), stent diameter (HR=2.48, 95% CI 1.77 to 3.48), history of peripheral vascular disease (HR=2.17, 95% CI 1.17 to 4.00), history of transit ischemic attack (HR=1.45, 95% CI 1.05 to 2.14), and left-side involvement (HR=1.33, 95% CI 1.04 to 1.69) were included in the nomogram. The C-indexes at 6 and 12 months were 0.650 and 0.611 in the training set, and 0.713 and 0.603 in the validation set, respectively. Compared with low-risk patients, the intermediate- and high-level group had 1.46 (95% CI 1.05 to 2.04; p=0.0235) and 2.28 (95% CI 1.64 to 3.17; p<0.0001) higher chances of developing ISR in 2 years, respectively.

CONCLUSIONS

A nomogram and a risk evaluation table were developed with good predictive ability for in-stent restenosis among patients with VAOS, which could serve as a practical approach for individualized risk evaluation.

Elevated non-HDL-C/HDL-C ratio increases the 1-year risk of recurrent stroke in older patients with non-disabling ischemic cerebrovascular events: results from the Xi'an Stroke Registry Study of China

BACKGROUND

Few studies have explored the prognostic role of nontraditional lipid-related indicators in non-disabling ischemic cerebrovascular events (NICE). In this study, we aimed to investigate the relationship between the ratio of non-high-density lipoprotein cholesterol to high-density lipoprotein cholesterol (non-HDL-C/HDL-C) and the1-year risk of recurrent stroke in patients with NICE.

METHODS

Total cholesterol (TC), HDL-C, and patient information were collected at admission. Recurrent stroke events were followed up 3, 6, and 12 months after onset. Non-HDL-C levels were calculated by subtracting HDL-C from TC. The non-HDL-C/HDL-C ratio was treated as a continuous variable and in quartiles (Q1-Q4). Stratified multivariate Cox regression was used to investigate the relationship between the non-HDL-C/HDL-C ratio and the 1-year risk of recurrent stroke in patients with NICE.

RESULTS

Overall, 1,659 patients with NICE were enrolled. For each unit increase in the non-HDL-C/HDL-C ratio, the 1-year risk of recurrent stroke in patients aged ≥ 65 years (older patients) with NICE increased by 64% in the adjusted model (hazard ratio [HR]: 1.64, 95%confidence interval [CI]:1.18-2.27, P = 0.003), and the HRs were 3.21 and 4.24 times higher in the Q3 and Q4 groups than that in the Q1 group, which was considered to be the reference (adjusted model Q3: HR: 3.21, 95%CI: 1.05-9.83, P = 0.041; adjusted model Q4: HR: 4.24, 95%CI: 1.30-13.85, P = 0.017). However, there was no significant difference in patients younger than 65 years. Both curve fitting and Kaplan-Meier cumulative risk analysis showed that an elevated non-HDL-C/HDL-C ratio significantly increased the 1-year risk of recurrent stroke in older patients with NICE. The optimal range for the non-HDL-C/HDL-C ratio should be no higher than the Q2 group (2.256-2.939). Stratified Cox regression analysis showed that these results tended to be stable for different comorbidities (all P for interaction > 0.05).

CONCLUSIONS

Elevated non-HDL-C/HDL-C ratios significantly increased the 1-year risk of recurrent stroke in older patients with NICE. Therefore, clinicians need to pay more attention to this indicator when managing older patients with NICE.

Prediction of Late Hospital Arrival in Patients with Mild and Rapidly Improving Acute Ischemic Stroke in a Rural Area of China

Purpose

Among all ischemic stroke patients, more than half are mild and rapidly improving acute ischemic stroke (MaRAIS) patients. However, many MaRAIS patients do not recognize the disease early on, and thus they delay access to the treatment that would be most effective if provided earlier. This is especially true in rural areas. The aim of this study was to develop and validate a late hospital arrival risk nomogram in a rural Chinese population of patients with MaRAIS.

Methods

We developed a prediction model based on a training dataset of 173 MaRAIS patients collected from September 9, 2019 to May 13, 2020. Data analyzed included demographics and disease characteristics. A least absolute shrinkage and selection operator (LASSO) regression model was used to optimize feature selection for the late hospital arrival risk model. Multivariable logistic regression analysis was applied to build a prediction model incorporating the features selected in the LASSO regression models. The discrimination, calibration, and clinical usefulness of the prediction model were assessed using the C-index, calibration plot, and decision curve analysis, respectively. Internal validation was then assessed using bootstrapping validation.

Results

Variables contained in the prediction nomogram included transportation mode, history of diabetes, knowledge of stroke symptoms, and thrombolytic therapy. The model had moderate predictive power with a C-index of 0.709 (95% confidence interval: 0.636-0.783) and good calibration. In the internal validation, the C-index reached 0.692. The risk threshold was 30-97% according to the analysis of the decision curve, and the nomogram could be applied in clinical practice.

Conclusion

This novel nomogram, which incorporates transportation mode, history of diabetes, knowledge of stroke symptoms, and thrombolytic therapy, was conveniently applied to facilitate individual late hospital arrival risk prediction among MaRAIS patients in a rural area of Shanghai, China.

A Novel Nomogram for Predicting Malignant Cerebral Edema After Endovascular Thrombectomy in Acute Ischemic Stroke: A Retrospective Cohort Study

BACKGROUND

Malignant cerebral edema (MCE) is a common and feared complication after endovascular thrombectomy (EVT) in acute ischemic stroke (AIS). This study aimed to establish a nomogram to predict MCE in anterior circulation large vessel occlusion stroke (LVOS) patients receiving EVT in order to guide the postoperative medical care in the acute phase.

METHODS

In this retrospective cohort study, 381 patients with anterior circulation LVOS receiving EVT were screened from 636 hospitalized patients with LVOS at 2 stroke medical centers. Clinical baseline data and imaging data were collected within 2-5 days of admission to the hospital. The patients were divided into 2 groups based on whether MCE occurred after EVT. Multivariate logistic regression analysis was used to evaluate the independent risk factors for MCE and to establish a nomogram.

RESULTS

Sixty-six patients out of 381 (17.32%) developed MCE. The independent risk factors for MCE included admission National Institutes of Health Stroke Scale (NIHSS) ≥16 (odds ratio [OR] 1.851; 95% CI 1.029-3.329; P = 0.038), ASPECT score (OR 0.621; 95% CI 0.519-0.744; P < 0.001), right hemisphere (OR 1.636; 95% CI 0.941-2.843; P = 0.079), collateral circulation (OR 0.155; 95% CI 0.074-0.324; P < 0.001), recanalization (OR 0.223; 95% CI 0.109-0.457; P < 0.001), hematocrit (OR, 0.937; 95% CI: 0.892-0.985; P =0.010), and glucose (OR 1.118; 95% CI 1.023-1.223; P = 0.036), which were adopted as parameters of the nomogram. The receiver operating characteristic curve analysis showed that the area under the curve of the nomogram in predicting MCE was 0.901(95% CI 0.848-0.940; P < 0.001). The Hosmer-Lemeshow test results were not significant (P = 0.685), demonstrating a good calibration of the nomogram.

CONCLUSIONS

The novel nomogram composed of admission NIHSS, ASPECT scores, right hemisphere, collateral circulation, recanalization, hematocrit, and serum glucose provide a potential predictor for MCE in patients with AIS after EVT.

To explore the regulatory effect of Buyang Huanwu Decoction on cerebral infarction based on quantitative proteomics

Buyang Huanwu Decoction (BYHW) contains chemical components such as ligustrazine, oxypaeoniflora, chlorogenic acid, and others. To explore the neuroprotective effect and potential target protein of BYHW in cerebral infarction (CI). A double-blind, randomized controlled trial was established and patients with CI were divided into the BYHW group (n = 35) and the control group (n = 30). To evaluate the efficacy by TCM syndrome score and clinical indicators, and to explore the changes of serum proteins by proteomics technology, so as to explore the mechanism of BYHW and potential target proteins. The study found that compared with the control group, the TCM syndrome score, including Deficiency of Vital Energy (DVE), Blood Stasis (BS), and NIHSS in the BYHW group decreased significantly (p < 0.05), and the Barthel Index (BI) score was significantly higher. A total of 99 differential regulatory proteins were identified by proteomics, which act on lipids and atherosclerosis, complement and coagulation cascade, and TNF-α signaling pathway. In addition, Elisa verified the results of proteomics and found that BYHW can reduce the neurological impairments focus on IL-1β, IL-6, TNF-α, MCP-1, MMP-9, and PAI-1. Significance: In this study, quantitative proteomics was used in combination with liquid chromatography-mass spectrometry (LC-MS/MS) to study the therapeutic effect of BYHW on cerebral infarction (CI) and potential changes in serum proteomics. In addition, the public proteomics database was used for bioinformatics analysis, and Elisa experiment verified the results of proteomics, further clarifying the potential protection mechanism of BYHW on CI.

Lifestyle, clinical, and occupational risk factors of recurrent stroke among the working-age group: A systematic review and meta-analysis

Background

Stroke recurrence is increasing in the working-age population. This study aimed to summarize and evaluate the risk factors for recurrent stroke among the working-age population.

Methods

Relevant studies were extracted from several databases following Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Fixed- or random-effects estimates of the pooled odds ratio (OR) and 95% confidence interval (CI) of risk factors for recurrent stroke were generated based on heterogeneity. Subgroup and publication bias analyses were also performed.

Results

Seventeen studies were included in the meta-analysis. Pooled effects results revealed that the risk of recurrent stroke in the working-age group was as follows: Diabetes (OR = 1.85, 95% CI: 1.47, 2.32), hypertension (OR = 1.27, 95% CI: 1.12, 1.44), smoking (OR = 1.52, 95% CI: 1.27, 1.81), history of cardiac disease (OR = 2.86, 95% CI: 2.22, 3.67), history of stroke (OR = 2.45, 95% CI: 1.81, 3.31), and National Institutes of Health stroke severity score (OR = 1.09, 95% CI: 1.03, 1.15).

Conclusion

These findings suggest that several factors, such as diabetes, hypertension, smoking, history of cardiac disease and stroke, and severity of a stroke, are potential risk factors for recurrent stroke in the working-age group. Therefore, strategies to reduce those risk factors should be adopted and attention paid to prevent recurrent stroke among working-age populations.

Experimental verification and comprehensive analysis of m7G methylation regulators in the subcluster classification of ischemic stroke

Background: Ischemic stroke (IS) is a fatal cerebrovascular disease involving several pathological mechanisms. Modification of 7-methylguanosine (m7G) has multiple regulatory functions. However, the expression pattern and mechanism of m7G in IS remain unknown. Herein, we aimed to explore the effect of m7G modification on IS. Methods: We screened significantly different m7G-regulated genes in Gene Expression Omnibus datasets, GSE58294 and GSE22255. The random forest (RF) algorithm was selected to identify key m7G-regulated genes that were subsequently validated using the middle cerebral artery occlusion (MCAO) model and quantitative polymerase chain reaction (qPCR). A risk model was subsequently generated using key m7G-regulated genes. Then, "ConsensusClusterPlus" package was used to distinguish different m7G clusters of patients with IS. Simultaneously, between two m7G clusters, differentially expressed genes (DEGs) and immune infiltration differences were also explored. Finally, we investigated functional enrichment and the mRNA-miRNA-transcription factor network of DEGs. Results: RF and qPCR confirmed that EIF3D, CYFIP2, NCBP2, DCPS, and NUDT1 were key m7G-related genes in IS that could accurately predict clinical risk (area under the curve = 0.967). NCBP2 was the most significantly associated gene with immune infiltration. Based on the expression profiles of these key m7G-related genes, the IS group could be divided into two clusters. According to the single-sample gene set enrichment analysis algorithm, four types of immune cells (immature dendritic cells, macrophages, natural killer T cells, and TH1 cells) were significantly different in the two m7G clusters. The functional enrichment of 282 DEGs between the two clusters was mainly concentrated in the "regulation of apoptotic signaling pathway," "cellular response to DNA damage stimulus," "adaptive immune system," and "pyroptosis." The miR-214-LTF-FOXJ1 axis may be a key regulatory pathway for IS. Conclusion: Our findings suggest that EIF3D, CYFIP2, NCBP2, DCPS, and NUDT1 may serve as potential diagnostic biomarkers for IS and that the m7G clusters developed by these genes provide more evidence for the regulation of m7G in IS.

Association of post-stroke-initiated antidepressants with long-term outcomes in young adults with ischaemic stroke

OBJECTIVE

We examined the association between initiation of antidepressants within the first year after ischaemic stroke (IS) in young adults and long-term fatal and non-fatal cardiovascular events, as well as all-cause mortality.

PATIENTS AND METHODS

The Helsinki Young Stroke Registry (HYSR) includes patients aged 15-49 years with their first-ever IS occurring 1994-2007. From nationwide registers, we obtained data on prescriptions (1993-2011) and outcomes of interest (1994-2011). Time of initiating post-stroke antidepressants (PSADs) was defined as time of the first filled prescription for antidepressants within the first year from IS. To account for non-random assignment of PSADs, we performed propensity score matching and studied the relationship between PSAD initiation and outcomes using Cox regression models with time-varying coefficients.

RESULTS

Of all patients (n = 888), 206 (23.2%) initiated PSADs within the first year, of which 203 (98.5%) could be matched to 406 non-initiators. In this matched sample of 609 patients, the median follow-up time was 8.1 (interquartile range [IQR] 5.0-12.6) years and 169 (28.9%) patients had any cardiovascular events, 95 (15.8%) had recurrent ischaemic or haemorrhagic strokes and 106 (17.4%) died. Adjusted for sociodemographics and cardiovascular comorbidities, PSAD initiation was associated with recurrent ischaemic or haemorrhagic stroke 5-10 years after IS (hazard ratio [HR] 3.07, 95% confidence interval [CI] 1.32-7.12). No association emerged between PSAD initiation and other outcomes.

CONCLUSIONS

In young adults, PSAD initiation within the first year after IS was associated with a heightened hazard of recurrent ischaemic or haemorrhagic stroke in the long term. Future studies are needed to verify the results and to further study the nature of this finding.KEY MESSAGESInitiation of post-stroke antidepressants (PSADs) within the first year after ischaemic stroke (IS) was associated with a heightened hazard of recurrent ischaemic or haemorrhagic stroke in the long term.Patients starting antidepressants after IS should be followed up more closely in case of recurrent events.Future studies are needed to verify the results and to further study the nature of this finding.

Genetic variation within the pri-let-7f-2 in the X chromosome predicting stroke risk in a Chinese Han population from Liaoning, China: From a case-control study to a new predictive nomogram

BACKGROUND AND OBJECTIVES

Stroke is the most common cause of disability and the second cause of death worldwide. Therefore, there is a need to identify patients at risk of developing stroke. This case-control study aimed to create and verify a gender-specific genetic signature-based nomogram to facilitate the prediction of ischemic stroke (IS) risk using only easily available clinical variables.

MATERIALS AND METHODS

A total of 1,803 IS patients and 1,456 healthy controls from the Liaoning province in China (Han population) were included which randomly divided into training cohort (70%) and validation cohort (30%) using the sample function in R software. The distribution of the pri-let-7f-2 rs17276588 variant genotype was analyzed. Following genotyping analysis, statistical analysis was used to identify relevant features. The features identified from the multivariate logistic regression, the least absolute shrinkage and selection operator (LASSO) regression, and univariate regression were used to create a multivariate prediction nomogram model. A calibration curve was used to determine the discrimination accuracy of the model in the training and validation cohorts. External validity was also performed.

RESULTS

The genotyping analysis identified the A allele as a potential risk factor for IS in both men and women. The nomogram identified the rs17276588 variant genotype and several clinical parameters, including age, diabetes mellitus, body mass index (BMI), hypertension, history of alcohol use, history of smoking, and hyperlipidemia as risk factors for developing IS. The calibration curves for the male and female models showed good consistency and applicability.

CONCLUSION

The pri-let-7f-2 rs17276588 variant genotype is highly linked to the incidence of IS in the northern Chinese Han population. The nomogram we devised, which combines genetic fingerprints and clinical data, has a lot of promise for predicting the risk of IS within the Chinese Han population.

A nomogram for individualized prediction of lower extremity deep venous thrombosis in stroke patients: A retrospective study

To develop and validate a nomogram for individualized prediction of lower extremity deep venous thrombosis (DVT) in stroke patients based on extremity function and daily living ability of stroke patients. In this study, 423 stroke patients admitted to the Rehabilitation Medical Center of the First Affiliated Hospital of Nanjing Medical University from December 2015 to February 2019 were taken as the subjects, who were divided into the DVT group (110) and No-DVT group (313) based on the existence of DVT. Inter-group comparison of baseline data was performed by 1-way Analysis of Variance, Kruskal-Wallis rank-sum test, or Pearson chi-square test. Data dimensions and predictive variables were selected by least absolute shrinkage and selection operator (LASSO); the prediction model was developed and the nomogram was prepared by binary logistics regression analysis; the performance of the nomogram was identified by the area under the receiver operating characteristic curve (AUC), Harrell's concordance index, and calibration curve; and the clinical effectiveness of the model was analyzed by clinical decision curve analysis. Age, Brunnstrom stage (lower extremity), and D-dimer were determined to be the independent predictors affecting DVT. The independent predictors mentioned above were developed and presented as a nomogram, with AUC and concordance index of 0.724 (95% confidence interval [CI]: 0.670-0.777), indicating the satisfactory discrimination ability of the nomogram. The P value of the results of the Hosmer-Lemeshow test was 0.732, indicating good fitting of the prediction model. Decision curve analysis showed that the clinical net benefit of this model was 6% to 50%. We developed a nomogram to predict lower extremity deep venous thrombosis in stroke patients, and the results showed that the nomogram had satisfactory prediction performance and clinical efficacy.

Conditional survival nomogram predicting real-time prognosis of locally advanced breast cancer: Analysis of population-based cohort with external validation

Background

Locally advanced breast cancer (LABC) is generally considered to have a relatively poor prognosis. However, with years of follow-up, what is its real-time survival and how to dynamically estimate an individualized prognosis? This study aimed to determine the conditional survival (CS) of LABC and develop a CS-nomogram to estimate overall survival (OS) in real-time.

Methods

LABC patients were recruited from the Surveillance, Epidemiology, and End Results (SEER) database (training and validation groups, n = 32,493) and our institution (testing group, n = 119). The Kaplan-Meier method estimated OS and calculated the CS at year (x+y) after giving x years of survival according to the formula CS(y|x) = OS(y+x)/OS(x). y represented the number of years of continued survival under the condition that the patient was determined to have survived for x years. Cox regression, best subset regression, and the least absolute shrinkage and selection operator (LASSO) regression were used to screen predictors, respectively, to determine the best model to develop the CS-nomogram and its network version. Risk stratification was constructed based on this model.

Results

CS analysis revealed a dynamic improvement in survival occurred with increasing follow-up time (7 year survival was adjusted from 63.0% at the time of initial diagnosis to 66.4, 72.0, 77.7, 83.5, 89.0, and 94.7% year by year [after surviving for 1-6 years, respectively]). In addition, this improvement was non-linear, with a relatively slow increase in the second year after diagnosis. The predictors identified were age, T and N status, grade, estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor 2 (HER 2), surgery, radiotherapy and chemotherapy. A CS-nomogram developed by these predictors and the CS formula was used to predict OS in real-time. The model's concordance indexes (C-indexes) in the training, validation and testing groups were 0.761, 0.768 and 0.810, which were well-calibrated according to the reality. In addition, the web version was easy to use and risk stratification facilitated the identification of high-risk patients.

Conclusions

The real-time prognosis of LABC improves dynamically and non-linearly over time, and the novel CS-nomogram can provide real-time and personalized prognostic information with satisfactory clinical utility.

Diffusion-weighted imaging-based radiomics for predicting 1-year ischemic stroke recurrence

Purpose

To investigate radiomics based on DWI (diffusion-weighted imaging) for predicting 1-year ischemic stroke recurrence.

Methods

A total of 1,580 ischemic stroke patients were enrolled in this retrospective study conducted from January 2018 to April 2021. Demographic and clinical characteristics were compared between recurrence and non-recurrence groups. On DWI, lesions were segmented using a 2D U-Net automatic segmentation network. Further, radiomics feature extraction was done using the segmented mask matrix on DWI and the corresponding ADC map. Additionally, radiomics features were extracted. The study participants were divided into a training cohort (n = 157, 57 recurrence patients, and 100 non-recurrence patients) and a test cohort (n = 846, 28 recurrence patients, 818 non-recurrence patients). A sparse representation feature selection model was performed to select features. Further classification was accomplished using a recurrent neural network (RNN). The area under the receiver operating characteristic curve values was obtained for model performance.

Results

A total of 1,003 ischemic stroke patients (682 men and 321 women; mean age: 65.90 ± 12.44 years) were included in the final analysis. About 85 patients (8.5%) recurred in 1 year, and patients in the recurrence group were older than the non-recurrence group (P = 0.003). The stroke subtype was significantly different between recurrence and non-recurrence groups, and cardioembolic stroke (11.3%) and large artery atherosclerosis patients (10.3%) showed a higher recurrence percentage (P = 0.005). Secondary prevention after discharge (statins, antiplatelets, and anticoagulants) was found significantly different between the two groups (P = 0.004). The area under the curve (AUC) of clinical-based model and radiomics-based model were 0.675 (95% CI: 0.643–0.707) and 0.779 (95% CI: 0.750–0.807), respectively. With an AUC of 0.847 (95% CI: 0.821–0.870), the model that combined clinical and radiomic characteristics performed better.

Conclusion

DWI-based radiomics could help to predict 1-year ischemic stroke recurrence.

Stroke health management: Novel strategies for the prevention of recurrent ischemic stroke

Objectives

The aim of the study was to assess the effect of the stroke health management model on the prognosis and recurrence of mild to moderate ischemic stroke, guided by the stroke health manager based on the patients' needs. In addition, up-to-date evidence of healthcare resource allocation, planning, and optimization is provided.

Methods

The current research was a retrospective, observational, single-center, history-controlled study with patients divided into two groups, namely, the intervention group and the control group, following the guidance of the stroke health manager. The control group patients received standard medical care during hospitalization, which consisted of advice on healthy lifestyle choices carried out by the bed nurse, but no structured education, WeChat group, or clinical consultation was included. The intervention group patients, in addition to the standard medical care, received health management and health education from the stroke health manager, and after hospital discharge, the patients were followed up over the telephone by the health manager to see if there was any recurrence or readmission.

Results

From 1 January 2018 to 31 December 2020, 382 patients with acute ischemic stroke were enrolled in this study. Through the univariate regression analysis, we found that SHM intervention was associated with a significantly lower risk of recurrence (HR = 0.459). We constructed a nomogram based on the significant variables from the regression analysis and also analyzed the association between the control group and the SHM intervention group among all subgroups using the Cox proportional hazards model to assess the effect of the stroke health management model. Most patients in this study had a total risk point between 170 and 270. The C-index value was 0.76, and the time-dependent AUC for predicting recurrence was &gt;0.7.

Conclusion

The stroke health manager-guided management model based on patients' needs can better control the risk factors of stroke and significantly reduce the recurrence rate of mild to moderate ischemic stroke within 1 year.

A novel nomogram to predict hemorrhagic transformation in ischemic stroke patients after intravenous thrombolysis

Background

Hemorrhagic transformation (HT) is the most serious complication of ischemic stroke patients after intravenous thrombolysis and leads to a poor clinical prognosis. This study aimed to determine the independent predictors associated with HT in stroke patients with intravenous thrombolysis and to establish and validate a nomogram that combines with predictors to predict the probability of HT after intravenous thrombolysis in patients with ischemic stroke.

Method

This study enrolled ischemic stroke patients with intravenous thrombolysis from December 2016 to June 2022. All the patients were divided into training and validation cohorts. The nomogram was composed of the significant predictors for HT in the training cohort as obtained by the multivariate logistic regression analysis. The area under the receiver operating characteristic curve was used to assess the discriminative performance of the nomogram. The calibration performance of the nomogram was assessed by the Hosmer–Lemeshow goodness-of-fit test and calibration plots. Decision curve analysis was used to test the clinical validity of the nomogram.

Results

A total of 394 patients with intravenous thrombolysis were enrolled in the study. In the training cohort (n = 257), 45 patients had HT after intravenous thrombolysis. Multivariate logistic regression analysis demonstrated early infarct signs (OR, 7.954; 95% CI, 3.553-17.803; P < 0.001), NIHSS scores (OR, 1.110; 95% CI, 1.054-1.168; P < 0.001), uric acid (OR, 0.993; 95% CI, 0.989–0.997; P = 0.001), and albumin-to-globulin ratio (OR, 0.109; 95% CI, 0.023–0.508; P = 0.005) were independent predictors for HT and constructed the nomogram. In the training and validation cohorts, the AUC of the nomogram was 0.859 and 0.839, respectively. The Hosmer–Lemeshow goodness-of-fit test and calibration plot showed good concordance between predicted and observed probability in the training and validation cohorts. Decision curve analysis indicated that the nomogram was significantly useful for predicting HT in the training and further confirmed in the validation cohort.

Conclusion

This study proposes a novel and practical nomogram based on early infarct signs, NIHSS scores, uric acid, and albumin-to-globulin ratio that can well predict the probability of HT after intravenous thrombolysis in patients with ischemic stroke.

Recurrence prediction of Essen Stroke Risk and Stroke Prognostic Instrument-II scores in ischemic stroke: A study of 5-year follow-up

OBJECTIVE

To evaluate the predictive accuracy of the Essen Stroke Risk Score and the Stroke Prognostic Instrument II score on the long-term recurrence in Chinese patients with acute ischemic stroke.

METHODS

Patients with acute ischemic stroke were enrolled and had completed ESRS and SPI-II scores. Patients were stratified according to the Essen Stroke Risk Score and Stroke Prognostic Instrument II score and were followed until stroke recurrence or composite endpoint event (stroke recurrence, myocardial infarction or cardiovascular death). We estimated stratified incidence rates and calculated the cumulative risks at 5 years using Kaplan-Meier estimates. We used receiver operating characteristic (ROC) curves to compare the predictive ability of the Essen Stroke Risk Score and Stroke Prognostic Instrument II score.

RESULTS

A total of 578 patients completed the follow-up. The cumulative 5-year event rates were 32.3% (95% CI, 28.2% to 36.4%) for recurrent stroke and 37.9% (95% CI: 33.8%-42.0%) for composite endpoint event. The cumulative risk of all outcomes increased with increasing risk scores. AUC for ESRS and SPI-II risk scores were 0.613 (95% CI: 0.565-0.661) and 0.613 (95% CI: 0.564-0.662) for 5-year stroke recurrence respectively and correspondingly 0.622 (95% CI: 0.576-0.668) and 0.627 (95% CI: 0.581-0.674) for composite endpoint events.

CONCLUSION

In Chinese patients with acute ischemic stroke, both Essen Stroke Risk Score and Stroke Prognostic Instrument II scores could equally stratify the risk of 5-year recurrent stroke and combined vascular events.

Risk factors for Drug-resistant Epilepsy (DRE) and a nomogram model to predict DRE development in post-traumatic epilepsy patients

Objectives

To identify factors affecting the development of drug‐resistant epilepsy (DRE), and establish a reliable nomogram to predict DRE development in post‐traumatic epilepsy (PTE) patients.

Methods

This study conducted a retrospective clinical analysis in patients with PTE who visited the Epilepsy Center, Beijing Tiantan Hospital from January 2013 to December 2018. All participants were followed up for at least 3 years, and the development of DRE was assessed. Data from January 2013 to December 2017 were used as development dataset for model building. Those independent predictors of DRE were included in the final multivariable logistic regression, and a derived nomogram was built. Data from January 2018 to December 2018 were used as validation dataset for internal validation.

Results

Complete clinical information was available for 2830 PTE patients (development dataset: 2023; validation dataset: 807), of which 21.06% (n = 596) developed DRE. Among all parameters of interest including gender, age at PTE, family history, severity of traumatic brain injury (TBI), single or multiple injuries, lesion location, post‐TBI treatments, acute seizures, PTE latency, seizure type, status epilepticus (SE), and electroencephalogram (EEG) findings, four predictors showed independent effect on DRE, they were age at PTE, seizure type, SE, and EEG findings. A model incorporating these four variables was created, and a nomogram to calculate the probability of DRE using the coefficients of the model was developed. The C‐index of the predictive model and the validation was 0.662 and 0.690, respectively. The goodness‐of‐fit test indicated good calibration for model development and validation (p = 0.272, 0.572).

Conclusions

The proposed nomogram achieved significant potential for clinical utility in the prediction of DRE among PTE patients. The risk of DRE for individual PTE patients can be estimated by using this nomogram, and identified high‐risk patients might benefit from non‐pharmacological therapies at an early stage.

Prediction of Ischemic Stroke Recurrence Based on COX Proportional Risk Regression Model and Evaluation of the Effectiveness of Patient Intensive Care Interventions

With the continuous improvement of medical technology and the aging of the population, the death rate of stroke is gradually decreasing, but the recurrence rate is still high, and the number of recurrences is increasing, resulting in disability and other symptoms, which brings great burden and distress to patients and their families. As the number of strokes increases, neurological impairment becomes more and more severe, affecting patients' ability to live, socialize, and work, and seriously reducing their quality of life. Clustered care is a combination of evidence-based linked interventions and a multidisciplinary team providing the best possible care through evidence-based research and highly operational practice, and it can improve outcomes for ischemic stroke patients more than implementation alone. This paper presents a Cox proportional risk regression-based model, using it to build the most used semi-parametric model for multifactorial survival analysis, due to its advantages of both parametric and nonparametric models, and to analyze the factors influencing survival time in study subjects with incomplete data. The proposed strategy has been found to be useful in predicting ischemic stroke recurrence and cluster care interventions for patients.

Predictors for the prognosis and recurrence of ischaemic stroke among young Chinese patients: a cohort study

OBJECTIVE

To identify predictors of young Chinese patients with ischaemic stroke outcome and recurrence of stroke.

DESIGN

Through considered the risk factors, neuroimaging findings, distribution, vascular stenosis, and the post-stroke treatment of young Chinese patients with ischemic stroke.

SETTING

The First Hospital of Jilin University.

PARTICIPANTS

We enrolled 579 patients (median age 45 years, range 15-49, men 81.0%) treated for the first occurrence of ischaemic stroke between January 2014 and December 2017.

MAIN OUTCOME MEASURES

We assessed stroke outcome based on the modified Rankin Scale (mRS) scores and recurrence of cerebrovascular events at 12 months. Multivariate logistic regression was used to identify the independent predictors of unfavourable outcomes (mRS score ≥2) and recurrence.

RESULTS

We observed stenosis in 295 patients (50.9%)-middle cerebral artery stenosis was the most common (18.1%). Of all 579 included patients, normal or mild, moderate and severe stenoses or occlusions were observed in 51.8%, 6.0% and 42.1% of patients, respectively. Unfavourable outcomes were observed in 91 patients (15.7%), including 10 patients (1.7%) who died. The rate of stroke recurrence was 7.9%. Independent predictors of unfavourable outcomes included a high National Institutes of Health Stroke Scale score (OR 1.151, 95% CI 1.094 to 1.210; p<0.001) and severe vascular stenosis or occlusion (OR 1.867, 95% CI 1.181 to 2.952; p=0.008). Predictors of recurrence included age of ≥45 years (OR 2.072, 95% CI 1.066 to 4.025; p=0.032) and atrial fibrillation (OR 15.207, 95% CI 4.273 to 54.120; p<0.001).

CONCLUSIONS

Our research shows that when developing prevention strategies for young people, measures that focus on mitigating risk factors should be considered. In addition, vascular screening of young populations is also of vital importance for stroke prevention and poor prognosis prediction.

A clinical model for predicting knee replacement in early-stage knee osteoarthritis: data from osteoarthritis initiative

BACKGROUND

Knee osteoarthritis (OA) progresses in a heterogeneous way, as a majority of the patients gradually worsen over decades while some undergo rapid progression and require knee replacement. The aim of this study was to develop a predictive model that enables quantified risk prediction of future knee replacement in patients with early-stage knee OA.

METHODS

Patients with early-stage knee OA, intact MRI measurements, and a follow-up time larger than 108 months were retrieved from the Osteoarthritis Initiative database. Twenty-five candidate predictors including demographic data, clinical outcomes, and radiographic parameters were selected. The presence or absence of knee replacement during the first 108 months of the follow-up was regarded as the primary outcome. Patients were randomly divided into derivation and validation groups in the ratio of three to one. Nomograms were developed based on multivariable logistic regressions of derivation group via R language. Those models were further tested in the validation group for external validation.

RESULTS

A total of 839 knees were enrolled, with 98 knees received knee replacement during the first 108 months. Glucocorticoid injection history, knee OA in the contralateral side, extensor muscle strength, area of cartilage deficiency, bone marrow lesion, and meniscus extrusion were selected to develop the nomogram after multivariable logistic regression analysis. The bias-corrected C-index and AUC of our nomogram in the validation group were 0.804 and 0.822, respectively.

CONCLUSION

Our predicting model provided simplified identification of patients with high risk of rapid progression in knee OA, which showed adequate predictive discrimination and calibration.

KEY POINTS

• Knee OA progresses in a heterogeneous way and rises to a challenge when making treatment strategies. • Our predicting model provided simplified identification of patients with high risk of rapid progression in knee OA.

A J-Shaped Curve Relationship Between Baseline Fasting Blood Glucose and 1-Year Stroke Recurrence in Non-diabetic Patients With Acute Cerebral Infarction in Xi'an, China: A Multicenter Observational Cohort Study

Background

The relationship between baseline fasting blood glucose (FBG) levels and 1-year stroke recurrence in non-diabetic patients with acute cerebral infarction (ACI) is unclear. We aimed to clarify this relationship in non-diabetic patients with ACI.

Methods

Baseline FBG levels and related information of the patients were collected at admission and the events of stroke recurrence were followed up 1, 3, 6, and 12 months after the patients were discharged. Baseline FBG levels were analyzed as continuous variables and quartiles (Q1–Q4). Multivariate Cox regression models and a two-piecewise linear regression model were used to investigate the relationship and determine the threshold effect between baseline FBG levels and 1-year stroke recurrence in non-diabetic patients with ACI.

Results

Overall, 1,634 non-diabetic patients with ACI were enrolled. After adjusting for potential confounding factors, the hazard is 2.24-fold higher in Q4 than those in Q2, being considered the reference in non-diabetic patients with ACI [hazard ratio (HR) = 2.24, 95%CI: 1.08–4.65, P = 0.031]. Plotting hazard ratios over baseline FBG levels suggested a J-shaped relationship for 1-year stroke recurrence. Further analysis revealed that the nadir value of baseline FBG levels is 4.6 mmol/L. The relationship was more significant in patients with atrial fibrillation than in those without (P for interaction = 0.009).

Conclusion

Lower and higher baseline FBG levels may lead to an increased risk of 1-year stroke recurrence in non-diabetic patients with ACI as shown by a J-shaped curve with a nadir value of 4.6 mmol/L.

Development and validation of a nomogram to predict postoperative pulmonary complications following thoracoscopic surgery

Background

Postoperative pulmonary complications (PPCs) after thoracoscopic surgery are common. This retrospective study aimed to develop a nomogram to predict PPCs in thoracoscopic surgery.

Methods

A total of 905 patients who underwent thoracoscopy were randomly enrolled and divided into a training cohort and a validation cohort at 80%:20%. The training cohort was used to develop a nomogram model, and the validation cohort was used to validate the model. Univariate and multivariable logistic regression were applied to screen risk factors for PPCs, and the nomogram was incorporated in the training cohort. The discriminative ability and calibration of the nomogram for predicting PPCs were assessed using C-indices and calibration plots.

Results

Among the patients, 207 (22.87%) presented PPCs, including 166 cases in the training cohort and 41 cases in the validation cohort. Using backward stepwise selection of clinically important variables with the Akaike information criterion (AIC) in the training cohort, the following seven variables were incorporated for predicting PPCs: American Society of Anesthesiologists (ASA) grade III/IV, operation time longer than 180 min, one-lung ventilation time longer than 60 min, and history of stroke, heart disease, chronic obstructive pulmonary disease (COPD) and smoking. With incorporation of these factors, the nomogram achieved good C-indices of 0.894 (95% confidence interval (CI) [0.866–0.921]) and 0.868 (95% CI [0.811–0.925]) in the training and validation cohorts, respectively, with well-fitted calibration curves.

Conclusion

The nomogram offers good predictive performance for PPCs after thoracoscopic surgery. This model may help distinguish the risk of PPCs and make reasonable treatment choices.

Clinical features and risk factors analysis of bronchitis obliterans due to refractory Mycoplasma pneumoniae pneumonia in children: a nomogram prediction model

Background

Early prediction of bronchitis obliterans (BO) is of great significance to the improvement of the long-term prognosis of children caused by refractory Mycoplasma pneumoniae pneumonia (RMPP). This study aimed to establish a nomogram model to predict the risk of BO in children due to RMPP.

Methods

A retrospective observation was conducted to study the clinical data of children with RMPP (1–14 years old) during acute infection. According to whether there is BO observed in the bronchoscope, children were divided into BO and the non-BO groups. The multivariate logistic regression model was used to construct the nomogram model.

Results

One hundred and forty-one children with RMPP were finally included, of which 65 (46.0%) children with RMPP were complicated by BO. According to the multivariate logistic regression analysis, WBC count, ALB level, consolidation range exceeding 2/3 of lung lobes, timing of macrolides, glucocorticoids or fiber bronchoscopy and plastic bronchitis were independent influencing factors for the occurrence of BO and were incorporated into the nomogram. The area under the receiver operating characteristic curve (AUC-ROC) value of nomogram was 0.899 (95% confidence interval [CI] 0.848–0.950). The Hosmer–Lemeshow test showed good calibration of the nomogram (p = 0.692).

Conclusion

A nomogram model found by seven risk factor was successfully constructed and can use to early prediction of children with BO due to RMPP.

A Prospective Study to Investigate Controlling Blood Pressure Under Transcranial Doppler After Endovascular Treatment in Patients With Occlusion of Anterior Circulation

Objective: The objective of this study was to evaluate the effect of blood pressure (BP) management with transcranial Doppler (TCD) guidance in patients with large-vessel occlusion in the anterior circulation after endovascular thrombectomy (EVT) on the long-term prognosis.

Methods: This was a prospective study; 232 patients were nonrandomized assigned to TCD-guided BP management (TBM) group or non-TCD-guided BP management (NBM) group. In the TBM group, BP was controlled according to TCD showing cerebral blood flow fluctuation. In the NBM group, BP was controlled according to the guidelines. The primary endpoint was a modified Rankin scale (mRS) score of 2 or lower at 90 days. The safety outcomes were the rates of symptomatic or any intracerebral hemorrhage (ICH) and mortality at 90 days.

Results: One hundred sixty-three patients were assigned to the TBM group, and 69 were assigned to the NBM group. In the propensity score-matched cohort (65 matches in both groups), there was significant difference in the proportion of participants with mRS 0–2 at 90 days according to BP management (adjusted odds ratio 3.34, 95% CI 1.36 to 8.22). There was no difference in the rates of symptomatic or any ICH and mortality between two groups. In inverse probability-weighted regression adjustment analysis, mortality decreased significantly in the TBM group than in the NBM group (adjusted odds ratio 0.86, 95% CI 0.76–0.99, p = 0.03).

Conclusion: In patients with acute ischemic stroke from large-vessel occlusion in the anterior circulation, BP management under TCD was superior to NBM in improving the clinical outcomes at 90 days.

Clinical Trial Registration: (URL: https://www.chictr.org.cn/showproj.aspx?proj=55484; Identifier: ChiCTR2000034443.

Safety and effect of Neuroform Atlas stent in the treatment of symptomatic intracranial stenosis: A single-center experience

Background

Vascular intracranial stenosis (IS) is a significant cause of acute ischemic stroke (AIS). This single-center study aims to show that symptomatic IS treatment by using the Neuroform Atlas stent (Stryker neurovascular, Kalamazoo, MI, USA) could be effective in reducing vessel stenosis.

Methods

Ten patients affected by AIS or TIA, in the vascular territory of high-grade intracranial atherosclerotic lesions (>70% of vessel stenosis), older than 18-year-old, were treated by implanting a Neuroform Atlas stent (diameter of 4.5mm in 80% and 4mm in 20%). 70% of the patients underwent pre-stenting intracranial angioplasty.

Results

Patients were between 54.8 and 83 years old (mean 68.46y ± 8.44y), 70% males and 30% females. At admission, 50% of all patients had an AIS and 50% a TIA. Restoration of the stenotic lumen was obtained after the endovascular procedure. The percentage mean of vascular stenosis was 83.7% ± 6.09% before treatment (t0), 52.2% ± 10.42% at the end of treatment (t1) and 46.2% ± 8.28% at the follow-up (t2). The IS percentage mean reduction between t0 and t1 was 31.5% ± 7.31%, and between t1 and t2 was 6% ± 5.47%, t0 and t2 of 37.5% ± 7.38%. Percentage reduction of IS was highly significant between time t0 and t1 (p = 0.005), and t0 and t2 (p = 0.005), also with a significant reduction between t1 and t2 (p = 0.012). No patient had experienced an increase of the ischemic area in the vascular territory of the target vessel at 3 months from the initial assessment. 10% of patients experienced a 3-months negative outcome (mRS = 5), 90% experienced a favorable outcome (mRS ≤2).

Conclusions

Intracranial stenosis endovascular treatment with Neuroform Atlas stent provides encouraging results, with a statistically significant association between the vascular caliber improvement and the endovascular treatment.

Prognostic factors and survival outcome of primary pulmonary acinar cell carcinoma

Purpose

The objective of our study was to investigate the epidemiologic characteristics and prognostic factors in patients with pulmonary acinar cell carcinoma (PACC).

Methods

PACC patients diagnosed between 1975 and 2016 were identified from the Surveillance, Epidemiology, and End Results (SEER) database. The trend in PACC incidence was assessed using joinpoint regression software. Overall survival (OS) and disease‐specific survival (DSS) were evaluated using the Kaplan–Meier method and log‐rank test. Univariate and multivariate Cox regression analysis was performed to identify the independent prognostic factors for OS and DSS. Nomograms to predict survival possibilities were constructed based on the identified independent prognostic factors.

Results

A total of 2918 patients were identified with PACC. The mean age was 65.2 ± 8.95 years with a female to male of 1.6:1. The incidence of PACC steadily increased by an annual percentage change (APC) of 3.2% (95% CI 2.1–4.4, p < 0.05). Multivariate Cox regression analysis revealed that age, gender, race, stage, grade, tumor size, number of positive lymph nodes, surgery, and chemotherapy were independent prognostic factors for survival. Nomograms specifically for PACC were constructed to predict 1‐ and 5‐year OS and DSS possibility, respectively. The concordance index (C‐index) and calibration plots showed the established nomograms had robust and accurate performance.

Conclusion

PACC was rare but the incidence has been steadily increasing over the past four decades. Survival has improved in recent years. Surgery or chemotherapy could provide better OS and DSS. The established nomograms specifically for PACC were robust and accurate in predicting 1‐ and 5‐year OS and DSS.

Trends of risk profile among middle-aged adults hospitalized for acute ischemic stroke in United States 2006-2017

BACKGROUND

Recent studies reported increasing trends in hospitalization of stroke patients aged 35-64 years.

AIM

To examine changes in risk factor profiles among patients aged 35-64 years hospitalized with acute ischemic stroke between 2006 and 2017 in the United States.

METHODS

We used data from the National Inpatient Sample of the Healthcare Cost and Utilization Project from 2006 through 2017. Principal ICD-9-CM/ICD-10-CM codes were used to identify acute ischemic stroke hospitalizations, and secondary codes were used to identify the presence of four major stroke risk factors: hypertension, diabetes, lipid disorders, and tobacco use. We used the relative percent change to assess the changes in the prevalence of risk profile between 2006-2007 and 2016-2017 and linear regression models to obtain the p values for the overall trends across six time periods.

RESULTS

Approximately 1.5 million acute ischemic stroke hospitalizations occurred during 2006-2017. The prevalence of having all four risk factors increased from 4.1% in 2006-2007 to 9.1% in 2016-2017 (relative percent change 122.0%, p < 0.001 for trend), prevalence of any three risk factors increased from 24.5% to 33.8% (relative percent change 38.0%, p < 0.001). Prevalence of only two risk factors decreased from 36.1% to 32.7% (p < 0.001), only one risk factor decreased from 25.2% to 18.1% (p < 0.001), and absence of risk factors decreased from 10.1% to 6.2% (p < 0.001). The most prevalent triad of risk factors was hypertension, diabetes, and lipid disorders (14.3% in 2006-2007 and 19.8% in 2016-2017), and the most common dyad risk factors was hypertension and lipid disorders (12.6% in 2006-2007 and 11.9% in 2016-2017).

CONCLUSIONS

The prevalence of hospitalized acute ischemic stroke patients aged 35-64 years with all four or any three of four major stroke risk factors increased by 122% and 38%, while those with only one risk factor or no risk factor has declined by 28% and 39%, respectively, from 2006 to 2017. Younger adults are increasingly at higher risk for stroke from preventable and treatable risk factors. This growing public health problem will require clinicians, healthcare systems, and public health efforts to implement more effective prevention strategies among this population.