Machine learning model for predicting stroke recurrence in adult stroke patients with moyamoya disease and factors of stroke recurrence.

OBJECT: The aim of this study was at building an effective machine learning model to contribute to the prediction of stroke recurrence in adult stroke patients subjected to moyamoya disease (MMD), while at analyzing the factors for stroke recurrence. METHODS: The data of this retrospective study originated from the database of JiangXi Province Medical Big Data Engineering & Technology Research Center. Moreover, the information of MMD patients admitted to the second affiliated hospital of Nanchang university from January 1st, 2007 to December 31st, 2019 was acquired. A total of 661 patients from January 1st, 2007 to February 28th, 2017 were covered in the training set, while the external validation set comprised 284 patients that fell into a scope from March 1st, 2017 to December 31st, 2019. First, the information regarding all the subjects was compared between the training set and the external validation set. The key influencing variables were screened out using the Lasso Regression Algorithm. Furthermore, the models for predicting stroke recurrence in 1, 2, and 3 years after the initial stroke were built based on five different machine learning algorithms, and all models were externally validated and then compared. Lastly, the CatBoost model with the optimal performance was explained using the SHapley Additive exPlanations (SHAP) interpretation model. RESULT: In general, 945 patients suffering from MMD were recruited, and the recurrence rate of acute stroke in 1, 2, and 3 years after the initial stroke reached 11.43%(108/945), 18.94%(179/945), and 23.17%(219/945), respectively. The CatBoost models exhibited the optimal prediction performance among all models; the area under the curve (AUC) of these models for predicting stroke recurrence in 1, 2, and 3 years was determined as 0.794 (0.787, 0.801), 0.813 (0.807, 0.818), and 0.789 (0.783, 0.795), respectively. As indicated by the results of the SHAP interpretation model, the high Suzuki stage, young adults (aged 18-44), no surgical treatment, and the presence of an aneurysm were likely to show significant correlations with the recurrence of stroke in adult stroke patients subjected to MMD. CONCLUSION: In adult stroke patients suffering from MMD, the CatBoost model was confirmed to be effective in stroke recurrence prediction, yielding accurate and reliable prediction outcomes. High Suzuki stage, young adults (aged 18-44 years), no surgical treatment, and the presence of an aneurysm are likely to be significantly correlated with the recurrence of stroke in adult stroke patients subjected to MMD.

High lipoprotein(a) concentration is associated with moyamoya disease.

BACKGROUND: Moyamoya disease (MMD) has attracted the attention of scholars because of its rarity and unknown etiology. METHODS: Data for this study were sourced from the Second Affiliated Hospital of Nanchang University. Regression analyses were conducted to examine the association in Lipoprotein [Lp(a)] and MMD. R and IBM SPSS were conducted. RESULTS: A cohort comprising 1012 MMD patients and 2024 controls was established through the propensity score matching method. Compared with controls, MMD patients showed higher median Lp(a) concentrations [18.5 (9.6-37.8) mg/dL vs. 14.9 (7.8-30.5) mg/dL, P < 0.001]. The odds ratios and 95% confidence intervals for Lp(a) were calculated in three models: unadjusted model, model 1 (adjusted for body mass index and systolic blood pressure), and model 2 (adjusted for model 1 plus triglyceride, C-reactive protein, homocysteine, and low-density lipoprotein cholesterol). Results were [1.613 (1.299-2.002), P < 0.001], [1.598 (1.286-1.986), P < 0.001], and [1.661 (1.330-2.074), P < 0.001], respectively. Furthermore, age, sex, or hypertension status had nothing to do with this relationship. CONCLUSIONS: Positive relationship exists between Lp(a) and MMD.

Feasibility of a clinical-radiomics combined model to predict the occurrence of stroke-associated pneumonia.

PURPOSE: To explore the predictive value of radiomics in predicting stroke-associated pneumonia (SAP) in acute ischemic stroke (AIS) patients and construct a prediction model based on clinical features and DWI-MRI radiomics features. METHODS: Univariate and multivariate logistic regression analyses were used to identify the independent clinical predictors for SAP. Pearson correlation analysis and the least absolute shrinkage and selection operator with ten-fold cross-validation were used to calculate the radiomics score for each feature and identify the predictive radiomics features for SAP. Multivariate logistic regression was used to combine the predictive radiomics features with the independent clinical predictors. The prediction performance of the SAP models was evaluated using receiver operating characteristics (ROC), calibration curves, decision curve analysis, and subgroup analyses. RESULTS: Triglycerides, the neutrophil-to-lymphocyte ratio, dysphagia, the National Institutes of Health Stroke Scale (NIHSS) score, and internal carotid artery stenosis were identified as clinically independent risk factors for SAP. The radiomics scores in patients with SAP were generally higher than in patients without SAP (P < 0. 05). There was a linear positive correlation between radiomics scores and NIHSS scores, as well as between radiomics scores and infarct volume. Infarct volume showed moderate performance in predicting the occurrence of SAP, with an AUC of 0.635. When compared with the other models, the combined prediction model achieved the best area under the ROC (AUC) in both training (AUC = 0.859, 95% CI 0.759-0.936) and validation (AUC = 0.830, 95% CI 0.758-0.896) cohorts (P < 0.05). The calibration curves and decision curve analysis further confirmed the clinical value of the nomogram. Subgroup analysis showed that this nomogram had potential generalization ability. CONCLUSION: The addition of the radiomics features to the clinical model improved the prediction of SAP in AIS patients, which verified its feasibility.

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.

Comparison of the Predictive Value of Inflammatory Biomarkers for the Risk of Stroke-Associated Pneumonia in Patients with Acute Ischemic Stroke.

Purpose: To investigate the predictive value of various inflammatory biomarkers in patients with acute ischemic stroke (AIS) and evaluate the relationship between stroke-associated pneumonia (SAP) and the best predictive index. Patients and Methods: We calculated the neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), monocyte-to-lymphocyte ratio (MLR), prognostic nutritional index (PNI), systemic inflammation response index (SIRI), systemic immune inflammation index (SII), Glasgow prognostic score (GPS), modified Glasgow prognostic score (mGPS), and prognostic index (PI). Variables were selectively included in the logistic regression analysis to explore the associations of NLR, PLR, MLR, PNI, SIRI, SII, GPS, mGPS, and PI with SAP. We assessed the predictive performance of biomarkers by analyzing receiver operating characteristic (ROC) curves. We further used restricted cubic splines (RCS) to investigate the association. Next, we conducted subgroup analyses to investigate whether specific populations were more susceptible to NLR. Results: NLR, PLR, MLR, SIRI, SII, GPS, mGPS, and PI increased significantly in SAP patients, and PNI was significantly decreased. After adjustment for potential confounders, the association of inflammatory biomarkers with SAP persisted. NLR showed the most favorable discriminative performance and was an independent risk factor predicting SAP. The RCS showed an increasing nonlinear trend of SAP risk with increasing NLR. The AUC of the combined indicator of NLR and C-reactive protein (CRP) was significantly higher than those of NLR and CRP alone (DeLong test, P<0.001). Subgroup analyses suggested good generalizability of the predictive effect. Conclusion: NLR, PLR, MLR, PNI, SIRI, SII, GPS, mGPS, and PI can predict the occurrence of SAP. Among the indices, the NLR was the best predictor of SAP occurrence. It can therefore be used for the early identification of SAP.

Short-term efficacy and safety of personalized antiplatelet therapy for patients with acute ischaemic stroke or transient ischaemic attack: A randomized clinical trial.

AIMS: The aim of this study was to determine whether the testing strategy for clopidogrel and/or aspirin resistance using CYP2C19 genotyping or urinary 11-dhTxB2 testing has an impact on clinical outcomes. METHODS: A multicentre, randomized, controlled trial was conducted at 14 centres in China from 2019 to 2021. For the intervention group, a specific antiplatelet strategy was assigned based on the CYP2C19 genotype and 11-dhTxB2, a urinary metabolite of aspirin, and the control group received nonguided (ie, standard of care) treatment. 11-dhTXB2 is a thromboxane A2 metabolite that can help quantify the effects of resistance to aspirin in individuals after ingestion. The primary efficacy outcome was new stroke, the secondary efficacy outcome was a poor functional prognosis (a modified Rankin scale score ≥3), and the primary safety outcome was bleeding, all within the 90-day follow-up period. RESULTS: A total of 2815 patients were screened and 2663 patients were enrolled in the trial, with 1344 subjects assigned to the intervention group and 1319 subjects assigned to the control group. A total of 60.1% were carriers of the CYP2C19 loss-of-function allele (*2, *3) and 8.71% tested positive for urinary 11-dhTxB2- indicating aspirin resistance in the intervention group. The primary outcome was not different between the intervention and control groups (P = .842). A total of 200 patients (14.88%) in the intervention group and 240 patients (18.20%) in the control group had a poor functional prognosis (hazard ratio 0.77, 95% confidence interval [CI] 0.63 to 0.95, P = .012). Bleeding events occurred in 49 patients (3.65%) in the intervention group and 72 patients (5.46%) in the control group (hazard ratio 0.66, 95% CI 0.45 to 0.95, P = .025). CONCLUSIONS: Personalized antiplatelet therapy based on the CYP2C19 genotype and 11-dhTxB2 levels was associated with favourable neurological function and reduced bleeding risk in acute ischaemic stroke and transient ischaemic attack patients. The results may help support the role of CYP2C19 genotyping and urinary 11-dhTxB2 testing in the provision of precise clinical treatment.

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.

Assessing electrocardiogram changes after ischemic stroke with artificial intelligence.

OBJECTIVE: Ischemic stroke (IS) with subsequent cerebrocardiac syndrome (CCS) has a poor prognosis. We aimed to investigate electrocardiogram (ECG) changes after IS with artificial intelligence (AI). METHODS: We collected ECGs from a healthy population and patients with IS, and then analyzed participant demographics and ECG parameters to identify abnormal features in post-IS ECGs. Next, we trained the convolutional neural network (CNN), random forest (RF) and support vector machine (SVM) models to automatically detect the changes in the ECGs; Additionally, We compared the CNN scores of good prognosis (mRS ≤ 2) and poor prognosis (mRS > 2) to assess the prognostic value of CNN model. Finally, we used gradient class activation map (Grad-CAM) to localize the key abnormalities. RESULTS: Among the 3506 ECGs of the IS patients, 2764 ECGs (78.84%) led to an abnormal diagnosis. Then we divided ECGs in the primary cohort into three groups, normal ECGs (N-Ns), abnormal ECGs after the first ischemic stroke (A-ISs), and normal ECGs after the first ischemic stroke (N-ISs). Basic demographic and ECG parameter analyses showed that heart rate, QT interval, and P-R interval were significantly different between 673 N-ISs and 3546 N-Ns (p < 0.05). The CNN has the best performance among the three models in distinguishing A-ISs and N-Ns (AUC: 0.88, 95%CI = 0.86-0.90). The prediction scores of the A-ISs and N-ISs obtained from the all three models are statistically different from the N-Ns (p < 0.001). Futhermore, the CNN scores of the two groups (mRS > 2 and mRS ≤ 2) were significantly different (p < 0.05). Finally, Grad-CAM revealed that the V4 lead may harbor the highest probability of abnormality. CONCLUSION: Our study showed that a high proportion of post-IS ECGs harbored abnormal changes. Our CNN model can systematically assess anomalies in and prognosticate post-IS ECGs.

Low lipoprotein(a) concentration is associated with atrial fibrillation: a large retrospective cohort study.

BACKGROUND AND AIMS: The role of serum lipoprotein(a) [Lp(a)] levels in atrial fibrillation (AF) is still uncertain, especially in the Chinese population. Here, we aimed to elucidate the potential relationship between Lp(a) quantiles and AF. METHODS: All data were collected through inpatients with electronic health records from the Second Affiliated Hospital of Nanchang University, Jiangxi Province, China. The propensity score matching (PSM) method was used to match control and case groups. Interactions between AF, Lp(a) quantiles, and other clinical indices were analyzed by logistic regression and stratified analysis. Statistical analyses were performed with IBM SPSS statistical software and R software. RESULTS: From 2017 to 2021, 4,511 patients with AF and 9,022 patients without AF were 1:2 matched by the propensity score matching method. A total of 46.9% of the study group was women, and the baseline mean age was 65 years. The AF group exhibited lower median Lp(a) than the non-AF group (15.95 vs. 16.90 mg/dL; P < 0.001). Based on the Lp(a) quantiles, the study population was divided into four groups: Q1 (≤ 8.71 mg/dL), Q2 (8.71-16.54 mg/dL), Q3 (16.54-32.42 mg/dL) and Q4 (> 32.42 mg/dL). The AF prevalence of each group decreased from 34.2% (Q1) to 30.9% (Q4) (P < 0.001). Lp(a) quantiles 1-3 significantly increased AF to 1.162-fold (1.049-1.286), 1.198-fold (1.083-1.327), and 1.111-fold (1.003-1.231) in the unadjusted logistic regression model, respectively. In the adjusted model, Lp(a) < 32.42 mg/dL still showed a significant inverse association with AF. In the stratified analysis, Lp(a) levels in female patients exhibited a significant negative correlation with AF (OR of Q1: 1.394[1.194-1.626], P = 0.001). Age and hypertension did not affect the adverse correlation. CONCLUSION: Low circulating Lp(a) levels were associated with AF, especially in the female Han population, suggesting that Lp(a) may be useful for risk stratification of AF in female individuals.

High lipoprotein(a) concentrations are associated with lower type 2 diabetes risk in the Chinese Han population: a large retrospective cohort study.

BACKGROUND: Lipoprotein (a) [Lp(a)] is a proven independent risk factor for coronary heart disease. It is also associated with type 2 diabetes mellitus (T2DM). However, the correlation between Lp(a) and T2DM has not been clearly elucidated. METHODS: This was a retrospective cohort study involving 9248 T2DM patients and 18,496 control individuals (1:2 matched). Patients were randomly selected from among inpatients in the Second Affiliated Hospital of Nanchang University between 2006 and 2017. Clinical characteristics were compared between the two groups. Spearman rank-order correlation coefficients were used to evaluate the strength and direction of monotonic associations of serum Lp(a) with other metabolic risk factors. Binary logistic regression analysis was used to establish the correlation between Lp(a) levels and T2DM risk. RESULTS: The median Lp(a) concentration was lower in T2DM patients than in controls (16.42 vs. 16.88 mg/dL). Based on four quartiles of Lp(a) levels, there was a decrease in T2DM risk from 33.7% (Q1) to 31.96% (Q4) (P for trend < 0.0001). Then, Lp(a) levels > 28.72 mg/dL (Q4) were associated with a significantly lower T2DM risk in the unadjusted model [0.924 (0.861, 0.992), P = 0.030]. Similar results were obtained in adjusted models 1 [Q4, 0.925 (0.862, 0.993), P = 0.031] and 2 [Q4, 0.919 (0.854, 0.990), P = 0.026]. Furthermore, in the stratified analysis, Q4 of Lp(a) was associated with a significantly lower T2DM risk among men [0.813 (0.734, 0.900), P < 0.001] and those age > 60 years [0.819 (0.737, 0.910), P < 0.001]. In contrast, the low-density lipoprotein cholesterol (LDL-C) levels and coronary heart disease (CHD) did not impact these correlations between Lp(a) and diabetes. CONCLUSIONS: There is an inverse association between Lp(a) levels and T2DM risk in the Chinese population. Male patients, especially those aged more than 60 years with Lp(a) > 28.72 mg/dL, are low-risk T2DM individuals, regardless of LDL-C levels and CHD status.

Personalized antiplatelet therapy based on clopidogrel/aspirin resistance tests in acute ischemic stroke and transient ischemic attack: Study protocol of a multi-center, single-blinded and randomized controlled trial.

BACKGROUND: Clopidogrel and aspirin are key intervention for acute ischemic stroke (AIS) and transient ischemic attack (TIA). However, with increased clinical application, many patients have shown clopidogrel resistance (CR) and/or aspirin resistance (AR) that affect antiplatelet therapy on AIS/TIA. At present, there is no research reported on personalized antiplatelet therapy guidelines for patients with CR and/or AR. Our study aims to assess the effect of personalized antiplatelet therapy based on CYP2C19 genotype and urine 11-dhTxB2 tests in patients with AIS or TIA. METHODS: This is a multi-center randomized controlled trial. Eligible patients with AIS/TIA from 14 comprehensive hospitals in Jiangxi province will be recruited after obtaining informed consent. Participants will be randomly divided into the intervention group and the control group at a ratio of 1:1. personalized antiplatelet therapy based on the CYP2C19 genotype/urine11-dhTxB2 tests will be given to the intervention group. Demographics, disease history, laboratory investigations, therapys, physiological tests, imaging reports and other clinical features will be collected. Clinical outcomes including stroke recurrence, Modified Rankin Scale (mRS) score, bleeding events and all-cause mortality will be assessed at the 1st, 3rd, 6th, and 12th-month post-discharge. DISCUSSION: Our study will conduct free antiplatelet resistance tests and personalized antiplatelet therapy for AIS/TIA patients with CR/AR, ultimately evaluating personalized therapy effectiveness through one-year follow-up. The research results will help to assess the impact of personalized antiplatelet therapy on the prognosis of stroke, thus providing reference for precise clinical treatment.

Does CYP2C19 polymorphisms affect neurological deterioration in stroke/TIA patients?: A systematic review and meta-analysis of prospective cohort studies.

BACKGROUND: The association between cytochrome P450 2C19 (CYP2C19) polymorphisms and neurological deterioration in stroke or transient ischemic attack (TIA) patients is not completely understood. Hence, we performed a systematic review and meta-analysis of prospective cohort studies to quantify this association. METHODS: PubMed, Cochrane Library, Excerpta Medica Database, China National Knowledge Infrastructure and WanFang databases were searched for studies published up to April 2019. Prospective cohort studies that reported an association between CYP2C19 polymorphisms and neurological deterioration in stroke/TIA patients were included. Data on risk ratio (RR) and 95% confidence intervals (CI) were extracted and pooled by the authors. Preferred Reporting Items for Systematic reviews and Meta-Analyses guidelines were followed. RESULTS: Twelve eligible studies were included. Twelve studies reported CYP2C19∗2, ∗3 loss-of-function alleles and 5 studies reported CYP2C19∗17 gain-of-function allele. Compared to non-carriers, carriers of CYP2C19∗2, ∗3 loss-of-function alleles had a significantly higher risk of neurological deterioration (RR, 1.63; 95%CI, 1.32-2.02). Conversely, carriers of CYP2C19∗17 gain-of-function allele had a significantly lower risk of neurological deterioration (RR, 0.520; 95%CI, 0.393-0.689) compared to non-carriers. CONCLUSIONS: This meta-analysis demonstrated that the carriers of CYP2C19∗2, ∗3 loss-of-function alleles have an increased risk of neurological deterioration compared to non-carriers in stroke or TIA patients. Additionally, CYP2C19∗17 gain-of-function allele can reduce the risk of neurological deterioration.

Extreme Gradient Boosting Model Has a Better Performance in Predicting the Risk of 90-Day Readmissions in Patients with Ischaemic Stroke.

OBJECT: Ischemic stroke readmission within 90 days of hospital discharge is an important quality of care metric. The readmission rates of ischemic stroke patients are usually higher than those of patients with other chronic diseases. Our aim was to identify the ischemic stroke readmission risk factors and establish a 90-day readmission prediction model for first-time ischemic stroke patients. METHODS: The readmission prediction model was developed using the extreme gradient boosting (XGboost) model, which can generate an ensemble of classification trees and assign a predictive risk score to each feature. The patient data were split into a training set (5159) and a validation set (911). The prediction results were evaluated with the receiver operating characteristic (ROC) curve and time-dependent ROC curve, which were compared with the outputs from the logistic regression (LR) model. RESULTS: A total of 6070 adult patients (39.6% female, median age 67 years) without any ischemic attack (IS) history were included, and 520 (8.6%) were readmitted within 90 days. The XGboost-based prediction model achieved a standard area under the curve (AUC) value of .782 (.729-.834), and the best time-dependent AUC value was .808 in 54 days for the validation set. In contrast, the LR model yielded a standard AUC value of .771 (.714-.828) and best time-dependent AUC value of .797. CONCLUSIONS: The XGboost model obtained a better risk prediction for 90-day readmission for first-time ischemic stroke patients than the LR model. This model can also reveal the high risk factors for stroke readmission in first-time ischemic stroke patients.

Predicting post-stroke pneumonia using deep neural network approaches.

BACKGROUND AND PURPOSE: Pneumonia is a common complication after stroke, causing an increased length of hospital stay and death. Therefore, the timely and accurate prediction of post-stroke pneumonia would be highly valuable in clinical practice. Previous pneumonia risk score models were often built on simple statistical methods such as logistic regression. This study aims to investigate post-stroke pneumonia prediction models using more advanced machine learning algorithms, specifically deep learning approaches. METHODS: Using a hospital's electronic health record(EHR) data from 2007-2017, 13,930 eligible patients with acute ischaemic stroke (AIS) were identified to build and evaluate the models (85% of the patients were used for training, and 15% were used for testing). In total, 1012 patients (7.23%) contracted pneumonia during hospitalization. A number of machine learning methods were developed and compared to predict pneumonia in the stroke population in China. In addition to the classic methods (i.e., logistic regression (LR), support vector machines (SVMs), extreme gradient boosting (XGBoost)), methods based on multiple layer perceptron (MLP) neural networks and recurrent neural network (RNNs) (i.e., attention-augmented gated recurrent unit (GRU)) are also implemented to make use of the temporal sequence information in electronic health record (EHR) systems. Prediction models for pneumonia were built for two time windows, i.e., within 7 days and within 14 days after stroke onset. In particular, pneumonia occurring within the 7-day window is considered highly associated with stroke (stroke-associated pneumonia, SAP). MAIN FINDINGS: The attention-augmented GRU model achieved the best performance based on an area under the receiver operating characteristic curve (AUC) of 0.928 for pneumonia prediction within 7 days and an AUC of 0.905 for pneumonia prediction within 14 days. This method outperformed the other machine learning-based methods and previously published pneumonia risk score models. Considering that pneumonia prediction after stroke requires a high sensitivity to facilitate its prevention at a relatively low cost (i.e., increasing the nursing level), we also compared the prediction performance using other evaluation criteria by setting the sensitivity to 0.90. The attention-augmented GRU achieved the optimal performance, with a specificity of 0.85, a positive predictive value (PPV) of 0.32 and a negative predictive value (NPV) of 0.99 for pneumonia within 7 days and a specificity of 0.82, a PPV of 0.29 and an NPV of 0.99 for pneumonia within 14 days. CONCLUSIONS: The deep learning-based predictive model is feasible for stroke patient management and achieves the optimal performance compared to many classic machine learning methods.

Interpreting stimulated plasma renin and aldosterone to select physiologically individualized therapy for resistant hypertension: importance of the class of stimulating drugs.

In the treatment of resistant hypertension, physiologically individualized therapy based on phenotyping with plasma renin activity (PRA) and plasma aldosterone significantly improves blood pressure control. Patients with a low-renin/low aldosterone (Liddle) phenotype respond best to amiloride, while those with low-renin/high aldosterone respond best to aldosterone antagonists, and those with high renin/high aldosterone (renal phenotype) respond best to angiotensin receptor blockers (ARB). However, it is important to measure PRA in a stimulated condition to distinguish between low levels due to high salt intake, licorice or nonsteroidal inflammatory drugs and low levels due to suppression by excess aldosterone secretion or renal tubular genetic variants causing retention of salt and water (Liddle phenotype). In the past, both diuretics and angiotensin converting inhibitors (ACEi) have been used for this purpose, and it has been assumed that these classes of drugs are equivalent. In this study of 2896 patients with hypertension, we evaluated that assumption. We found important differences among diuretics alone, ACEi/ARB alone, and ACEi/ARB + diuretics, which all stimulated PRA. However, ACEi/ARB lowers plasma aldosterone, and beta blockers lower PRA. Among patients with systolic pressure ≥ 180 mmHg ± diastolic pressure ≥ 100 mmHg stimulated only by diuretics, the phenotypes were 25% Liddle, 38% IA, 8.7% renal, and 28.3% mixed. In choosing physiologically individualized therapy based on PRA and aldosterone, it is important to consider the classes of stimulating drugs. Phenotypes are best distinguished by taking into account the aldosterone/PRA ratio in addition to the levels of PRA and aldosterone.

Extracting entities with attributes in clinical text via joint deep learning.

OBJECTIVE: Extracting clinical entities and their attributes is a fundamental task of natural language processing (NLP) in the medical domain. This task is typically recognized as 2 sequential subtasks in a pipeline, clinical entity or attribute recognition followed by entity-attribute relation extraction. One problem of pipeline methods is that errors from entity recognition are unavoidably passed to relation extraction. We propose a novel joint deep learning method to recognize clinical entities or attributes and extract entity-attribute relations simultaneously. MATERIALS AND METHODS: The proposed method integrates 2 state-of-the-art methods for named entity recognition and relation extraction, namely bidirectional long short-term memory with conditional random field and bidirectional long short-term memory, into a unified framework. In this method, relation constraints between clinical entities and attributes and weights of the 2 subtasks are also considered simultaneously. We compare the method with other related methods (ie, pipeline methods and other joint deep learning methods) on an existing English corpus from SemEval-2015 and a newly developed Chinese corpus. RESULTS: Our proposed method achieves the best F1 of 74.46% on entity recognition and the best F1 of 50.21% on relation extraction on the English corpus, and 89.32% and 88.13% on the Chinese corpora, respectively, which outperform the other methods on both tasks. CONCLUSIONS: The joint deep learning-based method could improve both entity recognition and relation extraction from clinical text in both English and Chinese, indicating that the approach is promising.

Nanotubular TiO2 regulates macrophage M2 polarization and increases macrophage secretion of VEGF to accelerate endothelialization via the ERK1/2 and PI3K/AKT pathways.

Background: Macrophages play important roles in the immune response to, and successful implantation of, biomaterials. Titanium nanotubes are considered promising heart valve stent materials owing to their effects on modulation of macrophage behavior. However, the effects of nanotube-regulated macrophages on endothelial cells, which are essential for stent endothelialization, are unknown. Therefore, in this study we evaluated the inflammatory responses of endothelial cells to titanium nanotubes prepared at different voltages. Methods and results: In this study we used three different voltages (20, 40, and 60 V) to produce titania nanotubes with three different diameters by anodic oxidation. The state of macrophages on the samples was assessed, and the supernatants were collected as conditioned media (CM) to stimulate human umbilical vein endothelial cells (HUVECs), with pure titanium as a control group. The results indicated that titanium dioxide (TiO2) nanotubes induced macrophage polarization toward the anti-inflammatory M2 state and increased the expression of arginase-1, mannose receptor, and interleukin 10. Further mechanistic analysis revealed that M2 macrophage polarization controlled by the TiO2 nanotube surface activated the phosphatidylinositol 3-kinase/AKT and extracellular signal-regulated kinase 1/2 pathways through release of vascular endothelial growth factor to influence endothelialization. Conclusion: Our findings expanded our understanding of the complex influence of nanotubes in implants and the macrophage inflammatory response. Furthermore, CM generated from culture on the TiO2 nanotube surface may represent an integrated research model for studying the interactions of two different cell types and may be a promising approach for accelerating stent endothelialization through immunoregulation.

Surface biofunctionalization of the decellularized porcine aortic valve with VEGF-loaded nanoparticles for accelerating endothelialization.

The original intention for building a tissue-engineered heart valve (TEHV) was to simulate a normal heart valve and overcome the insufficiency of the commonly used heart valve replacement in the clinic. The endothelialization of the TEHV is very important as the endothelialized TEHV can decrease platelet adhesion and delay the valvular calcification decline process. In this work, we encapsulated vascular endothelial growth factor (VEGF) into polycaprolactone (PCL) nanoparticles. Then, through the Michael addition reaction, PCL nanoparticles were introduced onto the decellularized aortic valve to prepare a hybrid valve. The encapsulation efficiency of the PCL nanoparticles for VEGF was up to 82%, and the in vitro accumulated release rate was slow without an evident initial burst release. In addition, the hybrid valve had a decreased hemolysis ratio and possessed antiplatelet adhesion capacity, and it was able to promote the adhesion and proliferation of endothelial cells, covering the surface with a dense cell layer to accelerate endothelialization. An experiment involving the subcutaneous implant in SD rats showed that at week 8, lots of blood capillaries were formed in the hybrid valve. Mechanics performance testing indicated that the mechanical property of the hybrid valve was partly improved. Taken together, we applied a nano-drug controlled release system to fabricate TEHV, and provide an approach for the biofunctionalization of the TEHV scaffold for accelerating endothelialization.

Biofunctionalization of decellularized porcine aortic valve with OPG-loaded PCL nanoparticles for anti-calcification.

Decellularized valve stents are widely used in tissue-engineered heart valves because they maintain the morphological structure of natural valves, have good histocompatibility and low immunogenicity. However, the surface of the cell valve loses the original endothelial cell coverage, exposing collagen and causing calcification and decay of the valve in advance. In this study, poly ε-caprolactone (PCL) nanoparticles loaded with osteoprotegerin (OPG) were bridged to a decellularized valve using a nanoparticle drug delivery system and tissue engineering technology to construct a new anti-calcification composite valve with sustained release function. The PCL nanoparticles loaded with OPG were prepared via an emulsion solvent evaporation method, which had a particle size of 133 nm and zeta potential of -27.8 mV. Transmission electron microscopy demonstrated that the prepared nanoparticles were round in shape, regular in size, and uniformly distributed, with an encapsulation efficiency of 75%, slow release in vitro, no burst release, no cytotoxicity to BMSCs, and contained OPG nanoparticles in vitro. There was a delay in the differentiation of BMSCs into osteoblasts. The decellularized valve modified by nanoparticles remained intact and its collagen fibers were continuous. After 8 weeks of subcutaneous implantation in rats, the morphological structure of the valve was almost complete, and the composite valve showed anti-calcification ability to a certain extent.