Characteristics and risk of stroke in emergency department patients with acute dizziness.

Background: Acute dizziness is a common symptom in the emergency department (ED), with strokes accounting for 3 %-5 % of cases. We investigated the risk of stroke in ED patients with acute dizziness and compared stroke characteristics diagnosed during and after the ED visit. Methods: We identified adult patients with acute dizziness, vertigo, or imbalance using a hospital research-based database. Patients with abdominal or flank pain were used as the comparison group. Patients with dizziness were 1:1 matched to comparison patients. Each patient was traced for up to one year until being hospitalized for a stroke. Results: Out of the 24,266 eligible patients, 589 (2.4 %) were hospitalized for stroke during the ED visit. For the remaining 23,677 patients, the risk of stroke at 7, 30, 90, and 365 days after ED discharge was 0.40 %, 0.52 %, 0.71 %, and 1.25 % respectively. Patients with dizziness had a higher risk of stroke compared to the comparison group at 7, 30, 90, and 365 days. The risk ratios decreased from 5.69 (95 % confidence interval [CI], 3.34-9.68) to 2.03 (95 % CI, 1.65-2.49). Compared to patients hospitalized for stroke during the ED visit, those hospitalized for stroke after the ED visit had greater stroke severity despite a lower initial triage acuity. Patients with early stroke (≤7 days) after ED discharge were less likely to have hypertension, diabetes, hyperlipidemia, and atrial fibrillation. They mostly experienced posterior circulation stroke. Patients with late stroke (>7 days) were older and less likely to have hypertension and hyperlipidemia but more likely to have a history of prior stroke and ischemic heart disease. Their strokes were mainly located in the anterior circulation territory. Conclusions: The risk of stroke after ED discharge was higher in patients with dizziness than in the comparison group, with gradually decreasing risk ratios in the following year. Patients hospitalized for stroke during and after the ED visit had different profiles of vascular risk factors and clinical characteristics.

Associations of diabetes status and glucose measures with outcomes after endovascular therapy in patients with acute ischemic stroke: an analysis of the nationwide TREAT-AIS registry.

Background: Hyperglycemia affects the outcomes of endovascular therapy (EVT) for acute ischemic stroke (AIS). This study compares the predictive ability of diabetes status and glucose measures on EVT outcomes using nationwide registry data. Methods: The study included 1,097 AIS patients who underwent EVT from the Taiwan Registry of Endovascular Thrombectomy for Acute Ischemic Stroke. The variables analyzed included diabetes status, admission glucose, glycated hemoglobin (HbA1c), admission glucose-to-HbA1c ratio (GAR), and outcomes such as 90-day poor functional outcome (modified Rankin Scale score ≥ 2) and symptomatic intracranial hemorrhage (SICH). Multivariable analyses investigated the independent effects of diabetes status and glucose measures on outcomes. A receiver operating characteristic (ROC) analysis was performed to compare their predictive abilities. Results: The multivariable analysis showed that individuals with known diabetes had a higher likelihood of poor functional outcomes (odds ratios [ORs] 2.10 to 2.58) and SICH (ORs 3.28 to 4.30) compared to those without diabetes. Higher quartiles of admission glucose and GAR were associated with poor functional outcomes and SICH. Higher quartiles of HbA1c were significantly associated with poor functional outcomes. However, patients in the second HbA1c quartile (5.6-5.8%) showed a non-significant tendency toward good functional outcomes compared to those in the lowest quartile (<5.6%). The ROC analysis indicated that diabetes status and admission glucose had higher predictive abilities for poor functional outcomes, while admission glucose and GAR were better predictors for SICH. Conclusion: In AIS patients undergoing EVT, diabetes status, admission glucose, and GAR were associated with 90-day poor functional outcomes and SICH. Admission glucose was likely the most suitable glucose measure for predicting outcomes after EVT.

Magnetic resonance imaging-based deep learning imaging biomarker for predicting functional outcomes after acute ischemic stroke.

PURPOSE: Clinical risk scores are essential for predicting outcomes in stroke patients. The advancements in deep learning (DL) techniques provide opportunities to develop prediction applications using magnetic resonance (MR) images. We aimed to develop an MR-based DL imaging biomarker for predicting outcomes in acute ischemic stroke (AIS) and evaluate its additional benefit to current risk scores. METHOD: This study included 3338 AIS patients. We trained a DL model using deep neural network architectures on MR images and radiomics to predict poor functional outcomes at three months post-stroke. The DL model generated a DL score, which served as the DL imaging biomarker. We compared the predictive performance of this biomarker to five risk scores on a holdout test set. Additionally, we assessed whether incorporating the imaging biomarker into the risk scores improved the predictive performance. RESULTS: The DL imaging biomarker achieved an area under the receiver operating characteristic curve (AUC) of 0.788. The AUCs of the five studied risk scores were 0.789, 0.793, 0.804, 0.810, and 0.826, respectively. The imaging biomarker's predictive performance was comparable to four of the risk scores but inferior to one (p = 0.038). Adding the imaging biomarker to the risk scores improved the AUCs (p-values) to 0.831 (0.003), 0.825 (0.001), 0.834 (0.003), 0.836 (0.003), and 0.839 (0.177), respectively. The net reclassification improvement and integrated discrimination improvement indices also showed significant improvements (all p < 0.001). CONCLUSIONS: Using DL techniques to create an MR-based imaging biomarker is feasible and enhances the predictive ability of current risk scores.

Machine learning-based survival analysis approaches for predicting the risk of pneumonia post-stroke discharge.

BACKGROUND: Post-stroke pneumonia (PSP) is common among stroke patients. PSP occurring after hospital discharge continues to increase the risk of poor functional outcomes and death among stroke survivors. Currently, there is no prediction model specifically designed to predict the occurrence of PSP beyond the acute stage of stroke. This study aimed to explore the use of machine learning (ML) methods in predicting the risk of PSP after hospital discharge. METHODS: This study analyzed data from 5,754 hospitalized stroke patients. The dataset was randomly divided into a training set and a holdout test set, with a ratio of 80:20. Several clinical and laboratory variables were utilized as predictors and different ML algorithms were employed to model time-to-event data. The ML model's predictive performance was compared to existing risk-scoring systems. A model-agnostic method based on Shapley additive explanations was utilized to interpret the ML model. RESULTS: The study found that 5.7% of the study patients experienced pneumonia within one year after discharge. Based on repeated 5-fold cross-validation on the training set, the random survival forest (RSF) model had the highest C-index among the various ML algorithms and traditional Cox regression analysis. The final RSF model achieved a C-index of 0.787 (95% confidence interval: 0.737-0.840) on the holdout test set, outperforming five existing risk-scoring systems. The top three important predictors were the Glasgow Coma Scale score, age, and length of hospital stay. CONCLUSIONS: The RSF model demonstrated superior discriminative ability compared to other ML algorithms and traditional Cox regression analysis, suggesting a non-linear relationship between predictors and outcomes. The developed ML model can be integrated into the hospital information system to provide personalized risk assessments.

Comparing Low- or Standard-Dose Alteplase in Endovascular Thrombectomy: Insights From a Nationwide Registry.

BACKGROUND: Timely intravenous thrombolysis and endovascular thrombectomy are the standard reperfusion treatments for large vessel occlusion stroke. Currently, it is unknown whether a low-dose thrombolytic agent (0.6 mg/kg alteplase) can offer similar efficacy to the standard dose (0.9 mg/kg alteplase). METHODS: We enrolled consecutive patients in the multicenter Taiwan Registry of Endovascular Thrombectomy for Acute Ischemic Stroke who had received combined thrombolysis (within 4.5 hours of onset) and thrombectomy treatment from January 2019 to April 2023. The choice of low- or standard-dose alteplase was based on the physician's discretion. The outcomes included successful reperfusion (modified Thrombolysis in Cerebral Infarction score, 2b-3), symptomatic intracerebral hemorrhage, 90-day modified Rankin Scale score, and 90-day mortality. The outcomes between the 2 groups were compared using multivariable logistic regression and inverse probability of treatment weighting-adjusted analysis. RESULTS: Among the 2242 patients in the Taiwan Registry of Endovascular Thrombectomy for Acute Ischemic Stroke, 734 (33%) received intravenous alteplase. Patients in the low-dose group (n=360) were older, had more women, more atrial fibrillation, and longer onset-to-needle time compared with the standard-dose group (n=374). In comparison to low-dose alteplase, standard-dose alteplase was associated with a lower rate of successful reperfusion (81% versus 87%; adjusted odds ratio, 0.63 [95% CI, 0.40-0.98]), a numerically higher incidence of symptomatic intracerebral hemorrhage (6.7% versus 3.9%; adjusted odds ratio, 1.81 [95% CI, 0.88-3.69]), but better 90-day modified Rankin Scale score (functional independence [modified Rankin Scale score, 0-2], 47% versus 31%; adjusted odds ratio, 1.91 [95% CI, 1.28-2.86]), and a numerically lower mortality rate (9% versus 15%; adjusted odds ratio, 0.73 [95% CI, 0.43-1.25]) after adjusting for covariates. Similar results were observed in the inverse probability of treatment weighting-adjusted models. The results were consistent across predefined subgroups and age strata. CONCLUSIONS: Despite the lower rate of successful reperfusion and higher risk of symptomatic intracerebral hemorrhage with standard-dose alteplase, standard-dose alteplase was associated with a better functional outcome in patients receiving combined thrombolysis and thrombectomy.

Validation of ICD-10-CM Diagnostic Codes for Identifying Patients with ST-Elevation and Non-ST-Elevation Myocardial Infarction in a National Health Insurance Claims Database.

Purpose: Distinguishing ST-elevation myocardial infarction (STEMI) and non-ST-elevation myocardial infarction (NSTEMI) is crucial in acute myocardial infarction (AMI) research due to their distinct characteristics. However, the accuracy of International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) codes for STEMI and NSTEMI in Taiwan's National Health Insurance (NHI) database remains unvalidated. Therefore, we developed and validated case definition algorithms for STEMI and NSTEMI using ICD-10-CM and NHI billing codes. Patients and Methods: We obtained claims data and medical records of inpatient visits from 2016 to 2021 from the hospital's research-based database. Potential STEMI and NSTEMI cases were identified using diagnostic codes, keywords, and procedure codes associated with AMI. Chart reviews were then conducted to confirm the cases. The performance of the developed algorithms for STEMI and NSTEMI was assessed and subsequently externally validated. Results: The algorithm that defined STEMI as any STEMI ICD code in the first three diagnosis fields had the highest performance, with a sensitivity of 93.6% (95% confidence interval [CI], 91.7-95.2%), a positive predictive value (PPV) of 89.4% (95% CI, 87.1-91.4%), and a kappa of 0.914 (95% CI, 0.900-0.928). The algorithm that used the NSTEMI ICD code listed in any diagnosis field performed best in identifying NSTEMI, with a sensitivity of 82.6% (95% CI, 80.7-84.4%), a PPV of 96.5% (95% CI, 95.4-97.4), and a kappa of 0.889 (95% CI, 0.878-0.901). The algorithm that included either STEMI or NSTEMI ICD codes listed in any diagnosis field showed excellent performance in defining AMI, with a sensitivity of 89.4% (95% CI, 88.2-90.6%), a PPV of 95.6% (95% CI, 94.7-96.4%), and a kappa of 0.923 (95% CI, 0.915-0.931). External validation confirmed these algorithms' efficacy. Conclusion: Our results provide valuable reference algorithms for identifying STEMI and NSTEMI cases in Taiwan's NHI database.

Clinical narratives as a predictor for prognosticating functional outcomes after intracerebral hemorrhage.

BACKGROUND: Intracerebral hemorrhage (ICH) is a devastating stroke type that causes high mortality rates and severe disability among survivors. Many prognostic models are available for prognosticating patients with ICH. This study aimed to investigate whether clinical narratives can improve the performance for predicting functional outcomes after ICH. METHODS: This study used data from the hospital stroke registry and electronic health records. The study population (n = 1363) was randomly divided into a training set (75%, n = 1023) and a holdout test set (25%, n = 340). Five risk scores for ICH were used as baseline prognostic models. Using natural language processing (NLP), text-based markers were generated from the clinical narratives of the training set through machine learning (ML) and deep learning (DL) approaches. The primary outcome was a poor functional outcome (modified Rankin Scale score of 3 to 6) at hospital discharge. The predictive performance was compared between the baseline models and models enhanced by incorporating the text-based markers using the holdout test set. RESULTS: The enhanced prognostic models outperformed the baseline models, regardless of whether ML or DL approaches were used. The areas under the receiver operating characteristic curve (AUCs) of the baseline models were between 0.760 and 0.892. Adding the text-based marker to the baseline models significantly increased the model discrimination, with AUCs ranging from 0.861 to 0.914. The net reclassification improvement and integrated discrimination improvement indices also showed significant improvements. CONCLUSIONS: Using NLP to extract textual information from clinical narratives could improve the predictive performance of all baseline prognostic models for ICH.

High Neutrophil-to-Lymphocyte Ratio Predicts One-Year Risk of Pneumonia Post-Stroke Discharge.

INTRODUCTION: The neutrophil-to-lymphocyte ratio (NLR) may predict stroke-associated pneumonia, which is generally defined as pneumonia occurring in the first week after stroke. However, little is known whether the initial NLR is associated with pneumonia risk during the long-term follow-up in stroke survivors. We aimed to determine the relationship between admission NLR and the risk of post-stroke pneumonia within 1 year after discharge from acute stroke care. METHODS: Hospital databases were searched to identify adult patients hospitalized for acute stroke. Admission NLR was extracted using differential leukocyte counts. The outcome of interest was hospitalized pneumonia occurring within 1 year after discharge from hospitalization for stroke. Multivariable Cox proportional-hazards models were used to determine the independent effects of the NLR on the risk of pneumonia. RESULTS: In this study, 5,741 patients with acute stroke (mean age, 68 years; men, 62.1%) were analyzed. The median NLR was 2.72 (interquartile range, 1.78-4.49). Of the patients, 342 (6.0%) developed pneumonia within 1 year after discharge. In the multivariable models, the NLR was a significant predictor of pneumonia after discharge whether it was analyzed as a continuous or dichotomized variable. The corresponding adjusted hazard ratios were 1.037 (95% confidence interval [CI], 1.013-1.061) and 1.361 (95% CI, 1.087-1.704), respectively. CONCLUSION: The NLR could predict the risk of post-stroke pneumonia up to 1 year after discharge from acute stroke care. It may help identify high-risk stroke survivors, for whom appropriate interventions can be targeted.

Incidence, prescription patterns and risk factors of antipsychotic initiation in elderly stroke survivors.

OBJECTIVES: Epidemiological data regarding antipsychotic initiation in elderly patients with stroke are limited. We aimed to investigate the incidence, prescription patterns and determinants of antipsychotic initiation in elderly patients with stroke. METHODS: We conducted a retrospective cohort study to identify patients aged above 65 years who had been admitted for stroke from the National Health Insurance Database (NHID). The index date was defined as the discharge date. The incidence and prescription pattern of antipsychotics were estimated using the NHID. To evaluate the determinants of antipsychotic initiation, the cohort identified from the NHID was linked to the Multicenter Stroke Registry (MSR). Demographics, comorbidities and concomitant medications were obtained from the NHID. Information including smoking status, body mass index, stroke severity and disability was retrieved by linking to the MSR. The outcome was antipsychotic initiation after the index date. Hazard ratios for antipsychotic initiation were estimated using the multivariable Cox model. RESULTS: In terms of prognosis, the first 2 months after a stroke was the highest-risk period for antipsychotic use. A high burden of coexisting diseases carried an increased risk of antipsychotic use; in particular, chronic kidney disease (CKD) had the highest adjusted hazard ratio (aHR = 1.73; 95% CI 1.29-2.31) as compared with other risk factors. Furthermore, stroke severity and disability were significant risk factors for antipsychotic initiation. CONCLUSIONS: Our study indicated that elderly stroke patients with chronic medical conditions, particularly CKD, and a higher stroke severity and disability were at greater risk of psychiatric disorders during the first 2 months after a stroke. CLINICAL TRIAL REGISTRATION: NA.

Utilization of screening and treatment for osteoporosis among stroke survivors.

Background: Stroke survivors are prone to osteoporosis and fractures. However, bone mineral density (BMD) testing and osteoporosis treatment were underutilized in patients with recent stroke. We aimed to examine whether stroke has an impact on the utilization of BMD testing and osteoporosis treatment as well as the determinants of their utilization in stroke patients using nationwide population-based data in Taiwan. Methods: We identified patients aged 55 years and older who were hospitalized for hemorrhagic or ischemic stroke as the stroke cohort, and age- and sex-matched patients hospitalized for reasons other than stroke, fracture, or fall as the non-stroke cohort. We used the Fine-Gray sub-distribution hazard competing risk regression model to determine the predictors for BMD testing and osteoporosis treatment. Results: A total of 32997 stroke patients and 32997 age- and sex-matched controls comprised the stroke and non-stroke cohorts, respectively. BMD testing and osteoporosis treatment were performed in 1.0% and 5.2% of the stroke patients, respectively, within one year after hospitalization while these measures were performed in 0.8% and 4.7% of the controls. Stroke patients were more likely to receive BMD testing (adjusted hazard ratio [HR] 1.33; 95% confidence interval [CI] 1.11-1.58) and osteoporosis treatment (adjusted HR 1.19; 95% CI 1.11-1.29). Female sex, osteoporosis, prior BMD testing, and low-trauma fractures after stroke increased the likelihood of using BMD testing and osteoporosis treatment whereas greater stroke severity reduced the likelihood of receiving both measures. Conclusions: Both BMD testing and osteoporosis treatment were underutilized among stroke survivors even though they had a higher chance of receiving both measures than non-stroke patients.

Application of machine learning and natural language processing for predicting stroke-associated pneumonia.

Background: Identifying patients at high risk of stroke-associated pneumonia (SAP) may permit targeting potential interventions to reduce its incidence. We aimed to explore the functionality of machine learning (ML) and natural language processing techniques on structured data and unstructured clinical text to predict SAP by comparing it to conventional risk scores. Methods: Linked data between a hospital stroke registry and a deidentified research-based database including electronic health records and administrative claims data was used. Natural language processing was applied to extract textual features from clinical notes. The random forest algorithm was used to build ML models. The predictive performance of ML models was compared with the A2DS2, ISAN, PNA, and ACDD4 scores using the area under the receiver operating characteristic curve (AUC). Results: Among 5,913 acute stroke patients hospitalized between Oct 2010 and Sep 2021, 450 (7.6%) developed SAP within the first 7 days after stroke onset. The ML model based on both textual features and structured variables had the highest AUC [0.840, 95% confidence interval (CI) 0.806-0.875], significantly higher than those of the ML model based on structured variables alone (0.828, 95% CI 0.793-0.863, P = 0.040), ACDD4 (0.807, 95% CI 0.766-0.849, P = 0.041), A2DS2 (0.803, 95% CI 0.762-0.845, P = 0.013), ISAN (0.795, 95% CI 0.752-0.837, P = 0.009), and PNA (0.778, 95% CI 0.735-0.822, P < 0.001). All models demonstrated adequate calibration except for the A2DS2 score. Conclusions: The ML model based on both textural features and structured variables performed better than conventional risk scores in predicting SAP. The workflow used to generate ML prediction models can be disseminated for local adaptation by individual healthcare organizations.

Automated risk assessment of newly detected atrial fibrillation poststroke from electronic health record data using machine learning and natural language processing.

Background: Timely detection of atrial fibrillation (AF) after stroke is highly clinically relevant, aiding decisions on the optimal strategies for secondary prevention of stroke. In the context of limited medical resources, it is crucial to set the right priorities of extended heart rhythm monitoring by stratifying patients into different risk groups likely to have newly detected AF (NDAF). This study aimed to develop an electronic health record (EHR)-based machine learning model to assess the risk of NDAF in an early stage after stroke. Methods: Linked data between a hospital stroke registry and a deidentified research-based database including EHRs and administrative claims data was used. Demographic features, physiological measurements, routine laboratory results, and clinical free text were extracted from EHRs. The extreme gradient boosting algorithm was used to build the prediction model. The prediction performance was evaluated by the C-index and was compared to that of the AS5F and CHASE-LESS scores. Results: The study population consisted of a training set of 4,064 and a temporal test set of 1,492 patients. During a median follow-up of 10.2 months, the incidence rate of NDAF was 87.0 per 1,000 person-year in the test set. On the test set, the model based on both structured and unstructured data achieved a C-index of 0.840, which was significantly higher than those of the AS5F (0.779, p = 0.023) and CHASE-LESS (0.768, p = 0.005) scores. Conclusions: It is feasible to build a machine learning model to assess the risk of NDAF based on EHR data available at the time of hospital admission. Inclusion of information derived from clinical free text can significantly improve the model performance and may outperform risk scores developed using traditional statistical methods. Further studies are needed to assess the clinical usefulness of the prediction model.

Validation of ICD-9-CM and ICD-10-CM Diagnostic Codes for Identifying Patients with Out-of-Hospital Cardiac Arrest in a National Health Insurance Claims Database.

Purpose: Taiwan's national health insurance (NHI) database is a valuable resource for large-scale epidemiological and long-term survival research for out-of-hospital cardiac arrest (OHCA). We developed and validated case definition algorithms for OHCA based on the International Classification of Diseases (ICD) diagnostic codes and billing codes for NHI reimbursement. Patients and Methods: Claims data and medical records of all emergency department visits from 2010 to 2020 were retrieved from the hospital's research-based database. Death-related diagnostic codes and keywords were used to identify potential OHCA cases, which were ascertained by chart reviews. We tested the performance of the developed OHCA algorithms and validated them on an external dataset. Results: The algorithm defining OHCA as any cardiac arrest (CA)-related ICD code in the first three diagnosis fields performed the best with a sensitivity of 89.5% (95% confidence interval [CI], 88.2-90.7%), a positive predictive value (PPV) of 90.6% (95% CI, 89.4-91.8%), and a kappa value of 0.900 (95% CI, 0.891-0.909). The second-best algorithm consists of any CA-related ICD code in any diagnosis field with a billing code for triage acuity level 1, achieving a sensitivity of 85.6% (95% CI, 84.1-87.0%), a PPV of 93.6% (95% CI, 92.5-94.5), and a kappa value of 0.894 (95% CI, 0.884-0.903). Both algorithms performed well in external validation. In subgroup analyses, the former algorithm performed the best in adult patients, outpatient claims, and during the ICD-9 era. The latter algorithm performed the best in the inpatient claims and during the ICD-10 era. The best algorithm for identifying pediatric OHCAs was any CA-related ICD code in the first three diagnosis fields with a billing code for triage acuity level 1. Conclusion: Our results may serve as a reference for future OHCA studies using the Taiwan NHI database.

Validation of Risk Scores for Predicting Atrial Fibrillation Detected After Stroke Based on an Electronic Medical Record Algorithm: A Registry-Claims-Electronic Medical Record Linked Data Study.

Background: Poststroke atrial fibrillation (AF) screening aids decisions regarding the optimal secondary prevention strategies in patients with acute ischemic stroke (AIS). We used an electronic medical record (EMR) algorithm to identify AF in a cohort of AIS patients, which were used to validate eight risk scores for predicting AF detected after stroke (AFDAS). Methods: We used linked data between a hospital stroke registry and a deidentified database including EMRs and administrative claims data. EMR algorithms were constructed to identify AF using diagnostic and medication codes as well as free clinical text. Based on the optimal EMR algorithm, the incidence rate of AFDAS was estimated. The predictive performance of 8 risk scores including AS5F, C2HEST, CHADS2, CHA2DS2-VASc, CHASE-LESS, HATCH, HAVOC, and Re-CHARGE-AF scores, were compared using the C-index, net reclassification improvement, integrated discrimination improvement, calibration curve, and decision curve analysis. Results: The algorithm that defines AF as any positive mention of AF-related keywords in electrocardiography or echocardiography reports, or presence of diagnostic codes of AF was used to identify AF. Among the 5,412 AIS patients without known AF at stroke admission, the incidence rate of AFDAS was 84.5 per 1,000 person-year. The CHASE-LESS and AS5F scores were well calibrated and showed comparable C-indices (0.741 versus 0.730, p = 0.223), which were significantly higher than the other risk scores. Conclusion: The CHASE-LESS and AS5F scores demonstrated adequate discrimination and calibration for predicting AFDAS. Both simple risk scores may help select patients for intensive AF monitoring.

Effectiveness of Standard-Dose vs. Low-Dose Alteplase for Acute Ischemic Stroke Within 3-4.5 h.

BACKGROUND: The efficacy and safety of intravenous alteplase administered 3-4.5 h after acute ischemic stroke have been demonstrated. However, whether responses differ between low-dose and standard-dose alteplase during this time window and whether certain subgroups benefit more remain unknown. PATIENTS AND METHODS: The current analysis was based on a multicenter matched-cohort study conducted in Taiwan. The treatment group comprised 378 patients receiving intravenous alteplase 3-4.5 h after stroke onset, and the control group comprised 378 age- and sex-matched patients who did not receive alteplase treatment during the same period. Standard- and low-dose alteplase was administered to patients at the physician's discretion. RESULTS: Overall, patients receiving alteplase exhibited more favorable outcomes than did controls [34.0 vs. 22.7%; odds ratio (OR): 1.75, 95% confidence interval (CI): 1.27-1.42], and the effectiveness was consistent in all subgroups. Although patients in the standard-dose group (n = 182) were younger than those in the low-dose (n = 192) group, the proportions of patients with favorable outcomes (36.3 vs. 31.8%; OR: 1.22, 95% CI: 0.80-1.88) and symptomatic hemorrhage (2.8 vs 4.2%; OR: 0.65, 95% CI: 0.21-2.02) were consistently comparable in a covariate-adjusted model and an age-matched cohort. In the subgroup analysis, patients with cardioembolism, atrial fibrillation, and hypercholesterolemia were more likely to achieve favorable outcomes after receiving standard-dose than low-dose alteplase. CONCLUSION: In the 3-4.5 h time window, the effectiveness and safety of standard-dose and low-dose alteplase may be comparable. A standard dose may be selected for patients with cardioembolism, atrial fibrillation, or hypercholesterolemia.

Risk of Incident Epilepsy After a Middle Cerebral Artery Territory Infarction.

Background: Among poststroke morbidities, poststroke epilepsy (PSE) has been identified as a significant clinical issue. Although middle cerebral artery (MCA) infarct is the most common type of stroke among all vascular territories, very few studies specifically focused on the risk factors leading to PSE in patients with MCA infarct. Methods: A population study in Taiwan has been conducted, linking the National Health Insurance Research Database and Hospital Stroke Registry, from 2001 to 2015 and 2006 to 2010, respectively. Patients were divided into MCA and non-MCA groups, and the diagnosis of incident epilepsy between the groups has been compared. The multivariable Cox proportional hazard model was used to identify the risk factors for developing PSE. The distribution of time to PSE was estimated using the Kaplan-Meier method. Results: In total, 1,838 patients were recruited, with 774 and 1,064 in the MCA and non-MCA groups, respectively. PSE incidence in the MCA group was 15.5% vs. 6.2% in the non-MCA group, with a hazard ratio of (95% CI) 2.06 (1.33-3.19). Factors significantly associated with PSE included atrial fibrillation, depression, National Institutes of Health Stroke Scale (NIHSS) scores of ≥ 16, and alert on arrival. For patients with MCA infarct, higher NIHSS and Glasgow coma scale scores, the presence of visual field defects and weakness, urination control impairment, and complications during hospitalization were associated with a higher risk for PSE development. Conclusions: This study established the conditions leading to a higher risk of PSE and identified the important clinical risk factors in patients experiencing MCA infarct. Efforts to manage these risk factors may be important in preventing PSE in patients with MCA infarct.

Validation of Stroke Risk Factors in Patients with Acute Ischemic Stroke, Transient Ischemic Attack, or Intracerebral Hemorrhage on Taiwan's National Health Insurance Claims Data.

Purpose: Taiwan has changed the coding system to the International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) coding since 2016. This study aimed to determine the optimal algorithms for identifying stroke risk factors in Taiwan's National Health Insurance (NHI) claims data. Patients and Methods: We retrospectively enrolled 4538 patients hospitalized for acute ischemic stroke (AIS), transient ischemic attack (TIA), or intracerebral hemorrhage (ICH) from two hospitals' stroke registries, which were linked to NHI claims data. We developed several algorithms based on ICD-10-CM diagnosis codes and prescription claims data to identify hypertension, diabetes, hyperlipidemia, atrial fibrillation (AF), and ischemic heart disease (IHD) using registry data as the reference standard. The agreement of risk factor status between claims and registry data was quantified by calculating the kappa statistic. Results: According to the registry data, the prevalence of hypertension, diabetes, hyperlipidemia, AF, and IHD among all patients was 77.5%, 41.5%, 47.9%, 12.1%, and 7.1%, respectively. In general, including diagnosis codes from prior inpatient or outpatient claims to those from the stroke hospitalization claims improved the agreement. Incorporating prescription data could improve the agreement for hypertension, diabetes, hyperlipidemia, and AF, but not for IHD. The kappa values of the optimal algorithms were 0.552 (95% confidence interval 0.524-0.580) for hypertension, 0.802 (0.784-0.820) for diabetes, 0.514 (0.490-0.539) for hyperlipidemia, 0.765 (0.734-0.795) for AF, and 0.518 (0.473-0.564) for IHD. Conclusion: Algorithms using diagnosis codes alone are sufficient to identify hypertension, AF, and IHD whereas algorithms combining both diagnosis codes and prescription data are more suitable for identifying diabetes and hyperlipidemia. The study results may provide a reference for future studies using Taiwan's NHI claims data.

Early Prediction of Functional Outcomes After Acute Ischemic Stroke Using Unstructured Clinical Text: Retrospective Cohort Study.

BACKGROUND: Several prognostic scores have been proposed to predict functional outcomes after an acute ischemic stroke (AIS). Most of these scores are based on structured information and have been used to develop prediction models via the logistic regression method. With the increased use of electronic health records and the progress in computational power, data-driven predictive modeling by using machine learning techniques is gaining popularity in clinical decision-making. OBJECTIVE: We aimed to investigate whether machine learning models created by using unstructured text could improve the prediction of functional outcomes at an early stage after AIS. METHODS: We identified all consecutive patients who were hospitalized for the first time for AIS from October 2007 to December 2019 by using a hospital stroke registry. The study population was randomly split into a training (n=2885) and test set (n=962). Free text in histories of present illness and computed tomography reports was transformed into input variables via natural language processing. Models were trained by using the extreme gradient boosting technique to predict a poor functional outcome at 90 days poststroke. Model performance on the test set was evaluated by using the area under the receiver operating characteristic curve (AUC). RESULTS: The AUCs of text-only models ranged from 0.768 to 0.807 and were comparable to that of the model using National Institutes of Health Stroke Scale (NIHSS) scores (0.811). Models using both patient age and text achieved AUCs of 0.823 and 0.825, which were similar to those of the model containing age and NIHSS scores (0.841); the model containing preadmission comorbidities, level of consciousness, age, and neurological deficit (PLAN) scores (0.837); and the model containing Acute Stroke Registry and Analysis of Lausanne (ASTRAL) scores (0.840). Adding variables from clinical text improved the predictive performance of the model containing age and NIHSS scores, the model containing PLAN scores, and the model containing ASTRAL scores (the AUC increased from 0.841 to 0.861, from 0.837 to 0.856, and from 0.840 to 0.860, respectively). CONCLUSIONS: Unstructured clinical text can be used to improve the performance of existing models for predicting poststroke functional outcomes. However, considering the different terminologies that are used across health systems, each individual health system may consider using the proposed methods to develop and validate its own models.

From Kidney Protection to Stroke Prevention: The Potential Role of Sodium Glucose Cotransporter-2 Inhibitors.

Chronic kidney disease (CKD) is an independent risk factor for stroke and covert cerebrovascular disease, and up to 40% of stroke patients have concomitant CKD. However, the so-called "cerebrorenal interaction" attracted less attention compared to its cardiorenal counterpart. Diabetes is the leading cause of CKD. The sodium-glucose cotransporter (SGLT) 2 inhibitor is a relatively new class of oral anti-diabetic drugs and has cardiorenal benefits in addition to glucose-lowering effects. In the present perspective, we would like to review the current status and future potential of the SGLT2 inhibitor in cerebro-renal interactions and strokes regardless of the status of diabetes. We propose the potential roles of baseline renal functions and SGLT1/2 dual inhibition in stroke prevention, as well as the additional benefits of reducing atrial fibrillation and hemorrhagic stroke for SGLT2 inhibitors. Further clinical trials are anticipated to test whether SGLT2 inhibitors can fulfill the long-standing unmet clinical need and stop such a vicious cycle of cerebro-renal interaction.