Derivation and Validation of a Scoring System for Intravenous Tissue Plasminogen Activator Use in Asian Patients

Traditional and machine learning models for predicting haemorrhagic transformation in ischaemic stroke: a systematic review and meta-analysis

BACKGROUND

Haemorrhagic transformation (HT) is a severe complication after ischaemic stroke, but identifying patients at high risks remains challenging. Although numerous prediction models have been developed for HT following thrombolysis, thrombectomy, or spontaneous occurrence, a comprehensive summary is lacking. This study aimed to review and compare traditional and machine learning-based HT prediction models, focusing on their development, validation, and diagnostic accuracy.

METHODS

PubMed and Ovid-Embase were searched for observational studies or randomised controlled trials related to traditional or machine learning-based models. Data were extracted according to Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies (CHARMS) checklist and risk of bias was assessed using the Prediction model Risk Of Bias ASsessment Tool (PROBAST). Performance data for prediction models that were externally validated at least twice and showed low risk of bias were meta-analysed.

RESULTS

A total of 100 studies were included, with 67 focusing on model development and 33 on model validation. Among 67 model development studies, 44 were traditional model studies involving 47 prediction models (with National Institutes of Health Stroke Scale score being the most frequently used predictor in 35 models), and 23 studies focused on machine learning prediction models (with support vector machines being the most common algorithm, used in 10 models). The 33 validation studies externally validated 34 traditional prediction models. Regarding study quality, 26 studies were assessed as having a low risk of bias, 11 as unclear, and 63 as high risk of bias. Meta-analysis of 15 studies validating eight models showed a pooled area under the receiver operating characteristic curve of approximately 0.70 for predicting HT.

CONCLUSION

While significant progress has been made in developing HT prediction models, both traditional and machine learning-based models still have limitations in methodological rigour, predictive accuracy, and clinical applicability. Future models should undergo more rigorous validation, adhere to standardised reporting frameworks, and prioritise predictors that are both statistically significant and clinically meaningful. Collaborative efforts across research groups are essential for validating these models in diverse populations and improving their broader applicability in clinical practice.

SYSTEMATIC REVIEW REGISTRATION

International Prospective Register of Systematic Reviews (CRD42022332816).

Predictive model for the risk of hemorrhagic transformation after rt-PA intravenous thrombolysis in patients with acute ischemic stroke: A systematic review and meta-analysis

OBJECTIVE

To systematically review the risk prediction model of Hemorrhages Transformation (HT) after intravenous thrombolysis in patients with Acute Ischemic Stroke (AIS).

METHODS

Web of Science, The Cochrane Library, PubMed, Embase, CINAHL, CNKI, CBM, WanFang, and VIP were searched from inception to February 25, 2023 for literature related to the risk prediction model for HT after thrombolysis in AIS.

RESULTS

A total of 17 included studies contained 26 prediction models, and the AUC of all models at the time of modeling ranged from 0.662 to 0.9854, 16 models had AUC>0.8, indicating that the models had good predictive performance. However, most of the included studies were at risk of bias. the results of the Meta-analysis showed that atrial fibrillation (OR=2.72, 95% CI:1.98-3.73), NIHSS score (OR=1.09, 95% CI:1.07-1.11), glucose (OR=1.12, 95% CI:1.06-1.18), moderate to severe leukoaraiosis (OR=3.47, 95% CI:1.61-7.52), hyperdense middle cerebral artery sign (OR=2.35, 95% CI:1.10-4.98), large cerebral infarction (OR=7.57, 95% CI:2.09-27.43), and early signs of infarction (OR=4.80, 95% CI:1.74-13.25) were effective predictors of HT after intravenous thrombolysis in patients with AIS.

CONCLUSIONS

The performance of the models for HT after thrombolysis in patients with AIS in the Chinese population is good, but there is some risk of bias. Future post-intravenous HT conversion prediction models for AIS patients in the Chinese population should focus on predictors such as atrial fibrillation, NIHSS score, glucose, moderate to severe leukoaraiosis, hyperdense middle cerebral artery sign, massive cerebral infarction, and early signs of infarction.

A weakly supervised deep learning model integrating noncontrasted computed tomography images and clinical factors facilitates haemorrhagic transformation prediction after intravenous thrombolysis in acute ischaemic stroke patients

BACKGROUND

Haemorrhage transformation (HT) is a serious complication of intravenous thrombolysis (IVT) in acute ischaemic stroke (AIS). Accurate and timely prediction of the risk of HT before IVT may change the treatment decision and improve clinical prognosis. We aimed to develop a deep learning method for predicting HT after IVT for AIS using noncontrast computed tomography (NCCT) images.

METHODS

We retrospectively collected data from 828 AIS patients undergoing recombinant tissue plasminogen activator (rt-PA) treatment within a 4.5-h time window (n = 665) or of undergoing urokinase treatment within a 6-h time window (n = 163) and divided them into the HT group (n = 69) and non-HT group (n = 759). HT was defined based on the criteria of the European Cooperative Acute Stroke Study-II trial. To address the problems of indiscernible features and imbalanced data, a weakly supervised deep learning (WSDL) model for HT prediction was constructed based on multiple instance learning and active learning using admission NCCT images and clinical information in addition to conventional deep learning models. Threefold cross-validation and transfer learning were performed to confirm the robustness of the network. Of note, the predictive value of the commonly used scales in clinics associated with NCCT images (i.e., the HAT and SEDAN score) was also analysed and compared to measure the feasibility of our proposed DL algorithms.

RESULTS

Compared to the conventional DL and ML models, the WSDL model had the highest AUC of 0.799 (95% CI 0.712-0.883). Significant differences were observed between the WSDL model and five ML models (P < 0.05). The prediction performance of the WSDL model outperforms the HAT and SEDAN scores at the optimal operating point (threshold = 1.5). Further subgroup analysis showed that the WSDL model performed better for symptomatic intracranial haemorrhage (AUC = 0.833, F1 score = 0.909).

CONCLUSIONS

Our WSDL model based on NCCT images had relatively good performance for predicting HT in AIS and may be suitable for assisting in clinical treatment decision-making.

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.

Recovery of walking ability in stroke patients through postacute care rehabilitation

BACKGROUND

Walking entails orchestration of the sensory, motor, balance, and coordination systems, and walking disability is a critical concern after stroke. How and to what extent these systems influence walking disability after stroke and recovery have not been comprehensively studied.

METHODS

We retrospectively analyzed patients with stroke in the Post-acute care-Cerebrovascular Diseases (PAC-CVD) program. We compared the characteristics of patient groups stratified by their ability to complete the 5-m walk test across various time points of rehabilitation. We then used stepwise linear regression to examine the degree to which each stroke characteristic and functional ability could predict patient gait performance.

RESULTS

Five hundred seventy-three patients were recruited, and their recovery of walking ability was defined by the timing of recovery in a 5-m walk test. The proportion of patients who could complete the 5-m walk test at admission, at 3 weeks of rehabilitation, at 6 weeks of rehabilitation, between 7 and 12 weeks of rehabilitation, and who could not complete the 5-m walk test after rehabilitation was 52.2%, 21.8%, 8.7%, 8.7%, and 8.6%, respectively. At postacute care discharge, patients who regained walking ability earlier had a higher chance of achieving higher levels of walking activity. Stepwise linear regression showed that Berg Balance Scale (BBS) (β: 0.011, p < .001), age (β: -0.005, p = .001), National Institutes of Health Stroke Scale (NIHSS) (6a + 6b; β: -0.042, p = .018), Mini-Nutritional assessment (MNA) (β: -0.007, p < .027), and Fugl-Meyer upper extremity assessment (FuglUE) (β: 0.002, p = .047) scores predicted patient's gait speed at discharge.

CONCLUSION

Balance, age, leg strength, nutritional status, and upper limb function before postacute care rehabilitation are predictors of walking performance after stroke.

Semantic Visualization in Functional Recovery Prediction of Intravenous Thrombolysis following Acute Ischemic Stroke in Patients by Using Biostatistics: An Exploratory Study

(1) Background: Intravenous thrombolysis following acute ischemic stroke (AIS) can reduce disability and increase the survival rate. We designed a functional recovery analysis by using semantic visualization to predict the recovery probability in AIS patients receiving intravenous thrombolysis; (2) Methods: We enrolled 131 AIS patients undergoing intravenous thrombolysis from 2011 to 2015 at the Medical Center in northern Taiwan. An additional 54 AIS patients were enrolled from another community hospital. A modified Rankin Score ≤2 after 3 months of follow-up was defined as favorable recovery. We used multivariable logistic regression with forward selection to construct a nomogram; (3) Results: The model included age and the National Institutes of Health Stroke Scale (NIHSS) score as immediate pretreatment parameters. A 5.23% increase in the functional recovery probability occurred for every 1-year reduction in age, and a 13.57% increase in the functional recovery probability occurred for every NIHSS score reduction. The sensitivity, specificity, and accuracy of the model in the validation dataset were 71.79%, 86.67%, and 75.93%, respectively, and the area under the receiver operating characteristic curve (AUC) was 0.867; (4) Conclusions: Semantic visualization-based functional recovery prediction models may help physicians assess the recovery probability before patients undergo emergency intravenous thrombolysis.

Symptomatic Intracranial Hemorrhage After Endovascular Stroke Treatment: External Validation of Prediction Models

BACKGROUND

Symptomatic intracranial hemorrhage (sICH) is a severe complication of reperfusion therapy for ischemic stroke. Multiple models have been developed to predict sICH or intracranial hemorrhage (ICH) after reperfusion therapy. We provide an overview of published models and validate their ability to predict sICH in patients treated with endovascular treatment in daily clinical practice.

METHODS

We conducted a systematic search to identify models either developed or validated to predict sICH or ICH after reperfusion therapy (intravenous thrombolysis and/or endovascular treatment) for ischemic stroke. Models were externally validated in the MR CLEAN Registry (n=3180; Multicenter Randomized Clinical Trial of Endovascular Treatment for Acute Ischemic Stroke in the Netherlands). The primary outcome was sICH according to the Heidelberg Bleeding Classification. Model performance was evaluated with discrimination (c-statistic, ideally 1; a c-statistic below 0.7 is considered poor in discrimination) and calibration (slope, ideally 1, and intercept, ideally 0).

RESULTS

We included 39 studies describing 40 models. The most frequently used predictors were baseline National Institutes of Health Stroke Scale (NIHSS; n=35), age (n=22), and glucose level (n=22). In the MR CLEAN Registry, sICH occurred in 188/3180 (5.9%) patients. Discrimination ranged from 0.51 (SPAN-100 [Stroke Prognostication Using Age and National Institutes of Health Stroke Scale]) to 0.61 (SITS-SICH [Safe Implementation of Treatments in Stroke Symptomatic Intracerebral Hemorrhage] and STARTING-SICH [STARTING Symptomatic Intracerebral Hemorrhage]). Best calibrated models were IST-3 (intercept, -0.15 [95% CI, -0.01 to -0.31]; slope, 0.80 [95% CI, 0.50-1.09]), SITS-SICH (intercept, 0.15 [95% CI, -0.01 to 0.30]; slope, 0.62 [95% CI, 0.38-0.87]), and STARTING-SICH (intercept, -0.03 [95% CI, -0.19 to 0.12]; slope, 0.56 [95% CI, 0.35-0.76]).

CONCLUSIONS

The investigated models to predict sICH or ICH discriminate poorly between patients with a low and high risk of sICH after endovascular treatment in daily clinical practice and are, therefore, not clinically useful for this patient population.

The SON2A2 score: A novel grading scale for predicting hemorrhage and outcomes after thrombolysis

Objectives

This study aimed to develop a score including novel putative predictors for predicting the risk of sICH and outcomes after thrombolytic therapy with intravenous (IV) recombinant tissue-type plasminogen activator (r-tPA) in acute ischemic stroke patients.

Methods

All patients with acute ischemic stroke treated with IV r-tPA at three university-based hospitals in Chongqing, China, from 2014 to 2019 were retrospectively studied. Potential risk factors associated with sICH (NINDS criteria) were determined with multivariate logistic regression, and we developed our score according to the magnitude of logistic regression coefficients. The score was validated in another independent cohort. Area under the receiver operating characteristic curve (AUC-ROC) was used to assess the performance of the score. Calibration was evaluated using the Hosmer-Lemeshow goodness-of-fit method.

Results

The SON2A2 score (0 to 8 points) consisted of history of smoking (no = 1, yes = 0, β = 0.81), onset-to-needle time (≥3.5 = 1,<3.5=0, β = 0.74), NIH Stroke Scale on admission (>10 = 2, ≤10 = 0, β = 1.22), neutrophil percentage (≥80.0% = 1, <80% = 0, β = 0.81), ASPECT score (≤11 = 2, >11 = 0, β = 1.30), and age (>65 years = 1, ≤65 years = 0, β = 0.89). The SON2A2 score was strongly associated with sICH (OR 1.98; 95%CI 1.675-2.34) and poor outcomes (OR 1.89; 95%CI 1.68-2.13). AUC-ROC in the derivation cohort was 0.82 (95%CI 0.77-0.86). Similar results were obtained in the validation cohort. The Hosmer-Lemeshow test revealed that predicted and observed event rates in derivation and validation cohorts were very close.

Conclusion

The SON2A2 score is a simple, efficient, quick, and easy-to-perform scale for predicting the risk of sICH and outcome after intravenous r-tPA thrombolysis within 4.5 h in patients with ischemic stroke, and risk assessment using this test has the potential for early and personalized management of this disease in high-risk patients.

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.

Development and validation of a nomogram to predict the 30-day mortality risk of patients with intracerebral hemorrhage

Background

Intracerebral hemorrhage (ICH) is a stroke syndrome with an unfavorable prognosis. Currently, there is no comprehensive clinical indicator for mortality prediction of ICH patients. The purpose of our study was to construct and evaluate a nomogram for predicting the 30-day mortality risk of ICH patients.

Methods

ICH patients were extracted from the MIMIC-III database according to the ICD-9 code and randomly divided into training and verification cohorts. The least absolute shrinkage and selection operator (LASSO) method and multivariate logistic regression were applied to determine independent risk factors. These risk factors were used to construct a nomogram model for predicting the 30-day mortality risk of ICH patients. The nomogram was verified by the area under the receiver operating characteristic curve (AUC), integrated discrimination improvement (IDI), net reclassification improvement (NRI), and decision curve analysis (DCA).

Results

A total of 890 ICH patients were included in the study. Logistic regression analysis revealed that age (OR = 1.05, P < 0.001), Glasgow Coma Scale score (OR = 0.91, P < 0.001), creatinine (OR = 1.30, P < 0.001), white blood cell count (OR = 1.10, P < 0.001), temperature (OR = 1.73, P < 0.001), glucose (OR = 1.01, P < 0.001), urine output (OR = 1.00, P = 0.020), and bleeding volume (OR = 1.02, P < 0.001) were independent risk factors for 30-day mortality of ICH patients. The calibration curve indicated that the nomogram was well calibrated. When predicting the 30-day mortality risk, the nomogram exhibited good discrimination in the training and validation cohorts (C-index: 0.782 and 0.778, respectively). The AUCs were 0.778, 0.733, and 0.728 for the nomogram, Simplified Acute Physiology Score II (SAPSII), and Oxford Acute Severity of Illness Score (OASIS), respectively, in the validation cohort. The IDI and NRI calculations and DCA analysis revealed that the nomogram model had a greater net benefit than the SAPSII and OASIS scoring systems.

Conclusion

This study identified independent risk factors for 30-day mortality of ICH patients and constructed a predictive nomogram model, which may help to improve the prognosis of ICH patients.

A New Nomogram for Predicting the Risk of Intracranial Hemorrhage in Acute Ischemic Stroke Patients After Intravenous Thrombolysis

Background

We aimed to develop and validate a new nomogram for predicting the risk of intracranial hemorrhage (ICH) in patients with acute ischemic stroke (AIS) after intravenous thrombolysis (IVT).

Methods

A retrospective study enrolled 553 patients with AIS treated with IVT. The patients were randomly divided into two cohorts: the training set (70%, n = 387) and the testing set (30%, n = 166). The factors in the predictive nomogram were filtered using multivariable logistic regression analysis. The performance of the nomogram was assessed based on the area under the receiver operating characteristic curve (AUC-ROC), calibration plots, and decision curve analysis (DCA).

Results

After multivariable logistic regression analysis, certain factors, such as smoking, National Institutes of Health of Stroke Scale (NIHSS) score, blood urea nitrogen-to-creatinine ratio (BUN/Cr), and neutrophil-to-lymphocyte ratio (NLR), were found to be independent predictors of ICH and were used to construct a nomogram. The AUC-ROC values of the nomogram were 0.887 (95% CI: 0.842–0.933) and 0.776 (95% CI: 0.681–0.872) in the training and testing sets, respectively. The AUC-ROC of the nomogram was higher than that of the Multicenter Stroke Survey (MSS), Glucose, Race, Age, Sex, Systolic blood Pressure, and Severity of stroke (GRASPS), and stroke prognostication using age and NIH Stroke Scale-100 positive index (SPAN-100) scores for predicting ICH in both the training and testing sets (p < 0.05). The calibration plot demonstrated good agreement in both the training and testing sets. DCA indicated that the nomogram was clinically useful.

Conclusions

The new nomogram, which included smoking, NIHSS, BUN/Cr, and NLR as variables, had the potential for predicting the risk of ICH in patients with AIS after IVT.

Nomogram to Predict the Number of Thrombectomy Device Passes for Acute Ischemic Stroke with Endovascular Thrombectomy

Purpose

This study aimed to determine the risk factors associated with the number of thrombectomy device passes and establish a nomogram for predicting the number of device pass attempts in patients with successful endovascular thrombectomy (EVT).

Methods

We enrolled patients from a signal comprehensive stroke center (CSC) who underwent EVT because of large vessel occlusion stroke. Multivariate logistic regression analysis was used to develop the best-fit nomogram for predicting the number of thrombectomy device passes. The discrimination and calibration of the nomogram were estimated using the area under the receiver operating characteristic curve (AUC-ROC) and a calibration plot with a bootstrap of 1000 resamples. A decision curve analysis (DCA) was used to measure the availability and effect of this predictive tool.

Results

In total, 130 patients (mean age 64.9 ± 11.1 years; 83 males) were included in the final analysis. Age (odds ratio [OR], 1.085; 95% confidence interval [CI], 1.005-1.172; p = 0.036), baseline Alberta Stroke Program Early computed tomography (ASPECTS) score (OR, 0.237; 95% CI, 0.115-0.486; p < 0.001), and homocysteine level (OR, 1.090; 95% CI, 1.028-1.155; p = 0.004) were independent predictors of device pass number and were thus incorporated into the nomogram. The AUC-ROC determined the discrimination ability of the nomogram, which was 0.921 (95% CI, 0.860-0.980), which indicated good predictive power. Moreover, the calibration plot revealed good predictive accuracy of the nomogram. The DCA demonstrated that when the threshold probabilities of the cohort ranged between 5.0% and 98.0%, the use of the nomogram to predict a device pass number > 3 provided greater net benefit than did "treat all" or "treat none" strategies.

Conclusion

The nomogram comprised age, baseline ASPECTS score, and homocysteine level, can predict a device pass number >3 in acute ischemic stroke (AIS) patients who are undergoing EVT.

Developing a Nomogram to Predict the Probability of Subsequent Vascular Events at 6-Month in Chinese Patients with Minor Ischemic Stroke

Purpose

To develop a nomogram to predict the risk of subsequent vascular events (SVE) at 6-month in Chinese patients with minor ischemic stroke (MIS).

Patients and Methods

We performed a retrospective analysis of 260 MIS patients, which were randomly divided into a derivation set (193 cases) and a verification set (67 cases) at a ratio of 3:1. Multi-factor logistic regression was used to construct a predictive model of SVE from the derivation set and verify it in the verification set.

Results

Finally, there were 51 cases (19.6%) of SVE in 260 MIS cases. Age, fasting blood glucose, metabolic syndrome, number of lesions found on MRI, and the infarct size were used to construct the prediction model and nomogram. The AUC in the derivation set was 0.901, with a sensitivity of 0.795, a specificity of 0.877, a positive likelihood ratio of 6.443, and a negative likelihood ratio of 0.234. The AUC in the verification set was 0.897, which was not significantly different from the derivation set (P = 0.937). The predictive model based on clinical parameters has good diagnostic efficiency and robustness.

Conclusion

The nomogram can provide personalized predictions for the 6-month SVE risk in Chinese MIS patients.

Risk Factors Associated with Outcomes of Recombinant Tissue Plasminogen Activator Therapy in Patients with Acute Ischemic Stroke

Ischemic stroke is the most common type of stroke, and early interventional treatment is associated with favorable outcomes. In the guidelines, thrombolytic therapy using recombinant tissue-type plasminogen activator (rt-PA) is recommended for eligible patients with acute ischemic stroke. However, the risk of hemorrhagic complications limits the use of rt-PA, and the risk factors for poor treatment outcomes need to be identified. To identify the risk factors associated with in-hospital poor outcomes in patients treated with rt-PA, we analyzed the electronic medical records of patients who were diagnosed with acute ischemic stroke and treated for rt-PA at Chang Gung Memorial Hospitals from 2006 to 2016. In-hospital death, intensive care unit (ICU) stay, or prolonged hospitalization were defined as unfavorable treatment outcomes. Medical history variables and laboratory test results were considered variables of interest to determine risk factors. Among 643 eligible patients, 537 (83.5%) and 106 (16.5%) patients had favorable and poor outcomes, respectively. In the multivariable analysis, risk factors associated with poor outcomes were female gender, higher stroke severity index (SSI), higher serum glucose levels, lower mean corpuscular hemoglobin concentration (MCHC), lower platelet counts, and anemia. The risk factors found in this research could help us study the treatment strategy for ischemic stroke.

Risk Factors and a Nomogram for Predicting Intracranial Hemorrhage in Stroke Patients Undergoing Thrombolysis

PURPOSE

Identifying stroke patients at risk of postthrombolysis intracranial hemorrhage (ICH) in the clinical setting is essential. We aimed to develop and evaluate a nomogram for predicting the probability of ICH in acute ischemic stroke patients undergoing thrombolysis.

PATIENTS AND METHODS

A retrospective observational study was conducted using data from 345 patients at a single center. The patients were randomly dichotomized into training (2/3; n=233) and validation (1/3; n=112) sets. A prediction model was developed by using a multivariable logistic regression analysis.

RESULTS

The nomogram comprised three variables: the presence of atrial fibrillation (odds ratio [OR]: 4.92, 95% confidence interval [CI]: 2.09-11.57), the National Institutes of Health Stroke Scale (NIHSS) score (OR: 1.11, 95% CI: 1.04-1.18) and the glucose level on admission (OR: 1.27, 95% CI: 1.08-1.50). The areas under the receiver operating characteristic curve of the nomogram for the training and validation sets were 0.828 (0.753-0.903) and 0.801 (0.690-0.911), respectively. The Hosmer-Lemeshow test revealed good calibration in both the training and validation sets (P = 0.509 and P = 0.342, respectively). The calibration plot also demonstrated good agreement. A decision curve analysis demonstrated that the nomogram was clinically useful.

CONCLUSION

We developed an easy-to-use nomogram model to predict ICH, and the nomogram may provide risk assessments for subsequent treatment in stroke patients undergoing thrombolysis.

Registry-based stroke research in Taiwan: past and future

Stroke registries are observational databases focusing on the clinical information and outcomes of stroke patients. They play an important role in the cycle of quality improvement. Registry data are collected from real-world experiences of stroke care and are suitable for measuring quality of care. By exposing inadequacies in performance measures of stroke care, research from stroke registries has changed how we manage stroke patients in Taiwan. With the success of various quality improvement campaigns, mortality from stroke and recurrence of stroke have decreased in the past decade. After the implementation of a nationwide stroke registry, researchers have been creatively expanding how they use and collect registry data for research. Through the use of the nationwide stroke registry as a common data model, researchers from many hospitals have built their own stroke registries with extended data elements to meet the needs of research. In collaboration with information technology professionals, stroke registry systems have changed from web-based, manual submission systems to automated fill-in systems in some hospitals. Furthermore, record linkage between stroke registries and administrative claims databases or other existing databases has widened the utility of registry data in research. Using stroke registry data as the reference standard, researchers have validated several algorithms for ascertaining the diagnosis of stroke and its risk factors from claims data, and have also developed a claims-based index to estimate stroke severity. By making better use of registry data, we believe that we will provide better care to patients with stroke.