We conducted this multicenter, prospective cohort study across 3 strategically selected medical centers in Lianyungang, China, representing distinct demographic populations: Lianyungang Clinical Medical College of Nanjing Medical University (urban core population), Guanyun County People’s Hospital (county or rural population), and Lianyungang Dongfang Hospital (eastern urban population).

This study was conducted in accordance with the Declaration of Helsinki and approved by the Institutional Review Board of Lianyungang Clinical Medical College (KY-20240403001‐01). The study was registered with the China Clinical Trial Registry (ChiCTR2400085504) and National Medical Research Registration System (MR-32-24-016371). Written informed consent was waived for the retrospective cohort and obtained from all participants or their legal representatives in the prospective validation cohort. All data were deidentified to protect patient privacy, and no participants received financial compensation.

From January 2017 through April 2024, we conducted a multicenter retrospective study enrolling consecutive adult patients (≥18 y) with AIS who received IVT. The model development cohort (n=1361) comprised patients treated at Lianyungang Clinical Medical College of Nanjing Medical University (January 2017-August 2023), while the external validation cohort (n=566) included patients from Dongfang Hospital and Guanyun County People’s Hospital (September 2023-April 2024).

To ensure methodological rigor, we implemented strict temporal and statistical separation protocols. The chronologically nonoverlapping cohorts maintained complete temporal independence, with feature normalization coefficients derived exclusively from the development dataset and subsequently applied to the validation cohort without modification. This unidirectional preprocessing workflow prevented statistical contamination between phases, preserving analytical integrity and enabling the unbiased evaluation of model generalizability across different clinical settings and time periods.

The inclusion criteria were as follows: (1) age ≥18 years with AIS in the hyperacute phase (<4.5 h from symptom onset), (2) treatment with recombinant tissue plasminogen activator or tenecteplase within 4.5 hours of onset, (3) stroke severity assessed using the NIHSS, and (4) blood pressure controlled to systolic <180 mm Hg or diastolic <115 mm Hg before thrombolysis. The exclusion criteria were as follows: (1) patients who underwent endovascular bridging therapy following IVT and (2) cases with incomplete clinical data.

Clinical data were prospectively collected using standardized electronic case report forms. Baseline characteristics included (1) demographics: age, gender, height, and weight; (2) risk factors: smoking, alcohol consumption, hypertension, diabetes mellitus, atrial fibrillation, valvular heart disease, and coronary artery disease; (3) medication history: antihypertensive, hypoglycemic, lipid-lowering, and anticoagulant therapies; (4) clinical parameters: NIHSS scores (baseline, 12 h, and 24 h post IVT) and blood pressure measurements; (5) laboratory tests: complete blood count, coagulation profile, and biochemical parameters; (6) imaging data: head computed tomography (CT), magnetic resonance imaging, and CT angiography (CTA); and (7) treatment metrics: onset-to-door time and door-to-needle time. The primary outcome was END, defined as an increase of ≥4 points in the NIHSS score within 24 hours post IVT compared to baseline. At least 2 independent neurologists classified stroke subtype according to the Trial of ORG 10172 in Acute Stroke Treatment (TOAST) criteria. In our study, all patients underwent CTA before IVT as part of the standardized acute stroke protocol. For quality control, 2 neuroradiologists and 2 stroke neurologists, blinded to outcomes, independently reviewed the CTA images, with consensus review for discordant readings.

To ensure temporal precedence and eliminate potential information leakage, all predictor variables were obtained prethrombolysis. Laboratory parameters, including red cell distribution width (RDW) and neutrophil count, were measured within 15-30 minutes of admission. The electronic medical record system was configured with timestamp restrictions to ensure that only pretreatment data were incorporated into the prediction model.

For model development, we employed stratified random sampling to partition the development cohort into training (80%) and internal validation (20%) sets, preserving class distribution. We conducted a systematic evaluation of 11 machine learning algorithms, including (1) base classifiers: regularized logistic regression, support vector machine, decision tree, multilayer perceptron, and naive bayes; ensemble methods: voting, stacking, bagging, boosting, random forest, and XGBoost; (2) model performance was comprehensively assessed using multiple metrics: (1) AUC; (2) accuracy, precision, recall, and F₁-score; and (3) confusion matrices.

To ensure robust validation, we implemented a 3-phase approach: (1) internal validation: using the held-out 20% development cohort, (2) external validation: with an independent cohort (n=566) from 2 additional medical centers, and (3) prospective validation: in 20 consecutive thrombolysis-eligible patients.

To identify the most influential predictors, we performed a comprehensive feature importance analysis on the optimal algorithm model, specifically employing permutation importance. This method quantifies each feature’s contribution by measuring the increase in prediction errors after randomly permuting the feature, thereby ensuring an unbiased assessment of relevance while accounting for feature correlations. Feature selection was optimized by balancing discriminative performance metrics (AUC, sensitivity or specificity, F₁-score) with clinical utility considerations, including indicator accessibility and cost-effectiveness.

We constructed several candidate models using the top 15 features ranked by feature importance from the best algorithm model. We systematically tested models with feature counts ranging from 2 to 15 using the following methods: (1) 5-fold cross-validation (AUC, accuracy, precision, recall, F₁-score); (2) DeLong test for ROC curve comparisons; and (3) clinical feasibility assessment, encompassing data acquisition time and cost-effectiveness, resource availability across health care institutions, and interobserver reliability for subjective indicators. The final model integrated statistical performance, feasibility scores, and cost-benefit analysis. The optimized model was deployed as a web-based decision support system with functionalities such as automatic data validation, an intuitive user interface, and real-time visualization. Minimal input requirements enable seamless clinical integration.

Using the combined dataset (development cohort: n=1361; external validation cohort: n=566), we conducted a systematic risk stratification analysis as follows:

Statistical analyses were performed using IBM SPSS Statistics 24 (IBM Corporation) and Python-based machine learning libraries. Categorical variables were expressed as numbers (percentages) and compared using χ² or Fisher exact tests. Continuous variables were presented as median (IQR) or mean (SD) and compared using the unpaired 2-tailed t test or Mann-Whitney U test, as appropriate.

Model discrimination was assessed using AUC values, and calibration was evaluated using calibration plots. Decision curve analysis was performed to assess the clinical utility of the ENDRAS model. The optimal cutoff value for risk stratification was determined using the Youden index. Statistical significance was set at P<.05 (2-tailed).

From January 2017 to April 2024, we initially identified 2567 patients with first-ever AIS who received IVT within 4.5 hours of symptom onset. After applying the predefined inclusion or exclusion criteria, 1927 patients were enrolled in the final analysis (development cohort: 1361 patients [Lianyungang Clinical College of Nanjing Medical University, The First People’s Hospital of Lianyungang]; external validation cohort: 566 patients [Lianyungang Dongfang Hospital and Guanyun County People’s Hospital]). The patient selection process is detailed in Figure 1.

A prospective validation cohort (n=20) was recruited to preliminarily validate the real-world performance of ENDRAS, with baseline characteristics shown in Table 3. In this small independent cohort comprising 5 END and 15 non-END patients, ENDRAS demonstrated preliminary discriminative capability with 80.0% sensitivity (4/5 patients with END correctly identified as high-risk) and 86.7% specificity (13/15 patients without END correctly classified as low-risk). The overall predictive accuracy was 85.0% (17/20), with positive and negative predictive values of 66.7% and 92.9%, respectively.

Machine learning methods leverage computational algorithms to model complex, nonlinear relationships between clinical variables, thereby overcoming the limitations of traditional risk assessment approaches. This has established them as superior predictive tools, demonstrating high accuracy and robust discriminatory performance in outcome prediction while maintaining clinical usability [17-19]. While prior studies [20,21] have explored the prediction of END using patient history, laboratory results, and biochemical markers, their clinical applicability remains limited due to insufficient validation. In cases of END following IVT, some predictive models define END as an increase of ≥2 points on the NIHSS score within 24 hours to 7 days [15,22,23]. However, this threshold may lack sensitivity in severe stroke cases (eg, baseline NIHSS ≥20), where a 2-point change in a total score of 42 could underestimate true clinical deterioration [24]. To address this limitation, recent predictive models have incorporated multifactorial risk assessments. For instance, 1 study [25] developed a model integrating 6 key predictors—age, diabetes, atrial fibrillation, antiplatelet therapy, C-reactive protein levels, and baseline NIHSS scores —to improve END risk stratification.

While predictive models for END continue to evolve, significant limitations persist in their clinical application. The model developed by Tian et al [26], which incorporates the neutrophil-to-lymphocyte ratio, mean platelet volume, body mass index, and atrial fibrillation, represents an advance in pre-IVT risk stratification. However, its utility is constrained by several factors: (1) limited validation in IVT-treated patients: the model has not been validated specifically in cohorts undergoing IVT, raising questions about its generalizability to this population; (2) methodological limitations: the use of manual grouping rather than randomized allocation may introduce selection bias, reducing the reliability of the predictive outcomes; and (3) incomplete risk factor integration: the model omits established predictors of END, such as baseline NIHSS scores and blood pressure parameters, which have been consistently associated with postthrombolysis neurological decline. To address the limitations in the existing END prediction models, we designed our research approach with several methodological improvements. Based on previous research findings, our study focused on key aspects to enhance predictive accuracy: (1) conducting prospective validation specifically in IVT-treated cohorts; (2) utilizing machine learning approaches to mitigate grouping bias; and (3) implementing comprehensive variable selection that incorporates established predictors such as NIHSS scores and hemodynamic markers.

The robust predictive performance of ENDRAS, coupled with the clear pathophysiological relevance of its component predictors, establishes a strong foundation for clinical implementation. ENDRAS provides real-time risk stratification for END in patients with prethrombolysis, addressing the current reliance on subjective assessment. We propose a risk-stratified management framework:

This framework complements standard guidelines, adding personalized risk assessment while preserving clinical judgment when patient-specific factors warrant deviation. Although our model development established 29% as the optimal probability threshold for creating a comprehensive optimal model (sensitivity: 95.24%, specificity: 97.58%, positive predictive value: 92.11%, negative predictive value: 98.57%), we recognize that different clinical environments may require different thresholds depending on their specific priorities.

Institutions with emergency departments, intensive care units, or those treating high-risk patients may opt for a lower threshold of 20% (high-sensitivity conservative model) to enhance “rule-out” capability and reduce missed END events, given its strong negative predictive value (98.61%) in high-stakes situations. For standard clinical pathways, multidisciplinary team decisions, and clinical trials, a threshold of 25% (high-performance balanced model) provides balanced and reliable performance across all metrics, delivering trustworthy positive and negative predictions.

In contrast, specialist clinics emphasizing resource efficiency and confirmatory testing may choose a threshold of 35% (precision diagnostic model) to limit false positives while preserving diagnostic accuracy. Resource-limited settings focused on cost containment and avoidance of overtreatment might implement a 40% threshold (high-specificity precision model), leveraging its “rule-in” capability to initiate treatment protocols based on highly specific positive results.

We recommend that institutions select the threshold most appropriate for their clinical context, monitoring resources, and risk management strategy. The detailed performance metrics and clinical interpretations for each threshold are provided in Table S3 in Multimedia Appendix 1.

An example of a high-risk case is as follows: a 72-year-old male with left MCA occlusion (NIHSS 18, ASPECTS 8) received an intravenous recombinant tissue plasminogen activator at 2.5 h post onset, with an ENDRAS score of 0.82. Hourly assessments detected subtle deterioration (+2 NIHSS points) at hour 4, triggering immediate advanced imaging that revealed salvageable penumbra. Emergent thrombectomy resulted in favorable outcomes (modified Rankin scale: 2). Without ENDRAS-guided monitoring, this intervention window might have been missed.

An example of a low-risk case is as follows: A 65-year-old female with small cortical infarct (NIHSS 4) received an intravenous recombinant tissue plasminogen activator at 3 h post onset, with an ENDRAS score of 0.28. Standard monitoring enabled early mobilization at 24 h and discharge planning by day 2, optimizing resource utilization without compromising safety.

These cases demonstrate how ENDRAS transforms from a predictive tool into a practical clinical decision support system guiding resource allocation, monitoring intensity, and intervention decisions. Prospective validation of these management protocols represents an important direction for future research.

Despite ENDRAS demonstrating robust predictive performance (AUC=0.988, 95% CI 0.983‐0.993), several limitations warrant consideration. While the model’s error profile is clinically acceptable (false-positive rate=2.42%, false-negative rate=4.76%), these misclassifications could still impact treatment decisions. The time span of the validation cohort (September 2023 to April 2024, totaling 8 mo) is relatively short, which does not allow for a thorough assessment of the model’s stability across different time periods. All validation data originate from hospitals within a single geographical area, limiting the evaluation of the model’s generalizability across different regional populations. We acknowledge a significant lack of statistical power in the prospective validation portion (n=20). Given an expected incidence rate of END events of 8%‐28%, this sample size is only projected to yield 1‐6 events, far below the recommended standard for prediction model validation.

Furthermore, a critical limitation is ENDRAS’s development and validation exclusively within Chinese stroke populations. Established ethnic variations in stroke pathophysiology—including higher intracranial atherosclerosis prevalence in East Asian populations versus predominant extracranial atherosclerosis in Western cohorts—may affect model performance across different demographic contexts.

We recognize that dependence on CTA for IAS assessment poses an implementation challenge for our prediction model in resource-limited settings. To address this limitation, we have conducted a sensitivity analysis using a modified model without IAS, which demonstrated reduced but still clinically meaningful performance (AUC=0.857, 95% CI 0.837‐0.877). The identification of IAS as a strong predictor is consistent with pathophysiological mechanisms and previous studies linking it to stroke progression.

We acknowledge that excluding patients receiving bridging therapy (IV thrombolysis followed by mechanical thrombectomy) introduces selection bias and restricts applicability to the broader stroke population. This exclusion was necessary due to substantially different neurological courses, monitoring protocols, and clinical trajectories between patients undergoing emergent endovascular intervention and those receiving IV thrombolysis alone.

To address these limitations, we have initiated several complementary research directions. First, recognizing the selection bias introduced by excluding bridging therapy patients, we are developing a dedicated prediction model for this population (ENDRAS-MT). Preliminary analysis (n=216) indicates distinct predictive features in this group, with procedural factors (time to groin puncture, number of passes) and angiographic findings (collateral status, thrombus location) emerging as important predictors. This complementary model will extend risk stratification to the broader population of patients with AIS eligible for reperfusion therapies.

Second, we acknowledge CTA as the current reference standard for intracranial stenosis evaluation, offering higher sensitivity than magnetic resonance angiography or transcranial Doppler. We plan to validate alternative IAS assessment methods (transcranial Doppler, magnetic resonance angiography) against CTA in future work to develop conversion algorithms across modalities, potentially enabling resource-adapted model implementation.

Third, to enhance generalizability and validation rigor, we are planning a larger multicenter prospective validation study (target n>200) across diverse Chinese regions with extended follow-ups (12+ mo), while establishing international collaborations to assess applicability in non-Asian populations. This expanded validation will provide more robust evidence for ENDRAS’s clinical utility across heterogeneous populations.

Finally, future development will focus on optimizing computational efficiency for real-time implementation, refining risk stratification thresholds, and integrating additional relevant biomarkers and imaging parameters to improve predictive accuracy. These comprehensive steps are essential for ENDRAS’s successful clinical translation and widespread adoption in acute stroke management. Information on the proportion of missing data and interrater reliability for imaging and classification variables was not available in this study. These aspects should be addressed in future research to improve data quality and reproducibility.

ENDRAS represents a promising approach in postthrombolysis stroke care by providing clinicians with an objective tool for END risk assessment. The model shows encouraging discriminative performance in combined development and external validation cohorts, suggesting potential clinical utility in identifying high-risk patients who may benefit from intensified monitoring and early intervention, though further validation in larger prospective studies is needed.

Future research should focus on prospective implementation studies across varied health care settings, refinement of risk thresholds to optimize clinical decision-making, and investigation of whether ENDRAS-guided management actually improves patient outcomes compared to standard care. As we continue to validate and refine this model, ENDRAS offers a promising framework for personalized postthrombolysis monitoring that balances resource utilization with patient safety.