Development and Validation of Multiple Machine Learning Models Integrating Neutrophil‐Lymphocyte Ratio for Prediction of Hemorrhagic Transformation After Intravenous Thrombolysis in Acute Ischemic Stroke
Bibliographic record
Abstract
BACKGROUND: Hemorrhagic transformation (HT) is a critical complication of intravenous thrombolysis (IVT) in acute ischemic stroke (AIS). This study developed and validated machine learning (ML) models integrating inflammatory biomarkers with clinical indicators to predict post-IVT HT. METHODS: In 1272 IVT-treated AIS patients, the least absolute shrinkage and selection operator (LASSO) regression identified five predictors from 17 variables, which were subsequently utilized to construct eight ML models. The models were trained (70% data) and tested (30% data). Furthermore, external validation conducted on an independent cohort substantiated the generalizability of the optimal model. The SHapley Additive exPlanations (SHAP) method explained feature importance. RESULTS: LASSO screened five significant predictors: the neutrophil-to-lymphocyte ratio (NLR), admission National Institutes of Health Stroke Scale (NIHSS) score, the Alberta Stroke Program Early CT Score (ASPECTS), blood glucose, and atrial fibrillation. Logistic regression (LR) achieved optimal performance with an AUC of 0.833 internally and 0.842 externally. SHAP analysis prioritized NIHSS as the top contributor, while the nomogram elucidated the variability in HT risk. CONCLUSION: Integrating NLR with stroke severity and neuroimaging biomarkers enhances the accuracy of HT predictions. The LR-based nomogram provided a practical tool for personalized IVT decisions, emphasizing the prognostic value of systemic inflammation in AIS management.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".