Risk Factors and a Prediction Model for Hemorrhagic Transformation in Acute Ischemic Stroke With Atrial Fibrillation
Bibliographic record
Abstract
OBJECTIVES: To identify the risk factors of hemorrhagic transformation (HT) and to establish a prediction model for HT in patients with acute ischemic stroke (AIS) and atrial fibrillation (AF). METHODS: From January 2015 to December 2018, patients with AIS and AF were enrolled. Demographics, lesion features, and blood test results were collected. Univariate and multivariate logistic regression analyses were used to identify the independent risk factors of HT. The receiver operating curve (ROC) curve was utilized to determine the cutoff values and the efficiency of the variables. A predictive model was subsequently developed based on the identified independent risk factors. RESULTS: A total of 259 patients were included. Age [odds ratio (OR): 1.094; 95% CI: 1.048-1.142; P <0.001], LDL-C (OR: 0.633; 95% CI: 0.407-0.983; P =0.042), uric acid (OR: 0.996; 95% CI: 0.991-0.999; P =0.031), Alberta Stroke Program Early CT Score (ASPECTS) (OR: 0.700; 95% CI: 0.563-0.870; P <0.001), cerebral cortex infarction (OR: 0.294; 95% CI: 0.168-0.515; P <0.001), and massive cerebral infarction (OR: 3.683; 95% CI: 3.025-5.378; P <0.001) were independently associated with HT. We have developed a model incorporating these variables. The area under the curve of the predictive model was 0.87 (95% CI: 0.83-0.92), demonstrating satisfactory predictive ability with a sensitivity of 83.5% and a specificity of 76.4%. CONCLUSIONS: Our predictive model, which integrates age, LDL-C, uric acid, ASPECTS, cerebral cortex infarction, and massive cerebral infarction, can be used to predict HT after AIS in patients with AF, thereby facilitating the mitigation of adverse outcomes.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".