Abstract 4146712: Prediction of Thrombosis in Coronary Artery Aneurysms Following Kawasaki Disease Using a Deep Learning Survival Approach
Bibliographic record
Abstract
Background: Prediction of thrombosis of coronary artery (CA) aneurysms in children with Kawasaki disease (KD) is challenging both because risk is determined by the interplay of complex anatomical, physiological and pharmacological factors and because of the rarity of events. Methods: We used data from 483 patients with at least one giant CA aneurysm (z>10) and enrolled in the International Kawasaki Disease Registry to train and evaluate the performance of a deep learning survival (DeepSurv) algorithm predicting CA thrombosis over time. Outcomes were predicted separately for each CA branch. Each patient’s total follow-up duration was divided into epochs; new epochs were created with any new echocardiogram, change in thromboprophylaxis (either antiplatelet or anticoagulation) or outcome. Data (absolute aneurysm size, z-score and architecture) from the most recent echocardiogram were used at the start of each new epoch. Censoring was done on the last day of the epoch or date of last follow-up for the last epoch. Three-fold cross-validation was used to estimate model performance. SHAP analysis was used to calculate variable importance. Results: Average duration of follow-up was 4.2 years per patient; 87 (18%) patients had thrombosis during the follow-up period. The prediction model for thrombosis identified a high-risk group of epochs (28%). The high-risk epochs had a substantially greater incidence rate (5.1; 95%CI: 3.8-6.7) vs. 0.5 (95%CI: 0.4-0.7) events/100 patient-years, p<0.001) and rate ratio (9.8; 95%CI: 6.3-15.4, p<0.001). SHAP analysis identified critical features in predicting thrombosis, including larger aneurysm size, irregular aneurysm shape, recent change in thromboprophylaxis and, for those events diagnosed in the acute phase of the disease, immunosuppression and degree of inflammation. Events missed by the prediction algorithm were most often associated with rapid changes in CA aneurysm size during the acute phase of KD. Conclusion: A deep learning survival approach can be used to combine multiple dimensions of risk into a comprehensive, time-dependent, prediction model for thrombosis. This model identified a group of situations for which patients may be at substantially higher risk of developing a clot and where enhanced thromboprophylaxis should be considered. Particular attention should be paid to patients with large, irregular aneurysms, especially in hyperinflammatory contexts and when changing thromboprophylaxis regimen.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".