Abstract O.34: NT-proBNP based Algorithm for Diagnosis and Treatment of Kawasaki Disease - Are we there yet?
Bibliographic record
Abstract
Background: Diagnosis of Kawasaki Disease (KD) can be confusing in the absence of a confirmatory test or pathognomonic finding, especially when clinical criteria are incomplete (iKD). We have lately proposed serum NT-proBNP as an adjunctive diagnostic test. Method: We retrospectively tested a new diagnostic algorithm to aid in diagnosis based on NT-proBNP (Z-score for age), coronary artery dilation (CAD) at onset, and abnormal serum albumin or CRP. The goal was to assess the performance of the algorithm with respect to CAD outcome (aneurysm, dilation, or occult dilation). Occult dilation is defined as variation of coronary artery Z-score >2 within the normal range (<2.5). Results: The algorithm was tested on 81 KD patients who had NT-proBNP on admission at our institution between 2008 and 2013. Age at diagnosis was 3.2 ± 2.6 years, with a median of 5 diagnostic criteria (range 3-6), of whom 31/81 (38.3%) had iKD. Aneurysms occurred in 16/81 (19.8%); higher prevalence in iKD, 12/31 (38.7%) versus 4/50 (8.0%) (p=0.001). CAD affected 35/81 (43.2%), and 30/81 (37.0%) had occult CAD. With the algorithm, 80/81 (98.8%) were to be treated: based on high NT-proBNP alone for 54/81 (66.7%), on onset CAD for 13/81 (16.0%), and on high CRP or low albumin for 13/81 (16.0%). (Figure 1) Results were similar when the algorithm was applied to patients with complete or incomplete criteria. The only patient “not-to-treat” with the algorithm had iKD and transient occult CAD. Conclusion: This NT-proBNP based algorithm is efficient to identify and treat patients at risk of coronary involvement, despite an apparent selection bias of CA involvement. This paves the way for a prospective validation trial of the algorithm.
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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 | 0.008 |
| 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 | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".