N‐terminal pro‐B‐type natriuretic peptide diagnostic algorithm versus American Heart Association algorithm for Kawasaki disease
Bibliographic record
Abstract
BACKGROUND: Diagnosis of Kawasaki disease (KD) can be challenging in the absence of a confirmatory test or pathognomonic finding, especially when clinical criteria are incomplete. We recently proposed serum N-terminal pro-B-type natriuretic peptide (NT-proBNP) as an adjunctive diagnostic test. METHODS: We retrospectively tested a new algorithm to help KD diagnosis based on NT-proBNP, coronary artery dilation (CAD) at onset, and abnormal serum albumin or C-reactive protein (CRP). The goal was to assess the performance of the algorithm and compare its performance with that of the 2004 American Heart Association (AHA)/American Academy of Pediatrics (AAP) algorithm. RESULTS: The algorithm was tested on 124 KD patients with NT-proBNP measured on admission at the present institutions between 2007 and 2013. Age at diagnosis was 3.4 ± 3.0 years, with a median of five diagnostic criteria; and 55 of the 124 patients (44%) had incomplete KD. CA complications occurred in 64 (52%), with aneurysm in 14 (11%). Using this algorithm, 120/124 (97%) were to be treated, based on high NT-proBNP alone for 79 (64%); on onset CAD for 14 (11%); and on high CRP or low albumin for 27 (22%). Using the AHA/AAP algorithm, 22/47 (47%) of the eligible patients with incomplete KD would not have been referred for treatment, compared with 3/55 (5%) with the NT-proBNP algorithm (P < 0.001). CONCLUSION: This NT-proBNP-based algorithm is efficient to identify and treat patients with KD, including those with incomplete KD. This study 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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".