Efficacy Analysis of a 12-Cytokine Panel for the Diagnosis of Kawasaki Disease and Prediction of Intravenous Immunoglobulin Resistance
Bibliographic record
Abstract
Weixing Kong,1,* Lichao Gao,2,* Jian Hu,2 Zhufei Xu,3 Qing Zhang,2 Yujia Wang,2 Songling Fu,2 Chunhong Xie,2 Fangqi Gong2,* 1Yongkang Women and Children’s Health Hospital, Yongkang, Zhejiang, People’s Republic of China; 2Department of Cardiology, Children’s Hospital, Zhejiang University School of Medicine, National Clinical Research Center for Child Health, Hangzhou, Zhejiang, People’s Republic of China; 3Department of Pulmonology, Children’s Hospital, Zhejiang University School of Medicine, National Clinical Research Center for Child Health, Hangzhou, Zhejiang, People’s Republic of China*These authors contributed equally to this workCorrespondence: Fangqi Gong, Department of Cardiology, Children’s Hospital, Zhejiang University School of Medicine, National Clinical Research Center for Child Health, No. 3333 Binsheng Road, Hangzhou, 310052, People’s Republic of China, Tel +86-571-86670012, Email gongfangqi@zju.edu.cnPurpose: To evaluate the diagnostic and predictive value of a 12-cytokine panel for Kawasaki disease (KD) and intravenous immunoglobulin (IVIG) resistance.Patients and Methods: A retrospective case-control study was conducted using clinical data from children diagnosed with KD at Children’s Hospital, Zhejiang University School of Medicine, between December 1, 2023, and March 30, 2025. Demographic characteristics, laboratory findings, and echocardiographic results were collected. KD patients were compared with febrile controls without KD. Differences in sex, age, 12-cytokine profile, complete blood count, C-reactive protein level, and erythrocyte sedimentation rate were analyzed. Additionally, cytokine levels were compared between IVIG-resistant and IVIG-responsive KD patients to assess their predictive value for IVIG resistance.Results: A total of 686 KD patients and 101 febrile non-KD controls were enrolled. Compared with controls, KD patients were significantly younger and presented higher levels of IFN-α, IL-10, IL-1β, IL-2, IL-4, IL-5, IL-6, and IL-8, as well as elevated neutrophil counts and white blood cell counts. Logistic regression analysis identified age (in months), IL-10, IL-5, and the absolute neutrophil count as independent predictors of KD diagnosis. Among the KD patients, 80 were IVIG resistant. Compared with IVIG-responsive patients, IVIG-resistant patients presented significantly higher levels of IFN-γ, IL-10, IL-17, IL-2, IL-5, IL-6, and IL-8 but lower levels of IFN-α. Logistic regression revealed that IL-10 and IL-8 were independent predictors of IVIG resistance. When the concentration of IL-10 exceeded 14.70 pg/mL, the sensitivity and specificity for predicting IVIG resistance were 0.675 and 0.748, respectively. Similarly, when the concentration of IL-8 exceeded 23.55 pg/mL, the sensitivity and specificity were 0.725 and 0.658, respectively.Conclusion: The 12-cytokine panel has potential as a diagnostic and predictive tool for KD. Elevated IL-10 and IL-5 levels are independent predictors of KD diagnosis, whereas elevated IL-10 and IL-8 levels are independent predictors of IVIG resistance. These findings support the clinical utility of cytokine profiling in KD management.Keywords: Kawasaki disease, 12-cytokine panel, intravenous immunoglobulin resistance, child
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".