Establishment and validation of risk prediction model to predict intravenous immunoglobulin-resistance in Kawasaki disease based on meta-analysis of 15 cohorts
Bibliographic record
Abstract
Abstract Background Pediatric Kawasaki disease (KD) patients showing resistance to intravenous immunoglobulin (IVIG) are at risk of coronary artery lesions; thus, early prediction of IVIG resistance is particularly important. Herein, we aimed to develop and verify a novel predictive risk model for IVIG resistance in KD based on meta-analyses. Methods PubMed, Embase, and Web of Science databases were searched for cohort studies on the risk factors for IVIG resistance from January 2006 to December 2022. Data were extracted from the screened literature, followed by quality assessment using the Newcastle-Ottawa scale. meta-analyses used Stata 17.0 software to extract the risk factors with significant combined effect sizes and combined risk values, followed by logistic regression prediction model construction. The model was prospective validated using data from 1007 pediatric KD cases attending the Children’s Hospital of Soochow University. The model’s predictive ability was assessed using the Hosmer–Lemeshow test and area under the receiver operating characteristic curve (AUC) and the clinical utility was assessed using decision curve analysis(DCA). Results Fifteen cohort studies reporting 4273 patients with IVIG resistance were included. The incidence of IVIG resistance was 16.2%. Six risk factors were reported ≥ 3 times with significant results for the combined effect size: male sex, rash, cervical lymphadenopathy, % neutrophils ≥ 80%, Age ≤ 12 months and platelet count ≤ 300 × 10 9 /L. The logistic scoring model had 83.8% specificity, 70.4% sensitivity, and an optimal cut-off value of 23.500. Conclusion The risk prediction model for IVIG resistance in KD showed a good predictive performance, and pediatricians should pay high attention to these high-risk patients and develop an appropriate individual regimens to prevent coronary complications.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".