ArticlePediatric cardiology2025
Differentiating Kawasaki Disease and Multisystem Inflammatory Syndrome in Children Using Blood Composite Scores: Insights into Clinical Outcomes and Predictive Indices.
Article in Pediatric cardiology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
3 citing papers in PubMed.
- The predictive value of the ratio of multi-dimensional inflammatory biomarkers in pediatric diseases from the perspective of pro-inflammatory/anti-inflammatory balance: a systematic review.Translational pediatrics · 2026Review
- Mean Platelet Volume in the Differential Diagnosis Between Kawasaki Disease and Multisystem Inflammatory Syndrome in Children.Indian journal of pediatrics · 2026Article
- Kawasaki disease vs. MIS-C in a child with congenital coronary artery anomaly: a case report.Frontiers in pediatrics · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The study sought to assess the clinical utility of complete blood count-derived composite scores, suggesting their potential as markers of inflammation and disease severity in Kawasaki disease (KD) and multisystem inflammatory syndrome in children (MIS-C) with Kawasaki-like features. This retrospective study analyzed data from 71 KD and 73 MIS-C patients and 70 healthy controls. The KD group showed a higher rate of coronary involvement (26.7% vs. 10.9%), while the MIS-C group had a higher intensive care unit (ICU) admission rate (34.2% vs. 2.8%). Platelet counts, lymphocyte counts, mean platelet volume (MPV), MPV/Lymphocyte (MPVLR), and MPV/Platelet (MPVPR) ratios demonstrated the highest specificities in distinguishing MIS-C than KD (84.5%, 83.1%, 91.1%, 88.7%, and 88.7%, respectively). Monocyte counts, MPV, and MPVPR demonstrated the highest specificities to predictive ICU admission in the MIS-C group (83.3%, 89.6%, and 89.6%, respectively). Lymphocyte counts, platelet/lymphocyte ratio (PLR), neutrophil/lymphocyte ratio (NLR), MPVLR, and Systemic Immune-Inflammation Index (SII) parameters were found to have high negative predictive values for predicting KD patients without coronary artery lesions (CALs) (85.7%, 86.1%, 87.1%, 87.1%, and 85.7%, respectively)., Systemic Inflammation Response Index (SIRI), MPVPR, and CRP were independently predictive of ICU admission in the MIS-C group, and lymphocyte count and IVIG resistance were also identified as significant predictors of CALs in the KD group. NLR, MPVLR, MPVPR, and NPR indices effectively differentiate MIS-C from KD and predict ICU admission in MIS-C. NLR, PLR, MPVLR, and SII are valuable in excluding CALs in KD with high negative predictive values. In addition, SIRI and MPVLR were independent predictors of ICU admission in MIS-C, and lymphocyte count was identified as an independent predictor of CALs in KD.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.