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How multisystem inflammatory syndrome in children discriminated from Kawasaki disease: a differentiating score based
Ali Sobh1, Doaa Mosad Mosa2, Nada Khaled3
1Department of Pediatrics, Mansoura University Children's Hospital, Mansoura University Faculty of Medicine, Mansoura, Egypt.
Insights
Differentiating multisystem inflammatory syndrome in children (MIS-C) from Kawasaki disease (KD) is challenging. A new scoring model using clinical and lab data helps distinguish MIS-C from KD, aiding timely treatment decisions.
Area of Science:
- Pediatric rheumatology
- Infectious diseases
- Critical care medicine
Background:
- Multisystem inflammatory syndrome in children (MIS-C) shares diagnostic overlap with Kawasaki disease (KD), affecting 25-50% of MIS-C patients.
- Accurate differentiation is crucial due to time-sensitive management protocols for both conditions.
- Clinical practice faces challenges in distinguishing MIS-C from KD, impacting patient care.
Purpose of the Study:
- To develop a predictive model for differentiating MIS-C from KD.
- To identify key clinical and laboratory features that distinguish between MIS-C and KD.
- To aid clinicians in making timely and accurate treatment decisions.
Main Methods:
- Retrospective analysis of clinical and laboratory data from hospitalized patients under 18 with MIS-C or KD.
- Comparison of demographic, clinical, and laboratory profiles between MIS-C and KD cohorts.
- Development of a discrimination score using logistic regression analysis.
Main Results:
- MIS-C patients exhibited a higher prevalence of abdominal pain, vomiting, and cervical lymphadenopathy compared to KD patients.
- Elevated liver enzymes (AST, ALT) and serum creatinine, along with a lower platelet count nadir, were observed in MIS-C.
- A predictive scoring model was generated, achieving an area under the curve of 0.70 for distinguishing MIS-C from KD.
Conclusions:
- A novel prediction model based on clinical and laboratory findings effectively differentiates MIS-C from KD.
- The model provides valuable support for clinicians in treatment decision-making for pediatric inflammatory conditions.
- Key differentiating factors include gastrointestinal symptoms, cervical lymphadenopathy, elevated liver enzymes, and lower platelet counts in MIS-C.
Background:
About 25-50% of multisystem inflammatory syndrome in children (MIS-C) patients meet the criteria for diagnosis of Kawasaki disease (KD). The differentiation of both conditions is so challenging on clinical practice as the management of both is time dependant and precise diagnosis is fundamental.
Method:
Data were collected from children < 18 years old hospitalized with MIS-C or KD. Patient demographics, clinical, and laboratory data were compared, and a discrimination score was created to assist in clinical differentiation.
Results:
72 patients with MIS-C and 18 with KD were included in the study. Patients with MIS-C had a higher prevalence of abdominal pain (p = 0.02), vomiting (p = 0.03), and cervical lymphadenopathy (p = 0.02) compared with KD cases. MIS-C patients had higher liver enzymes (aspartate aminotransferase (AST) (p = 0.04), alanine aminotransferase (ALT) (p = 0.03), serum creatinine (p = 0.03), and lower platelet count nadir (p = 0.02) than KD. Four variables were detected in the regression analysis model, and the independent predictors were utilized to generate a scoring model that distinguished MIS-C from KD with an area under the curve of 0.70.
Conclusion:
This study constructed a prediction model for differentiation of MIS-C from KD based on clinical and laboratory profiles. This model will be valuable to guide clinicians in the treatment decisions. Key Points • Children with MIS-C are more likely to have gastrointestinal symptoms, cervical lymphadenopathy, and respiratory involvement than KD patients. • Elevated liver enzymes and lower platelet count are more pronounced laboratory findings in MIS-C than KD. • This study constructed a prediction model for differentiation of MIS-C from KD based on clinical and laboratory profiles. This model will be valuable to guide clinicians in the treatment decisions.
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