# A data science approach to identify and manage Multisystem Inflammatory Syndrome in Children (MIS-C) associated with SARS-CoV-2 infection and Kawasaki disease in pediatric patients

> **NIH NIH R61** · JOHNS HOPKINS UNIVERSITY · 2021 · $917,957

## Abstract

Summary – Since the SARS-CoV-2 pandemic began, the emergence of an associated novel multisystem
inflammatory syndrome in children (MIS-C) has been reported. Interestingly, patients with MIS-C follow a
presentation, management and clinical course that are somewhat similar to that of patients with Kawasaki
disease (KD). Currently, the reason for such an overlap in clinical features and management is unclear and
whether this overlap is the result of a partially shared etiology or pathophysiology is the subject of fierce
debates. The degree of overlap implies that some of the clinical prediction tools that we have developed in the
past for KD could be repurposed to accelerate the development of clinical support decision tools for MIS-C. In
this study, we will first (R61 component) systematically address the overlap between KD and MIS-C and create
salient machine-learning based prediction models for diagnosis/identification (Aim #1), management (Aim #2),
and short- and long-term outcomes (Aim #3) of MIS-C based on our previously developed predictive models for
KD in a process akin to transfer learning. Secondly (R33 component), we will validate and evaluate the
performance and clinical utility of these models in a predictive clinical decision support system for the diagnosis
and management of pediatric patients presenting with features indicative of either MIS-C or KD. In this study we
will include 3 groups of patients: 1) patients with SARS-CoV-2 infection with MIS-C (CDC criteria) regardless of
whether they have overlapping signs of KD, 2) patients with SARS-CoV-2 infection investigated for but
eventually not diagnosed with MIS-C, and 3) patients with KD but without SARS-CoV-2 infection. Targeted data
will be collected from enrolled patients (900 for training and 450 for validation) for deep phenotyping and
biomarker measurements. Physician feedback on the predictions generated by the algorithm will be used to
establish clinical utility. Data required for model training will be accrued in the first two years of activity (R61
period of the grant); the development of algorithms and their internal validation will occur concurrently. In the
following 2 years (R33 period of the grant), we will perform external validation, establish clinical utility, add real-
time epidemiological surveillance data to the models and finally package, and certify the algorithms for future
deployment and for the integration in electronic health records. This project will be a collaboration with the
International Kawasaki Disease Registry (IKDR) Consortium. The IKDR Consortium has an active KD and
pediatric COVID registry in 35 sites across the world and the number of sites is currently expanding to 60+ sites.
More than 600 MIS-C patients have already been identified at IKDR centers, making this project clearly feasible
and perfectly positioning IKDR to perform this study. We strongly believe that the use of emerging data science
methods and of our previously developed algori...

## Key facts

- **NIH application ID:** 10272448
- **Project number:** 1R61HD105591-01
- **Recipient organization:** JOHNS HOPKINS UNIVERSITY
- **Principal Investigator:** Nagib Dahdah
- **Activity code:** R61 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $917,957
- **Award type:** 1
- **Project period:** 2021-01-01 → 2022-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10272448

## Citation

> US National Institutes of Health, RePORTER application 10272448, A data science approach to identify and manage Multisystem Inflammatory Syndrome in Children (MIS-C) associated with SARS-CoV-2 infection and Kawasaki disease in pediatric patients (1R61HD105591-01). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10272448. Licensed CC0.

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