Using data science to improve diagnosis and management of MIS-C in children
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
This study is looking at a condition called multisystem inflammatory syndrome in children (MIS-C), which has come up during the COVID-19 pandemic, and it aims to create helpful tools for doctors to better diagnose and treat kids who show signs of MIS-C or Kawasaki disease.
Quick facts
| Grant type | NIH-funded research |
|---|---|
| Study type | NIH-funded research |
| Funding institution | Johns Hopkins University NIH-funded |
| Lab location | 1 site (Baltimore, United States) |
| Project ID | NIH-10733695 on NIH RePORTER |
What this research studies
This research investigates the multisystem inflammatory syndrome in children (MIS-C) that has emerged during the SARS-CoV-2 pandemic, particularly its similarities to Kawasaki disease. The study aims to develop machine-learning models to enhance the diagnosis and management of MIS-C by leveraging existing predictive tools for Kawasaki disease. It will involve systematic analysis of clinical features and outcomes, followed by validation of these models in a clinical decision support system to assist healthcare providers in making informed decisions for pediatric patients. The research will focus on children presenting with symptoms indicative of either MIS-C or Kawasaki disease.
Who could benefit from this research
Good fit: Ideal candidates for this research include children aged 0-21 who are presenting with symptoms of MIS-C or Kawasaki disease.
Not a fit: Patients who do not exhibit symptoms of MIS-C or Kawasaki disease may not benefit from this research.
Why it matters
Potential benefit: If successful, this research could lead to more accurate and timely diagnosis and management of MIS-C in children, potentially improving patient outcomes.
How similar studies have performed: Previous research has shown success in using machine-learning models for similar clinical decision-making processes, indicating a promising approach for this study.
Where this research is happening
Baltimore, United States
- Johns Hopkins University — Baltimore, United States (Active)
Researchers
- Principal investigator: Barnes, Benjamin — Johns Hopkins University
- Study coordinator: Barnes, Benjamin
About this research
- This is an active NIH-funded research project — typically early-stage science, not a clinical trial accepting patient enrollment.
- Some NIH-funded labs run parallel clinical studies or seek volunteers for related work. To check, contact the principal investigator or institution listed above.
- For full project details, budget, and progress reports, visit the official NIH RePORTER page below.