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Identifying and Validating Pediatric Hospitalizations for MIS-C Through Administrative Data
Katherine A Auger1,2,3, Matt Hall4, Staci D Arnold5
1Division of Hospital Medicine.
Insights
Researchers developed algorithms to accurately identify hospitalizations for multisystem inflammatory syndrome in children (MIS-C) using administrative data. These tools enable crucial research into this evolving pediatric condition.
Area of Science:
- Pediatric Health Research
- Epidemiology
- Health Informatics
Background:
- Multisystem inflammatory syndrome in children (MIS-C) is a rare condition, making large-scale research challenging for individual children's hospitals.
- Identifying MIS-C patients in administrative databases is difficult, hindering generalizable research efforts.
Conclusions:
- High-sensitivity algorithms are suitable for epidemiological research on MIS-C.
- High-PPV algorithms are valuable for comparative effectiveness studies.
- Accurate identification of MIS-C hospitalizations is essential for understanding and managing this novel pediatric syndrome.
Background:
Individual children's hospitals care for a small number of patients with multisystem inflammatory syndrome in children (MIS-C). Administrative databases offer an opportunity to conduct generalizable research; however, identifying patients with MIS-C is challenging.
Methods:
We developed and validated algorithms to identify MIS-C hospitalizations in administrative databases. We developed 10 approaches using diagnostic codes and medication billing data and applied them to the Pediatric Health Information System from January 2020 to August 2021. We reviewed medical records at 7 geographically diverse hospitals to compare potential cases of MIS-C identified by algorithms to each participating hospital's list of patients with MIS-C (used for public health reporting).
Results:
The sites had 245 hospitalizations for MIS-C in 2020 and 358 additional MIS-C hospitalizations through August 2021. One algorithm for the identification of cases in 2020 had a sensitivity of 82%, a low false positive rate of 22%, and a positive predictive value (PPV) of 78%. For hospitalizations in 2021, the sensitivity of the MIS-C diagnosis code was 98% with 84% PPV.
Conclusion:
We developed high-sensitivity algorithms to use for epidemiologic research and high-PPV algorithms for comparative effectiveness research. Accurate algorithms to identify MIS-C hospitalizations can facilitate important research for understanding this novel entity as it evolves during new waves.
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