Identifying and Validating Pediatric Hospitalizations for MIS-C Through Administrative Data.
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...
| Publicado en: | Pediatrics Vol. 151; no. 5; pp. 1 - 11 |
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| Autores principales: | , , , , , , , , , , , , , , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
American Academy of Pediatrics
May2023
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=164235437&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 164235437 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 00314005 PDT jtl: Pediatrics issn: 00314005 maglogo: N pubinfo: dt: May2023 vid: 151 iid: 5 pid: 1659 pub: American Academy of Pediatrics place: Elk Grove Village, Illinois artinfo: ui: 164235437 164235437 164235437 10.1542/peds.2022-059872 164235437 ppf: 1 ppct: 10 formats: tig: atl: Identifying and Validating Pediatric Hospitalizations for MIS-C Through Administrative Data. aug: au: Auger, Katherine A. Hall, Matt Arnold, Staci D. Bhumbra, Samina Bryan, Mersine A. Hartley, David Ivancie, Rebecca Katragadda, Harita Kazmier, Katie Jacob, Seethal A. Jerardi, Karen E. Molloy, Matthew J. Parikh, Kavita Schondelmeyer, Amanda C. Shah, Samir S. Brady, Patrick W. affil: Division of Hospital Medicine, Cincinnati Children's Hospital, Cincinnati, Ohio sug: subj: Multisystem Inflammatory Syndrome Diagnosis Infant, Hospitalized Human Record Review Health Information Systems Algorithms Sensitivity and Specificity False Positive Results Predictive Value of Tests Funding Source International Classification of Diseases Infant Infant: 1-23 months ab: 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. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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