Impact of anakinra use on clinical outcomes in children with moderate or severe multisystem inflammatory syndrome in children: a propensity score matched retrospective cohort study

BACKGROUND

The treatment of children with multisystem inflammatory syndrome in children (MIS-C) related to SARS-CoV-2 infection involves immunomodulatory therapies such as IVIG and steroids. Anakinra, an interleukin-1 receptor inhibitor, has also been used, but its effectiveness is not established yet. As optimal regimens for MIS-C remain unknown, we aimed to assess the effect of anakinra in reducing hospital stay in patients with MIS-C.

METHODS

We included children admitted from May 2020 to May 2021 diagnosed with MIS-C based on CDC criteria. The exposure of interest was anakinra use at any point during admission. The anakinra exposed group and the anakinra unexposed group were propensity score matched based on demographic and clinical severity indicators at initial presentation. Our primary outcome was length of hospital stay. Secondary outcomes were duration of vasoactive support, vasoactive inotropic score (VIS), level of respiratory support, time to fever resolution, reduction of CRP levels, and length of ICU stay. We used Wilcoxon rank sum, t-test, Chi square and Fisher's exact tests.

RESULTS

Of 138 children diagnosed with MIS-C, 79% had moderate or severe illness and 41% received anakinra. Of those, 31 patients who received anakinra were propensity score matched to 31 who did not. The length of stay in the hospital but not in the ICU was longer in the anakinra group. There were no differences in median duration of vasoactive support, fever resolution, CRP reduction, or VIS.

CONCLUSIONS

In patients with moderate to severe MIS-C, use of anakinra was associated with longer duration of hospital stay.

Epidemiology and Severity of Illness of MIS-C and Kawasaki Disease During the COVID-19 Pandemic

BACKGROUND AND OBJECTIVES

Multisystem inflammatory syndrome in children (MIS-C) is a novel, severe condition following severe acute respiratory syndrome coronavirus 2 infection. Large epidemiologic studies comparing MIS-C to Kawasaki disease (KD) and evaluating the evolving epidemiology of MIS-C over time are lacking. We sought to understand the illness severity of MIS-C compared with KD and evaluate changes in MIS-C illness severity over time during the coronavirus disease 2019 pandemic compared with KD.

METHODS

We included hospitalizations of children with MIS-C and KD from April 2020 to May 2022 from the Pediatric Health Information System administrative database. Our primary outcome measure was the presence of shock, defined as the use of vasoactive/inotropic cardiac support or extracorporeal membrane oxygenation. We examined the volume of MIS-C and KD hospitalizations and the proportion of hospitalizations with shock over time using 2-week intervals. We compared the proportion of hospitalizations with shock in MIS-C and KD patients over time using generalized estimating equations adjusting for hospital clustering and age, with time as a fixed effect.

RESULTS

We identified 4868 hospitalizations for MIS-C and 2387 hospitalizations for KD. There was a higher proportion of hospitalizations with shock in MIS-C compared with KD (38.7% vs 5.1%). In our models with time as a fixed effect, we observed a significant decrease in the odds of shock over time in MIS-C patients (odds ratio 0.98, P < .001) but not in KD patients (odds ratio 1.00, P = .062).

CONCLUSIONS

We provide further evidence that MIS-C is a distinct condition from KD. MIS-C was a source of lower morbidity as the pandemic progressed.

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 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.