Eliminating Monitor Overuse (EMO) Type III Effectiveness-Deimplementation Cluster-Randomized Trial: Statistical Analysis Plan

Background: Deimplementing overused health interventions is essential to maximizing quality and value while minimizing harm, waste, and inefficiencies. Three national guidelines discourage continuous pulse oximetry (SpO2) monitoring in children who are not receiving supplemental oxygen, but the guideline-discordant practice remains prevalent, making it a prime target for deimplementation. This paper details the statistical analysis plan for the Eliminating Monitor Overuse (EMO) SpO2 trial, which compares the effect of two competing deimplementation strategies (unlearning only vs. unlearning plus substitution) on the sustainment of deimplementation of SpO2 monitoring in children with bronchiolitis who are in room air. Methods: The EMO Trial is a hybrid type 3 effectiveness-deimplementation trial with a longitudinal cluster-randomized design, conducted in Pediatric Research in Inpatient Settings Network hospitals. The primary outcome is deimplementation sustainment, analyzed as a longitudinal difference-in-differences comparison between study arms. This analysis will use generalized hierarchical mixed-effects models for longitudinal clustering outcomes. Secondary outcomes include the length of hospital stay and oxygen supplementation duration, modeled using linear mixed-effects regressions. Using the well-established counterfactual approach, we will also perform a mediation analysis of hospital-level mechanistic measures on the association between the deimplementation strategy and the sustainment outcome. Discussion: We anticipate that the EMO Trial will advance the science of deimplementation by providing new insights into the processes, mechanisms, and likelihood of sustained practice change using rigorously designed deimplementation strategies. This pre-specified statistical analysis plan will mitigate reporting bias and support data-driven approaches

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 \u3c .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\u27s 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\u27s 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