Statistical Analysis
QI macros add-in for Excel 2020.01 (KnowWare International, Denver, Colorado) was used to generate the statistical process control charts of the outcome measures. To adjust for the seasonal variation impacting the number of patients in the PICU with critical asthma, subjects were divided into groups of 10. The upper control limit and lower control limit were calculated as 3 standard deviations above and below the center line. We considered 8 consecutive points above or below the center line to represent a special cause variation, prompting a change in the center line. Subject demographics, clinical characteristics, and balancing measures were compared among five groups of subjects: the pre-intervention, initial post-implementation (PDSA 1), inclusion of patients on continuous albuterol (PDSA 2), rate wean increment change (PDSA 3), and after the HFNC holiday (PDSA 4) using Kruskal-Wallis tests for continuous variables and chi square tests for categorical variables. A multivariable linear regression model was constructed attempting to control for known confounders (race, sex, intermittent magnesium doses, and PRISM-III score) a priori to determine our interventions impacts on HFNC duration. Statistical analyses of the subjects’ characteristics were performed using Stata17. A cutoff P value of <0.05 was considered statistically significant. The Standards for Quality Improvement Reporting Excellence (SQUIRE) 2.0 Guidelines (22) were followed during the preparation of this manuscript (supplement table 1).