A randomized, embedded, pragmatic, Bayesian clinical trial examining clinical decision support for high flow nasal cannula management in children with bronchiolitis: design and statistical analysis plan.

BACKGROUND: High flow nasal cannula (HFNC) has been increasingly adopted in the past 2 decades as a mode of respiratory support for children hospitalized with bronchiolitis. The growing use of HFNC despite a paucity of high-quality data regarding the therapy's efficacy has led to concerns about overutilization. We developed an electronic health record (EHR) embedded, quality improvement (QI) oriented clinical trial to determine whether standardized management of HFNC weaning guided by clinical decision support (CDS) results in a reduction in the duration of HFNC compared to usual care for children with bronchiolitis. METHODS: The design and summary of the statistical analysis plan for the REspiratory SupporT for Efficient and cost-Effective Care (REST EEC; "rest easy") trial are presented. The investigators hypothesize that CDS-coupled, standardized HFNC weaning will reduce the duration of HFNC, the trial's primary endpoint, for children with bronchiolitis compared to usual care. Data supporting trial design and eventual analyses are collected from the EHR and other real world data sources using existing informatics infrastructure and QI data sources. The trial workflow, including randomization and deployment of the intervention, is embedded within the EHR of a large children's hospital using existing vendor features. Trial simulations indicate that by assuming a true hazard ratio effect size of 1.27, equivalent to a 6-h reduction in the median duration of HFNC, and enrolling a maximum of 350 children, there will be a > 0.75 probability of declaring superiority (interim analysis posterior probability of intervention effect > 0.99 or final analysis posterior probability of intervention effect > 0.9) and a > 0.85 probability of declaring superiority or the CDS intervention showing promise (final analysis posterior probability of intervention effect > 0.8). Iterative plan-do-study-act cycles are used to monitor the trial and provide targeted education to the workforce. DISCUSSION: Through incorporation of the trial into usual care workflows, relying on QI tools and resources to support trial conduct, and relying on Bayesian inference to determine whether the intervention is superior to usual care, REST EEC is a learning health system intervention that blends health system operations with active evidence generation to optimize the use of HFNC and associated patient outcomes. TRIAL REGISTRATION: ClinicalTrials.gov NCT05909566. Registered on June 18, 2023.

Retrospective Validation of a Computerized Physiologic Equation to Predict Minute Ventilation Needs in Critically Ill Children.

OBJECTIVES: Mechanical ventilation (MV) is pervasive among critically ill children. We sought to validate a computerized physiologic equation to predict minute ventilation requirements in children and test its performance against clinician actions in an in silico trial. DESIGN: Retrospective, electronic medical record linkage, cohort study. SETTING: Quaternary PICU. PATIENTS: Patients undergoing invasive MV, serial arterial blood gas (ABG) analysis within 1-6 hours, and pharmacologic neuromuscular blockade (NMB). MEASUREMENTS AND MAIN RESULTS: ABG values were filtered to those occurring during periods of NMB. Simultaneous ABG and minute ventilation data were linked to predict serial Pa co2 and pH values using previously published physiologic equations. There were 15,121 included ABGs across 500 encounters among 484 patients, with a median (interquartile range [IQR]) of 20 (10-43) ABGs per encounter at a duration of 3.6 (2.1-4.2) hours. The median (IQR) Pa co2 prediction error was 0.00 (-3.07 to 3.00) mm Hg. In Bland-Altman analysis, the mean error was -0.10 mm Hg (95% CI, -0.21 to 0.01 mm Hg). A nested, in silico trial of ABGs meeting criteria for weaning (respiratory alkalosis) or escalation (respiratory acidosis), compared the performance of recommended ventilator changes versus clinician decisions. There were 1,499 of 15,121 ABGs (9.9%) among 278 of 644 (43.2%) encounters included in the trial. Calculated predictions were favorable to clinician actions in 1124 of 1499 ABGs (75.0%), equivalent to clinician choices in 26 of 1499 ABGs (1.7%), and worse than clinician decisions in 349 of 1499 ABGs (23.3%). Calculated recommendations were favorable to clinician decisions in sensitivity analyses limiting respiratory rate, analyzing only when clinicians made changes, excluding asthma, and excluding acute respiratory distress syndrome. CONCLUSIONS: A computerized equation to predict minute ventilation requirements outperformed clinicians' ventilator adjustments in 75% of ABGs from critically ill children in this retrospective analysis. Prospective validation studies are needed.