Objective: The aim of this study was to explore heterogeneity in
the cost-effectiveness of high-flow nasal cannula (HFNC) therapy
compared with continuous positive airway pressure (CPAP)
in children following extubation.
Design: Using data from the FIRST-line support for Assistance
in Breathing in Children (FIRST-ABC) trial, we explore heterogeneity
at the individual and subgroup levels using a causal
forest approach, alongside a seemingly unrelated regression
(SUR) approach for comparison.
Settings: FIRST-ABC is a noninferiority randomized controlled
trial (ISRCTN60048867) including children in UK paediatric
intensive care units, which compared HFNC with CPAP as the
first-line mode of noninvasive respiratory support.
Patients: In the step-down FIRST-ABC, 600 children clinically
assessed to require noninvasive respiratory support were randomly
assigned to HFNC and CPAP groups with 1:1 treatment allocation
ratio. In this analysis, 118 patients were excluded because they
did not consent to accessing their medical records, did not consent
to follow-up questionnaire or did not receive respiratory support.
Measurements and Main Results: The primary outcome of this
study is the incremental net monetary benefit (INB) of HFNC
compared with CPAP using a willingness-to-pay threshold of
£20,000 per QALY gain. INB is calculated based on total costs
and quality adjusted life years (QALYs) at 6 months. The findings
suggest modest heterogeneity in cost-effectiveness of HFNC
compared with CPAP at the subgroup level, while greater heterogeneity
is detected at the individual level.
Conclusions: The estimated overall INB of HFNC is smaller than
the INB for patients with better baseline status suggesting that
HFNC can be more cost-effective among less severely ill patients.
Key Words: causal forest, cost-effectiveness, heterogenous effects,
machine learning