Abstract
Objectives: To investigate heterogeneity in the cost-effectiveness of high-flow nasal cannula (HFNC) therapy compared with continuous positive airway pressure (CPAP) for acutely ill children requiring noninvasive respiratory support. Methods: Using data from the First-line Support for Assistance in Breathing in Children trial, we explore heterogeneity at the patient and subgroup levels using 2 causal forest approaches and a seemingly unrelated regression approach for comparison. First-line Support for Assistance in Breathing in Children is a noninferiority randomized controlled trial (ISRCTN60048867) involving 24 UK pediatric intensive care units. The Step-up trial focuses on acutely ill children aged 0 to 15 years, requiring noninvasive respiratory support. A total of 600 children were randomly assigned to HFNC and CPAP groups in a 1:1 allocation ratio, with 94 patients excluded because of data unavailability. Results: The primary outcome is the incremental net monetary benefit (INB) of HFNC compared with CPAP, using a willingness-to-pay threshold of £20 000 per quality-adjusted life year gain. INB is derived from total costs and quality-adjusted life years at 6 months. Subgroup analysis showed that some subgroups, such as male children, those aged less than 12 months, and those without severe respiratory distress at randomization, had more favorable INB results. Patient-level analysis revealed heterogeneity in INB estimates, particularly driven by the cost component, with greater uncertainty for those with higher INBs. Conclusions: The estimated overall INB of HFNC is significantly larger for specific patient subgroups, suggesting that the cost-effectiveness of HFNC can be heterogeneous, which highlights the importance of considering patient characteristics in evaluating the cost-effectiveness of HFNC.
| Original language | English |
|---|---|
| Pages (from-to) | 60-69 |
| Number of pages | 10 |
| Journal | Value in Health |
| Volume | 28 |
| Issue number | 1 |
| DOIs | |
| Publication status | Published - Jan 2025 |
Keywords
- causal forest
- cost-effectiveness
- heterogeneous effects
- machine learning
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