Innovation in Enrichment: Is Persistence Enough?
- Resource Type
- Article
- Authors
- Schenck, Edward J.; Siempos, Ilias I.
- Source
- Critical Care Medicine. May2024, Vol. 52 Issue 5, p853-856. 4p.
- Subject
- *ADULT respiratory distress syndrome
*MACHINE learning
- Language
- ISSN
- 0090-3493
The article discusses the use of clinical trial enrichment to improve the success of trials targeting patients who receive mechanical ventilation. Enrichment involves identifying a more homogeneous population with a higher likelihood of benefiting from a potentially risky therapy. The authors propose using machine learning methods to identify patients with hypoxemic respiratory failure (HRF) who are likely to persist on mechanical ventilation, as persistent ventilation increases the risk of poor outcomes. They present a framework for utilizing machine learning methods for trial enrichment and demonstrate the potential benefits and limitations of this approach. However, it is important to consider the patients who would not be included in an enriched trial and to assess the broader population before implementing this model in clinical practice. [Extracted from the article]