Individualized Treatment Effects of Bougie versus Stylet for Tracheal Intubation in Critical Illness
Open accessAbstract
Rationale: A recent randomized trial found that using a bougie did not increase the incidence of successful intubation on first attempt in critically ill adults. The average effect of treatment in a trial population, however, may differ from effects for individuals. Objective: We hypothesized that application of a machine learning model to data from a clinical trial could estimate the effect of treatment (bougie vs. stylet) for individual patients based on their baseline characteristics (“individualized treatment effects”). Methods: This was a secondary analysis of the BOUGIE (Bougie or Stylet in Patients Undergoing Intubation Emergently) trial. A causal forest algorithm was used to model differences in outcome probabilities by randomized group assignment (bougie vs. stylet) for each patient in the first half of the trial (training cohort). This model was used to predict individualized treatment effects for each patient in the second half (validation cohort). Measurements and Main Results: Of 1,102 patients in the BOUGIE trial, 558 (50.6%) were the training cohort, and 544 (49.4%) were the validation cohort. In the validation cohort, individualized treatment effects predicted by the model significantly modified the effect of trial group assignment on the primary outcome (P value for interaction = 0.02; adjusted qini coefficient, 2.46). The most important model variables were difficult airway characteristics, body mass index, and Acute Physiology and Chronic Health Evaluation II score. Conclusions: In this hypothesis-generating secondary analysis of a randomized trial with no average treatment effect and no treatment effect in any prespecified subgroups, a causal forest machine learning algorithm identified patients who appeared to benefit from the use of a bougie over a stylet and from the use of a stylet over a bougie using complex interactions between baseline patient and operator characteristics.
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AI might help researchers design and enrol tailored patient subgroups for precise RCTs.
“Instead of using large, heterogeneous trial populations, AI might help researchers design and enrol tailored patient subgroups for precise RCTs [63, 64].”
From Implementing Artificial Intelligence in Critical Care Medicine: a consensus of 22. (2025) · cites this paper as [64]
The Discussion (page not provided) says the machine-learning method can distinguish patients likely to benefit from different treatments and may inform methods for future trials. It does not say researchers could use it to design trials or enroll tailored patient subgroups; this study analyzed patients already enrolled in a trial. (LLM, conf=0.84)
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Self-citation
- Authors
- Seitz, K. P., Spicer, A. B., Casey, J. D., Buell, K. G., Qian, E. T., Graham Linck, E. J., Driver, B. E., Self, W. H., Ginde, A. A., Trent, S. A., Gandotra, S., Smith, L. M., Page, D. B., Vonderhaar, D. J., West, J. R., Joffe, A. M., Doerschug, K. C., Hughes, C. G., Whitson, M. R., Prekker, M. E., Rice, T. W., Sinha, P., Semler, M. W., & Churpek, M. M.
- Affiliations
- Division of Allergy, Pulmonary, and Critical Care Medicine, Department of Medicine, and
- University of Wisconsin–Madison (US)
- University of Chicago (US)
- Division of Pulmonary and Critical Care, Department of Medicine, University of Wisconsin-Madison, Madison, Wisconsin
- Department of Emergency Medicine and
- Vanderbilt University (US)
- University of Colorado Anschutz (US)
- Denver Health Medical Center (US)
- Division of Pulmonary, Allergy and Critical Care Medicine, Department of Medicine, and
- Atrium Health Pulmonary Critical Care Medicine, Charlotte, North Carolina
- University of Alabama (US)
- Louisiana State University (US)
- Lincoln Medical Center (US)
- University of Washington (US)
- University of Iowa (US)
- Hennepin County Medical Center (US)
- Washington University in St. Louis (US)
- ORCID
- Seitz, Kevin P.
- Spicer, Alexandra B.
- Casey, Jonathan D.
- Buell, Kevin G.
- Qian, Edward T.
- Driver, Brian E.
- Self, Wesley H.
- Ginde, Adit A.
- Trent, Stacy A.
- Gandotra, Sheetal
- Smith, Lane M.
- Page, David B.
- West, Jason R.
- Joffe, Aaron M.
- Doerschug, Kevin C.
- Hughes, Christopher G.
- Whitson, Micah R.
- Prekker, Matthew E.
- Rice, Todd W.
- Sinha, Pratik
- Semler, Matthew W.
- Churpek, Matthew M.
- Venue
- American Journal of Respiratory and Critical Care Medicine
- Publisher
- Oxford University Press (OUP)
- Published
- 2023-06
- Volume
- 207
- Issue
- 12
- Pages
- 1602-1611
- ISSN
- 1073-449X (ISSN-L)
- 1535-4970
- PMC
- PMC10273111
- OpenAlex
- W4323294178
- Cited by
- 32
- References
- 11
- Citations by year
- 2026: 3 · 2025: 13 · 2024: 12 · 2023: 4
- FWCI
- 10.52
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- American Journal of Respiratory and Critical Care Medicine (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- NIH (T32HL087738, K23HL153584, UL1 RR024975)
- National Heart, Lung, and Blood Institute (K23HL143053, R01HL157262)
- Topics
- Health Sciences › Medicine › Anesthesiology and Pain Medicine › Airway Management and Intubation Techniques
- Health Sciences › Medicine › Pulmonary and Respiratory Medicine › Respiratory Support and Mechanisms
- Health Sciences › Medicine › Pulmonary and Respiratory Medicine › Tracheal and airway disorders
- Keywords
- bougie, stylet, endotracheal intubation, individual treatment effects, causal forest, difficult airway, critical illness, first-attempt intubation success, body mass index, APACHE II score
- MeSH terms
- Adult, Calibration, Humans, Intubation, Intratracheal, Laryngoscopy, Critical Illness
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