Individualized Treatment Effects of Bougie versus Stylet for Tracheal Intubation in Critical Illness
Year: 2023
Type: article
Abstract: 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.
Authors: Kevin P. Seitz, Alexandra B. Spicer, Jonathan D. Casey, Kevin G. Buell, Edward T. Qian, Emma J. Graham Linck, Brian E. Driver, Wesley H. Self, Adit A. Ginde, Stacy A. Trent, Sheetal Gandotra, Lane M. Smith, David B. Page, Derek J. Vonderhaar, Jason R. West, Aaron M. Joffe, Kevin C. Doerschug, Christopher G. Hughes, Micah R. Whitson, Matthew E. Prekker, Todd W. Rice, Pratik Sinha, Matthew W. Semler, Matthew M. Churpek
Institutions: University of Wisconsin–Madison, University of Chicago, University of Chicago Medical Center, UW Health University Hospital, Vanderbilt University, Vanderbilt University Medical Center, University of Colorado Anschutz, University of Colorado Denver, Denver Health Medical Center, University of Alabama, University of Alabama at Birmingham, UAB Medicine, Louisiana State University, Ochsner Health System, Louisiana State University Health Sciences Center New Orleans, Lincoln Medical Center, University of Washington, University of Iowa, Hennepin County Medical Center, Washington University in St. Louis
Language: English
Subfield: Anesthesiology and Pain Medicine
Field: Medicine
Domain: Health Sciences
Open Access status: green
Awards: Developing a clinical decision support tool for the identification, diagnosis, and treatment of critical illness in hospitalized patients, G2340152788, Oxygen Saturation Targets during Mechanical Ventilation of Critically Ill Adults: A Clinical Trial, G4656749393, G9000043901, G9000132384, Clinical and Translational Training Program in Pulmonary Medicine, G949362299
OpenAlex ID: W4323294178