In American journal of respiratory and critical care medicine ; h5-index 108.0
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 : Secondary analysis of the Bougie or Stylet in Patients Undergoing Intubation Emergently (BOUGIE) 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 BOUGIE, 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 APACHE 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 use of a bougie over a stylet and from use of a stylet over a bougie using complex interactions between baseline patient and operator characteristics.
Seitz Kevin P, Spicer Alexandra B, Casey Jonathan D, Buell Kevin G, Qian Edward T, Graham Linck Emma J, Driver Brian E, Self Wesley H, Ginde Adit A, Trent Stacy A, Gandotra Sheetal, Smith Lane M, Page David B, Vonderhaar Derek J, 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
2023-Mar-06
Critical illness, Intubation, Machine learning, Prediction models
