Individualized Treatment Effects of Bougie versus Stylet for Tracheal Intubation in Critical Illness.

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.

Individualized Treatment Effects of Oxygen Targets in Mechanically Ventilated Critically Ill Adults.

Importance: Among critically ill adults, randomized trials have not found oxygenation targets to affect outcomes overall. Whether the effects of oxygenation targets differ based on an individual's characteristics is unknown. Objective: To determine whether an individual's characteristics modify the effect of lower vs higher peripheral oxygenation-saturation (Spo2) targets on mortality. Design, Setting, and Participants: A machine learning model to predict the effect of treatment with a lower vs higher Spo2 target on mortality for individual patients was derived in the Pragmatic Investigation of Optimal Oxygen Targets (PILOT) trial and externally validated in the Intensive Care Unit Randomized Trial Comparing Two Approaches to Oxygen Therapy (ICU-ROX) trial. Critically ill adults received invasive mechanical ventilation in an intensive care unit (ICU) in the United States between July 2018 and August 2021 for PILOT (n = 1682) and in 21 ICUs in Australia and New Zealand between September 2015 and May 2018 for ICU-ROX (n = 965). Exposures: Randomization to a lower vs higher Spo2 target group. Main Outcome and Measure: 28-Day mortality. Results: In the ICU-ROX validation cohort, the predicted effect of treatment with a lower vs higher Spo2 target for individual patients ranged from a 27.2% absolute reduction to a 34.4% absolute increase in 28-day mortality. For example, patients predicted to benefit from a lower Spo2 target had a higher prevalence of acute brain injury, whereas patients predicted to benefit from a higher Spo2 target had a higher prevalence of sepsis and abnormally elevated vital signs. Patients predicted to benefit from a lower Spo2 target experienced lower mortality when randomized to the lower Spo2 group, whereas patients predicted to benefit from a higher Spo2 target experienced lower mortality when randomized to the higher Spo2 group (likelihood ratio test for effect modification P = .02). The use of a Spo2 target predicted to be best for each patient, instead of the randomized Spo2 target, would have reduced the absolute overall mortality by 6.4% (95% CI, 1.9%-10.9%). Conclusion and relevance: Oxygenation targets that are individualized using machine learning analyses of randomized trials may reduce mortality for critically ill adults. A prospective trial evaluating the use of individualized oxygenation targets is needed.

Machine Learning-Driven Analysis of Individualized Treatment Effects Comparing Buprenorphine and Naltrexone in Opioid Use Disorder Relapse Prevention.

OBJECTIVE: A trial comparing extended-release naltrexone and sublingual buprenorphine-naloxone demonstrated higher relapse rates in individuals randomized to extended-release naltrexone. The effectiveness of treatment might vary based on patient characteristics. We hypothesized that causal machine learning would identify individualized treatment effects for each medication. METHODS: This is a secondary analysis of a multicenter randomized trial that compared the effectiveness of extended-release naltrexone versus buprenorphine-naloxone for preventing relapse of opioid misuse. Three machine learning models were derived using all trial participants with 50% randomly selected for training (n = 285) and the remaining 50% for validation. Individualized treatment effect was measured by the Qini value and c-for-benefit, with the absence of relapse denoting treatment success. Patients were grouped into quartiles by predicted individualized treatment effect to examine differences in characteristics and the observed treatment effects. RESULTS: The best-performing model had a Qini value of 4.45 (95% confidence interval, 1.02-7.83) and a c-for-benefit of 0.63 (95% confidence interval, 0.53-0.68). The quartile most likely to benefit from buprenorphine-naloxone had a 35% absolute benefit from this treatment, and at study entry, they had a high median opioid withdrawal score (P < 0.001), used cocaine on more days over the prior 30 days than other quartiles (P < 0.001), and had highest proportions with alcohol and cocaine use disorder (P ≤ 0.02). Quartile 4 individuals were predicted to be most likely to benefit from extended-release naltrexone, with the greatest proportion having heroin drug preference (P = 0.02) and all experiencing homelessness (P < 0.001). CONCLUSIONS: Causal machine learning identified differing individualized treatment effects between medications based on characteristics associated with preventing relapse.

metPropagate: network-guided propagation of metabolomic information for prioritization of metabolic disease genes.

Many inborn errors of metabolism (IEMs) are amenable to treatment, therefore early diagnosis is imperative. Whole-exome sequencing (WES) variant prioritization coupled with phenotype-guided clinical and bioinformatics expertise is typically used to identify disease-causing variants; however, it can be challenging to identify the causal candidate gene when a large number of rare and potentially pathogenic variants are detected. Here, we present a network-based approach, metPropagate, that uses untargeted metabolomics (UM) data from a single patient and a group of controls to prioritize candidate genes in patients with suspected IEMs. We validate metPropagate on 107 patients with IEMs diagnosed in Miller et al. (2015) and 11 patients with both CNS and metabolic abnormalities. The metPropagate method ranks candidate genes by label propagation, a graph-smoothing algorithm that considers each gene's metabolic perturbation in addition to the network of interactions between neighbors. metPropagate was able to prioritize at least one causative gene in the top 20th percentile of candidate genes for 92% of patients with known IEMs. Applied to patients with suspected neurometabolic disease, metPropagate placed at least one causative gene in the top 20th percentile in 9/11 patients, and ranked the causative gene more highly than Exomiser's phenotype-based ranking in 6/11 patients. Interestingly, ranking by a weighted combination of metPropagate and Exomiser scores resulted in improved prioritization. The results of this study indicate that network-based analysis of UM data can provide an additional mode of evidence to prioritize causal genes in patients with suspected IEMs.