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

Seitz, Kevin P.; Spicer, A.; Casey, Jonathan D; Buell, Kevin G; Qian, Edward T; Linck, Emma J Graham; 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, Matthew J.; Prekker, Matthew E.; Rice, Todd W.; Sinha, Pratik; Semler, Matthew W.; Churpek, Matthew M

doi:10.1164/rccm.202209-1799oc

Cited by 15 publications

(11 citation statements)

References 11 publications

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“…14,15 Data from randomized trials can be analyzed using machine learning methods to predict individualized treatment effects, defined as the predicted difference in outcome between 2 treatments for an individual patient based on his or her unique characteristics. [16][17][18][19] To test the hypothesis that the effect of peripheral oxygenation-saturation (SpO 2 ) targets on mortality would differ based on individual patient characteristics, this study derived and externally validated predicted individualized treatment effects using 2 temporally and geographically distinct randomized trials of lower vs higher SpO 2 targets in critically ill patients receiving mechanical ventilation.…”

Section: Resultsmentioning

confidence: 99%

“…Prior studies examining predicted individualized treatment effects in a single randomized trial did not partition or partitioned the dataset using train-test or time-series splits. 16,17,[33][34][35] The externally validation of models in a separate randomized trial, as done in this study, represents the next important step in accurately identifying individualized treatment effects.…”

Section: Discussionmentioning

confidence: 99%

“…This is important because individualized treatment effect models use supervised machine learning algorithms that can overfit the derivation cohort and produce excessively optimistic results. Prior studies examining predicted individualized treatment effects in a single randomized trial did not partition or partitioned the dataset using train-test or time-series splits . The externally validation of models in a separate randomized trial, as done in this study, represents the next important step in accurately identifying individualized treatment effects.…”

Section: Discussionmentioning

confidence: 99%

“…Such nonrandom variation in the magnitude or direction of treatment effect, referred to as heterogeneity of treatment effect , is not adequately addressed by traditional one-at-a-time subgroup analyses because each patient has multiple characteristics that may each influence the effect of the intervention on outcomes . Data from randomized trials can be analyzed using machine learning methods to predict individualized treatment effects, defined as the predicted difference in outcome between 2 treatments for an individual patient based on his or her unique characteristics …”

Section: Introductionmentioning

confidence: 99%

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

Buell,

Spicer,

Casey

et al. 2024

JAMA

Self Cite

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ImportanceAmong 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.ObjectiveTo determine whether an individual’s characteristics modify the effect of lower vs higher peripheral oxygenation-saturation (Spo2) targets on mortality.Design, Setting, and ParticipantsA 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).ExposuresRandomization to a lower vs higher Spo2 target group.Main Outcome and Measure28-Day mortality.ResultsIn 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 relevanceOxygenation 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.

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Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Buell,

Spicer,

Casey

et al. 2024

JAMA

Self Cite

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“…Most used recommended steps to reduce risks of over-fitting, including coefficient shrinkage methods and internal cross-validation. Six (40,41,44,59,69,79) applied effect model findings to external datasets for validation. Only four studies (52,75,78,79) used performance metrics designed for risk prediction without also reporting performance for predicting treatment effects.…”

Section: Resultsmentioning

confidence: 99%

Impact of the PATH Statement on Analysis and Reporting of Heterogeneity of Treatment Effect in Clinical Trials: A Scoping Review

Selby,

Maas,

Fireman

et al. 2024

Preprint

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Background: The Predictive Approaches to Treatment Effect Heterogeneity (PATH) Statement provides guidance for using predictive modeling to identify differences (i.e., heterogeneity) in treatment effects (benefits and harms) among participants in randomized clinical trials (RCTs). It distinguished risk modeling, which uses a multivariable model to predict risk of trial outcome(s) and then examines treatment effects within strata of predicted risk, from effect modeling, which predicts trial outcomes using models that include treatment, individual participant characteristics and interactions of treatment with selected characteristics. Purpose: To describe studies of heterogeneous treatment effects (HTE) that use predictive modeling in RCT data and cite the PATH Statement, Data Sources: The Cited By functions in PubMed, Google Scholar, Web of Science and SCOPUS databases (Jan 7, 2020 - June 5, 2023). Study Selection: 42 reports presenting 45 predictive models. Data Extraction: Double review with adjudication to identify risk and effect modeling and examine consistency with Statement consensus statements. Credibility of HTE findings was assessed using criteria adapted from the Instrument to assess Credibility of Effect Modification Analyses (ICEMAN). Clinical importance of credible HTE findings was also assessed. Data Synthesis: The numbers of reports, especially risk modeling reports, increased year-on-year. Consistency with consensus statements was high, except for two: only 15 of 32 studies with positive overall findings included a risk model; and most effect models explored many candidate covariates with little prior evidence for effect modification. Risk modeling was more likely than effect modeling to identify both credible HTE (14/19 vs 5/26) and clinically important HTE (10/19 vs 4/26). Limitations: Risk of reviewer bias: reviewers assessing credibility and clinical importance were not blinded to adherence to PATH recommendations. Conclusions: The PATH Statement appears to be influencing research practice. Risk modeling often uncovered clinically important HTE; effect modeling was more often exploratory.

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Individualized treatment in critical care: the oxygenation paradigm

Buell,

Semler,

Churpek

2024

Intensive Care Med

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

Cited by 15 publications

References 11 publications

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

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

Impact of the PATH Statement on Analysis and Reporting of Heterogeneity of Treatment Effect in Clinical Trials: A Scoping Review

Individualized treatment in critical care: the oxygenation paradigm

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