The Heterogeneous Effect of High PEEP strategies on Survival in Acute Respiratory Distress Syndrome: preliminary results of a data-driven analysis of randomized trials
Bibliographic record
Abstract
ABSTRACT Background Mixed trial results suggest that some ventilated patients with acute respiratory distress syndrome (ARDS) benefit from high PEEP while others may be harmed, indicating heterogeneity of treatment effect (HTE). This study applies data-driven predictive approaches to uncover HTE and re-examines previously hypothesized HTE. This manuscript serves as a pre-registration of planned external validation of our trained models. Methods We identified eight randomized trials, and obtained individual patient data (IPD) from three of them (ALVEOLI, LOVS, EXPRESS), as our train cohort. We used effect modelling to predict individualized treatment effects (predicted 28-day mortality risk difference between PEEP strategies) across patient subgroups stratified by observed tertiles (≤8 cmH 2 O, 9–11 cmH 2 O, ≥12 cmH 2 O). Candidate effect modelling methods included meta-learners and technique-specific methods. Optimal methods were selected through ‘leave-one-trial-out’ cross-validation, evaluating the methods’ performances in each PEEP tertile using AUC-benefit. We trained final models using the best performing methods implemented with or without forward selection (which yielded sufficient AUC-benefit), and additional final models by selecting the variables that yielded consistency in the forward selections performed in the cross validation, if any. We further evaluated earlier hypothesized HTE comparing (1) patients with baseline PaO2/FiO2 ≤ 200 versus > 200 mmHg, and (2) patients with hypoinflammatory versus hyperinflammatory subphenotypes. Preliminary findings In the lower PEEP tertile (≤8 cmH 2 O), an X-learner implemented without, and an S-learner implemented with forward selection (both with flexible base learners), yielded the highest AUC benefits and were used to train final models. In the high PEEP tertile (≥12 cmH 2 O), only the causal forest implemented with forward selection yielded an AUC benefit exceeding zero. Respiratory-system compliance (C RS ) was consistently selected in the forward selections of cross validation, and was used to train an extra final causal forest model, with predicted effects shifting from harm to benefit for C RS 26.5 mL/cmH 2 O or higher. Higher PEEP benefited patients with baseline PaO 2 /FiO 2 ≤200 mmHg (OR 0.80, 95% CI 0.66–0.98), incurred harm among those with PaO 2 /FiO 2 >200 mmHg (OR 1.74, 95% CI 1.02–2.98; interaction P=0.01). This HTE was strongest when PaO 2 /FiO 2 was measured at low PEEP (≤8 cmH 2 O), reduced at mid-level PEEP (9–11 cmH 2 O), and negligible at high PEEP (≥12 cmH 2 O). A second-order interaction showed significant heterogeneity of HTE (ie, second-order heterogeneity) across PEEP tertiles (P=0.03). Preliminary Conclusions Our preliminary findings indicated that baseline C RS ≥ 26.5 mL/cmH 2 O predicts benefit, while C RS < 26.5 mL/cmH 2 O predicts harm from high PEEP when C RS is measured at high baseline PEEP (≥12 cmH 2 O). Similarly, baseline PaO 2 /FiO 2 ≤ 200 mmHg predicts benefit, while PaO 2 /FiO 2 > 200 mmHg predicts harm from high PEEP when PaO 2 /FiO 2 is measured at a low baseline PEEP (≤8 cmH 2 O). Using data from the LOVS trial, we investigated HTE for high PEEP between hypo- and hyperinflammatory subphenotypes but found none, despite significant HTE observed earlier in the ALVEOLI trial.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.206 | 0.300 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.025 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".