Mortality As a Measure of Treatment Effect in Clinical Trials Recruiting Critically Ill Patients*
Bibliographic record
Abstract
OBJECTIVES: All-cause mortality is a common measure of treatment effect in ICU-based randomized clinical trials (RCTs). We sought to understand the performance characteristics of a mortality endpoint by evaluating its temporal course, responsiveness to differential treatment effects, and impact when used as an outcome measure in trials of acute illness. DATA SOURCES: We searched OVID Medline for RCTs published from 1990 to 2018. STUDY SELECTION: We reviewed RCTs that had randomized greater than or equal to 100 patients, were published in one of five high-impact general medical or eight critical care journals, and reported mortality at two or more distinct time points. We excluded trials recruiting pediatric or neonatal patients and cluster RCTs. DATA EXTRACTION: Mortality by randomization group was recorded from the article or estimated from survival curves. Trial impact was assessed by inclusion of results in clinical practice guidelines. DATA SYNTHESIS: From 2,592 potentially eligible trials, we included 343 RCTs (228,784 adult patients). While one third of all deaths by 180 days had occurred by day 7, the risk difference between study arms continued to increase until day 60 (p = 0.01) and possibly day 90 (p = 0.07) and remained stable thereafter. The number of deaths at ICU discharge approximated those at 28-30 days (95% [interquartile range [IQR], 86-106%]), and deaths at hospital discharge approximated those at 60 days (99% [IQR, 94-104%]). Only 13 of 43 interventions (30.2%) showing a mortality benefit have been adopted into widespread clinical practice. CONCLUSIONS: Our findings provide a conceptual framework for choosing a time horizon and interpreting mortality outcome in trials of acute illness. Differential mortality effects persist for 60 to 90 days following recruitment. Location-based measures approximate time-based measures for trials conducted outside the United States. The documentation of a mortality reduction has had a modest impact on practice.
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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 | 0.370 | 0.649 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.012 | 0.017 |
| Bibliometrics | 0.013 | 0.017 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".