Predictive enrichment using biomarkers in studies of critically-ill patients with sepsis: a systematic review
Bibliographic record
Abstract
BACKGROUND: Sepsis remains one of the most prevalent conditions necessitating admission to intensive care units and is associated with high mortality. Unfortunately, randomized trials examining therapeutics for critically-ill patients with sepsis have been disproportionately negative, failing to identify effective interventions, likely due to the dilution of treatment effects across highly diverse populations. Designing trials to identify patients that are most likely to benefit from an intervention based on biologic mechanism, known as predictive enrichment, may be an effective strategy for enhancing clinical trial efficacy. We conducted a systematic review to summarize the use of biological markers (biomarkers) used for predictive enrichment in randomized controlled trials (RCTs) including patients with sepsis. METHODS: We conducted this review following PRISMA-ScR guidelines. We searched OVID Medline, Embase, and the Cochrane Central Register of Controlled Trials databases. We included RCTs that enrolled adult patients with sepsis that used molecular biomarkers for predictive enrichment in their study design. We summarize study characteristics, biomarkers used, predictive enrichment strategies, and trial outcomes narratively without statistical pooling. Risk of bias of individual studies was assessed using the Cochrane Risk of Bias tool. RESULTS: Of the 1,718 citations found with the search, we included 12 eligible studies. All studies used either blood-based circulating proteins or lipopolysaccharide markers for predictive enrichment. Five studies (42%) reported statistically significant impacts on the primary outcome, with three showing patient-important benefit (reduced mortality or disease severity) and two demonstrating improvements in surrogate outcomes related to biomarkers. Interventions that demonstrated benefit included immune, antimicrobial or coagulation modulating therapies. These five trials that showed benefit used suPAR, AT, mHLA-DR, IL-6 and EAA as biomarkers for predictive enrichment. CONCLUSIONS: This review characterizes the use of biomarkers for predictive enrichment in randomized trials of critically-ill patients with sepsis. Although the studies were heterogeneous in design and limited in number, those that employed biomarker-based enrichment strategies demonstrate a promising signal for enhanced clinical trial efficiency. The use of biomarkers for predictive enrichment in critical care trial design requires further exploration, investigation and validation.
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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 | 0.022 | 0.104 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.009 |
| Bibliometrics | 0.018 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".