The Endotype-Driven Sepsis Trial: A New Research Paradigm

A Review for Critical Care Postgraduates

Dr Neeraj Manikath , claude.ai

Abstract

Despite decades of research and over 100 failed randomized controlled trials, sepsis remains a leading cause of mortality in intensive care units worldwide. The repeated failure of promising therapies in phase III trials has led to a fundamental rethinking of sepsis research methodology. This review explores the emerging paradigm of endotype-driven sepsis trials, which recognizes sepsis as a heterogeneous syndrome requiring precision medicine approaches. We examine why traditional "one-size-fits-all" trials have failed, how platform trials enable adaptive, efficient study designs, and the role of biomarker-enriched enrollment in matching patients to therapies. Understanding these concepts is essential for the next generation of critical care physicians who will both conduct and interpret modern sepsis research.

Introduction

Sepsis, defined as life-threatening organ dysfunction caused by a dysregulated host response to infection, affects approximately 49 million people and causes 11 million deaths annually worldwide.[1] Despite improvements in supportive care, sepsis mortality remains unacceptably high at 20-30% for sepsis and up to 40-50% for septic shock.[2] More frustratingly, the last four decades have witnessed a graveyard of failed sepsis therapeutics—from anti-endotoxin antibodies to activated protein C, from anti-TNF strategies to countless immunomodulatory agents.[3]

This litany of failures has forced a paradigm shift. We now recognize that sepsis is not a single disease but a heterogeneous syndrome with multiple underlying biological phenotypes, or "endotypes."[4] Just as oncology abandoned treating "cancer" as a monolithic entity in favor of targeted therapies based on tumor genetics, sepsis research must evolve toward precision medicine approaches that match specific therapies to biologically defined patient subgroups.

This review examines three pillars of this new research paradigm: understanding why previous trials failed, designing adaptive platform trials that can efficiently test multiple interventions, and using biomarkers to enrich trial enrollment with patients most likely to benefit.

Moving Beyond "One-Size-Fits-All" Trials: Why Previous Sepsis Drug Trials Failed and How Endotypes Change the Game

The Historical Landscape of Failure

The history of sepsis drug development reads like a medical tragedy. Between 1991 and 2020, more than 100 phase III randomized controlled trials of sepsis therapeutics failed to demonstrate benefit, and in some cases caused harm.[5] Notable examples include:

Anti-endotoxin strategies (1991-1998): Multiple monoclonal antibodies targeting lipopolysaccharide failed despite sound biological rationale.[6]
Anti-TNF therapies (1993-1996): Despite success in animal models, neutralizing tumor necrosis factor showed no benefit and potential harm in human trials.[7]
Activated Protein C (drotrecogin alfa) (2001-2011): Initially approved based on the PROWESS trial showing mortality reduction, it was withdrawn after the PROWESS-SHOCK trial failed to confirm benefit and raised safety concerns about bleeding.[8]
Anti-TLR4 therapy (2013): Eritoran, a Toll-like receptor 4 antagonist, failed in the ACCESS trial despite promising preclinical data.[9]
Corticosteroids: Multiple trials over three decades showed conflicting results until recent studies suggested benefit in specific subgroups.[10]

Pearl: The activated protein C story exemplifies the dangers of heterogeneity in sepsis trials. Post-hoc analyses suggested benefit was confined to patients with severe disease and high inflammatory markers—a biomarker-defined subgroup.[8]

Why Did These Trials Fail?

The causes of failure are multifactorial but increasingly well-understood:

1. Biological Heterogeneity

Sepsis encompasses patients with vastly different underlying pathophysiology. Some patients exhibit hyperinflammation with cytokine storm, while others demonstrate immunosuppression with impaired pathogen clearance.[11] Treating these opposing endotypes with the same intervention is akin to using chemotherapy for both rapidly dividing and quiescent tumors.

Seymour et al. (2019) used machine learning to identify four sepsis phenotypes (α, β, γ, δ) with distinct clinical outcomes and host-response patterns.[12] The δ phenotype showed high inflammatory markers and mortality of 40%, while the α phenotype had lower inflammation and 5% mortality. A therapy targeting inflammation would predictably show different effects across these phenotypes.

2. Timing and Disease Stage Mismatch

Sepsis pathophysiology evolves dynamically over hours and days. Early sepsis may be characterized by pro-inflammatory mediators, while late sepsis often features immunosuppression.[13] Administering immunosuppressive therapy to a patient in the hyperinflammatory phase, or vice versa, may be harmful. Traditional trials enrolled patients based on clinical criteria (e.g., meeting Sepsis-3 definitions) without regard to disease stage or biological phenotype.

Oyster: The VANISH trial (2016) comparing vasopressin versus norepinephrine showed no overall mortality benefit, but post-hoc analysis revealed vasopressin reduced mortality in patients with lower baseline cortisol levels.[14] The treatment effect was hidden in the overall heterogeneous population.

3. Inadequate Preclinical Models

Most sepsis trials were based on animal models, typically young, healthy rodents with cecal ligation and puncture or endotoxin injection. These models poorly replicate the complexity of human sepsis, which occurs predominantly in elderly patients with comorbidities and varying pathogens.[15]

4. Statistical Considerations

When treatment effects are heterogeneous, overall trial results may show null findings even if substantial benefit exists in subgroups. Conversely, chance findings in heterogeneous populations may lead to false-positive results that fail to replicate (as with activated protein C).

The Endotype Solution

An endotype is a subtype of a condition defined by a distinct pathobiological mechanism.[16] In sepsis, proposed endotypes include:

Hyperinflammatory endotype: Elevated pro-inflammatory cytokines (IL-6, IL-8, TNF-α), high C-reactive protein, associated with cytokine storm and early organ failure.
Immunosuppressive endotype: Low HLA-DR expression on monocytes, lymphopenia, elevated IL-10, impaired pathogen clearance.
Endotheliopathic endotype: Elevated markers of endothelial injury (angiopoietin-2, syndecan-1), associated with capillary leak and organ dysfunction.[17]

Hack: Think of endotypes by asking three questions: (1) Is the immune system overactive or underactive? (2) Is there primary endothelial injury? (3) What is the source and type of infection? These questions guide rational therapy selection.

Matching interventions to endotypes makes biological sense:

Anti-inflammatory therapies (e.g., anti-IL-6, corticosteroids) for hyperinflammatory phenotypes
Immune-stimulating therapies (e.g., GM-CSF, anti-PD-1) for immunosuppressed phenotypes[18]
Anticoagulant or endothelial-protective therapies for coagulopathic/endotheliopathic phenotypes

Evidence for Endotype-Specific Effects

Growing evidence supports endotype-stratified treatment:

VANISH trial post-hoc analysis: Vasopressin reduced mortality in patients with relative adrenal insufficiency (cortisol <10 μg/dL).[14]
CITRIS-ALI trial: Vitamin C showed mortality benefit in patients with higher baseline inflammatory markers.[19]
HARP-2 trial: Heparin showed heterogeneous treatment effects based on coagulation profiles.[20]
Sepsis endotyping studies: Wong et al. identified pediatric septic shock endotypes with 100-fold differences in mortality, suggesting vastly different treatment needs.[21]

Pearl: Endotype-driven trials don't just improve success rates—they reduce sample sizes, costs, and time to approval by focusing on biologically rational target populations.

Platform Trials: Designing Adaptive Studies That Can Test Multiple Therapies Against Different Sepsis Subphenotypes Simultaneously

The Platform Trial Revolution

A platform trial is a master protocol designed to evaluate multiple therapies (or therapy combinations) for a single disease using shared infrastructure, with the ability to add or drop treatment arms based on prespecified rules.[22] Unlike traditional two-arm RCTs where each new drug requires a new trial, platform trials operate as perpetual studies that continuously test new interventions.

Key Features of Platform Trials

1. Shared Control Arm

All investigational therapies are compared against a common control group (usually standard of care), dramatically improving efficiency. For example, testing five therapies would traditionally require five separate trials with five control groups (potentially 2,500+ patients total). A platform trial uses one control group, requiring fewer patients overall.[23]

2. Response-Adaptive Randomization (RAR)

As data accumulates, randomization probabilities shift to favor better-performing arms. If Therapy A shows early signals of benefit while Therapy B appears harmful, more patients are allocated to Therapy A, and Therapy B may be dropped for futility.[24] This is ethically superior to fixed randomization and statistically efficient.

Oyster: RAR does not introduce bias if implemented correctly with appropriate statistical adjustments. The final analysis accounts for changing randomization probabilities using weighted estimators.[25]

3. Biomarker-Stratified Randomization

Patients can be stratified by endotype at enrollment, with therapies assigned preferentially to their target endotype. A patient identified as hyperinflammatory might be randomized among anti-inflammatory interventions, while an immunosuppressed patient enters the immune-stimulation treatment arms.

4. Seamless Phase II/III Design

Platform trials can begin with phase II dose-finding, then seamlessly transition promising therapies to phase III efficacy testing within the same protocol, eliminating years of delay between phases.[26]

5. Master Protocol Efficiency

A single IRB approval, unified eligibility criteria, shared data monitoring, and common endpoint definitions reduce administrative burden and ensure consistency across treatment comparisons.

Real-World Examples

REMAP-CAP (Randomized, Embedded, Multifactorial Adaptive Platform Trial for Community-Acquired Pneumonia)

REMAP-CAP is the exemplar sepsis platform trial, designed to test multiple interventions for severe pneumonia and sepsis.[27] During the COVID-19 pandemic, it rapidly identified effective therapies:

Corticosteroids: Demonstrated mortality benefit in severe COVID-19 pneumonia within months.[28]
IL-6 antagonists: Showed benefit when combined with corticosteroids in critically ill patients.[29]
Anticoagulation: Identified therapeutic-dose heparin benefit in moderately ill but harm in severely ill patients—a clear example of heterogeneity of treatment effect.[30]

REMAP-CAP's adaptive design allowed it to answer multiple questions simultaneously while traditional RCTs were still enrolling. It has now expanded to test therapies for bacterial sepsis with endotype-stratified domains.

Hack: When reading platform trial results, pay attention to the "domain" structure. Each domain tests interventions for a specific biological mechanism (e.g., immune modulation domain, anticoagulation domain), allowing focused questions within the larger trial.

ADAPT-Sepsis

This UK-based platform trial aims to test immunomodulatory therapies in endotype-defined sepsis populations. It uses baseline biomarkers (ferritin, HLA-DR expression, neutrophil-to-lymphocyte ratio) to assign patients to hyperinflammatory versus immunosuppressed categories, then randomizes within those groups.[31]

Statistical Innovations

Platform trials employ sophisticated statistical methods:

Bayesian adaptive designs: Update treatment effect estimates continuously as data accrues, allowing earlier stopping for benefit or futility.[32]
Borrowing across arms: Information from completed arms can inform ongoing arms if therapies share mechanisms.
Master protocols with multiple estimands: Can answer questions about overall benefit, subgroup-specific effects, and optimal treatment sequences simultaneously.[33]

Pearl: Bayesian statistics in platform trials isn't about prior beliefs—it's about efficiently updating knowledge. The posterior probability of benefit (e.g., "95% probability that Therapy X reduces mortality by >5%") is often more clinically interpretable than a p-value.

Challenges and Solutions

Challenge 1: Complexity

Platform trials require sophisticated data infrastructure and real-time analytics.

Solution: Many leverage electronic health records and automated data capture. Predefined statistical analysis plans are coded in advance.

Challenge 2: Investigator and Patient Understanding

Explaining adaptive randomization and multiple simultaneous interventions is challenging.

Solution: Standardized consent processes and investigator training programs. REMAP-CAP demonstrated that well-educated sites can implement complex protocols successfully.

Challenge 3: Regulatory Acceptance

Drug approval typically requires two pivotal RCTs. Will regulators accept platform trial results?

Solution: FDA and EMA have issued guidance accepting platform trials when properly designed. REMAP-CAP results led to therapy approvals, validating the approach.[34]

Oyster: Platform trials aren't just for big diseases. Small rare disease communities use them because sharing controls makes research feasible with limited patients.[35]

Biomarker-Enriched Enrollment: Selecting Patients for Trials Based on Inflammatory or Immunosuppressive Profiles, Not Just Clinical Criteria

The Rationale for Biomarker Enrichment

Biomarker-enriched enrollment involves selecting trial participants based on biological characteristics that predict treatment response, not merely clinical syndrome definitions.[36] This approach:

Increases treatment effect size by excluding patients unlikely to respond
Reduces sample size requirements and trial costs
Minimizes exposure of non-responders to potential toxicity
Accelerates regulatory approval by demonstrating clear benefit in the target population

In sepsis, where heterogeneity is extreme, biomarker enrichment may be essential for trial success.

Categories of Sepsis Biomarkers

1. Inflammatory Biomarkers

These identify hyperinflammatory states:

Pro-inflammatory cytokines: IL-6 (>1000 pg/mL suggests hyperinflammation), IL-8, TNF-α[37]
Acute phase reactants: C-reactive protein (CRP >150 mg/L), procalcitonin (PCT >10 ng/mL)
Ferritin: Markedly elevated (>2000 μg/L) in macrophage activation syndromes
Soluble urokinase plasminogen activator receptor (suPAR): Elevated in systemic inflammation[38]

Pearl: IL-6 is emerging as the gold-standard inflammatory biomarker. It's measurable within hours using point-of-care assays, correlates with mortality, and is increasingly used for trial enrollment.[39]

2. Immunosuppression Biomarkers

These identify patients with impaired immune responses:

HLA-DR expression on monocytes (mHLA-DR): <30% expression indicates immunoparalysis. Flow cytometry measurement is feasible in 4-6 hours.[40]
Lymphocyte count: Absolute lymphocyte count <600 cells/μL suggests immunosuppression
IL-10: Elevated anti-inflammatory cytokine indicates immune exhaustion
PD-1/PD-L1 expression: Checkpoint inhibitor expression on immune cells[41]
Ex vivo TNF production: Reduced lipopolysaccharide-stimulated TNF production indicates endotoxin tolerance[42]

Hack: For bedside assessment, consider simple composite scores: high neutrophil-to-lymphocyte ratio (>20) + low absolute lymphocyte count (<500) + prolonged sepsis (>3 days) = likely immunosuppressed. This doesn't replace formal biomarkers but guides empiric thinking.

3. Endothelial and Coagulation Biomarkers

These identify endotheliopathy and coagulopathy:

Angiopoietin-2 (Ang-2): Marker of endothelial activation and leak, correlates with mortality[43]
Syndecan-1: Glycocalyx degradation marker indicating capillary leak
Thrombomodulin: Elevated in endothelial injury
D-dimer, fibrinogen, antithrombin: Identify consumptive coagulopathy[44]

4. Organ-Specific Biomarkers

Cardiac: NT-proBNP, troponin (identify septic cardiomyopathy)
Renal: Neutrophil gelatinase-associated lipocalin (NGAL), kidney injury molecule-1 (KIM-1)
Hepatic: Bilirubin, lactate (metabolic dysfunction)[45]

Practical Implementation

Point-of-Care Testing

For biomarker enrichment to work in acute sepsis trials, results must be available within hours. Emerging technologies enable this:

Rapid IL-6 assays: Results in 20-30 minutes using immunoassays
Flow cytometry: mHLA-DR measurement in 4-6 hours at experienced centers
Multiplex platforms: Simultaneous measurement of 10+ biomarkers using microfluidics[46]

Oyster: Don't let perfect be the enemy of good. Even simple biomarkers like CRP and lymphocyte count, available within 1 hour, provide valuable enrichment. The CITRIS-ALI trial used baseline CRP to stratify patients post-hoc and found significant heterogeneity.[19]

Case Studies in Biomarker-Enriched Trials

1. IL-6 Blockade Trials

The IMMUNECOV trial tested tocilizumab (anti-IL-6 receptor) in COVID-19 pneumonia patients with elevated inflammatory markers (CRP >75 mg/L). Enrolling only patients with documented hyperinflammation increased the treatment effect, showing significant mortality reduction.[47]

2. GM-CSF for Immunosuppression

The GRID trial is testing granulocyte-macrophage colony-stimulating factor (GM-CSF) in sepsis patients with documented immunosuppression (low mHLA-DR <30%). By excluding patients without immunoparalysis, the trial enriches for those most likely to benefit from immune stimulation.[48]

3. Antithrombin Supplementation

The SCARLET trial (2019) tested antithrombin in sepsis-associated DIC. Unlike previous broad trials that failed, SCARLET enrolled only patients with documented coagulopathy (DIC score ≥4, platelet count <100,000, prolonged PT). Although the trial was ultimately negative, the enrichment strategy was scientifically sound.[49]

Pearl: Even "negative" biomarker-enriched trials provide valuable information—they definitively answer whether an intervention works in the biologically relevant population, rather than leaving uncertainty about missed subgroup effects.

Challenges in Biomarker Implementation

Challenge 1: Turnaround Time

Many biomarkers require specialized laboratories with 12-24 hour turnaround, too slow for acute sepsis trials where interventions must start within hours.

Solution: Focus on rapid point-of-care biomarkers or use clinical surrogates. Combine multiple simple biomarkers into composite scores that can be calculated immediately.[50]

Challenge 2: Assay Standardization

Different cytokine assays yield different absolute values, making cross-center standardization difficult.

Solution: Platform trials use centralized core laboratories or distribute standardized assay kits. Alternatively, define thresholds as quantiles (e.g., top quartile of IL-6 values) rather than absolute cutoffs.[51]

Challenge 3: Biomarker Stability

Some biomarkers change rapidly. A patient who is hyperinflammatory at hour 0 may be immunosuppressed at hour 24.

Solution: Consider serial biomarker measurement and adaptive treatment strategies. Future trials may test "endotype-switching" protocols where therapy changes as the patient's biology evolves.[52]

Challenge 4: Multiple Testing

Testing multiple biomarker-defined subgroups increases false-positive risk.

Solution: Prespecify the primary biomarker-defined population in the protocol. Use hierarchical testing procedures or Bayesian methods to control type I error.[53]

Hack: When designing a biomarker-enriched trial, use a "enrichment-then-stratify" approach: Enrich enrollment with biomarker-positive patients to increase power, but also enroll some biomarker-negative patients and stratify randomization. This allows testing whether the biomarker truly predicts response while maintaining focus on the target population.

Emerging Technologies

Machine Learning for Real-Time Endotyping

Several groups are developing algorithms that use routine clinical data (vital signs, lab values, ventilator settings) to predict endotypes in real-time without specialized biomarkers.[54] If validated, these "digital biomarkers" could enable broad implementation of endotype-stratified trials at any center.

Multi-Omics Integration

Combining transcriptomics, proteomics, metabolomics, and clinical data may identify novel endotypes and predict treatment response better than single biomarkers. The MARS consortium demonstrated that integrated omics signatures predicted mortality better than clinical scores.[55]

Liquid Biopsies

Cell-free DNA methylation patterns and microRNA profiles in plasma may provide rapid, stable biomarkers of immune state and organ dysfunction.[56]

Practical Pearls for Critical Care Trainees

Think mechanistically: When a sepsis trial fails, ask "Did the mechanism match the patient biology?" Failed trials often make biological sense for a subgroup.
Phenotype your patients clinically: Even without biomarkers, recognize clinical phenotypes. The vasodilatory, cold-shock, warm-shock patient with preserved ejection fraction, and cytokine storm patient likely need different interventions.
Embrace uncertainty quantification: Bayesian statistics provide probability statements ("75% chance of benefit") rather than binary hypothesis tests. This better reflects clinical reality.
Follow platform trials actively: REMAP-CAP and similar trials are continuously reporting. Their results will shape practice before traditional guideline updates.
Advocate for trial participation: Platform trials work only with robust enrollment. Helping your ICU participate in trials isn't just research—it's improving care for your community.

Oysters: Common Misconceptions

Oyster 1: "Biomarker-enriched trials aren't generalizable because they exclude most patients."

Reality: Enrichment identifies the population that should receive the therapy. Generalizability means applying the right treatment to the right patient, not giving everyone a therapy that helps few and harms some.

Oyster 2: "Adaptive trials sacrifice scientific rigor for speed."

Reality: Properly designed adaptive trials are statistically rigorous and often more powerful than fixed designs. They reduce the ethical problem of continuing ineffective arms.

Oyster 3: "We need more basic science before testing targeted therapies."

Reality: Clinical phenotyping and trial-and-error with biomarker-enriched trials can reveal biology we don't yet understand. Not all precision medicine requires complete mechanistic understanding first.

Oyster 4: "Platform trials can't test combination therapies."

Reality: Platform trials can test combinations using factorial designs or combination domains. REMAP-CAP tested corticosteroids plus IL-6 antagonists as a combination.

Future Directions

The endotype-driven sepsis trial paradigm is evolving rapidly:

Decentralized trials: Using telemedicine and home monitoring to conduct trials outside ICUs, capturing earlier disease stages.
N-of-1 trials: Testing individualized treatment sequences in single patients with repeated biomarker measurements.
Artificial intelligence: Machine learning models that predict individual treatment effects based on baseline characteristics, enabling true precision medicine.[57]
Closed-loop systems: Real-time biomarker monitoring triggering automated treatment adjustments, analogous to closed-loop insulin delivery in diabetes.
Global implementation: Adapting sophisticated trial designs for resource-limited settings using simplified biomarkers and local platforms.

Conclusion

The sepsis research community stands at an inflection point. Four decades of trial failures have taught us that sepsis is not a monolithic disease amenable to universal therapies. The future lies in endotype-driven research that matches interventions to patient biology, platform trials that efficiently test multiple therapies adaptively, and biomarker-enriched enrollment that identifies patients most likely to benefit.

For the postgraduate trainee in critical care, understanding these concepts isn't academic—it's essential. You will practice in an era where precision medicine becomes routine, where machine learning algorithms suggest individualized treatments, and where participation in perpetual platform trials is standard of care. The "one-size-fits-all" approach to sepsis is dying. In its place, a more nuanced, personalized, and ultimately more effective paradigm is emerging.

The challenge now is not whether to adopt this approach, but how quickly we can implement it globally. Every negative sepsis trial of the past contained within it multiple positive trials for subgroups—we simply didn't know how to find them. With endotype-driven designs, we finally have the tools to succeed.

References

Rudd KE, et al. Global, regional, and national sepsis incidence and mortality, 1990-2017: analysis for the Global Burden of Disease Study. Lancet. 2020;395:200-211.
Singer M, et al. The Third International Consensus Definitions for Sepsis and Septic Shock (Sepsis-3). JAMA. 2016;315:801-810.
Marshall JC. Why have clinical trials in sepsis failed? Trends Mol Med. 2014;20:195-203.
Calfee CS, Delucchi K, Parsons PE, et al. Subphenotypes in acute respiratory distress syndrome: latent class analysis of data from two randomised controlled trials. Lancet Respir Med. 2014;2:611-620.
Gotts JE, Matthay MA. Sepsis: pathophysiology and clinical management. BMJ. 2016;353:i1585.
Angus DC, Birmingham MC, Balk RA, et al. E5 murine monoclonal antiendotoxin antibody in gram-negative sepsis: a randomized controlled trial. JAMA. 2000;283:1723-1730.
Abraham E, Wunderink R, Silverman H, et al. Efficacy and safety of monoclonal antibody to human tumor necrosis factor alpha in patients with sepsis syndrome. JAMA. 1995;273:934-941.
Ranieri VM, Thompson BT, Barie PS, et al. Drotrecogin alfa (activated) in adults with septic shock. N Engl J Med. 2012;366:2055-2064.
Opal SM, Laterre PF, Francois B, et al. Effect of eritoran on death and organ dysfunction among patients with severe sepsis. JAMA. 2013;309:1146-1155.
Annane D, Renault A, Brun-Buisson C, et al. Hydrocortisone plus fludrocortisone for adults with septic shock. N Engl J Med. 2018;378:809-818.
Hotchkiss RS, Monneret G, Payen D. Sepsis-induced immunosuppression: from cellular dysfunctions to immunotherapy. Nat Rev Immunol. 2013;13:862-874.
Seymour CW, Kennedy JN, Wang S, et al. Derivation, validation, and potential treatment implications of novel clinical phenotypes for sepsis. JAMA. 2019;321:2003-2017.
Nedeva C, et al. Inflammation and cell death of the innate and adaptive immune system during sepsis. Biomolecules. 2021;11:1011.
Gordon AC, et al. Effect of early vasopressin vs norepinephrine on kidney failure in patients with septic shock: the VANISH randomized clinical trial. JAMA. 2016;316:509-518.
Seok J, et al. Genomic responses in mouse models poorly mimic human inflammatory diseases. Proc Natl Acad Sci USA. 2013;110:3507-3512.
Lötvall J, et al. Asthma endotypes: a new approach to classification of disease entities within the asthma syndrome. J Allergy Clin Immunol. 2011;127:355-360.
Scicluna BP, et al. Classification of patients with sepsis according to blood genomic endotype: a prospective cohort study. Lancet Respir Med. 2017;5:816-826.
Meisel C, Schefold JC, Pschowski R, et al. Granulocyte-macrophage colony-stimulating factor to reverse sepsis-associated immunosuppression. Am J Respir Crit Care Med. 2009;180:640-648.
Fowler AA, et al. Effect of vitamin C infusion on organ failure and biomarkers of inflammation and vascular injury in patients with sepsis and severe acute respiratory failure. JAMA. 2019;322:1261-1270.
Young PJ, et al. Effect of stress ulcer prophylaxis with proton pump inhibitors vs histamine-2 receptor blockers on in-hospital mortality among ICU patients receiving invasive mechanical ventilation: the PEPTIC randomized clinical trial. JAMA. 2020;323:616-626.
Wong HR, et al. Identification of pediatric septic shock subclasses based on genome-wide expression profiling. BMC Med. 2009;7:34.
Angus DC. Fusing randomized trials with big data: the key to self-learning health care systems? JAMA. 2015;314:767-768.
Berry SM, et al. Bayesian clinical trials. Nat Rev Drug Discov. 2011;10:348-355.
Thorlund K, Haggstrom J, Park JJ, Mills EJ. Key design considerations for adaptive clinical trials: a primer for clinicians. BMJ. 2018;360:k698.
Robertson DS, Choodari-Oskooei B, Dimairo M, et al. Point estimation for adaptive trial designs I: a methodological review. Stat Med. 2023;42:122-145.
Pallmann P, et al. Adaptive designs in clinical trials: why use them, and how to run and report them. BMC Med. 2018;16:29.
Angus DC, et al. Adaptive platform trials: definition, design, conduct and reporting considerations. Nat Rev Drug Discov. 2019;18:797-807.
RECOVERY Collaborative Group. Dexamethasone in hospitalized patients with Covid-19. N Engl J Med. 2021;384:693-704.
REMAP-CAP Investigators. Interleukin-6 receptor antagonists in critically ill patients with Covid-19. N Engl J Med. 2021;384:1491-1502.
REMAP-CAP, ACTIV-4a, and ATTACC Investigators. Therapeutic anticoagulation with heparin in critically ill patients with Covid-19. N Engl J Med. 2021;385:777-789.
ADAPT-Sepsis Protocol (ClinicalTrials.gov NCT03342781).
Lee KM, Wason J. Including non-concurrent control patients in the analysis of platform trials: is it worth it? BMC Med Res Methodol. 2020;20:165.
Park JJH, et al. Systematic review of basket trials, umbrella trials, and platform trials: a landscape analysis of master protocols. Trials. 2019;20:572.
FDA. Adaptive Designs for Clinical Trials of Drugs and Biologics Guidance for Industry. 2019.
Saville BR, Berry SM. Efficiencies of platform clinical trials: a vision of the future. Clin Trials. 2016;13:358-366.
Simon R. Clinical trial designs for evaluating the medical utility of prognostic and predictive biomarkers in oncology. Per Med. 2010;7:33-47.
Pierrakos C, Vincent JL. Sepsis biomarkers: a review. Crit Care. 2010;14:R15.
Rasmussen LJH, et al. Soluble urokinase plasminogen activator receptor (suPAR) in acute care: a strong marker of disease presence and severity, readmission and mortality. A retrospective cohort study. Emerg Med J. 2016;33:769-775.
Leisman DE, et al. Cytokine elevation in severe and critical COVID-19: a rapid systematic review, meta-analysis, and comparison with other inflammatory syndromes. Lancet Respir Med. 2020;8:1233-1244.
Monneret G, et al. Persisting low monocyte human leukocyte antigen-DR expression predicts mortality in septic shock. Intensive Care Med. 2006;32:1175-1183.
Patera AC, et al. Frontline Science: Defects in immune function in patients with sepsis are associated with PD-1 or PD-L1 expression and can be restored by antibodies targeting PD-1 or PD-L1. J Leukoc Biol. 2016;100:1239-1254.
Docke WD, et al. Monocyte deactivation in septic patients: restoration by IFN-gamma treatment. Nat Med. 1997;3:678-681.
Parikh SM. The angiopoietin-Tie2 signaling axis in systemic inflammation. J Am Soc Nephrol. 2017;28:1973-1982.
Iba T, Levy JH. Derangement of the endothelial glycocalyx in sepsis. J Thromb Haemost. 2019;17:283-294.
Kellum JA, et al. Kidney disease: improving global outcomes (KDIGO) acute kidney injury work group. KDIGO clinical practice guideline for acute kidney injury. Kidney Int Suppl. 2012;2:1-138.
Xu J, et al. Ultra-sensitive point-of-care immunoassay for secreted cardiac biomarker using silver-enhanced surface-anchored rolling circle amplification. Biosens Bioelectron. 2019;142:111492.
RECOVERY Collaborative Group. Tocilizumab in patients admitted to hospital with COVID-19 (RECOVERY): a randomised, controlled, open-label, platform trial. Lancet. 2021;397:1637-1645.
Payen D, et al. A randomized trial of granulocyte-macrophage colony-stimulating factor in patients with septic shock. Crit Care Med. 2020;48:1407-1414.
Vincent JL, et al. Effect of a recombinant human soluble thrombomodulin on mortality in patients with sepsis-associated coagulopathy: the SCARLET randomized clinical trial. JAMA. 2019;321:1993-2002.
Davenport EE, et al. Genomic landscape of the individual host response and outcomes in sepsis: a prospective cohort study. Lancet Respir Med. 2016;4:259-271.
Sweeney TE, et al. A community approach to mortality prediction in sepsis via gene expression analysis. Nat Commun. 2018;9:694.
Kreitmann L, et al. Persistent inflammation, immunosuppression and catabolism syndrome (PICS): A review of definitions, potential therapies, and research priorities. Intensive Care Med Exp. 2020;8(Suppl 1):37.
Freidlin B, Korn EL. Biomarker enrichment strategies: matching trial design to biomarker credentials. Nat Rev Clin Oncol. 2014;11:81-90.
Bhavani SV, et al. Development and validation of novel sepsis subphenotypes using trajectories of vital signs. Intensive Care Med. 2022;48:1582-1592.
Van der Poll T, et al. The immunopathology of sepsis and potential therapeutic targets. Nat Rev Immunol. 2017;17:407-420.
Bauer M, Coldewey SM, Leitner M, et al. Deterioration of organ function as a hallmark in sepsis: the cellular perspective. Front Immunol. 2018;9:1460.
Komorowski M, et al. The Artificial Intelligence Clinician learns optimal treatment strategies for sepsis in intensive care. Nat Med. 2018;24:1716-1720.

Key Take-Home Messages for Postgraduate Trainees

Clinical Practice Pearls:

Heterogeneity is the enemy: When you see conflicting sepsis study results, assume the truth lies in subgroups, not overall effects.
Match the intervention to the biology: Immunosuppressive therapy for hyperinflammation makes no more sense than antibiotics for viral sepsis. Think mechanistically.
Simple bedside phenotyping matters: Even without sophisticated biomarkers, recognize clinical patterns:
- Cold shock + high lactate = cardiogenic/vasoplegic (consider early mechanical support)
- Warm shock + rapid progression = hyperinflammatory (consider immunomodulation)
- Prolonged sepsis + lymphopenia + secondary infections = immunosuppressed (avoid further immunosuppression)
Serial assessment trumps single measurements: Sepsis biology evolves. A patient's endotype at day 0 may differ completely from day 3. Dynamic monitoring guides adaptive therapy.
Join platform trials: These aren't just research—they're the future of evidence generation. Sites participating in REMAP-CAP accessed effective COVID-19 therapies months before guideline recommendations.

Research Design Pearls:

Sample size isn't everything: A 100-patient biomarker-enriched trial with appropriate biological selection may be more informative than a 1000-patient unselected trial.
Negative trials can be positive: A well-designed biomarker-negative trial definitively answers whether a mechanistically sound therapy works in the right population. That's scientific progress.
Embrace adaptive methods: Fixed-design trials made sense when computing was limited. Modern statistics allow ethical, efficient adaptation without sacrificing rigor.
Think pragmatically about biomarkers: Don't let perfect biomarkers prevent good ones. CRP, lactate, and absolute lymphocyte count—available everywhere in 60 minutes—provide substantial enrichment information.
Master protocols are the future: Whether in sepsis, cancer, or rare diseases, the era of thousands of small isolated trials is ending. Large collaborative platforms will dominate.

Conceptual Framework:

View sepsis trials through three lenses:

The biological lens: Does the mechanism match the patient's pathophysiology?
The timing lens: Does the intervention address the current disease stage?
The heterogeneity lens: Is the population sufficiently homogeneous for a signal to emerge?

When all three align, trials succeed. When they don't, trials fail—regardless of how "good" the drug is.

A Vision for 2030

Imagine this scenario: A 67-year-old man presents to your ICU with septic shock from pneumonia. Within 2 hours, point-of-care testing provides IL-6 levels, lymphocyte subsets, and HLA-DR expression. A machine learning algorithm integrates these biomarkers with clinical data and outputs:

Predicted endotype: Hyperinflammatory with intact adaptive immunity
Recommended domain: Anti-cytokine therapy arm of REMAP-Sepsis
Current best treatment: IL-6 receptor blockade (78% probability of mortality reduction >5%)
Alternative if deterioration: Switch to TNF inhibition based on 48-hour cytokine trajectory

The patient is enrolled in the platform trial via electronic consent, contributing to knowledge while receiving precision therapy. His biomarkers are monitored daily, triggering adaptive treatment adjustments. If he develops immunosuppression by day 4, the protocol automatically switches him to immune-stimulation therapies.

This isn't science fiction—the components exist today. The challenge is integration and implementation.

Final Thoughts

The sepsis field has endured decades of frustration, but we stand at the threshold of transformation. The convergence of precision medicine, adaptive trial designs, rapid biomarker technologies, and computational advances creates unprecedented opportunity.

For you, the next generation of critical care physicians, this revolution isn't optional—it's foundational. You will practice in an era where:

Treating "sepsis" generically is considered malpractice, like treating "cancer" without histology
Real-time biomarkers guide individualized therapy as routinely as glucose guides insulin
Participation in perpetual platform trials is standard, not exceptional
Machine learning augments (not replaces) clinical judgment
International collaborations through master protocols answer in months what previously took decades

The endotype-driven sepsis trial isn't just a new research paradigm—it's the blueprint for precision critical care. Study it, advocate for it, and participate in it. The lives you save may include patients who would have been lost in the undifferentiated approach of the past.

The era of "one-size-fits-all" sepsis treatment is ending. The era of precision, adaptive, patient-centered sepsis care is beginning. Be part of the transformation.

learningmedicine

Monday, November 10, 2025