What is a simple way to subphenotype ICU patients without using ferritin, (C-reactive protein)?

Simple Subphenotyping Approaches for ICU Patients Without Ferritin

Use routinely collected clinical parameters combined with C-reactive protein (CRP) to identify ICU subphenotypes, as machine learning algorithms have successfully identified distinct sepsis subphenotypes using more than 25 routine clinical parameters from electronic health records, and parsimonious 3-4 variable classifiers can reliably stratify patients without requiring specialized biomarkers. 1

Practical Clinical Parameter-Based Approaches

Routine Clinical Data Method

• Machine learning models have identified four distinct sepsis subphenotypes using >25 routinely measured clinical parameters available in standard electronic health data 1
• These parameters include vital signs, laboratory values, organ dysfunction scores, and demographic data that are universally available 1
• This approach requires no specialized biomarkers and can be implemented globally 1

Parsimonious Classifier Approach

• For ARDS patients, validated 3-4 variable classifiers using clinical data and plasma inflammatory markers (excluding ferritin) can reliably identify subphenotypes with differential mortality and treatment responses 1
• These simplified classifiers have been operationalized in clinical trials (NCT04009330) 1
• The parsimonious approach maintains accuracy while improving feasibility for bedside implementation 1

CRP-Based Stratification

CRP as a Primary Biomarker

• CRP demonstrates strong diagnostic accuracy (AUC 0.922) for sepsis subphenotyping and can differentiate inflammatory states 2
• CRP kinetics (serial measurements) provide additional predictive value for identifying patients developing complications 3
• CRP combined with clinical parameters can stratify patients into risk groups without requiring ferritin 4

Specific CRP Thresholds

• CRP >7.1 mg/dL (71 mg/L) identifies high-risk patients with increased mortality (AUROC 0.76) 4
• CRP <20 mg/L has high negative predictive value (0.91) for ruling out bloodstream infections in critically ill patients 5
• Serial CRP measurements showing increasing trends predict ICU-acquired infections before clinical diagnosis 3

Alternative Biomarker Combinations

Procalcitonin (PCT) Integration

• PCT shows the highest diagnostic accuracy (AUC 0.989) for sepsis identification and can be used as an alternative primary biomarker 2
• PCT >0.4 ng/mL threshold provides negative predictive value of 0.91 for bloodstream infections 5
• PCT enables differentiation between Gram-positive and Gram-negative bacterial infections 2

Multi-Biomarker Panels Without Ferritin

• Combining CRP with PCT and presepsin provides complementary information for sepsis subphenotyping when measured within 24 hours of ICU admission 2
• Mid-regional pro-adrenomedullin (MR-proADM) demonstrates superior prognostic value, particularly in septic shock (p=0.00001) 2
• Presepsin (soluble CD14-ST) shows excellent diagnostic accuracy (AUC 0.948) and increases further in septic shock 2

Organ Dysfunction Scoring Systems

SOFA Score Integration

• The Sequential Organ Failure Assessment (SOFA) score quantifies organ dysfunction severity and serves as a prognostic indicator 6
• Number of acquired organ system failures is the most important prognostic indicator for ICU patients 6
• SOFA scores can be combined with biomarkers to create integrated subphenotyping algorithms 1

Physiologic Subphenotyping

• Respiratory system compliance in ARDS patients identifies subgroups with differential treatment responses to low tidal volume ventilation 1
• Physiologic measurements are readily identifiable using routine clinical data without specialized biomarkers 1
• These measurements tie directly to intervention mechanisms and predict heterogeneity of treatment effect 1

Practical Implementation Strategy

Step-by-Step Approach

1. Collect routine clinical parameters including vital signs, laboratory values, and organ dysfunction scores within 24 hours of ICU admission 1, 2
2. Measure CRP and PCT as primary inflammatory biomarkers (both widely available and cost-effective) 2, 5
3. Calculate SOFA score to quantify organ dysfunction severity 6
4. Apply validated parsimonious classifiers using 3-4 variables to assign subphenotype 1
5. Monitor biomarker kinetics with serial measurements to identify evolving clinical trajectories 3, 7

Risk Stratification Without Ferritin

• Low-risk group: Normal CRP (<20 mg/L) and PCT (<0.4 ng/mL) with low SOFA scores 5
• Intermediate-risk group: Elevated single biomarker (either CRP or PCT) 4
• High-risk group: Very high CRP (>71 mg/L) with elevated PCT and high SOFA scores 4, 7

Important Caveats

Global Applicability Considerations

• Subphenotyping approaches must be readily measurable with acceptable operating characteristics to allow global implementation 1
• Most subphenotyping studies derive from North American/European cohorts, requiring validation in diverse populations 1
• Low- and middle-income countries bear the greatest sepsis burden but are underrepresented in subphenotyping research 1

Limitations of Biomarker-Only Approaches

• Clinical severity criteria alone achieve prognostic enrichment but generally fail to enrich for treatment response 1
• Subphenotypes must be prospectively identifiable and predict treatment responsiveness to have clinical utility 1
• Integration of clinical, biological, and physiologic data provides more robust subphenotyping than single modalities 1

Validation Requirements

• Subphenotypes must be consistently reproducible across multiple populations to avoid overinterpreting signals from single datasets 1
• Pragmatic and parsimonious approaches facilitate implementation but require prospective validation 1
• Machine learning models may lack transparency, creating challenges for clinical decision-making 1