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Assessment of Bedside-Adaptable Models to Predict Molecular Sepsis Subtypes in a Resource-Limited Setting: A
Barnabas Bakamutumaho1, Julius J Lutwama1, Nicholas Owor1
1Department of Arbovirology, Emerging and Re-emerging Infectious Diseases, Uganda Virus Research Institute, Entebbe, Uganda.
Medrxiv : the Preprint Server for Health Sciences
|July 2, 2026
Summary
Bedside clinical models show moderate accuracy for sepsis subtyping in Uganda, but lack discriminatory power for precision care. Enhanced laboratory capacity is crucial for improving sepsis management in low-resource settings.
Area of Science:
- Global Health
- Infectious Diseases
- Molecular Diagnostics
Background:
- Sepsis subtypes identified in low- and middle-income countries (LMICs) are challenging to evaluate due to limited molecular diagnostics.
- Resource-limited settings require adaptable tools for sepsis stratification.
Purpose of the Study:
- To assess if bedside clinical and rapid microbiologic data can accurately stratify Ugandan adults with sepsis by molecular subtype.
- To evaluate bedside-adaptable classifier models against transcriptomic and proteomic sepsis subtypes.
Main Methods:
- Secondary analysis of two prospective observational sepsis cohorts in Uganda.
- Testing bedside-adaptable clinical and clinico-microbiologic models against molecular subtype assignments.
Main Results:
- Clinical models showed moderate discrimination (AUROC 0.73-0.75) and strong calibration (E avg ≤0.015) for Uganda-derived subtypes.
- Adding rapid diagnostics (HIV, malaria, TB) yielded similar performance.
- Models showed moderate performance against high-income country-derived sepsis frameworks.
Conclusions:
- Bedside-adaptable models offer acceptable calibration but modest discrimination for molecular sepsis stratification in LMICs.
- Improved laboratory capacity and scalable molecular biomarker assays are needed for precision sepsis care in resource-limited settings.