Development of a Novel Clinicomolecular Risk Index to Enhance Mortality Prediction and Immunological Stratification of Adults Hospitalized with Sepsis in Sub-Saharan Africa: A Pilot Study from Uganda

Simple item page
dc.contributor.authorMatthew, J. Cummings
dc.contributor.authorBakamutumaho, Barnabas
dc.contributor.authorKomal, Jain
dc.contributor.authorAdam, Price
dc.contributor.authorOwor, Nicholas
dc.contributor.authorKayiwa, John
dc.contributor.authorNamulondo, Joyce
dc.contributor.authorByaruhanga, Timothy
dc.contributor.authorMuwanga, Moses
dc.contributor.authorNsereko, Christopher
dc.contributor.authorStephen, Sameroff
dc.contributor.authorW. Ian, Lipkin
dc.contributor.authorLutwama, Julius J.
dc.contributor.authorMax, R. O’Donnell
dc.date.accessioned2025-03-03T17:37:27Z
dc.date.available2025-03-03T17:37:27Z
dc.date.issued2023
dc.description.abstractThe global burden of sepsis is concentrated in sub-Saharan Africa (SSA), where epidemic HIV and unique pathogen diversity challenge the effective management of severe infections. In this context, patient stratification based on biomarkers of a dysregulated host response may identify subgroups more likely to respond to targeted immunomodulatory therapeutics. In a prospective cohort of adults hospitalized with suspected sepsis in Uganda, we applied machine learning methods to develop a prediction model for 30-day mortality that integrates physiology-based risk scores with soluble biomarkers reflective of key domains of sepsis immunopathology. After model evaluation and internal validation, whole-blood RNA sequencing data were analyzed to compare biological pathway enrichment and inferred immune cell profiles between patients assigned differential model-based risks of mortality. Of 260 eligible adults (median age, 32 years; interquartile range, 26–43 years; 59.2% female, 53.9% living with HIV), 62 (23.8%) died by 30 days after hospital discharge. Among 14 biomarkers, soluble tumor necrosis factor receptor 1 (sTNFR1) and angiopoietin 2 (Ang-2) demonstrated the greatest importance for mortality prediction in machine learning models. A clinicomolecular model integrating sTNFR1 and Ang-2 with the Universal Vital Assessment (UVA) risk score optimized 30-day mortality prediction across multiple performance metrics. Patients assigned to the high-risk, UVA-based clinicomolecular subgroup exhibited a transcriptional profile defined by proinflammatory innate immune and necroptotic pathway activation, T-cell exhaustion, and expansion of key immune cell subsets including regulatory and gamma-delta T cells. Clinicomolecular stratification of adults with suspected sepsis in Uganda enhanced 30-day mortality prediction and identified a high-risk subgroup with a therapeutically targetable immunological profile. Further studies are needed to advance pathobiologically informed sepsis management in SSA.
dc.identifier.citationCummings, M. J., Bakamutumaho, B., Jain, K., Price, A., Owor, N., Kayiwa, J., ... & O’Donnell, M. R. (2023). Development of a Novel Clinicomolecular Risk Index to Enhance Mortality Prediction and Immunological Stratification of Adults Hospitalized with Sepsis in Sub-Saharan Africa: A Pilot Study from Uganda. The American Journal of Tropical Medicine and Hygiene, 108(3), 619.
dc.identifier.urihttps://pmc.ncbi.nlm.nih.gov/articles/PMC9978552/
dc.identifier.urihttps://nru.uncst.go.ug/handle/123456789/10033
dc.language.isoen
dc.publisherThe American Journal of Tropical Medicine and Hygiene
dc.titleDevelopment of a Novel Clinicomolecular Risk Index to Enhance Mortality Prediction and Immunological Stratification of Adults Hospitalized with Sepsis in Sub-Saharan Africa: A Pilot Study from Uganda
dc.typeArticle
Files
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
Development of a Novel Clinicomolecular Risk Index to Enhance Mortality Prediction.pdf
Size:
1.4 MB
Format:
Adobe Portable Document Format
Download
License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
1.71 KB
Format:
Item-specific license agreed upon to submission
Description:
Download
Collections
Medical and Health Sciences