Severity: Warning
Message: file_get_contents(https://...@pubfacts.com&api_key=b8daa3ad693db53b1410957c26c9a51b4908&a=1): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests
Filename: helpers/my_audit_helper.php
Line Number: 176
Backtrace:
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 176
Function: file_get_contents
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 250
Function: simplexml_load_file_from_url
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 3122
Function: getPubMedXML
File: /var/www/html/application/controllers/Detail.php
Line: 575
Function: pubMedSearch_Global
File: /var/www/html/application/controllers/Detail.php
Line: 489
Function: pubMedGetRelatedKeyword
File: /var/www/html/index.php
Line: 316
Function: require_once
The 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.
Download full-text PDF |
Source |
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9978552 | PMC |
http://dx.doi.org/10.4269/ajtmh.22-0483 | DOI Listing |
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