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Published on: December 9, 2022
Predicting Mortality in Tanzanian Children With Sepsis Using Point-of-Care Biomarkers
Abigail M Sorensen1, Raya Y Mussa2, Scott P Oltman1,3,4
1University of California, San Francisco, San Francisco, California.
Insights
A new model combining point-of-care biomarkers like procalcitonin (PCT) and clinical signs accurately predicts child sepsis mortality in Tanzania. This tool aids risk stratification in resource-limited settings.
Area of Science:
- Pediatrics
- Infectious Diseases
- Global Health
Background:
- Sepsis is a major cause of child mortality globally, especially in resource-limited settings (RLS).
- Effective risk-stratification tools using accessible data are crucial for pediatric sepsis management in RLS.
- Tanzanian children with sepsis face a high mortality burden.
Purpose of the Study:
- To evaluate point-of-care (POC) biomarkers and clinical features for predicting in-hospital mortality in Tanzanian children with sepsis.
- To develop and validate a predictive model for pediatric sepsis mortality using readily available data.
- To assess the performance of POC biomarkers, including procalcitonin (PCT), C-reactive protein, ferritin, and lactate.
Main Methods:
- Prospective observational cohort study of 755 children (28 days-14 years) with sepsis in Dar es Salaam, Tanzania.
- Evaluation of POC biomarkers (PCT, CRP, ferritin, lactate) and clinical characteristics for association with mortality.
- Development of predictive models using LASSO regression and assessment of performance via AUC and classification metrics.
Main Results:
- 19.6% of enrolled children died during hospitalization.
- Procalcitonin (PCT) and clinical factors (malnutrition, breathing difficulty, altered mental status) were significant predictors of mortality (p<0.001).
- A multivariable model combining PCT and clinical characteristics achieved high discrimination (AUC 0.87), outperforming individual predictors.
Conclusions:
- A combined POC biomarker and clinical characteristics model effectively predicts mortality in pediatric sepsis patients in Tanzania.
- This integrated approach facilitates timely risk stratification and targeted interventions for improved outcomes in RLS.
- POC biomarkers alongside clinical data offer a promising strategy for managing pediatric sepsis in resource-limited environments.
Background And Objectives:
Sepsis is a leading cause of child mortality worldwide, disproportionately affecting children in resource-limited settings (RLSs). Effective risk-stratification tools using readily available data are urgently needed for this population. Therefore, the study objective was to evaluate the performance of point-of-care (POC) biomarkers and clinical characteristics for predicting in-hospital mortality among Tanzanian children with sepsis.
Methods:
We conducted a prospective observational cohort study of children (aged 28 days-14 years) with sepsis presenting to Muhimbili National Hospital in Dar es Salaam, Tanzania (July 2022-November 2024). POC biomarkers (procalcitonin [PCT], C-reactive protein, ferritin, and lactate) and clinical characteristics were evaluated for their association with mortality. We used the least absolute shrinkage and selection operator regression to construct predictive models of mortality. We evaluated model performance using the area under the receiver operating characteristic curve (AUC) and classification metrics, including sensitivity and specificity.
Results:
Among the 755 enrolled participants, 19.6% (n = 148) died during hospitalization. PCT and tested clinical characteristics were significantly associated with mortality (all P < .001). A multivariable model incorporating PCT, malnutrition, breathing difficulty, and altered mental status demonstrated strong discrimination (AUC 0.87, 95% CI [0.84-0.90]), outperforming individual biomarkers and clinical characteristics alone.
Conclusions:
A combined POC biomarker and clinical characteristics model was highly predictive of mortality among children with sepsis in Tanzania. Integrating POC biomarkers with easy-to-measure clinical characteristics associated with severity may enable timely risk stratification and inform targeted interventions to improve pediatric sepsis outcomes in RLSs.
