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Improving decision making for massive transfusions in a resource poor setting: a preliminary study in Kenya
Elisabeth D Riviello1, Stephen Letchford2, Earl Francis Cook3
1Department of Medicine, Division of Pulmonary and Critical Care Medicine, Brigham and Women's Hospital, Boston, Massachusetts, United States of America.
Plos One
|May 29, 2015
Summary
Survival rates for massive transfusions in Kenya were higher than expected. A predictive model identified key survival factors, aiding resource allocation for life-saving care.
Area of Science:
- Medical research
- Clinical outcomes
- Resource allocation
Background:
- Finite resources impact patient care in resource-poor settings.
- Blood transfusion is a scarce resource.
- Limited research exists on predictors of survival in massive transfusion patients.
Purpose of the Study:
- Develop a prediction model for survival in patients receiving massive transfusions.
- Identify key predictors of survival in this patient group.
- Inform prioritization decisions for blood use in emergencies.
Main Methods:
- Retrospective analysis of 95 patients receiving ≥5 units of whole blood within 48 hours (2004-2010) at a rural Kenyan hospital.
- Collected presenting characteristics and in-hospital survival data from patient charts.
- Developed a logistic regression model with stepwise selection and validated using ROC curve analysis (AUC=0.757).
Main Results:
- 74% of patients survived to discharge.
- Number of units transfused did not predict mortality; no futility threshold identified.
- Initial blood pressure, absence of comorbidities, and transfusion indication were key predictors of survival.
Conclusions:
- The study developed a preliminary model to predict survival in massive bleeding patients.
- Survival rates were higher than clinical perception, challenging assumptions about transfusion futility.
- The model aids prioritization but must be balanced with equity, acceptability, affordability, and sustainability considerations.