The development of critical care in resource-poor settings will rely on the stepwise introduction of service improvements, leveraging human resources through training, a focus on sustainable technology, ongoing analysis of cost effectiveness, and the sharing of context-specific best practices. Although prevention, public health, and disease-specific agendas dominate many current conversations in global health, this is nonetheless a time ripe for the development of critical care. Leaders in global health funding hope to improve quality and length of life. Critical care is an integral part of the continuum of care necessary to make that possible.

BCPAP has contributed significantly to favorable outcomes for preterm infants with RDS at AIC Kijabe Hospital. The use of this simple technology should be considered and studied for expansion to all hospitals in Kenya that care for preterm infants.

BackgroundThe reality of finite resources has a real-world impact on a patient’s ability to receive life-saving care in resource-poor settings. Blood for transfusion is an example of a scarce resource. Very few studies have looked at predictors of survival in patients requiring massive transfusion. We used data from a rural hospital in Kenya to develop a prediction model of survival among patients receiving massive transfusion.MethodsPatients who received five or more units of whole blood within 48 hours between 2004 and 2010 were identified from a blood registry in a rural hospital in Kenya. Presenting characteristics and in-hospital survival were collected from charts. Using stepwise selection, a logistic model was developed to predict who would survive with massive transfusion versus those who would die despite transfusion. An ROC curve was created from this model to quantify its predictive power.ResultsNinety-five patients with data available met inclusion criteria, and 74% survived to discharge. The number of units transfused was not a predictor of mortality, and no threshold for futility could be identified. Preliminary results suggest that initial blood pressure, lack of comorbidities, and indication for transfusion are the most important predictors of survival. The ROC curve derived from our model demonstrates an area under the curve (AUC) equal to 0.757, with optimism of 0.023 based on a bootstrap validation.ConclusionsThis study provides a framework for making prioritization decisions for the use of whole blood in the setting of massive bleeding. Our analysis demonstrated an overall survival rate for patients receiving massive transfusion that was higher than clinical perception. Our analysis also produced a preliminary model to predict survival in patients with massive bleeding. Prediction analyses can contribute to more efficient prioritization decisions; these decisions must also include other considerations such as equity, acceptability, affordability and sustainability.