Critical care in resource-poor settings: lessons learned and future directions.

CONTEXT: Critical care faces the same challenges as other aspects of healthcare in the developing world. However, critical care faces an additional challenge in that it has often been deemed too costly or complicated for resource-poor settings. This lack of prioritization is not justified. Hospital care for the sickest patients affects overall mortality, and public health interventions depend on community confidence in healthcare to ensure participation and adherence. Some of the most effective critical care interventions, including rapid fluid resuscitation, early antibiotics, and patient monitoring, are relatively inexpensive. Although cost-effectiveness studies on critical care in resource-poor settings have not been done, evidence from the surgical literature suggests that even resource-intensive interventions can be cost effective in comparison to immunizations and human immunodeficiency virus care. In the developing world, where many critically ill patients are younger and have fewer comorbidities, critical care presents a remarkable opportunity to provide significant incremental benefit, arguably much more so than in the developed world. ESSENTIAL CONSIDERATIONS: Key areas of consideration in developing critical care in resource-poor settings include: Personnel and training, equipment and support services, ethics, and research. Strategies for training and retaining skilled labor include tying education to service commitment and developing protocols for even complex processes. Equipment and support services need to focus on technologies that are affordable and sustainable. Ethical decision making must be based on data when possible and on transparent articulated policies always. Research should be performed in resource-poor settings and focus on needs assessment, prognostication, and cost effectiveness. FUTURE DIRECTIONS: 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.

Improving decision making for massive transfusions in a resource poor setting: a preliminary study in Kenya.

BACKGROUND: The 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. METHODS: Patients 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. RESULTS: Ninety-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. CONCLUSIONS: This 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.