Using multivariate sequential patterns to improve survival prediction in intensive care burn unit

Resuscitation and stabilization are key issues in Intensive Care Burn Units and early survival predictions help to decide the best clinical action during these phases. Current survival scores of burns focus on clinical variables such as age or the body surface area. However, the evolution of other parameters (e.g. diuresis or fluid balance) during the first days is also valuable knowledge. In this work we suggest a methodology and we propose a Temporal Data Mining algorithm to estimate the survival condition from the patient’s evolution. Experiments conducted on 480 patients show the improvement of survival prediction.

Update in the management of critically ill burned patients

The management of critically ill burn patients is challenging. These patients have to be managed in specialized centers, where the expertise of physicians and nursing personnel guarantees the best treatment. Mortality of burn patients has improved over the past decades due to a better understanding of burn shock pathophysiology, optimal surgical management, infection control and nutritional support. Indeed, a more aggressive resuscitation, early excision and grafting, the judicious use of topical antibiotics, and the provision of an adequate calorie and protein intake are key to attain best survival results. General advances in critical care have also to be implemented, including protective ventilation, glycemic control, selective decontamination of the digestive tract, and implementation of sedation protocols.