End-of-life care in a cardiology department: have we improved?

nd-of-life care is not usually a priority in cardiology departments. We sought to evaluate the changes in end-of-life care after the introduction of a do-not-resuscitate (DNR) order protocol. Retrospective analysis of all deaths in a cardiology department in two periods, before and after the introduction of the protocol. Comparison of demographic characteristics, use of DNR orders, and end-of-life care issues between both periods, according to the presence in the second period of the new DNR sheet (Group A), a conventional DNR order (Group B) or the absence of any DNR order (Group C). The number of deaths was similar in both periods (n = 198 vs. n = 197). The rate of patients dying with a DNR order increased significantly (57.1% vs. 68.5%; P = 0.02). Only 4% of patients in both periods were aware of the decision taken about cardiopulmonary resuscitation. Patients in Group A received the DNR order one day earlier, and 24.5% received it within the first 24 h of admission (vs. 2.6% in the first period; P < 0.001). All patients in Group A with an implantable cardioverter defibrillator (ICD) had shock therapies deactivated (vs. 25.0% in the first period; P = 0.02). The introduction of a DNR order protocol may improve end-of-life care in cardiac patients by increasing the use and shortening the time of registration of DNR orders. It may also contribute to increase ICD deactivation in patients with these orders in place. However, the introduction of the sheet in late stages of the disease failed to improve patient participation.

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.