[Delirium in cardiology: an old problem, a new emergency]. / Il delirium in cardiologia: un vecchio problema, una nuova emergenza.

Delirium is a common cognitive behavioral disorder, with acute onset, frequent in elderly hospitalized patients. This condition has long been the subject of research in the critical area, with the development of targeted prevention and management protocols. In the cardiology field, however, awareness of delirium is poor. The problem of delirium has recently begun to involve practitioners since the publication of first studies showing the increase of adverse events in patients with this condition. The pathophysiology of delirium is unclear and the risk factors are based on clinical conditions and factors related to patient's care itself that need to be readily identified. Thus, delirium is a clinical manifestation that can easily be confused with other conditions. Notwithstanding, delirium can be prevented and treated when clinically evident, with a number of non-pharmacological interventions based on a multidisciplinary approach. Pharmacological therapy, due to its unclear effectiveness, should be reserved to patients with severe agitation or at risk of injuring themselves and others. The purpose of this review is to increase the awareness in healthcare professionals about the recent data on etiology, prevention, treatment and prognosis of delirium and to put the basis for a protocol that could be used in Cardiology departments.

Patient-specific seizure prediction based on heart rate variability and recurrence quantification analysis.

Epilepsy is often associated with modifications in autonomic nervous system, which usually precede the onset of seizures of several minutes. Thus, there is a great interest in identifying these modifications enough time in advance to prevent a dangerous effect and to intervene. In addition, these changes can be a risk factor for epileptic patients and can increase the possibility of death. Notably autonomic changes associated to seizures are highly depended of seizure type, localization and lateralization. The aim of this study was to develop a patient-specific approach to predict seizures using electrocardiogram (ECG) features. Specifically, from the RR series, both time and frequency variables and features obtained by the recurrence quantification analysis were used. The algorithm was applied in a dataset of 15 patients with 38 different types of seizures. A feature selection step, was used to identify those features that were more significant in discriminating preictal and interictal phases. A preictal interval of 15 minutes was selected. A support vector machine (SVM) classifier was then built to classify preictal and interictal phases. First, a classifier was set up to classify preictal and interictal segments of each patient and an average sensibility of 89.06% was obtained, with a number of false positive per hour (FP/h) of 0.41. Then, in those patients who had at least 3 seizures, a double-cross-validation approach was used to predict unseen seizures on the basis of a training on previous ones. The results were quite variable according to seizure type, achieving the best performance in patients with more stereotypical seizure. The results of the proposed approach show that it is feasible to predict seizure in advance, considering patient-specific, and possible seizure specific, characteristics.