%0 Journal Article
%T Predicting discharge of palliative care inpatients by measuring their heart rate variability
%A Eva Katharina Masel
%A Herbert H. Watzke
%A Katharina A. Kierner
%A Patrick Huber
%A Romina Nemecek
%A Sophie Schur
%J SCIE-indexed Journal
%D 2014
%X ¡°When will I be discharged from the hospital?¡± is one of the most commonly questions asked by patients. Home discharge is a major challenge for patients with advanced cancer as well as their families and health care professionals (1). The likelihood and the time point of discharge are important for planning discharge management. In contrast to other fields of medicine, we have few validated tools to predict the likelihood of discharge in patients with advanced cancer (1,2). The improvement of symptoms on the Edmonton symptom assessment scale (ESAS) is a validated tool for the assessment of physical symptoms in palliative care. It constitutes an important prognostic factor and is correlated with the improvement of survival (3). However, at the time of admission to a palliative care unit (PCU) one cannot predict whether the patient¡¯s symptoms will improve. Estimation of the probability of discharge in such patients has not been investigated yet. In an EU-wide assessment it was found that friends and relatives provided three billion unpaid care hours (5.2 hours per EU citizen), amounting to about 23.2 billion Euros (4). Thus, discharge is obviously a major challenge for patients with advanced cancer and their families. Referral to palliative care tends to occur rather late. In view of the high cost of palliative care and the fact that functionally dependent patients are unlikely to return home (5,6), it would be meaningful to focus on prognosis as well as the likelihood of discharge. A predictor of home discharge at the time of admission would enable caregivers to accompany patients and family members effectively through this process. It would also permit timely organization of home care, ideally enabling a patient to spend as much time as possible in the home environment. We hypothesized that analysis of the autonomic nervous system and the detection of dysautonomia might influence the likelihood of patients being discharged from a PCU and serve as a predictor. Autonomic dysfunction is associated with shorter survival. Every organ in the body is innervated by the autonomic nervous system. In advanced cancer patients, dysfunction of the autonomic nervous system identified by measuring heart rate variability (HRV) was found to be associated with a lower performance status and shorter survival (7,8). A patient population being admitted to a PCU has not been studied so far. We hypothesized that the degree of autonomic dysfunction is correlated with a shorter survival probability. Analysis of HRV is a means of obtaining data on standardized parameters of the
%U http://apm.amegroups.com/article/view/4407/5886