Young caregivers in the end-of-life setting: a population-based profile of an emerging group.

PURPOSE: Little is known about young caregivers of people with advanced life-limiting illness. Better understanding of the needs and characteristics of these young caregivers can inform development of palliative care and other support services. METHODS: A population-based analysis of caregivers was performed from piloted questions included in the 2001-2007 face-to-face annual health surveys of 23,706 South Australians on the death of a loved one, caregiving provided, and characteristics of the deceased individual and caregiver. The survey was representative of the population by age, gender, and region of residence. FINDINGS: Most active care was provided by older, close family members, but large numbers of young people (ages 15-29) also provided assistance to individuals with advanced life-limiting illness. They comprised 14.4% of those undertaking "hands-on" care on a daily or intermittent basis, whom we grouped together as active caregivers. Almost as many young males as females participate in active caregiving (men represent 46%); most provide care while being employed, including 38% who work full-time. Over half of those engaged in hands-on care indicated the experience to be worse or much worse than expected, with young people more frequently reporting dissatisfaction thereof. Young caregivers also exhibited an increased perception of the need for assistance with grief. CONCLUSION: Young people can be integral to end-of-life care, and represent a significant cohort of active caregivers with unique needs and experiences. They may have a more negative experience as caregivers, and increased needs for grief counseling services compared to other age cohorts of caregivers.

An evaluation of remifentanil-sevoflurane response surface models in patients emerging from anesthesia: model improvement using effect-site sevoflurane concentrations.

INTRODUCTION: We previously reported models that characterized the synergistic interaction between remifentanil and sevoflurane in blunting responses to verbal and painful stimuli. This preliminary study evaluated the ability of these models to predict a return of responsiveness during emergence from anesthesia and a response to tibial pressure when patients required analgesics in the recovery room. We hypothesized that model predictions would be consistent with observed responses. We also hypothesized that under non-steady-state conditions, accounting for the lag time between sevoflurane effect-site concentration (Ce) and end-tidal (ET) concentration would improve predictions. METHODS: Twenty patients received a sevoflurane, remifentanil, and fentanyl anesthetic. Two model predictions of responsiveness were recorded at emergence: an ET-based and a Ce-based prediction. Similarly, 2 predictions of a response to noxious stimuli were recorded when patients first required analgesics in the recovery room. Model predictions were compared with observations with graphical and temporal analyses. RESULTS: While patients were anesthetized, model predictions indicated a high likelihood that patients would be unresponsive (> or = 99%). However, after termination of the anesthetic, models exhibited a wide range of predictions at emergence (1%-97%). Although wide, the Ce-based predictions of responsiveness were better distributed over a percentage ranking of observations than the ET-based predictions. For the ET-based model, 45% of the patients awoke within 2 min of the 50% model predicted probability of unresponsiveness and 65% awoke within 4 min. For the Ce-based model, 45% of the patients awoke within 1 min of the 50% model predicted probability of unresponsiveness and 85% awoke within 3.2 min. Predictions of a response to a painful stimulus in the recovery room were similar for the Ce- and ET-based models. DISCUSSION: Results confirmed, in part, our study hypothesis; accounting for the lag time between Ce and ET sevoflurane concentrations improved model predictions of responsiveness but had no effect on predicting a response to a noxious stimulus in the recovery room. These models may be useful in predicting events of clinical interest but large-scale evaluations with numerous patients are needed to better characterize model performance.