Search

Objective To explore end-of-life care in the ward and intensive care unit (ICU) environment in nine Australian hospitals in a retrospective observational study. Methods In total, 1693 in-hospital deaths, 356 in ICU, were reviewed, including patient demographics, advance care plans, life-sustaining treatments, recognition of dying by clinicians and evidence of the palliative approach to patient care. Results Most patients (n = 1430, 84%) were aged ≥60 years, with a low percentage (n = 208, 12%) having an end-of-life care plan on admission. Following admission, 82% (n = 1391) of patients were recognised as dying, but the time between recognition of dying to death was short (ICU (staying 4–48 h) median 0.34 days (first quartile (Q1), third quartile (Q3): 0.16, 0.72); Ward (staying more than 48 h) median 2.1 days (Q1, Q3: 0.96, 4.3)). Although 41% (n = 621) patients were referred for specialist palliative care, most referrals were within the last few days of life (2.3 days (0.88, 5.9)) and 62% of patients (n = 1047) experienced active intervention in their final 48 h. Conclusions Late recognition of dying can expose patients to active interventions and minimises timely palliative care. To attain alignment to the National Consensus Statement to improve experiences of end-of-life care, a nationally coordinated approach is needed. What is known about the topic? The majority of Australian patient deaths occur in hospitals whose care needs to align to the Australian Commission on Safety and Quality in Health Care’s National Consensus Statement, essential elements of safe and high-quality end-of-life care. What does this paper add? The largest Australian study of hospital deaths reveals only 12% of patients have existing advance care plans, recognition of death is predominantly within the last 48 h of life, with 60% receiving investigations and interventions during this time with late symptom relief. What are the implications for practitioners? Given the poor alignment with the National Consensus Statement, a nationally coordinated approach would improve the patient experience of end-of-life care.

BACKGROUND Given the stable number of potential organ donors after brain death, donors after circulatory death have been an increasing source of organs procured for transplant. Among the most important considerations for donation after circulatory death (DCD) is the prediction that death will occur within a reasonable period of time after the withdrawal of cardiorespiratory support (WCRS). Accurate prediction of time to death is necessary for the procurement process. We aimed to develop simple predictive rules for death in less than 60 min and test the accuracy of these rules in a pool of potential DCD donors. METHODS A multicenter prospective longitudinal cohort design of DCD eligible patients (n=318), with the primary binary outcome being death in less than 60 min after withdrawal of cardiorespiratory support conducted in 28 accredited intensive care units (ICUs) in Australia. We used a random split-half method to produce two samples, first to develop the predictive classification rules and then to estimate accuracy in an independent sample. RESULTS The best classification model used only three simple classification rules to produce an overall efficiency of 0.79 (0.72-0.85), sensitivity of 0.82 (0.73-0.90), and a positive predictive value of 0.80 (0.70-0.87) in the independent sample. Using only intensive care unit specialist prediction (a single classification rule) produced comparable efficiency 0.80 (0.73-0.86), sensitivity 0.87 (0.78-0.93), and positive predictive value 0.78 (0.68-0.86). CONCLUSION This best predictive model missed only 18% of all potential donors. A positive prediction would be incorrect on only 20% of occasions, meaning there is an acceptable level of lost opportunity costs involved in the unnecessary assembly of transplantation teams and theatres.

Objectives Half of all ICU patients die within 60 minutes of withdrawal of cardiorespiratory support. Prediction of which patients die before and after 60 minutes would allow changes in service organization to improve patient palliation, family grieving, and allocation of ICU beds. This study tested various predictors of death within 60 minutes and explored which clinical variables ICU specialists used to make their prediction. Design and settings Prospective longitudinal cohort design (n = 765) of consecutive adult patients having withdrawal of cardiorespiratory support, in 28 ICUs in Australia. Primary outcome was death within 60 minutes following withdrawal of cardiorespiratory support. A random split-half method was used to make two independent samples for development and testing of the predictive indices. The secondary outcome was ICU Specialist prediction of death within 60 minutes. Measurements and main results Death within 60 minutes of withdrawal of cardiorespiratory support occurred in 377 (49.3%). ICU specialist opinion was the best individual predictor, with an unadjusted odds ratio of 15.42 (95% CI, 9.33-25.49) and an adjusted odds ratio of 8.44 (4.30-16.58). A predictive index incorporating the ICU specialist opinion and clinical variables had an area under the curve of 0.89 (0.86-0.92) and 0.84 (0.80-0.88) in the development and test sets, respectively; and a second index using only clinical variables had an area under the curve of 0.86 (0.82-0.89) and 0.78 (0.73-0.83). The ICU specialist prediction of death within 60 minutes was independently associated with five clinical variables: pH, Glasgow Coma Scale, spontaneous respiratory rate, positive end-expiratory pressure, and systolic blood pressure. Conclusion ICU specialist opinion is probably the current clinical standard for predicting death within 60 minutes of withdrawal of cardiorespiratory support. This approach is supported by this study, although predictive indices restricted to clinical variables are only marginally inferior. Either approach has a clinically useful level of prediction that would allow ICU service organization to be modified to improve care for patients and families and use ICU beds more efficiently.