Clinical registries, which capture information about the health and healthcare use of patients with a health condition or treatment, often contain patient-reported outcomes (PROs) that provide insights about the patient’s perspectives on their health. Missing data can affect the value of PRO data for healthcare decision-making. We compared the precision and bias of several missing data methods when estimating longitudinal change in PRO scores. This research conducted analyses of clinical registry data and simulated data. Registry data were from a population-based regional joint replacement registry for Manitoba, Canada; the study cohort consisted of 5631 patients having total knee arthroplasty between 2009 and 2015. PROs were measured using the 12-item Short Form Survey, version 2 (SF-12v2) at pre- and post-operative occasions. The simulation cohort was a subset of 3000 patients from the study cohort with complete PRO information at both pre- and post-operative occasions. Linear mixed-effects models based on complete case analysis (CCA), maximum likelihood (ML) and multiple imputation (MI) without and with an auxiliary variable (MI-Aux) were used to estimate longitudinal change in PRO scores. In the simulated data, bias, root mean squared error (RMSE), and 95% confidence interval (CI) coverage and width were estimated under varying amounts and types of missing data. Three thousand two hundred thirty (57.4%) patients in the study cohort had complete data on the SF-12v2 at both occasions. In this cohort, mixed-effects models based on CCA resulted in substantially wider 95% CIs than models based on ML and MI methods. The latter two methods produced similar estimates and 95% CI widths. In the simulation cohort, when 50% of the data were missing, the MI-Aux method, in which a single hypothetical auxiliary variable was strongly correlated (i.e., 0.8) with the outcome, reduced the 95% CI width by up to 14% and bias and RMSE by up to 50 and 45%, respectively, when compared with the MI method. Missing data can substantially affect the precision of estimated change in PRO scores from clinical registry data. Inclusion of auxiliary information in MI models can increase precision and reduce bias, but identifying the optimal auxiliary variable(s) may be challenging.

Much of what we understand about the design of healthcare systems to support care of the dying comes from our experiences with providing palliative care for dying cancer patients. It is increasingly recognized that in addition to cancer, high quality end of life care should be an integral part of care that is provided for those with other advancing chronic life-limiting conditions. A “palliative approach” has been articulated as one way of conceptualizing this care. However, there is a lack of conceptual clarity regarding the essential characteristics of a palliative approach to care. The goal of this research was to delineate the key characteristics of a palliative approach found in the empiric literature in order to establish conceptual clarity. We conducted a knowledge synthesis of empirical peer-reviewed literature. Search terms pertaining to “palliative care” and “chronic life-limiting conditions” were identified. A comprehensive database search of 11 research databases for the intersection of these terms yielded 190,204 documents. A subsequent computer-assisted approach using statistical predictive classification methods was used to identify relevant documents, resulting in a final yield of 91 studies. Narrative synthesis methods and thematic analysis were used to then identify and conceptualize key characteristics of a palliative approach. The following three overarching themes were conceptualized to delineate a palliative approach: (1) upstream orientation towards the needs of people who have life-limiting conditions and their families, (2) adaptation of palliative care knowledge and expertise, (3) operationalization of a palliative approach through integration into systems and models of care that do not specialize in palliative care. Our findings provide much needed conceptual clarity regarding a palliative approach. Such clarity is of fundamental importance for the development of healthcare systems that facilitate the integration of a palliative approach in the care of people who have chronic life-limiting conditions.

Nurses are increasingly being called on to engage in spiritual care with their patients. A diverse body of theoretical and empirical literature addresses spirituality as it relates to nursing practice, yet there is little consensus about what spiritual nursing care entails. The purpose of this article is to conceptualize spiritual care in relation to nursing practice. A brief historical review indicates that our current understandings of spiritual nursing care have been shaped by three eras characterized by particular approaches: the religious approach, the scientific approach, and the existential approach. We draw elements from each of these approaches to propose attributes of spiritual care in the context of nursing practice. We propose that spiritual nursing care is an intuitive, interpersonal, altruistic, and integrative expression that is contingent on the nurse’s awareness of the transcendent dimension of life but that reflects the patient’s reality.