Derivation and Validation of a Risk Model for Emergency Department Palliative Care Needs Assessment using the Screen for Palliative and End of Life Care Needs in the Emergency Department (SPEED) Tool Pubblico
Moulia, Danielle Louise (2014)
Abstract
Background: A key setting for the provision of palliative care is the emergency department (ED) where important decisions regarding patient treatment and next site of care are determined. One barrier to the provision of palliative care in the ED is identifying patients who would benefit from a palliative care consult. The Screen for Palliative and End of Life Care Needs in the Emergency Department is a 5-question screening that can be completed either by a patient or proxy (SPEED informer). It assesses 5 domains of unmet palliative care needs - physical symptoms (pain), psychological distress, access to care, medication management, and goals of care alignment.
Objective: To derive and validate a risk model to predict a palliative care event (palliative care consult, discharge to hospice, or in-hospital death) for cancer patients with an ED visit and subsequent hospital admission using data available upon arrival, including data from the SPEED tool.
Methods: We performed a retrospective derivation and temporal validation of a risk model for a palliative care event (PCE). We developed a multivariate logistic regression model to predict PCEs based on SPEED data and other patient characteristics available upon arrival to the ED. We assessed model performance using a receiver operating characteristic curve and visual inspection of quintile plots.
Results: Eleven factors were identified as predictive of a PCE, including SPEED score, proxy SPEED informer, age, EMS arrival, emergent or immediate ED acuity, the number of ED visits within the last 90 days, metastatic cancer, cardiac arrhythmias, coagulopathy, depression and weight loss. In validation, the risk model had an area under the curve of 0.72 and calibration showed an underestimation of risk in the second and third quintiles.
Conclusions: A risk model based on SPEED score has been
successfully derived, but needs a larger dataset for proper
validation. If the predictive ability of the model is confirmed, a
risk model can efficiently identify cancer patients arriving to the
ED who may benefit from early initiation of a palliative care
consult.
Table of Contents
Literature Review.......................................1
Manuscript................................................9
Introduction..............................................9
Methods...................................................11
Results.....................................................16
Discussion.................................................20
References................................................25
Tables......................................................32
Figures.....................................................42
Appendix A: IRB
Approval...........................51
Appendix B: Modeling Strategy....................53
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