EAPC - ipac

442 EAPC Abstracts
142 Oral Presentation
Assessment and measurement of quality of life and other
symptoms
Developing a Measure of Quality of Death and Dying (QODD) in
the Pediatric Intensive Care Unit (PICU)
Authors: Mildred Solomon Center for Applied Ethics Education
Development Center U. STATES
Adena Cohen-Bearak Education Development Center Newton U. STATES
Deborah Sellers Education Development Center Newton U. STATES
Sarah McGraw New England Research Institutes Watertown U. STATES
Robert Truog Education Development Center Newton U. STATES
Background: End-of-life care in the PICU poses challenges distinct from
those for adults. Relatively little information is available about the quality
of dying and death in the PICU. We present the results from a qualitative
study to develop a measure of QODD in the PICU. Our aims are to
describe constructs and indicators through: 1) a systematic literature
review and 2) focus groups with PICU providers. These findings, combined
with interviews with bereaved family members, will inform the
development of a PICU QODD instrument. Methods: A Medline search
identified 99 relevant articles. A moderator conducted six focus groups
about death and dying in the PICU with 65 PICU providers (physicians,
nurses and psychosocial) from two teaching hospitals. The study team
abstracted themes and specific indicators on decision-making, family and
clinician concerns, and quality of care from the 99 articles. Using both
inductive and deductive approaches, three coders first read the transcripts
and coded for the domains identified in the literature and new domains
emerging from the transcripts. Second, they identified sub-themes and topics
within each domain. Results: Eight core domains with indicators were
identified: 1) Decisions; 2) Conflict; 3) Communication; 4) Continuity of
care; 5) Emotional and psychosocial needs of the family; 6) Pain and other
symptoms; 7) Choices around the circumstances of death and;
8) Bereavement. A meta-theme, underlying all of the focus group findings,
was the notion of the uniqueness of each child, family, and the circumstances
of death. Conclusions: Some domains and indicators are unique to
the PICU; unlike for adults, autonomy is not a core domain.
Accommodating the uniqueness of each circumstance presents methodological
challenges for the development of a tool. Measures should address
the subjective assessment of satisfaction with care and alignment with
hopes and priorities.
143 Oral Presentation
Assessment and measurement of quality of life and other
symptoms
Evaluation of the Palliative Prognostic Score (PaP) and routinely
collected clinical data in prognostication of survival for
patients referred to a palliative care consultation service in an
acute care hospital
Authors: Yoko Tarumi Oncology/Palliative Care Medicine University of
Alberta CANADA
Francis Lau University of Victoria, Health Information Science Victoria
CANADA
Lorelei Sawchuk Royal Alexandra Hospital, Palliative Care Program
Edmonton CANADA
Hue Quan Capital Health Regional Palliative Care Program Edmonton
CANADA
Sharon Watanabe University of Alberta, Onclogy/Palliative Care Medicine
Edmonton CANADA
Background: The PaP is a validated tool for survival prognostication in palliative
care patients. The purpose of this study is to further validate the PaP
and examine the additional prognostic utility of routinely collected clinical
data. Methods: Cancer and non-cancer patients referred to a palliative care
consultation service at an acute care hospital were included. This was a
prospective cohort study on survival prediction based on PaP and other routinely
collected clinical data: Palliative Performance (PPS), Folstein Mini
Mental State Examination Score (MMSE), Edmonton Symptom
Assessment Scale. Data were collected at initial consultation, and again at
the time of final decision making for discharge planning. Other predictor
variables were obtained via routinely collected administrative data including
age group, gender, primary diagnosis, problems at referral, location and
date of discharge, and location and date of death. Statistical Analysis:
1) Kaplan-Meier (KM) survival analysis for above listed variables;
2) Hazard ratios for death with above variables; 3) Calculation of PaP using
both PPS and KPS; 4) Survival rate (%) by above variables. Results: A total
312 cases have been included in the preliminary data analysis. 95 cases
have been censored due to unavailability of date of death (expected to be
ready by the end of 2007). KM analysis for PaP showed 30 day survival
rates that are consistent with previous studies in each risk category: A
(>70%), B (30–70%), and C (