Research

Catherine Ryan, PhD, RN, APN , CCRN, Funded Projects

Prediction of Mortality in Advanced Heart Failure Patients Using Symptom Clusters

Funding Source: National Institute of Nursing Research

Dates: 2/15/08– 2/14/11

Abstract: Our long-term goal is to prognosticate end of life based on the symptom experience of people with advanced heart failure (AHF). Accurately predicting the course of illness for people with AHF would allow for patients to make their choices about end-of-life care and to direct care efficiently and appropriately. In advanced heart failure, patients, families, and physicians need realistic estimates of outcomes in order to plan for continuing care and make decisions regarding whether care is aggressive and curative, palliative, or hospice. Symptoms and particularly symptom clusters are emerging as potentially very relevant to predicting the course of chronic illness although this has not been studied in AHF. Through this study, we will determine feasibility of two symptom clustering methods and generate preliminary data about prognostic symptom clusters on which to base future studies of mortality in AHF. This exploratory study will include 250 non-institutionalized adults who are being followed in heart failure clinics or their homes and whose primary care providers have stated that they would not be surprised if death occurred within 2 years. In adults with AHF and utilizing a symptom Q Sort and the Computerized Heart Failure Symptoms Questionnaire (C-HFSQ), Aim 1 is to describe: (1) the symptoms (presence, severity) and symptom clusters reported by 250 adults with AHF at baseline and by those surviving at each of 3, 6, 9, and 12 month time points; (2) symptom clusters generated from each measure (Q-Sort and C-HFSQ) by group, (within 3 months of death and at 12 months survival) and the convergence of the clusters by method (Q-analysis and latent class analysis) at each time-point. Aim 2. Utilizing last reported C-HFSQ data (within 3 months of death or at 12 months), to determine the probability that cluster(s) of symptoms differentiate people with AHF who die during the 12-month study from those who survive. Patients will be asked to report their own heart failure symptoms at baseline and every 3 months for one year (3, 6, 9, and 12 months) or until death to identify the clusters of symptoms that predict increased risk for mortality within 3 months. These data will be analyzed using descriptive statistics, Q methodology, and latent class cluster analysis.