Center for End-of-Life Transition Research (CEoLTR)

Projects

Project #1 (Dr. Ryan)

Predicti on of M ortality in Advanced Heart Failure Patien ts Using Sympt om Clusters

PI (Dr. Ryan)

Our l ong-term g oal is t o pr ogn osticate end of life based on the sympt om experience of pe ople with advanced heart failure (AHF). Accurately predicting the c ourse of illness f or pe ople with AHF w ould all ow f or patien ts t o make their c h oices ab out end- of-life care and t o direct care efficiently and appr opriately. In advanced heart failure, patien ts, f amilies, and physicians need realistic estimates of outc omes in order t o plan f or c ontinuing care and make decisi ons regarding whether care is aggressive and curative, palliative, or h ospice. Sympt oms and particularly sympt om clusters are emerging as p otentially very relevant t o predicting the c ourse of chr onic illness alt h ough this has n ot been studied in AHF. Thr ough this study, we will determine feasibility of tw o sympt om clustering met h ods and generate preliminary data ab out pr ogn ostic sympt om clusters on which t o base future studies of m ortality in AHF. This expl orat ory study will include 250 n on-instituti onalized adul ts w h o are being f oll owed in heart failure clinics or their h omes and w h ose primary care pr oviders have stated that they w ould n ot be surprised if death occurred within 2 years. In adul ts with AHF and utilizing a sympt om Q S ort and the C omputerized Heart Failure Sympt oms Questi onnaire (C-HFSQ), Aim 1 is t o des cribe: (1) the sympt oms (presence, severity) and sympt om clusters rep orted by 250 adul ts with AHF at baseline and by t h ose surviving at each of 3, 6, 9, and 12 m onth time p oin ts; (2) sympt om clusters generated fr om each measure (Q-S ort and C-HFSQ) by gr oup, (within 3 m onths of death and at 12 m onths survival) and the c onvergence of the clusters by met h od (Q-analysis and latent class analysis) at each time-p oint. Aim 2. Utilizing last rep orted C-HFSQ data (within 3 m onths of death or at 12 m onths), t o determine the pr obability that cluster(s) of sympt oms differentiate pe ople with AHF w h o die during the 12-m onth study fr om t h ose w h o survive. Patien ts will be asked t o rep ort their own heart failure sympt oms at baseline and every 3 m onths f or one year (3, 6, 9, and 12 m onths) or until death t o identify the clusters of sympt oms that predict in creased risk f or m ortality within 3 m onths. These data will be analyzed using des criptive statistics, Q met h od ol ogy, and latent class cluster analysis.

 

Since heart disease ranks at the t op of reas ons f or death in the U.S. and heart failure is the m ost c omm on reas on f or h ospitalizati ons and is in creasing, understanding ways t o predict outc omes of heart failure will lead t o m ore effective interventi ons, including at end of life, t o reduce impact on s ociety and the healthcare system.