Oops, you're using an old version of your browser so some of the features on this page may not be displaying properly.

MINIMAL Requirements: Google Chrome 24+Mozilla Firefox 20+Internet Explorer 11Opera 15–18Apple Safari 7SeaMonkey 2.15-2.23

Poster session 05

1565P - Predictive elements of cancer treatment symptom burden and the effectiveness of symptom treatment approaches

Date

10 Sep 2022

Session

Poster session 05

Topics

Supportive Care and Symptom Management

Tumour Site

Presenters

Kathi Mooney

Citation

Annals of Oncology (2022) 33 (suppl_7): S713-S742. 10.1016/annonc/annonc1075

Authors

K. Mooney1, E. Iacob2, G. Donaldson3

Author affiliations

  • 1 Cancer Control And Population Sciences Research Program, Huntsman Cancer Institute, 84124 - Salt Lake City/US
  • 2 College Of Nursing, University of Utah, 84112 - Salt Lake City/US
  • 3 School Of Medicine, University of Utah, 84132 - Salt Lake City/US

Resources

Login to get immediate access to this content.

If you do not have an ESMO account, please create one for free.

Abstract 1565P

Background

In the era of precision medicine, precision symptom management is also important to advance. Symptom interventions delivered by a combination of electronic and clinician interaction allows for differential delivery of cancer symptom treatment elements. Both prognostic and predictive relationships could form the basis of tailored symptom care.

Methods

387 cancer patients receiving chemotherapy were randomized to four different symptom treatment approaches using a combination of automated self-management coaching, nurse practitioner-delivered care and decision support algorithms. A total symptom burden score, comprised of 11 common physical and psychosocial chemotherapy-related symptoms was assessed daily for six months. Prognostic and predictive relationships were evaluated between this daily symptom score and pre-randomization covariates derived from PROMIS-29 assessments of anxiety, depression, fatigue, pain, physical function, social function, and sleep disturbance. A prognostic analysis examined the symptom burden trajectory as a function of the pre-randomization covariate battery, while a predictive analysis investigated how burden trajectories for each of the 4 treatment interventions varied as a function of covariate levels. Both analyses used mixed effects linear models with quadratic time effects and random intercepts estimated under maximum likelihood.

Results

Participants were on average 59 years old, female (57.6%) with a variety of cancer diagnoses and stage 3 or 4 disease (72%). Demographic and disease covariates were not prognostic of symptom burden; however, significant (p<.001) prognostic relationships emerged between subsequent symptom burden trajectories and anxiety, social function, and sleep disturbance. Anxiety, depression, pain, physical function, social function, sleep disturbance (all p<=.001) and fatigue (p=.009) predicted differential intervention change trajectories in the predictive analysis.

Conclusions

Pre-treatment psychosocial covariates predict subsequent symptom burden and can identify in advance those most likely to benefit from individual intervention components that reduce symptom burden.

Clinical trial identification

NCT02779725.

Editorial acknowledgement

Legal entity responsible for the study

The authors.

Funding

U.S. National Cancer Institute.

Disclosure

K. Mooney: Financial Interests, Personal, Advisory Board: Reimagine Care. All other authors have declared no conflicts of interest.

This site uses cookies. Some of these cookies are essential, while others help us improve your experience by providing insights into how the site is being used.

For more detailed information on the cookies we use, please check our Privacy Policy.

Customise settings
  • Necessary cookies enable core functionality. The website cannot function properly without these cookies, and you can only disable them by changing your browser preferences.