Identifying the trajectory of SPPADE symptoms in patients with cancer using electronic health record data.

Authors

Kurt Kroenke, Veronica Grzegorczyk, Minji Lee, Jeph Herrin, Sandra A Mitchell, Linda L Chlan, Kathryn J Ruddy, Deirdre R Pachman, Joan M Griffin, Jennifer L Ridgeway, Jessica D Austin, Andrea L Cheville

Published in

Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer. Volume 34. Issue 10. Sep 23, 2026. Epub Sep 23, 2026.

Abstract

The longitudinal course (trajectory) of symptoms in patients with cancer has been understudied. Sleep disturbance, pain, physical function impairment, anxiety, depression, and energy deficit/fatigue (SPPADE) are particularly common in patients with cancer. This study characterizes the trajectory patterns of SPPADE symptoms in a large and diverse sample of patients with cancer and identifies factors associated with distinct trajectories.
To identify the optimal number and functional forms of the symptom trajectories, latent class growth analysis was conducted. Electronic health record (EHR) data were analyzed from 14,590 patients in the Enhanced EHR-facilitated Cancer Symptom Control (E2C2) trial cohort with at least 3 symptom assessments within a 12-month period. Each SPPADE symptom was rated on a 0-10 numeric rating scale. Multivariable multinomial logistic regression was used to determine factors associated with membership in the symptom trajectory subgroups.
Four distinct trajectory patterns were similar across symptoms. The average sample distribution of membership in a trajectory class across the 6 symptoms was 65.5% for low severity stable, 15.5% for high severity stable, 10.7% for improving, and 8.4% for worsening. Being employed, married/partnered, better educated, older, and not having metastatic disease were independently associated with membership in the low stable trajectory of SPPADE symptoms. Cancer site generally did not influence symptom trajectory class membership.
For oncology practices that routinely use patient-reported outcomes to assess symptoms, EHR data can be a source for characterizing symptom trajectories. Knowledge of factors associated with trajectory class membership could be useful in clinical research and for tailoring evidence-based symptom management strategies.

PMID:
42776292
Bibliographic data and abstract were imported from PubMed on 24 Sep 2026.

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