ArticleSupportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer2026
Identifying the trajectory of SPPADE symptoms in patients with cancer using electronic health record data.
Article in Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
purposeThe 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.
methodsTo 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.
resultsFour 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.
conclusionFor 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.
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