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Cancer survival analysis focuses on quantifying and interpreting the time from a key starting point, such as diagnosis or the initiation of treatment, to a specific endpoint, such as remission or death. This analysis provides critical insights into treatment effectiveness and factors that influence patient outcomes, helping to shape clinical decisions and guide prognostic evaluations. A cornerstone of oncology research, survival analysis tackles the challenges of skewed, non-normally...
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Association Between Patient-Reported Outcomes-Derived Symptom Complexity and Overall Survival Among Patients With

Linda Watson1,2, Claire Link1, Siwei Qi1

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Summary
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Higher symptom complexity in cancer patients is linked to a shorter overall survival (OS). This patient-reported outcomes (PROs) algorithm can help predict survival and guide clinical decisions.

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Area of Science:

  • Oncology
  • Patient-Reported Outcomes
  • Prognostic Biomarkers

Background:

  • Cancer Care Alberta (CCA) developed a patient-reported outcomes (PROs)-derived algorithm to score symptom complexity.
  • The algorithm categorizes patients into low, moderate, or high symptom complexity groups.
  • The prognostic utility of this algorithm for overall survival (OS) was investigated.

Purpose of the Study:

  • To examine the association between symptom complexity scores and overall survival (OS) in cancer patients.
  • To determine the prognostic value of the CCA symptom complexity algorithm.

Main Methods:

  • Included 5,841 adult patients with initial oncology consultations between October 2019 and April 2020.
  • Assessed symptom complexity using a PRO questionnaire within 30 days of consultation.
  • Analyzed OS using Kaplan-Meier curves and Cox proportional hazards models, adjusting for covariates.

Main Results:

  • Higher baseline symptom complexity was significantly associated with shorter OS (low: 64.7 weeks, moderate: 39.0 weeks, high: 25.7 weeks).
  • Patients with moderate complexity had a 69% higher risk of death (HR 1.69), and high complexity patients had a 142% higher risk of death (HR 2.42) compared to low complexity.
  • Demographic and clinical variables differed across complexity levels.

Conclusions:

  • Increased symptom complexity, as measured by the CCA algorithm, is a significant predictor of shorter OS.
  • The PRO-derived symptom complexity score demonstrates utility in predicting cancer patient survival.
  • Findings can inform clinical decision-making for treatment planning and supportive care.