Validating a Patient-Reported Outcomes–Derived Algorithm for Classifying Symptom Complexity Levels Among Patients With Cancer
Background: The patient-reported outcomes (PROs) symptom complexity algorithm, derived from self-reported symptom scores using the Edmonton Symptom Assessment System and concerns indicated on the Canadian Problem Checklist, has not been validated extensively. Methods: This is a retrospective chart review study using data from the Alberta Cancer Registry and electronic medical records from Alberta Health Services. The sample includes patients with cancer who visited a cancer facility in Alberta, Canada, from February 2016 through November 2017 (n=1,466). Results: The effect size (d=1.2) indicates that the magnitude of difference in health status between the severe- and low-complexity groups is large. The symptom complexity algorithm effectively classified subgroups of patients with cancer with distinct health status. Using Karnofsky performance status, the algorithm shows a sensitivity of 70.3%, specificity of 84.1%, positive predictive value of 79.1%, negative predictive value of 76.7%, and accuracy of 77.7%. An area under the receiver operating characteristic of 0.824 was found for the complexity algorithm, which is generally regarded as good, This same finding was also regarded as superior to the alternative algorithm generated by 2-step cluster analysis (area under the curve, 0.721). Conclusions: The validity of the PRO-derived symptom complexity algorithm is established in this study. The algorithm demonstrated satisfactory accuracy against a clinician-driven complexity assessment and a strong correlation with the known group analysis. Furthermore, the algorithm showed a higher screening capacity compared with the algorithm generated from 2-step cluster analysis, reinforcing the importance of contextualization when classifying patients’ symptoms, rather than purely relying on statistical outcomes. The algorithm carries importance in clinical settings, acting as a symptom complexity flag, helping healthcare teams identify which patients may need more timely, targeted, and individualized patient symptom management.
Submitted August 13, 2019; accepted for publication May 7, 2020.
Author contributions: Study concept and design: Watson. Sample preparation: Photitai, Chmielewski, Smith. Critical feedback, research, and analysis: All authors. Manuscript preparation: Watson, Qi, DeIure. Manuscript editing: DeIure, Photitai.
Disclosures: The authors have disclosed that they have not received any financial consideration from any person or organization to support the preparation, analysis, results, or discussion of this article.
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