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Exploring differences in adverse symptom event grading thresholds between clinicians and patients in the clinical
Thomas M Atkinson1, Lauren J Rogak2, Narre Heon2
1Department of Psychiatry and Behavioral Sciences, Memorial Sloan Kettering Cancer Center, 641 Lexington Ave., 7th Floor, New York, NY, 10022, USA. atkinsot@mskcc.org.
Purpose:
Symptomatic adverse event (AE) monitoring is essential in cancer clinical trials to assess patient safety, as well as inform decisions related to treatment and continued trial participation. As prior research has demonstrated that conventional concordance metrics (e.g., intraclass correlation) may not capture nuanced aspects of the association between clinician and patient-graded AEs, we aimed to characterize differences in AE grading thresholds between doctors (MDs), registered nurses (RNs), and patients using the Bayesian Graded Item Response Model (GRM).
Methods:
From the medical charts of 393 patients aged 26-91 (M = 62.39; 43% male) receiving chemotherapy, we retrospectively extracted MD, RN and patient AE ratings. Patients reported using previously developed Common Terminology Criteria for Adverse Events (CTCAE) patient-language adaptations called STAR (Symptom Tracking and Reporting). A GRM was fitted to calculate the latent grading thresholds between MDs, RNs and patients.
Results:
Clinicians have overall higher average grading thresholds than patients when assessing diarrhea, dyspnea, nausea and vomiting. However, RNs have lower grading thresholds than patients and MDs when assessing constipation. The GRM shows higher variability in patients' AE grading thresholds than those obtained from clinicians.
Conclusions:
The present study provides evidence to support the notion that patients report some AEs that clinicians might not consider noteworthy until they are more severe. The availability of GRM methodology could serve to enhance clinical understanding of the patient symptomatic experience and facilitate discussion where AE grading discrepancies exist. Future work should focus on capturing explicit AE grading decision criteria from MDs, RNs, and patients.
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