ReCAP: Identifying Severe Adverse Event Clusters Using the National Cancer Institutes Common Terminology Criteria for Adverse Events [CARE DELIVERY]

CONTEXT & QUESTION ASKED:

Exploring the relationship among adverse events is important because those that arise from a common mechanism are amenable to a common intervention, which can improve symptom management, quality of life, and treatment adherence. To date, symptom cluster studies have used patient-reported data, which are not always available in clinical trials. In this study, we proposed using the National Cancer Institute Common Terminology Criteria for Adverse Events (CTCAE) to identify adverse event clusters because the CTCAE are collected as standard practice and can therefore be used when patient-reported outcomes are unavailable. Hence, is it feasible to identify severe adverse events clusters from data captured using the CTCAE in clinical trials?

SUMMARY ANSWER:

Six severe adverse events clusters were identified in patients with advanced prostate cancer. Identifying adverse events clusters using CTCAE data from clinical trials is feasible.

METHODS:

A variable-based hierarchical cluster analysis was conducted using the CTCAE data captured from 323 patients who experienced at least one grade 3 or higher adverse event in an advanced prostate cancer randomized clinical trial conducted by SWOG (S9916).

BIAS, CONFOUNDING FACTOR(S), DRAWBACKS:

The difficulty of using adverse event data from clinical trials is that often not all adverse events are recorded. In our study, only the highest severity grade for each adverse event type was recorded, and only grade 3 or higher adverse events were captured reliably. Moreover, in contrast to previous publications on symptom cluster that used patient-reported outcomes, the CTCAE is clinician reported and may not accurately reflect the presence of patient symptoms and the severity of these symptoms.

REAL-LIFE IMPLICATIONS:

Capturing adverse events using the CTCAE, which is standard practice in all clinical trials, can be used to understand the relationships among adverse events and to identify adverse events clusters when patient-reported outcomes are unavailable.

FIG 2.

Dendrogram for the cluster analysis. PRBC, packed RBC; w/o, without. Numbers in the figure are Spearman rank correlation, and Ps are from the Mantel-Haenszel ² test.

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