Effects of a Geriatric Assessment Intervention on Patient-Reported Symptomatic Toxicity in Older Adults With Advanced Cancer

Abstract

PURPOSE Providing a geriatric assessment (GA) summary with management recommendations to oncologists reduces clinician-rated toxicity in older patients with advanced cancer receiving treatment. This secondary analysis of a national cluster randomized clinical trial (ClinicalTrials.gov identifier: NCT02054741 ) aims to assess the effects of a GA intervention on symptomatic toxicity measured by Patient-Reported Outcomes Common Terminology Criteria for Adverse Events (PRO-CTCAE). METHODS From 2014 to 2019, the study enrolled patients age ≥ 70 years, with advanced solid tumors or lymphoma and ≥ 1 GA domain impairment, who were initiating a regimen with high prevalence of toxicity. Patients completed PRO-CTCAEs, including the severity of 24 symptoms (11 classified as core symptoms) at enrollment, 4-6 weeks, 3 months, and 6 months. Symptoms were scored as grade ≥ 2 (at least moderate) and grade ≥ 3 (severe/very severe). Symptomatic toxicity was determined by an increase in severity during treatment. A generalized estimating equation model was used to assess the effects of the GA intervention on symptomatic toxicity. RESULTS Mean age was 77 years (range, 70-96 years), 43% were female, and 88% were White, 59% had GI or lung cancers, and 27% received prior chemotherapy. In 706 patients who provided PRO-CTCAEs at baseline, 86.1% reported at least one moderate symptom and 49.7% reported severe/very severe symptoms at regimen initiation. In 623 patients with follow-up PRO-CTCAE data, compared with usual care, fewer patients in the GA intervention arm reported grade ≥ 2 symptomatic toxicity (overall: 88.9% v 94.8%, P = .035; core symptoms: 83.4% v 91.7%, P = .001). The results for grade ≥ 3 toxicity were comparable but not significant ( P > .05). CONCLUSION In the presence of a high baseline symptom burden, a GA intervention for older patients with advanced cancer reduces patient-reported symptomatic toxicity.

An Unsupervised Machine Learning Approach to Evaluating the Association of Symptom Clusters With Adverse Outcomes Among Older Adults With Advanced Cancer

“…The GAP70+ trial collected 27 PRO-CTCAE symptom items with multiple attributes . The investigators selected those items based on their relevance to older adults when designing the trial in 2013 .…”

“…Clinic notes and discharge summaries were reviewed by blinded clinicians at the URCC NCORP Research Base, and the treating physician was queried if there was any discrepancy. Detailed sociodemographic information, cancer diagnosis and stage, cancer treatments, Karnofsky performance status (KPS), and 8 GA domains (eg, polypharmacy, functional status, comorbidity) were collected as described previously . Race (American Indian or Alaskan Native, Asian, Black or African American, Native Hawaiian or other Pacific Islander, and White) and ethnicity (Hispanic or Latino, non-Hispanic, and unknown) was self-reported by patients.…”

“…The GAP70+ trial collected 27 PRO-CTCAE symptom items with multiple attributes. 2 , 8 , 18 The investigators selected those items based on their relevance to older adults when designing the trial in 2013. 18 For this analysis, we included 24 PRO-CTCAE items with severity attributes collected before treatment.…”

“… 2 , 8 , 18 The investigators selected those items based on their relevance to older adults when designing the trial in 2013. 18 For this analysis, we included 24 PRO-CTCAE items with severity attributes collected before treatment. Severity responses for each item range from 0 to 4, corresponding to none, mild, moderate, severe, and very severe.…”

“…17 To standardize the analysis of PRO-CTCAE data, the NCI formed a Cancer Treatment Tolerability Consortium with 4 research teams in 2018, as part of the Cancer Moonshot initiative. 2,18 PRO-CTCAE items were developed to be analyzed and reported individually 19 ; however, one methodologic initiative is examining a summative score that would capture overall symptom burden at baseline or cumulative symptomatic toxic effects during treatment. One potential approach is to use machine learning algorithms 20 in addition to traditional methods, such as total score (eg, the scoring of MD Anderson Symptom Inventory).…”