Cheville, Andrea

11108 Background: Advances in cancer treatment have led to a growing population of patients living with metastatic cancer. Symptom burden for these patients is understudied. Our objective is to report the prevalence and severity of SPPADE symptoms (sleep disturbance, pain, physical function impairment, anxiety, depression, and low energy /fatigue) in a large cohort of patients with metastatic cancer. Methods: These data are drawn from the Enhanced, EHR-facilitated Cancer Symptom Control (E2C2) stepped wedge pragmatic trial evaluating a symptom management intervention for cancer patients (NCT03892967). This analysis was limited to those identified with metastatic disease using ICD- codes and a natural language processing algorithm prior to or within 30 days of their baseline symptom survey. Symptom severity was measured on a 0-10 scale with scores of 1-3 categorized as “mild”, 4-6 as “moderate”, and ≥7 as “severe.” A SPPADE composite score was calculated by summing six individual symptom scores and is an indicator of total symptom burden. We report descriptive statistics for each symptom by severity category. We also test for differences in SPPADE composite score by race, disability status, employment status, education and cancer type using Kruskal-Wallis tests. Results: 12,824 patients with metastatic disease completed a survey between March 2019 and January 2023. Discretized score distributions for the SPPADE symptoms are reported in the Table. The SPPADE composite mean score for the cohort was 17.6 (SD +/- 12.0). Significant differences were found in total symptom burden by sociodemographic and clinical characteristics ( p<0.01): Higher total symptom burden (Mean [SD])was experienced by individuals who were American Indian/Alaska Native (AI/AN) (23.8[13.5]) or African American (AA) (20.5[14.7]), disabled (24.0[12.8]) or unemployed (21.1[13.0]), and had a high school education (20.0[13.3]) or less than high school education (18.5[12.4]). There were also statistically significant differences ( p<0.01) in symptom burden across cancer types, with patients with metastatic lung cancer reporting the highest symptom burden (20.0[12.2]) and those with metastatic melanoma reporting the lowest (14.7[11.4]). Conclusions: SPPADE symptoms are common in patients with metastatic cancer, especially fatigue, sleep disturbance, and physical function impairment. Symptom burden is especially high in AI/AN, AA, disabled/unemployed, and less educated patients. Further research is needed to tailor symptom interventions among those living with metastatic disease. [Table: see text]

Abstract Background: SPPADE symptoms (Sleep disturbance, Pain, Physical function impairment, Anxiety, Depression/distress, and Energy deficits/fatigue) may result from cancer and its treatment. The aim of this analysis was to characterize SPPADE symptoms in patients with breast cancer. Methods. SPPADE data were drawn from the Enhanced, EHR-facilitated Cancer Symptom Control (E2C2) study, a stepped-wedge pragmatic cluster-randomized trial designed to evaluate a symptom management intervention in routine practice (NCT03892967). Symptom severity was measured on a 0-10 scale via a survey administered by patient portal, in-clinic tablet, or interactive voice response system (by phone) prior to, during, or soon after an outpatient oncology encounter. Scores of 4-6 were categorized as “moderate” and ≥7 as “severe.” In this analysis, only the data from each patient’s initial survey were included. Results. A total of 9,651 patients with breast cancer completed an initial E2C2 survey between March 2019 and January 2023. The proportion of patients with moderate or severe SPPADE symptoms were as follows: 22% moderate and 10% severe for insomnia; 18% moderate and 7% severe for pain; 17% moderate and 7% severe for anxiety; 15% moderate and 5% severe for emotional distress; 25% moderate and 12% severe for fatigue; and 22% moderate and 8% severe for impaired physical function. Table 1 presents score means and standard deviations by demographic subgroup. Conclusions. SPPADE symptoms are common in patients with breast cancer. These observations warrant routine implementation of symptom surveillance and management in breast oncology. Funding acknowledgement: The Improving the Management of symPtoms during And following Cancer Treatment (IMPACT) Consortium is a Cancer Moonshot Research Initiative under the authorization of the 2016 United States 21st Century Cures Act. This research was supported by the National Cancer Institute of the NIH, UM1CA233033 (Mayo Clinic, Rochester, MN). Insomnia, Pain, Anxiety, Distress, Fatigue, and Functional Impairment in Patients with Breast Cancer Citation Format: Kathryn Ruddy, Veronica Grzegorczyk, Deirdre Pachman, Sandra Mitchell, Bolun Liu, Deanne Smith, Ciara O'Sullivan, Shawna Ehlers, Jeph Herrin, Parvez Rahman, Andrea Cheville. Insomnia, Pain, Anxiety, Distress, Fatigue, and Functional Impairment in Patients with Breast Cancer [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO2-12-09.

31 Background: Incidence of early-onset colorectal cancer (eoCRC, <50 years of age at diagnosis) is increasing globally. However, little is known regarding symptom burden in this sub-population. We compared symptoms in patients with eoCRC to those of patients with average-onset CRC (aoCRC, diagnosed at age ≥ 50). Methods: E2C2 is a stepped-wedge, cluster-randomized pragmatic trial evaluating a collaborative care model for the assessment and management of six symptoms [Sleep disturbance, Pain, Physical function impairment, Anxiety, emotional Distress, and Energy deficit (SPPADE)]. Patients were asked to complete assessments rating each of these symptoms on an 11-point numerical rating scale. Symptom severity was classified as mild (0-3), moderate (4-6), or severe (7-10). We conducted a subgroup analysis of patients with CRC, comparing SPPADE symptoms on an initial survey, regardless of timing in treatment course, between patients with eoCRC and aoCRC. Results: Patients with CRC (n=4281) completed a survey between March 2019 and December 2022. Patient demographics and SPPADE symptoms are reported in the table. Patients with eoCRC were more likely to report severe sleep disturbance ( p<0.0001), pain ( p=0.03485), anxiety ( p<0.0001), emotional distress ( p<0.0001), and fatigue ( p=0.01083). In patients with eoCRC, women were more likely to report severe pain (12.6% vs 6.9%, p=0.0069), anxiety (16.1% vs 10.0%, p=0.012), emotional distress (12.1% vs 5.4%, p=0.00066), fatigue (18.9% vs 13.4%, p=0.038), and physical function impairment (10.1% vs 5.8%, p=0.026) compared to men. Conclusions: Patients with eoCRC reported higher symptom severity than patients with aoCRC. This suggests unique needs for symptom management in patients with eoCRC. Additional research is needed to evaluate how time since diagnosis, disease characteristics, cancer-directed treatment patterns, and co-occurring symptoms are associated with specific symptoms in this population. [Table: see text]

AbstractCancer and its treatment produce deleterious symptoms across the phases of care. Poorly controlled symptoms negatively affect quality of life and result in increased health-care needs and hospitalization. The Improving the Management of symPtoms during And following Cancer Treatment (IMPACT) Consortium was created to develop 3 large-scale, systematic symptom management systems, deployed through electronic health record platforms, and to test them in pragmatic, randomized, hybrid effectiveness and implementation trials. Here, we describe the IMPACT Consortium’s conceptual framework, its organizational components, and plans for evaluation. The study designs and lessons learned are highlighted in the context of disruptions related to the COVID-19 pandemic.

Abstract Background Electronic health record–linked portals may improve health-care quality for patients with cancer. Barriers to portal access and use undermine interventions that rely on portals to reduce cancer care disparities. This study examined portal access and persistence of portal use and associations with patient and structural factors before the implementation of 3 portal-based interventions within the Improving the Management of symPtoms during And following Cancer Treatment (IMPACT) Consortium. Methods Portal use data were extracted from electronic health records for the 12 months preceding intervention implementation. Sociodemographic factors, mode of accessing portals (web vs mobile), and number of clinical encounters before intervention implementation were also extracted. Rurality was derived using rural-urban commuting area codes. Broadband access was estimated using the 2015-2019 American Community Survey. Multiple logistic regression models tested the associations of these factors with portal access (ever accessed or never accessed) and persistence of portal use (accessed the portal ≤20 weeks vs ≥21 weeks in the 35-week study period). Results Of 28 942 eligible patients, 10 061 (35%) never accessed the portal. Male sex, membership in a racial and ethnic minority group, rural dwelling, not working, and limited broadband access were associated with lower odds of portal access. Younger age and more clinical encounters were associated with higher odds of portal access. Of those with portal access, 25% were persistent users. Using multiple modalities for portal access, being middle-aged, and having more clinical encounters were associated with persistent portal use. Conclusion Patient and structural factors affect portal access and use and may exacerbate disparities in electronic health record–based cancer symptom surveillance and management.

Abstract Background Physical activity is associated with decreased breast cancer recurrence and mortality, as well as fewer treatment-related symptoms. Nevertheless, most breast cancer survivors do not meet physical activity guidelines. The purpose of this manuscript is to characterize physical activity trends over time in breast cancer survivors. Methods Mayo Clinic Breast Disease Registry participants received surveys at baseline and at 1 and 4 years after diagnosis; breast cancer recurrence and/or metastatic disease were exclusion criteria. Participants were considered to be meeting guidelines if they self-reported at least 150 minutes of moderate-intensity (eg, fast walking) and/or strenuous (eg, jogging) physical activity per week. Statistical analyses include analysis of covariance methods, paired t tests, conditional logistic regression models, and McNemar tests of homogeneity. Results A total of 171 participants were included in the analysis. The amount of total physical activity decreased over time (P = .07). Mild-intensity physical activity (eg, easy walking) decreased most over time (P = .05). Among participants aged 18-49 years, mild-intensity (P = .05) and moderate-intensity (P = .02) physical activity decreased over time. Strenuous-intensity physical activity levels decreased over time among participants with a normal body mass index (P = .002) and with obesity (P = .01). Conclusions We found a trend-level decrease in total physical activity over time, driven mostly by a decrease in mild-intensity physical activity. Young breast cancer survivors are especially likely to reduce their physical activity over time. Further research on implementing physical activity guidelines in clinical practice is warranted.

Abstract Embedded pragmatic clinical trials (ePCTs) play a vital role in addressing current population health problems, and their use of electronic health record (EHR) systems promises efficiencies that will increase the speed and volume of relevant and generalizable research. However, as the number of ePCTs using EHR-derived data grows, so does the risk that research will become more vulnerable to biases due to differences in data capture and access to care for different subsets of the population, thereby propagating inequities in health and the healthcare system. We identify 3 challenges—incomplete and variable capture of data on social determinants of health, lack of representation of vulnerable populations that do not access or receive treatment, and data loss due to variable use of technology—that exacerbate bias when working with EHR data and offer recommendations and examples of ways to actively mitigate bias.

e13552 Background: Real-world data from Electronic Health Records (EHR) have been widely used for patient identification to build study cohorts for clinical research. Traditionally, diagnosis codes in the EHR, such International Classification of Diseases (ICD), are used to identify the target patients. However, the accuracy of this approach is dependent on the accuracy of ICD coding, with potential errors especially for tumor types that are frequent locations for metastases (which may contribute to mis-coding). In this study, we attempted to develop a Machine Learning (ML) based approach on EHR data to improve the accuracy of identification of patients with lung cancer. Methods: We used survey respondents in the Enhanced, EHR-facilitated Cancer Symptom Control (E2C2, NCT03892967) cluster-randomized trial at Mayo Clinic as our initial pan-cancer cohort. E2C2 includes adults receiving Medical Hematology/Oncology care for a solid or liquid tumor at Mayo Clinic. We collected cancer diagnoses from the individually abstracted Mayo Clinic Cancer Registry to annotate the cancer type for the patients. Lung cancer related ICD-9 (162.X) and ICD-10 (C34.X) codes were used to build a search query on the Mayo’s EHR to find target patients from the E2C2 cohort, and to investigate the ICD-based lung cancer patient identification performance. Diagnosis, radiation oncology treatment (CPT 77261 - 77799), and antineoplastic drug administration data were collected from EHR as variables. Logistic Regression (LR), Support vector machine (SVM), Random Forest (RF), and eXtreme Gradient Boosting (XGB) were selected to build models for lung cancer patient identification. 10-fold cross-validation was implemented to assess the models. Precision, Recall, F1 Score, and area under the curve (AUC) were selected to measure the performance. Results: We collected 13,893 patients with a specific cancer diagnosis, and 1,394 were identified as having lung cancer. The identification performance across different methods is shown. The ICD-based method only had 0.65 precision. It means we collected a lot of false positive cases (other cancer but no lung cancer patient), as we mentioned in the background. SVM gets the best precision results, but its recall and F1 score are not good enough. XGB shows the best F1 Score and AUC, which also means this method achieved the best and most balanced performance. Conclusions: In this study, we found that XGB-based methods achieved the best identification performance for lung cancer. In the future, we will investigate if this is also true for the identification of other cancer types. [Table: see text]

Background. Mortality risk assessment before kidney transplantation (KT) is imperfect. An emerging risk factor for death in nontransplant populations is physiological age as determined by the application of artificial intelligence to the electrocardiogram (ECG). The aim of this study was to examine the relationship between ECG age and KT waitlist mortality. Methods. We applied a previously developed convolutional neural network to the ECGs of KT candidates evaluated 2014 to 2019 to determine ECG age. We used a Cox proportional hazard model to examine whether ECG age was associated with waitlist mortality. Results. Of the 2183 patients evaluated, 59.1% were male, 81.4% were white, and 11.4% died during follow-up. Mean ECG age was 59.0 ± 12.0 y and mean chronological age at ECG was 53.3 ± 13.6 y. After adjusting for chronological age, comorbidities, and other characteristics associated with mortality, each increase in ECG age of >10 y than the average ECG age for patients of a similar chronological age was associated with an increase in mortality risk (hazard ratio 3.59 per 10-y increase; 95% confidence interval, 2.06-5.72; P < 0.0001). Conclusions. ECG age is a risk factor for KT waitlist mortality. Determining ECG age through artificial intelligence may help guide risk-benefit assessment when evaluating candidates for KT.

Abstract We previously described the hypotheses, outcomes, design, and analysis for E2C2, a pragmatic stepped-wedge trial to assess an intervention to improve symptom control in patients with cancer. Subsequent consideration of the design and cohort led to the addition of a second primary hypothesis. This article describes and presents the rationale for this second hypothesis. This addendum also details a revised analytic approach, necessitated by inconsistencies in the original analytic plan. The design, outcomes, and other aspects of the protocol remain unchanged.