Gatten, Clare A.

e13660 Background: We previously showed success with collection of structured data elements within the electronic health record using the minimum common oncology data elements (henceforth, mCODE tool) supported by soft-stop close visit validation (CVV) and staff support interventions in a limited cohort. Since the additional interventions are resource-intensive and difficult to apply across an entire department, we therefore implemented a department-wide roll-out supported by the least resource-intensive interventions while monitoring uptake. Methods: The mCODE tool collects five data elements: stage, disease status, performance status, intent of treatment, and intent to change therapy. The Department of Oncology providers received education on the mCODE tool and were asked to implement use at a faculty meeting; soft-stop CVV was enabled at that time. The percentage of data captured for each data element was collected from the notes of providers who did not participate in prior pilots for the 4 months post-implementation. mCODE tool usage was defined as entry of at least one data element except stage (as stage has long been a structured data element in Epic, can be entered independently of the mCODE tool, and many providers were entering stage prior to mCODE tool introduction). Results: 86 MDs and 20 APPs for a total of 106 providers were included, resulting in 14,973 visits and 8,745 unique patients. 73.6% of providers (n =78) utilized the mCODE tool. Of those who utilized the tool, median overall data capture across the four data elements was 24.0%. 22.6% of providers (n =24) achieved > 80% overall data capture and 32.1% (n =34) had over 50% overall data capture despite minimal intervention. 75.6% of MDs (n = 65) utilized the tool with a median data capture of 16.3% compared to 65% of APPs (n = 13) with a median data capture of 90.3%. Rates of data capture for individual data elements were similar. Conclusions: Use of soft-stop CVV appears to drive utilization of the mCODE tool but is insufficient to promote > 80% data capture for most providers without more intervention. Supportive interventions such as individualized assistance with incorporation into existing templates and directed communication from leadership remain necessary to promote use of structured data elements and will be implemented in sequence to understand how we achieve the target data capture ( > 80%) at scale.

3626 Background: For patients (pts) with dMMR stage III colon cancer, the addition of atezolizumab (atezo) to adjuvant 5-fluorouracil, leucovorin plus oxaliplatin (mFOLFOX6) significantly improved disease-free survival in ATOMIC (NCT02912559). Here we present HRQOL data. Methods: Pts with surgically resected stage III dMMR colon cancer were randomized (1:1) to receive mFOLFOX6 + atezo (840 mg IV q2 weeks) for 12 cycles (6 months [mo]) followed by atezo monotherapy for 13 cycles (12 mo total) versus mFOLFOX6 for 12 cycles. HRQOL was an exploratory endpoint measured using the FACT-C (includes FACT-G), FACT/GOG-NTX, EQ5D-5L, and PRO-CTCAE. Optional participation was by paper surveys at trial registration, prior to treatment cycles 4 and 7, and at 6, 12, and 36 mo after registration. Mean changes from baseline (BL) were compared between treatment arms using general linear mixed models (negative mean changes indicate worsening). At 6 mo, a two-sided 95% confidence interval excluding a difference of 1.9 points (FACT/GOG-NTX) and 6 points (FACT-C Trial Outcome Index [TOI]) favoring the mFOLFOX6 arm would indicate non-inferiority of the addition of atezo. Rates of side effect bother (FACT-G item GP5) and pt-reported adverse events (AEs) by PRO-CTCAE (composite scores, baseline adjusted) were compared using Fisher’s exact tests. Results: Of 712 randomized pts, 581 (285 atezo+mFOLFOX6; 296 mFOLFOX6) consented to participate in the survey. Pts completed 2398/3021 (79%) of expected surveys across all time points (1218/1489 [82%] atezo+mFOLFOX6; 1180/1532 [77%] mFOLFOX6). The difference in mean changes from BL between arms indicated non-inferiority of the addition of atezo at 6 mo (table); differences remained small at 12 and 36 mo. No significant differences in mean changes from BL between arms were found at 6, 12, or 36 mo for any scale. At 6 mo, 16% vs 20% of pts in each arm reported at least “quite a bit” of side effect bother (p=0.34), favoring atezo. Across cycles, atezo was associated with higher rates of pt-reported itchy skin (62% vs 48%), cough (53% vs 43%), and shortness of breath (51% vs 41%) [all p<0.05]. Conclusions: Addition of atezo to mFOLFOX6 slightly increases pt-reported AEs, but its effect on HRQOL is minimal and not clinically meaningful. Results support tolerability of atezo+mFOLFOX6 as standard of care adjuvant treatment for pts with dMMR stage III colon cancer. Support: UG1CA189823, U10CA180821, U10CA180882; Genentech, a member of the Roche group; https://acknowledgments.alliancefound.org. Clinical trial information: NCT02912559 . Scale Mo Mean change from BL – atezo+mFOLFOX6 Mean change from BL – mFOLFOX6 Diff (95% CI) FACT/GOG-NTX 6 -10.0 -10.6 0.6 (-0.8, 2.1) 12 -8.0 -9.4 1.4 (-0.2, 2.9) 36 -7.2 -6.9 -0.4 (-2.1, 1.4) FACT-C TOI 6 -4.1 -2.5 -1.6 (-3.7, 0.6) 12 2.1 1.8 0.3 (-1.9, 2.6) 36 0.7 1.8 -1.1 (-3.7, 1.5)

LBA1 Background: Standard adjuvant chemotherapy of stage III colon cancer consists of a fluoropyrimidine plus oxaliplatin. In patients (pts) with stage III colon cancer and deficient mismatch repair (dMMR), the benefit of an immune checkpoint inhibitor combined with adjuvant chemotherapy is unknown. The phase III ATOMIC trial (NCT02912559) was conducted to determine whether atezolizumab (atezo), an anti-PD-L1 antibody, can improve pt outcomes when added to adjuvant 5-fluorouracil, leucovorin plus oxaliplatin (mFOLFOX6) in pts with stage III dMMR tumors. Methods: We conducted an NCI-sponsored, multicenter and randomized phase III trial in pts with surgically resected stage III dMMR colon adenocarcinoma (any T, N 1,2 M 0 ). Pts, age > 12 years (yr), were accrued at NCTN sites and the German AIO. Tumor dMMR was determined by local immunohistochemistry and centrally verified. Pts were randomized 1:1 to mFOLFOX6 plus atezo (840 mg IV q2 weeks) for 12 cycles (6 months)[mo] followed by atezo monotherapy for 13 cycles (12 mo total) versus mFOLFOX6 alone for 12 cycles. Randomization stratification factors were N-stage (N 1 /N 1c vs N 2 ), T-stage (T 1 -T 3 vs T 4 ) and site (proximal vs distal). The primary endpoint was disease-free survival (DFS); secondary endpoints were overall survival and adverse event (AE) profile (CTCAE, PRO-CTCAE). Primary efficacy analysis was done in the intent-to-treat population; DFS was compared by arm (stratified log-rank test). Hazard ratio (HR) and 95% confidence interval (CI) were calculated using a stratified Cox model; 3-yr DFS was determined by Kaplan-Meier method. Among 700 pts., 165 DFS events with two interim analyses (50% , 75% of events) yielded 90% power to detect HR 0.6 (3-yr DFS 75% vs. 84.2%) assuming exponential survival and 1-sided alpha (0.025). Results: From 9/2017 to 1/2023, 712 pts were randomized (1 pediatric) to either atezo plus mFOLFOX6 (n= 355; atezo arm) or mFOLFOX6 (n= 357). Median pt age was 64 yr. 55.1% were female. Among tumors, 83.8% were proximal, 46.1% were clinical low risk (T 1-3 N 1 ) and 53.9% high risk (T 4 and/or N 2 ). At the second interim analysis, median pt follow-up was 37.2 mo (interquartile range, 24.2 to 55.5) and 124 DFS events were observed. Three-year DFS was 86.4 % (95% CI, 81.8 to 89.9) in the atezo arm and 76.6 % (95% CI, 71.3 to 81.0) in the mFOLFOX6 arm (HR, 0.50; 95% CI, 0.35 to 0.72). Stratified log-rank p-value was <0.0001, crossing the pre-specified efficacy boundary of 0.009. Efficacy for the atezo arm was consistent across subgroups, including pts >70 yr and low- and high-risk groups. Treatment-related > grade 3 AEs occurred in 71.7 % of pts in the atezo arm vs 62.1 % in the mFOLFOX6 arm. Conclusion: The addition of atezolizumab to mFOLFOX6 significantly improved DFS and should be considered the new adjuvant standard of care for patients with dMMR stage III colon cancer. Support: U10CA180821, U10CA180882, U24CA196171; Genentech, a member of the Roche group; https://acknowledgments.alliancefound.org. Clinical trial information: NCT02912559 .

Abstract Background A plant-based diet is associated with better survival among patients with nonmetastatic colorectal cancer (CRC), but its association in metastatic CRC is unknown. Methods Using an National Cancer Institute–sponsored trial (CALGB/SWOG 80405), we included 1284 patients who completed validated food frequency questionnaires at the initiation of metastatic CRC treatment. We calculated 3 indices: overall plant-based diet index (PDI), which emphasized consumption of all plant foods while reducing animal food intake; healthful plant-based diet index (hPDI), which emphasized consumption of healthful plant foods such as whole grains, fruits, and vegetables; and unhealthful plant-based diet index (uPDI), which emphasized consumption of less healthful plant foods such as fruit juices, refined grains, and sugar-sweetened beverages. We estimated the associations of 3 indices (quintiles) with overall survival (OS) and progression-free survival (PFS) using multivariable Cox proportional hazards regression. Results We observed 1100 deaths and 1204 progression events (median follow-up = 6.1 years). Compared with the lowest quintile, patients in the highest quintile of PDI had significantly better survival (hazard ratio [HR] for OS = 0.76 [0.62-0.94], Ptrend = .004; PFS = 0.81 [0.66-0.99], Ptrend = .09). Similar findings were observed for hPDI (HR for OS = 0.81 [0.65-1.01], Ptrend = .053; PFS = 0.80 [0.65-0.98], Ptrend = .04), whereas uPDI was not associated with worse survival (HR for OS = 1.16 [0.94-1.43], Ptrend = .21; PFS = 1.12 [0.92-1.36], Ptrend = .42). Conclusions Our study suggests that a plant-based diet, especially when rich in healthful plant foods, is associated with better survival among patients with metastatic CRC. The cause of survival benefits warrants further investigation.

AbstractIntroductionStructured data capture requires defined languages such as minimal Common Oncology Data Elements (mCODE). This pilot assessed the feasibility of capturing 5 mCODE categories (stage, disease status, performance status (PS), intent of therapy and intent to change therapy).MethodsA tool (SmartPhrase) using existing and custom structured data elements was Built to capture 4 data categories (disease status, PS, intent of therapy and intent to change therapy) typically documented as free‐text within notes. Existing functionality for stage was supported by the Build. Participant survey data, presence of data (per encounter), and time in chart were collected prior to go‐live and repeat timepoints. The anticipated outcome was capture of >50% sustained over time without undue burden.ResultsPre‐intervention (5‐weeks before go‐live), participants had 1390 encounters (1207 patients). The median percent capture across all participants was 32% for stage; no structured data was available for other categories pre‐intervention. During a 6‐month pilot with 14 participants across three sites, 4995 encounters (3071 patients) occurred. The median percent capture across all participants and all post‐intervention months increased to 64% for stage and 81%–82% for the other data categories post‐intervention. No increase in participant time in chart was noted. Participants reported that data were meaningful to capture.ConclusionsStructured data can be captured (1) in real‐time, (2) sustained over time without (3) undue provider burden using note‐based tools. Our system is expanding the pilot, with integration of these data into clinical decision support, practice dashboards and potential for clinical trial matching.