Taylor, Bradley

Background Reductions in blood pressure (BP) control among patients with hypertension were observed early in the COVID‐19 pandemic. The degree to which BP control may have returned to prepandemic levels is unknown. Methods Individuals aged 18 to 85 years with hypertension from 17 health systems participating in the National Patient‐Centered Clinical Research Network were identified using electronic health record data collected as part of routine care. BP control (percentage of patients whose most recent BP measurement was <140/<90 mm Hg) was estimated in a series of 12‐month rolling measurement periods from 2017 through 2022 (January 1, 2017 through December 31, 2017; April 1, 2017 through March 31, 2018; … January 1, 2022 through December 31, 2022). Differences in average BP control between 2022 (January 1, 2022 through December 31, 2022) and 2019 (January 1, 2019 through December 31, 2019) were estimated overall (adjusted for age, sex, and race and ethnicity) and by race and ethnicity (adjusted for age and sex). Results Our sample included 1 193 314 persons with hypertension in 2019 (48.9% aged 65–85 years, 52.9% men, 66.2% non‐Hispanic White) and 1 499 418 individuals in 2022 (50.6% aged 65–85 years, 47.1% men, 62.7% non‐Hispanic White). The weighted average BP control dropped from 65.3% in 2019 to 61.8% in 2020 and then partially recovered to 62.6% in 2022 (adjusted mean difference, −2.6 percentage points [95% CI, −5.0 to −0.2]). Non‐Hispanic Asian individuals experienced the largest temporal drop in BP control, declining from 68.4% in 2019 to 63.9% in 2022. Conclusions BP control was disrupted during the COVID‐19 pandemic and had not fully rebounded to prepandemic levels by the end of 2022. Continued surveillance is needed to determine whether the decline in BP control will persist and will result in future adverse cardiovascular events.

Background Cardiovascular disease (CVD) is a leading cause of death in cancer survivors. Predicting CVD risk in this population remains challenging. Risk prediction models that use machine learning algorithms could provide an objective method for accurate risk prediction to facilitate the prevention and management of CVD in cancer survivors. We evaluated previously tested machine learning algorithms and logistic regression (regularized by machine learning methods), in addition to testing and validating newer, more complex machine learning algorithms, for CVD prediction in cancer survivors. Methods This multicenter study used a database of 3835 multiracial cancer survivors with 89 clinical, laboratory, and echocardiographic features over 20 years. Models were trained using repeated random and time‐split samples and tested on a separate cohort of 329 patients. Model performance was assessed using the area under the receiver operating characteristic curve. Results Regularized logistic regression achieved an area under the receiver operating characteristic curve of 0.845 (heart failure), 0.783 (atrial fibrillation), 0.792 (coronary artery disease), and 0.806 (composite CVD). These are comparable to 0.837 and 0.848 for heart failure, using Bayesian additive regression tree and random forest as more advanced machine learning models, respectively. De novo composite CVD (post‐cancer diagnosis) was also predicted with an area under the receiver operating characteristic curve of 0.826 using regularized logistic regression, compared with 0.735 and 0.802 using decision tree and random forest, respectively. Conclusions Regularized logistic regression and advanced machine learning models demonstrated similar predictive performance, with institutional transferability. These tools may support risk stratification and prevention strategies in cardio‐oncology using longitudinal data. Registration URL: https://www.clinicaltrials.gov ; Unique identifier: NCT05377320.

596 Background: Genomic alterations (GA) that drive cancer development and predict therapeutic response remain elusive in patients (pts) with KRAS WT PDAC. We interrogated our institutional database to identify actionable GAs in pts with metastatic, KRAS WT PDAC and analyzed the therapeutic impact of matched TT. Methods: We reviewed electronic medical records of KRAS WT PDAC pts (n=24) who underwent comprehensive genomic profiling (CGP) utilizing Foundation One CDx (25.0%) or Tempus (75.0%) between 2015-2021. Duration of response (DOR) was calculated from date of treatment (Tx) initiation to Tx discontinuation. Overall survival (OS) was measured from the date of the diagnosis (Dx) of advanced disease (AD) to death or last follow-up. OS was estimated using the Kaplan-Meier method, with at-risk periods left-truncated at the time of CGP. The effect of covariates on survival was evaluated using Cox proportional hazards regression. Results: Of the 24 KRAS WT pts, 14 (58%) had AD: 8 (57%) pts had metastatic disease at or shortly after Dx, 6 (43%) pts developed metachronous recurrence. Median age at Dx for pts with AD was 65, and 57% were female. Seven of 14 pts with AD (50%) had highly actionable GA (HAGA), (Table). Pts with HAGA demonstrated durable responses to TT (Table) with manageable toxicities. Pts with HAGA had a median OS of 28 mo compared to 5.9 mo for those without (Hazard Ratio = 0.47, p = 0.33). Conclusions: The sustained therapeutic benefit noted with TT matched to HAGA in pts with KRAS WT PDAC underscores the need for systematic interrogation of the somatic genome in PDAC pts. Optimal sequencing of cytotoxic therapy with TT and its impact on modulating clonal selection pressure in pts with KRAS WT PDAC merits prospective evaluation.[Table: see text]

191 Background: Somatic alterations in KRAS and BRAF have prognostic as well as predictive impact in pts with mCRC; however, the differential impact of various somatic alterations in these genes need further characterization. We analyzed the prognostic impact of specific somatic mutations in K RAS and BRAF in mCRC pts. Methods: We retrospectively reviewed the electronic medical records of pts with mCRC at our institution who underwent comprehensive genomic profiling (CGP) utilizing the Foundation One assay. Prevalence of genetic alterations was estimated using proportions and compared between groups using a chi-squared test. Patients were followed for survival from metastatic diagnosis until death or last follow-up, with left truncation at the time of CGP. Kaplan-Meier estimates were used to estimate overall survival, and groups were compared using a Cox-regression based likelihood ratio test. Results: 192 pts were identified - median age at diagnosis was 55 years, 62% (119/192) presented with metachronous metastatic disease, and 28% (54/192) had a rectal primary. Somatic mutations in KRAS were found in 49% (95/192) pts, and 53% (50/95) had a left sided primary (p = 0.3). Majority of the KRAS mutations localized to codon 12 (72/95 -76%), KRAS G12C comprised 12% (11/95). Median Overall Survival (mOS) of KRAS mutated pts was 3.0 years compared to 3.5 years for KRAS wild type (WT) pts (p = 0.5). Median OS of pts with different KRAS mutations were as follows: codon 12 mutations (excluding G12C) - 2.7 years; KRAS G12C – 5.2 years; non-codon 12 KRAS mutations - 4.8 years. BRAF mutations were identified in 7.8% (15/192) pts, and 67% (10/15) had a right sided primary (p = 0.062). BRAF V600E represented the most common alteration in BRAF – 87% (13/15). Patients with BRAF mutation had a mOS of 1.8 years compared to 3.1 years for BRAF WT pts (p = 0.2). Median OS of pts with different BRAF mutations were as follows: BRAF V600E – 1.8 years and BRAF non V600E - 2.1 years (p = 0.4). Conclusions: The numerically higher mOS in pts with KRAS G12C and non-codon 12 KRAS mutations merit further biologic characterization with functional assays. Individualized therapeutic strategies must be conceptualized for mCRC pts with specific RAS/BRAF mutations, considering their widely disparate prognosis and putative downstream signaling mechanisms. Dynamic molecular simulation to understand conformational changes in proteins associated with specific mutations will be pivotal to optimizing precision therapeutic strategies.

4136 Background: The genomic alterations which characterize PDAC holds great promise for novel therapeutic interventions. Constitutive signaling via mutated KRAS is considered the signature pathognomonic alteration in PDAC, less than 10% of patients (pts) have tumors which are KRAS wild type (WT). We retrospectively reviewed our institutional genomic database to characterize PDAC pts with KRAS WT tumors. Methods: We reviewed electronic medical records of PDAC pts who underwent comprehensive genomic profiling (CPG) utilizing Foundation One CDx (50.6%) or TEMPUS (49.4%) between 2015-2020. Demographic and disease characteristics were compared between cohorts using Wilcoxon rank-sum test or chi-square tests. Left truncation at the time of CGP was used to account for the time of entry into the study cohort. Kaplan-Meier method was used for survival curve estimation, and log-rank test was used for between-group comparison. Cox regression was used to adjust for confounders. Results: We identified 235 patients: median age at diagnosis was 65 years and 52% were male. Clinical stages at diagnosis were localized (resectable/borderline resectable), locally advanced, or metastatic in 105 (44.7%), 61 (26.0%), and 69 (29.4%) patients, respectively. KRAS status was mutated in 212 (90%) patients: the most common alterations being G12D (48%), G12V (28%) and G12R (14%). KRAS WT status was noted in 23 (9.8%) pts, actionable genomic alterations in this subgroup are summarized in the table. Baseline demographic and treatment characteristics were similar between patients with KRAS mutated and WT tumors. Of the 23 patients with KRAS WT tumors, 16 (69.6%) completed all planned curative intent therapy compared to 121 (57.3%) of the 212 KRAS mutated pts (p=0.26). Median Overall Survival of patients with KRAS mutated tumors was 18.6 months compared to 44.1 months for WT pts (p=0.03). Adjusting for stage, WT vs. mutated status was associated with a 62% decreased hazard of death (HR 0.38 [0.18-0.83]; p=0.016). Conclusions: Patients with KRAS WT PDAC appear to have a distinct biology compared to those with KRAS mutations, meriting exploration in larger data sets. Further, comprehensive whole genome or transcriptomic characterization of KRAS WT tumors is necessary to identify putative driver alterations as well as actionable therapeutic targets.[Table: see text]