Citations

e18790 Background: Patients with SARS-CoV-2 with a diagnosis of cancer have increased risk of severe COVID-19 outcomes compared to patients without cancer. However, little is known regarding outcomes of patients with COVID-19 and cancer in the setting of human immunodeficiency virus (HIV). Given the unique risks of this population, we sought to understand COVID-19 outcomes using registry data. Methods: This is a descriptive research study utilizing the CCC19 registry, an international multi-institutional registry with healthcare provider-reported cases of patients with cancer and COVID-19. Between March 2020-December 2021, 116 persons with HIV (PWH) and 10,642 persons without HIV (PWOH) with laboratory-confirmed SARS-CoV-2 infection were identified as eligible for the analysis. Results: Median follow-up time for both groups was 90 days, with interquartile range (IQR) 30-180 days. Most PWH were actively receiving antiretroviral therapy (ART) at the time of COVID-19 diagnosis, with 71% (n = 82) having named drug information available; bictegravir/emtricitabine/tenofovir was the most common ART (n = 25). PWH were of younger age (median 57.5 yrs [IQR 46.5-63.25] vs 65 yrs [IQR 55-74]), male (81% vs 47%), and either non-Hispanic Black or Hispanic (71% vs 34%) compared to PWOH. 12% of PWH (n = 14) were current smokers compared to 6% of PWOH (n = 638), and more than half in each group were never smokers (51% of PWH and 53% of PWOH). The following comorbidities were identified in PWH vs PWOH: cardiovascular (16% vs 20%), pulmonary (16% vs 20%), renal (15% vs 14%), and diabetes mellitus (18% vs 27%). A higher proportion of PWH had hematologic malignancy compared to PWOH (33% vs 19%). More PWH had active cancer which was progressing at the time of SARS-CoV-2 infection compared to PWOH (24% vs 14%). 44% of PWH (n = 51) had received active systemic anticancer therapy within the 3 months preceding SARS-CoV-2 infection (including cytotoxic, targeted, endocrine therapies, and immunotherapy) compared to 51% of PWOH (n = 5,420). PWH had an increased rate of hospitalization (58% vs 55%) compared to PWOH. Although a lower proportion of PWH required supplemental oxygen during hospitalization compared to PWOH (34% vs 38%) and ICU admission rates were identical between the two groups (16% vs 16%), PWH had an increased rate of mechanical ventilation (14% vs 10%) and death (24% vs 18%) compared to PWOH. Conclusions: This is the first known study describing outcomes of patients with cancer and COVID-19 in the PWH population from a large multinational dataset. PWH have characteristics associated with adverse outcomes in prior analyses (male sex, non-Hispanic Black or Hispanic, hematologic malignancy, progressing cancer) but are notably younger and have fewer comorbidities. HIV infection may portend increased risk of severe COVID-19 and death; however, additional analyses, including multivariable regression, are warranted.

TPS4177 Background: The anti–PD-L1 antibody atezolizumab (ATEZO) prevents PD-L1 from interacting with PD-1 and B7.1, thus reinvigorating antitumor T cell activity. Anti-VEGF bevacizumab (BEV) increases dendritic cell maturation, enhances T cell infiltration, and reduces myeloid-derived suppressor cells and regulatory T cells in tumors. ATEZO + BEV is FDA approved for first-line treatment of advanced HCC based on the IMbrave 150 study. Locoregional radiotherapy (e.g., Y90 TARE) enhances the diversity of the intratumoral T cell receptor repertoire and increases tumor antigen release. We hypothesize that the Y90 TARE + BEV + ATEZO combination induces synergistic tumor killing and prolongs progression-free survival in patients (pts) receiving Y90 TARE (HR = 0.6 when compared to Y-90 TARE alone). Methods: Eligible pts have HCC that cannot be surgically resected (confirmed by pathology review), is at least BCLC stage B, and is outside Milan criteria. Other requirements include ECOG PS of 0-1 at screening, measurable disease by RECIST 1.1, no prior systemic therapy, and FLR estimated at ≥ 40% post local therapy. Pts must have a pretreatment liver biopsy taken and then be randomized 1:1 to TARE (Arm A) or TARE + ATEZO + BEV (Arm B). Pts will have TARE mapping followed by TARE treatment. In Arm B, pts will begin TARE treatment followed by BEV + ATEZO (4 wks [± 1 wk] later). Pts will have abdominal MRI or CT scans every 12 weeks and CT scans of the chest every 24 wks. The primary study objective is to assess and compare pts' progression-free survival (per mRECIST 1.1) in each study arm. The main secondary objective is to determine the safety and tolerability (CTCAE v5) of pts in Arm B. Exploratory objectives are to define the use of cellular and circulating biomarkers in the prediction of improved clinical outcomes of pts in Arm B. Symptoms experienced by pts in both arms using patient-reported outcomes will be assessed. Disease progression will be captured by both RECIST 1.1 and mRECIST. We plan to assess the safety of Y90 TARE + BEV + ATEZO in the first 10 pts randomized to Arm B for two cycles, and if there are no grade ≥ 3 unexpected toxicities possibly, probably, or definitely related to combined TARE + BEV + ATEZO, continue to accrue 128 pts in total (current enrollment n- = 5). Pts will continue study treatment (Arm B) for a total of 24 months from initiation of TARE or until intolerable toxicity or disease progression occur, whichever is earlier. Enrollment began in September 2020. Clinical trial information: NCT04541173.

Abstract Internal validation is the most popular evaluation strategy used for drug–target predictive models. The simple random shuffling in the cross-validation, however, is not always ideal to handle large, diverse and copious datasets as it could potentially introduce bias. Hence, these predictive models cannot be comprehensively evaluated to provide insight into their general performance on a variety of use-cases (e.g. permutations of different levels of connectiveness and categories in drug and target space, as well as validations based on different data sources). In this work, we introduce a benchmark, BETA, that aims to address this gap by (i) providing an extensive multipartite network consisting of 0.97 million biomedical concepts and 8.5 million associations, in addition to 62 million drug–drug and protein–protein similarities and (ii) presenting evaluation strategies that reflect seven cases (i.e. general, screening with different connectivity, target and drug screening based on categories, searching for specific drugs and targets and drug repurposing for specific diseases), a total of seven Tests (consisting of 344 Tasks in total) across multiple sampling and validation strategies. Six state-of-the-art methods covering two broad input data types (chemical structure- and gene sequence-based and network-based) were tested across all the developed Tasks. The best-worst performing cases have been analyzed to demonstrate the ability of the proposed benchmark to identify limitations of the tested methods for running over the benchmark tasks. The results highlight BETA as a benchmark in the selection of computational strategies for drug repurposing and target discovery.

Introduction: The Framingham Heart Study Dementia Risk Score (FDRS) was developed in a general population of elderly individuals. It is unknown how the FDRS variables predict dementia in heart failure (HF) and atrial fibrillation (AF) populations. Thus, we aimed to evaluate the predictive ability of the FDRS variables in population-based cohorts of individuals with HF and AF and to determine whether the addition of other comorbidities and risk factors improves risk prediction for dementia. Methods: Residents aged ≥50 from 7 southeast MN counties with a first diagnosis of HF (ICD-9 428/ICD-10 I50) or AF (ICD-9 427.3/ICD-10 I48 with AF on electrocardiogram/Holter monitor) between 1/1/2013 and 12/31/2017 were identified. Patients with dementia (defined by ICD codes) prior to or within 6 months after their index event and patients who died within 6 months after index were excluded. For both cohorts, models were constructed to predict dementia ≥ 6 months post index including the variables in the FDRS (age, marital status, BMI, stroke/TIA, diabetes and cancer). Additional comorbidities and risk factors were added to the models. For all models c-statistics were calculated. Results: Among 3,291 patients with HF (mean age 76 years, 53% male), 626 cases of dementia occurred and among 4,107 patients with AF (mean age 74 years, 57% male), 736 cases of dementia occurred. Among patients with HF, the FDRS variables predicted dementia with a c-statistic of 0.68 (Table). Adding additonal comorbidities and risk factors impoved the c-statistic slightly to 0.69 (p=0.01). Among patients with AF, the FDRS variables also performed well (c-stastic: 0.73; Table) and adding other comorbidities and risk factors improved the performance (c-statistic: 0.75; p<0.01). Conclusions: The variables from the FDRS predict dementia well in both HF and AF populations. The addition of comorbidities and other CVD risk factors only modestly improved prediction, thus indicating that the FDRS variables are appropriate to predict dementia in patients with HF and AF.

Objective We examined the association between stay-at-home order implementation and the incidence of COVID-19 infections and deaths in rural versus urban counties of the United States. Design We used an interrupted time-series analysis using a mixed effects zero-inflated Poisson model with random intercept by county and standardised by population to examine the associations between stay-at-home orders and county-level counts of daily new COVID-19 cases and deaths in rural versus urban counties between 22 January 2020 and 10 June 2020. We secondarily examined the association between stay-at-home orders and mobility in rural versus urban counties using Google Community Mobility Reports. Interventions Issuance of stay-at-home orders. Primary and secondary outcome measures Co-primary outcomes were COVID-19 daily incidence of cases (14-day lagged) and mortality (26-day lagged). Secondary outcome was mobility. Results Stay-at-home orders were implemented later (median 30 March 2020 vs 28 March 2020) and were shorter in duration (median 35 vs 54 days) in rural compared with urban counties. Indoor mobility was, on average, 2.6%–6.9% higher in rural than urban counties both during and after stay-at-home orders. Compared with the baseline (pre-stay-at-home) period, the number of new COVID-19 cases increased under stay-at-home by incidence risk ratio (IRR) 1.60 (95% CI, 1.57 to 1.64) in rural and 1.36 (95% CI, 1.30 to 1.42) in urban counties, while the number of new COVID-19 deaths increased by IRR 14.21 (95% CI, 11.02 to 18.34) in rural and IRR 2.93 in urban counties (95% CI, 1.82 to 4.73). For each day under stay-at-home orders, the number of new cases changed by a factor of 0.982 (95% CI, 0.981 to 0.982) in rural and 0.952 (95% CI, 0.951 to 0.953) in urban counties compared with prior to stay-at-home, while number of new deaths changed by a factor of 0.977 (95% CI, 0.976 to 0.977) in rural counties and 0.935 (95% CI, 0.933 to 0.936) in urban counties. Each day after stay-at-home orders expired, the number of new cases changed by a factor of 0.995 (95% CI, 0.994 to 0.995) in rural and 0.997 (95% CI, 0.995 to 0.999) in urban counties compared with prior to stay-at-home, while number of new deaths changed by a factor of 0.969 (95% CI, 0.968 to 0.970) in rural counties and 0.928 (95% CI, 0.926 to 0.929) in urban counties. Conclusion Stay-at-home orders decreased mobility, slowed the spread of COVID-19 and mitigated COVID-19 mortality, but did so less effectively in rural than in urban counties. This necessitates a critical re-evaluation of how stay-at-home orders are designed, communicated and implemented in rural areas.

Human genetic studies support an inverse causal relationship between leukocyte telomere length (LTL) and coronary artery disease (CAD), but directionally mixed effects for LTL and diverse malignancies. Clonal hematopoiesis of indeterminate potential (CHIP), characterized by expansion of hematopoietic cells bearing leukemogenic mutations, predisposes both hematologic malignancy and CAD. TERT (which encodes telomerase reverse transcriptase) is the most significantly associated germline locus for CHIP in genome-wide association studies. Here, we investigated the relationship between CHIP, LTL, and CAD in the Trans-Omics for Precision Medicine (TOPMed) program ( n = 63,302) and UK Biobank ( n = 47,080). Bidirectional Mendelian randomization studies were consistent with longer genetically imputed LTL increasing propensity to develop CHIP, but CHIP then, in turn, hastens to shorten measured LTL (mLTL). We also demonstrated evidence of modest mediation between CHIP and CAD by mLTL. Our data promote an understanding of potential causal relationships across CHIP and LTL toward prevention of CAD.

Background Electronic medical records are increasingly used to identify disease cohorts; however, computable phenotypes using electronic medical record data are often unable to distinguish between prevalent and incident cases. Methods and Results We identified all Olmsted County, Minnesota residents aged ≥18 with a first‐ever International Classification of Diseases, Ninth Revision (ICD‐9) diagnostic code for atrial fibrillation or atrial flutter from 2000 to 2014 (N=6177), and a random sample with an International Classification of Diseases, Tenth Revision (ICD‐10) code from 2016 to 2018 (N=200). Trained nurse abstractors reviewed all medical records to validate the events and ascertain the date of onset (incidence date). Various algorithms based on number and types of codes (inpatient/outpatient), medications, and procedures were evaluated. Positive predictive value (PPV) and sensitivity of the algorithms were calculated. The lowest PPV was observed for 1 code (64.4%), and the highest PPV was observed for 2 codes (any type) >7 days apart but within 1 year (71.6%). Requiring either 1 inpatient or 2 outpatient codes separated by >7 days but within 1 year had the best balance between PPV (69.9%) and sensitivity (95.5%). PPVs were slightly higher using ICD‐10 codes. Requiring an anticoagulant or antiarrhythmic prescription or electrical cardioversion in addition to diagnostic code(s) modestly improved the PPVs at the expense of large reductions in sensitivity. Conclusions We developed simple, exportable, computable phenotypes for atrial fibrillation using structured electronic medical record data. However, use of diagnostic codes to identify incident atrial fibrillation is prone to some misclassification. Further study is warranted to determine whether more complex phenotypes, including unstructured data sources or using machine learning techniques, may improve the accuracy of identifying incident atrial fibrillation.

INTRODUCTION: Despite studies showing improved safety, efficacy, and cost-effectiveness of endoscopic resection for nonmalignant colorectal polyps, colectomy rates for nonmalignant colorectal polyps have been increasing in the United States and Europe. Given this alarming trend, we aimed to investigate whether colectomy rates for nonmalignant colorectal polyps are increasing or declining in a large, integrated, community-based healthcare system with access to advanced endoscopic resection procedures. METHODS: We identified all individuals aged 50–85 years who underwent a colonoscopy between 2008 and 2018 and were diagnosed with a nonmalignant colorectal polyp(s) at the Kaiser Permanente Northern California integrated healthcare system. Among these individuals, we identified those who underwent a colectomy for nonmalignant colorectal polyps within 12 months after the colonoscopy. We calculated annual colectomy rates for nonmalignant colorectal polyps and stratified rates by age, sex, and race and ethnicity. Changes in rates over time were tested by the Cochran-Armitage test for a linear trend. RESULTS: Among 229,730 patients who were diagnosed with nonmalignant colorectal polyps between 2008 and 2018, 1,611 patients underwent a colectomy. Colectomy rates for nonmalignant colorectal polyps decreased significantly from 125 per 10,000 patients with nonmalignant polyps in 2008 to 12 per 10,000 patients with nonmalignant polyps in 2018 ( P < 0.001 for trend). When stratified by age, sex, and race and ethnicity, colectomy rates for nonmalignant colorectal polyps also significantly declined from 2008 to 2018. DISCUSSION: In a large, ethnically diverse, community-based population in the United States, we found that colectomy rates for nonmalignant colorectal polyps declined significantly over the past decade likely because of the establishment of advanced endoscopy centers, improved care coordination, and an organized colorectal cancer screening program.

Abstract Objective. 4D-CBCT provides phase-resolved images valuable for radiomics analysis for outcome prediction throughout treatment courses. However, 4D-CBCT suffers from streak artifacts caused by under-sampling, which severely degrades the accuracy of radiomic features. Previously we developed group-patient-trained deep learning methods to enhance the 4D-CBCT quality for radiomics analysis, which was not optimized for individual patients. In this study, a patient-specific model was developed to further improve the accuracy of 4D-CBCT based radiomics analysis for individual patients. Approach. This patient-specific model was trained with intra-patient data. Specifically, patient planning 4D-CT was augmented through image translation, rotation, and deformation to generate 305 CT volumes from 10 volumes to simulate possible patient positions during the onboard image acquisition. 72 projections were simulated from 4D-CT for each phase and were used to reconstruct 4D-CBCT using FDK back-projection algorithm. The patient-specific model was trained using these 305 paired sets of patient-specific 4D-CT and 4D-CBCT data to enhance the 4D-CBCT image to match with 4D-CT images as ground truth. For model testing, 4D-CBCT were simulated from a separate set of 4D-CT scan images acquired from the same patient and were then enhanced by this patient-specific model. Radiomics features were then extracted from the testing 4D-CT, 4D-CBCT, and enhanced 4D-CBCT image sets for comparison. The patient-specific model was tested using 4 lung-SBRT patients’ data and compared with the performance of the group-based model. The impact of model dimensionality, region of interest (ROI) selection, and loss function on the model accuracy was also investigated. Main results. Compared with a group-based model, the patient-specific training model further improved the accuracy of radiomic features, especially for features with large errors in the group-based model. For example, the 3D whole-body and ROI loss-based patient-specific model reduces the errors of the first-order median feature by 83.67%, the wavelet LLL feature maximum by 91.98%, and the wavelet HLL skewness feature by 15.0% on average for the four patients tested. In addition, the patient-specific models with different dimensionality (2D versus 3D) or loss functions (L1 versus L1 + VGG + GAN) achieved comparable results for improving the radiomics accuracy. Using whole-body or whole-body+ROI L1 loss for the model achieved better results than using the ROI L1 loss alone as the loss function. Significance. This study demonstrated that the patient-specific model is more effective than the group-based model on improving the accuracy of the 4D-CBCT radiomic features analysis, which could potentially improve the precision for outcome prediction in radiotherapy.