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ImportanceCetuximab-based and carboplatin-based chemoradiotherapy (CRT) are often used for patients with locally advanced head and neck cancer who are ineligible for cisplatin. There are no prospective head-to-head data comparing cetuximab-based and carboplatin-based regimens for radiosensitization.ObjectiveTo compare survival with cetuximab-based and carboplatin-based CRT in locally advanced head and neck squamous cell carcinoma (HNSCC).Design, Setting, and ParticipantsThis cohort study included US veterans who received a diagnosis of HNSCC between January 2006 and December 2020 and were treated with systemic therapy and radiation. Data cutoff was March 1, 2022 and data analysis was conducted from April-May 2022.ExposuresCisplatin, cetuximab, or carboplatin-based systemic therapy as captured in VA medication data and cancer registry.Main Outcomes and MeasuresOverall survival by systemic therapy was estimated using Kaplan-Meier methods. We used propensity score and inverse probability weighting to achieve covariate balance between cetuximab-treated and carboplatin-treated patients and used Cox regression to estimate cause-specific hazard ratios of death associated with carboplatin vs cetuximab. We also performed subgroup analyses of patients with oropharynx vs nonoropharynx primary sites.ResultsA total of 8290 patients (median [IQR] age, 63 [58-68] years; 8201 men [98.9%]; 1225 [15.8%] Black or African American and 6424 [82.6%] White individuals) with nonmetastatic HNSCC were treated with CRT with cisplatin (5566 [67%]), carboplatin (1231 [15%]), or cetuximab (1493 [18%]). Compared with cisplatin-treated patients, patients treated with carboplatin and cetuximab were older with worse performance status scores and higher comorbidity burden. Median (IQR) overall survival was 74.4 (22.3-162.2) months in patients treated with cisplatin radiotherapy (RT), 43.4 (15.3-123.8) months in patients treated with carboplatin RT, and 31.1 (12.4-87.8) months in patients treated with cetuximab RT. After propensity score and inverse probability weighting, carboplatin was associated with improved overall survival compared with cetuximab (cause-specific hazard ratio, 0.85; 95% CI, 0.78-0.93; P = .001). This difference was prominent in the oropharynx subgroup.Conclusions and RelevanceIn this cohort study of a US veteran population with HNSCC undergoing treatment with CRT, almost a third of patients were ineligible to receive treatment with cisplatin and received cetuximab-based or carboplatin-based radiosensitization. After propensity score matching, carboplatin-based systemic therapy was associated with 15% improvement in overall survival compared with cetuximab, suggesting that carboplatin may be the preferred radiosensitizer, particularly in oropharynx cancers.

Black Veterans have higher a incidence of localized and metastatic prostate cancer compared to White Veterans yet are underrepresented in reports of frequencies of somatic and germline alterations. This retrospective analysis of somatic and putative germline alterations was conducted in a large cohort of Veterans with prostate cancer (N = 835 Black, 1613 White) who underwent next generation sequencing through the VA Precision Oncology Program, which facilitates molecular testing for Veterans with metastatic cancer. No differences were observed in gene alterations for FDA approved targetable therapies (13.5% in Black Veterans vs. 15.5% in White Veterans, P = .21), nor in any potentially actionable alterations (25.5% vs. 28.7%, P =.1). Black Veterans had higher rates of BRAF (5.5% vs. 2.6%, P < .001) alterations, White Veterans TMPRSS2 fusions (27.2% vs. 11.7%, P < .0001). Putative germline alteration rates were higher in White Veterans (12.0% vs. 6.1%, P < .0001). Racial disparities in outcome are unlikely attributable to acquired somatic alterations in actionable pathways.

PURPOSE Prostate cancer (PCa) is among the leading causes of cancer deaths. While localized PCa has a 5-year survival rate approaching 100%, this rate drops to 31% for metastatic prostate cancer (mPCa). Thus, timely identification of mPCa is a crucial step toward measuring and improving access to innovations that reduce PCa mortality. Yet, methods to identify patients diagnosed with mPCa remain elusive. Cancer registries provide detailed data at diagnosis but are not updated throughout treatment. This study reports on the development and validation of a natural language processing (NLP) algorithm deployed on oncology, urology, and radiology clinical notes to identify patients with a diagnosis or history of mPCa in the Department of Veterans Affairs. PATIENTS AND METHODS Using a broad set of diagnosis and histology codes, the Veterans Affairs Corporate Data Warehouse was queried to identify all Veterans with PCa. An NLP algorithm was developed to identify patients with any history or progression of mPCa. The NLP algorithm was prototyped and developed iteratively using patient notes, grouped into development, training, and validation subsets. RESULTS A total of 1,144,610 Veterans were diagnosed with PCa between January 2000 and October 2020, among which 76,082 (6.6%) were identified by NLP as having mPCa at some point during their care. The NLP system performed with a specificity of 0.979 and sensitivity of 0.919. CONCLUSION Clinical documentation of mPCa is highly reliable. NLP can be leveraged to improve PCa data. When compared to other methods, NLP identified a significantly greater number of patients. NLP can be used to augment cancer registry data, facilitate research inquiries, and identify patients who may benefit from innovations in mPCa treatment.

6057 Background: Cardiovascular disease and stroke are important causes of long-term morbidity and mortality in patients with oropharyngeal squamous cell carcinoma (OPSCC). Cancer treatments including radiotherapy to the neck and chemotherapy have been associated with increased risk of stroke. In the era of treatment de-intensification for OPSCC, up-front surgical treatment has been proposed as one strategy that allows for de-escalation or avoidance of (chemo)radiotherapy. We sought to quantify the cumulative incidence of stroke in patients treated for non-metastatic OPSCC, and then evaluate whether patients receiving up-front surgery for OPSCC have decreased risk of stroke compared to those undergoing non-surgical treatment. Methods: We identified a cohort of 10,436 United States veterans diagnosed with non-metastatic OPSCC from 2000-2020, of whom 2,717 received up-front surgery (with or without perioperative radiotherapy or chemoradiotherapy) and 7,719 received non-surgical therapy (definitive radiotherapy or chemoradiotherapy). We estimated the cumulative incidence of stroke in this population, accounting for death as a competing risk. To assess the association between up-front surgery and risk of stroke, we generated a propensity score for the probability of receiving surgical treatment and used inverse probability weighting to construct pseudo-populations balanced on all potential confounders. Cox regression models of the inverse probability weighted population were used to estimate the cause-specific hazard ratio of stroke associated with surgical vs non-surgical treatment. Results: The 10-year cumulative incidence of stroke was 12.5% (95% CI 11.8-13.23) and death was 57.3% (95% CI 56.2-58.4). Up-front surgical patients who underwent perioperative (chemo)radiotherapy had shorter radiation and chemotherapy courses compared to non-surgical patients, suggestive of lower treatment intensity. Propensity score generation and inverse probability weighting yielded good overlap and covariate balance between surgical and non-surgical treatment groups. The inverse probability weighted cause-specific hazard ratio of stroke associated with up-front surgical treatment was 0.77 (95% CI 0.66-0.91, p = 0.002). This association was consistent across subgroups defined by age ( > /≤65 years) and baseline cardiovascular risk factors (hypertension, hyperlipidemia, diabetes). Conclusions: In over 10,000 US veterans with OPSCC, cumulative incidence of stroke was 12.5% at 10 years. Up-front surgical treatment was associated with a 23% reduced risk of stroke compared to definitive (chemo)radiotherapy. These findings present an important additional risk-benefit consideration to factor into treatment decisions and patient counseling, and should motivate future studies to examine cardiovascular events in this high-risk population.

Objective To best meet our point-of-care research (POC-R) needs, we developed ProjectFlow, a configurable, clinical research workflow management application. In this article, we describe ProjectFlow and how it is used to manage study processes for the Diuretic Comparison Project (DCP) and the Research Precision Oncology Program (RePOP). Materials and methods The Veterans Health Administration (VHA) is the largest integrated health care system in the United States. ProjectFlow is a flexible web-based workflow management tool specifically created to facilitate conduct of our clinical research initiatives within the VHA. The application was developed using the Grails web framework and allows researchers to create custom workflows using Business Process Model and Notation. Results As of January 2021, ProjectFlow has facilitated management of study recruitment, enrollment, randomization, and drug orders for over 10 000 patients for the DCP clinical trial. It has also helped us evaluate over 3800 patients for recruitment and enroll over 370 of them into RePOP for use in data sharing partnerships and predictive analytics aimed at optimizing cancer treatment in the VHA. Discussion The POC-R study design embeds research processes within day-to-day clinical care and leverages longitudinal electronic health record (EHR) data for study recruitment, monitoring, and outcome reporting. Software that allows flexibility in study workflow creation and integrates with enterprise EHR systems is critical to the success of POC-R. Conclusions We developed a flexible web-based informatics solution called ProjectFlow that supports custom research workflow configuration and has ability to integrate data from existing VHA EHR systems.

Background The risk factors associated with the stages of Coronavirus Disease-2019 (COVID-19) disease progression are not well known. We aim to identify risk factors specific to each state of COVID-19 progression from SARS-CoV-2 infection through death. Methods and results We included 648,202 participants from the Veteran Affairs Million Veteran Program (2011-). We identified characteristics and 1,809 ICD code-based phenotypes from the electronic health record. We used logistic regression to examine the association of age, sex, body mass index (BMI), race, and prevalent phenotypes to the stages of COVID-19 disease progression: infection, hospitalization, intensive care unit (ICU) admission, and 30-day mortality (separate models for each). Models were adjusted for age, sex, race, ethnicity, number of visit months and ICD codes, state infection rate and controlled for multiple testing using false discovery rate (≤0.1). As of August 10, 2020, 5,929 individuals were SARS-CoV-2 positive and among those, 1,463 (25%) were hospitalized, 579 (10%) were in ICU, and 398 (7%) died. We observed a lower risk in women vs. men for ICU and mortality (Odds Ratio (95% CI): 0.48 (0.30–0.76) and 0.59 (0.31–1.15), respectively) and a higher risk in Black vs. Other race patients for hospitalization and ICU (OR (95%CI): 1.53 (1.32–1.77) and 1.63 (1.32–2.02), respectively). We observed an increased risk of all COVID-19 disease states with older age and BMI ≥35 vs. 20–24 kg/m2. Renal failure, respiratory failure, morbid obesity, acid-base balance disorder, white blood cell diseases, hydronephrosis and bacterial infections were associated with an increased risk of ICU admissions; sepsis, chronic skin ulcers, acid-base balance disorder and acidosis were associated with mortality. Conclusions Older age, higher BMI, males and patients with a history of respiratory, kidney, bacterial or metabolic comorbidities experienced greater COVID-19 severity. Future studies to investigate the underlying mechanisms associated with these phenotype clusters and COVID-19 are warranted.

Introduction: CYP2C19 reduced function (RF) alleles (*2, *3) have been shown to impair clopidogrel effectiveness following percutaneous coronary intervention (PCI) in the setting of acute coronary syndromes (ACS); however, this association has not been explored in the Veterans Health Administration. Hypothesis: CYP2C19 RF alleles are associated with major adverse cardiac events (MACE) in patients treated with clopidogrel Methods: MACE and CYP2C19 genotype were determined in MVP participants who underwent PCI between 2009-2017 with 1 year follow-up. Age was restricted to Results: Among 4,490 Veterans (age 59.1 ± 5 years, 18% African American, 6% Hispanic, 44% ACS) who were prescribed clopidogrel following PCI, 1,261 (28%) had RF CYP2C19 alleles. The overall MACE rate was 7.0%. MACE was associated with ACS as initial presentation, diagnosis of diabetes, chronic kidney disease, peripheral vascular disease, congestive heart failure, history of stroke, prior MI. Among the subgroup presenting with ACS, the adjusted risk of MACE was greater in participants with RF alleles compared to those with wildtype alleles (HR 1.38, 95% CI 1.002-1.916). There was no impact of RF alleles on MACE risk in those in the non-ACS group (HR 1.00, 95% CI 0.702-1.429). Conclusions: These data from the largest integrated health system in the US demonstrate that in Veterans presenting with ACS undergoing PCI, there was a higher risk of MACE in CYP2C19 RF carriers prescribed clopidogrel vs. wildtype carriers. The impact of the CYP2C19-clopidogrel interaction was not observed in the non-ACS patients. Further studies should explore the role of pharmacogenetic testing in Veterans.

72 Background: Identifying cancer cases within the electronic health record (EHR) or claims data can be challenging because diagnosis codes are often entered into patient records during routine screenings or as “rule out” diagnosis codes when the patient is referred to a procedure. To improve accuracy of prostate cancer (PCa) case ascertainment, we compared algorithms that used diagnoses codes to natural language processing (NLP) tools applied to clinical notes and pathology reports to identify Veterans with prostate cancer (PCa). Methods: This is a retrospective observational cohort study using VA EHR data to identify veterans diagnosed with PCa between 2000 and 2020. Using International Classification of Diseases (ICD-10 CM or ICD-9 CM) diagnosis and procedure codes, we identified veterans who may have PCa. We deployed validated NLP tools to identify the presence of Gleason score, metastatic PCa, and castration sensitivity to identify evidence of PCa within the notes. We conducted a descriptive analysis to compare the results of algorithms that relied exclusively on diagnosis codes compared to use of NLP tools. Results: From 2000 through 2020,1,031,296 veterans had one or more PCa diagnosis code. This number decreased by 11% for each additional PCa diagnosis code required. When we required 4 or more PCa diagnosis codes to be present, only 746,350 veterans had PCa. When we deployed NLP tools to identify mention of a Gleason score or an indicator of mPCa, only 685,847 Veterans had these indicators of PCa, a 35% decrease in the number of PCa cases with a single diagnosis code. Chart review of patients with their first PCa diagnosis codes in 2019 and 4 or more codes in their records illustrated no evidence of Gleason score or mPCa disease in their EHR. Analysis of their pathology reports revealed that these patients had prostatic intraepithelial neoplasia or atypical small acinar proliferation and had not yet developed prostate cancer. Conclusions: Accurate ascertainment of PCa using EHR and claims data requires using NLP tools and clinical notes combined with structured data sources such as diagnosis codes. Relying on ICD diagnosis codes alone will overestimate the burden of PCa up to 30%.

47 Background: Practice guidelines have been modified to recommend hereditary and tumor gene mutation testing in patients with metastatic castration-resistant prostate cancer (mCRPC) to identify patients for molecularly targeted therapies. Identifying appropriate candidates for testing can be challenging in electronic health records and claims data. In this study, we used natural language processing (NLP) algorithms to identify veterans with mCRPC, reported gene mutation testing rates and identified factors associated with testing. Methods: This is a retrospective observational cohort study using NLP to identify veterans diagnosed with mCRPC between 2016 and 2020. Patient and facility characteristics were reported descriptively. Chi-square and t-tests were used to determine whether differences were statistically significant at a significance level of 0.05 based on receipt of testing. Generalized linear mixed models with binomial error distributions and logit links accounting for clustering by facility were used to determine which factors were independently associated with testing. Results: 9,282 veterans were diagnosed with mCRPC between 2016 and 2020, as determined by NLP algorithms identifying diagnosis of metastatic disease and castration-resistant disease. Among these patients, 381 died within 45 days of their diagnosis, and were excluded from analysis. In the analytic cohort of 8,901 veterans, 1,282 (14%) patients received testing. Of these, 1,041 (81%) received tumor tissue testing and 292 (23%) received hereditary testing. In bivariate analyses, age, race, ethnicity, Commission on Cancer (COC) facility certification, and facility complexity rating differed between veterans who received the test versus who did not (mean age of 73 versus 77, p < 0.0001; 30% versus 24% Black, p < 0.0001; 93% versus 92% non-Hispanic, p = 0.04; 64% versus 63% COC-certified facility, p = 0.04; and 59% versus 52% most complex facility, p < 0.0001). In multivariate analyses, older age and lower facility complexity rating were associated with lower odds of testing (for every 10-year increase in age, adjusted odds ratio [aOR], 95% confidence interval [CI]: 0.54, 0.50-0.58; Mid-high and low complexity facilities compared to highest complexity facilities: aOR, 95% CI: 0.52, 0.32-0.85 and 0.39, 0.22-0.71, respectively). Conclusions: Gene mutation testing in veterans with mCRPC is underutilized. Older age and being seen in a lower complexity facility are independently associated with a lower odds of testing. Patient and facility barriers to testing should be identified to improve guideline concordant care.

29 Background: Early-stage prostate cancer (PCa) is typically detected on prostate-specific antigen (PSA) screening and subsequent prostate biopsy, healthcare interventions that are considered routine elective procedures. The COVID-19 pandemic disrupted routine healthcare screenings and interventions. We sought to determine whether delayed screening and diagnostic workup of PCa was associated with increased rates of incident PCa, high-grade Gleason, and metastatic disease at diagnosis. Methods: We used the Corporate Data Warehouse of the Veterans Health Administration to collect PSA, prostate biopsy, PCa diagnosis, Gleason score, and metastasis information of White and Black Veterans aged 40 years or older newly diagnosed with PCa from January 2019 through June 2021. For each month, we calculated rates of PSA, prostate biopsy, and incident PCa per 100,000 men and plotted monthly rates by race. We performed descriptive analyses to compare age at first PSA, age at diagnosis, baseline PSA, Gleason scores, and metastasis pre- and post-January 2020, the month in which the U.S. declared COVID-19 a public health emergency. Results: The decrease in the rate of PSA screening immediately after January 2020 was similar in both White and Black Veterans (7% vs. 6%). However, the magnitude of reduction in the rate of prostate biopsy and the rate diagnosis of incident PCa were five times larger among Black Veterans compared to White Veterans (11% vs. 2% for both biopsy and diagnosis rates). Among the 17,771 White and 9,610 Black Veterans with incident PCa, the rate of Gleason of 4+3 or greater and the rate of metastatic disease at diagnosis increased three months after the pandemic in both race groups with comparable magnitude (2-3% increase in high-grade Gleason, 1% increase in metastatic disease). Conclusions: Utilization patterns indicated that while the decrease in PSA screening after the pandemic was similar in White and Black Veterans, reductions in prostate biopsy and diagnosis of incident PCa were five times greater in Blacks than Whites. Further research including risk-adjusted modeling is needed to determine whether Black Veterans were disproportionately affected by the pandemic-related disruptions in PSA screening and diagnostic workup of prostate cancer.[Table: see text]

178 Background: Prostate cancer comprises one third of male Veteran cancers and is their second leading cause of cancer death. Metastatic prostate cancer is lethal. Next Generation Sequencing (NGS) of somatic tumors is recommended for metastatic prostate to identify actionable alterations targeted with approved therapies. Veterans with prostate cancers harboring alterations in genes involved in the DNA damage response (e.g. BRCA1/2) or high microsatellite instability (MSI-High) may be eligible for PARP inhibitors or checkpoint blockade immunotherapy, respectively. Potential candidates may be identified for ongoing clinical trials of novel precision oncology approaches. Methods: This is a retrospective analysis of clinical, genomic, demographic data from Veterans with metastatic prostate cancer who underwent somatic NGS using the Foundation Medicine NGS platform from 2019-February 2021. To be included, prostate cancer was submitted diagnosis for the NGS testing and metastatic disease determined by the VINCI natural language processing tool. Variables included demographic, clinical, and pathological characteristics (self-identified race/ethnicity, age, rurality of residence, Gleason score, specimen site, other cancer diagnosis, mutation frequency). Primary outcome was mutation rates in homologous recombination (HR) genes under current FDA approval for olaparib (ATM, BARD1, BRCA1, BRCA2, BRIP1, CDK12, CHEK1, CHEK2, FANCL, PALB2, RAD51B, RAD51C, RAD51D, RAD54L) or MSI-High. Raw variant data, submitted diagnosis, and clinical data were extracted from the NGS reports and harmonized for further variant annotation. Variant data included chromosome, position, reference and alternate allele, total depth, variant allele depth, and quality scores. Variants were annotated using ANNOVAR. Likely oncogenic and oncogenic mutations were identified using OncoKB. Results: 1,597 Veterans with metastatic prostate cancer underwent FMI NGS testing (63% White, 33% African American, 4% other). Median age was 66 years, 78.6% of cases from >60 years. Of the 1,597 who underwent blood or tumor testing, at least one likely oncogenic mutation in an HR gene under FDA approval for olaparib was found in 369 (23.1%) of Veterans (19% of tissue-based tests, 32.9% of blood-based tests). Of 651 liquid biopsy tests with at least one HR gene mutation, 125 of 214 (52%) had mutations at a variant allele frequency (VAF)

60 Background: Veterans may benefit from promising innovations in treatments for mPCa. The Veterans Affairs (VA) and Prostate Cancer Foundation (PCF) leadership issued a challenge to identify, in real time, the national census of Veterans receiving care for mPCa. Administrative diagnostic and procedural coding do not accurately identify the risk status or disease state of prostate cancer (PCa). This study reports the development and validation of NLP tools deployed on clinical notes to identify risk status or disease state. Methods: Using diagnosis and histology codes, we queried the VA Corporate Data Warehouse to identify Veterans with prostate cancer. We included structured laboratory tests, medications, procedures, and surgeries related to prostate cancer diagnosis or treatment in the analysis. Using structured data, we identified 1000 likely mPCa cases and controls. Medical records were reviewed to confirm status and to extract term dictionaries related to cancer, anatomy, metastasis, and other diagnostic concepts. We went through several iterations of testing to refine and validate the NLP tool on a limited set of known cases and controls. We deployed the tool on all cancer, urology, pathology, and radiation oncology notes. Results: The NLP system was able to identify the patients' history of metastatic disease with 0.975 precision and 0.828 recall. Among the 1,081,137 Veterans with prostate cancer, NLP identified 63,222 (5.8%) with mPCa. There are 16,282 Veterans alive with mPCa. Mean age of diagnosis was 67 and 8,847 (54.3%) were diagnosed in the VA. Demographics were: White 9,756 (60%), Black 4,466 (27%), and other 2,060 (13%). Conclusions: NLP is a reliable tool for identifying Veterans who may benefit from novel innovations in mPCa diagnosis and treatment.[Table: see text]