Abiraterone or Enzalutamide for Patients With Metastatic Castration-Resistant Prostate Cancer.

Importance: Abiraterone acetate and enzalutamide are recommended as preferred treatments for metastatic castration-resistant prostate cancer (mCRPC), but differences in their relative efficacy are unclear due to a lack of head-to-head clinical trials. Clear guidance is needed for making informed mCRPC therapeutic choices. Objective: To compare clinical outcomes in patients with mCRPC treated with abiraterone acetate or enzalutamide. Design, Setting, and Participants: This retrospective, multicenter cohort study included patients with mCRPC in the US Department of Veterans Affairs health care system who initiated treatment with abiraterone acetate or enzalutamide between January 1, 2014, and October 30, 2022. Exposures: Abiraterone acetate or enzalutamide. Main Outcomes and Measures: The study used inverse probability of treatment weighting to balance baseline characteristics between patients initiating abiraterone acetate or enzalutamide and evaluated restricted mean survival time (RMST) differences in overall survival (OS), prostate cancer-specific survival (PCS), time to next treatment switching or death (TTS), and time to prostate-specific antigen (PSA) response (TTR) at different time points after treatment initiation. Results: The study included 5779 patients (median age, 74.42 years [IQR, 68.94-82.14 years]). Median follow-up was between 38 and 60 months. Patients initiating enzalutamide on average had longer OS than those initiating abiraterone acetate, with RMSTs of 24.29 months (95% CI, 23.58-24.99 months) and 23.38 months (95% CI, 22.85-23.92 months), respectively, and a difference in RMST of 0.90 months (95% CI, 0.02-1.79 months) at 4 years. Similarly, TTS and TTR were improved in patients initiating enzalutamide, with an RMST at 4 years of 1.95 months (95% CI, 0.92-2.99 months) longer for TTS and 3.57 months (95% CI, 1.76-5.38 months) shorter for TTR. For PCS, the RMST at 2 years was 0.48 months (95% CI, 0.01-0.95 months) longer. An examination of subgroups identified that enzalutamide initiation was associated with longer RMST in OS among patients without prior docetaxel treatment (1.14 months; 95% CI, 0.19-2.10 months) and in those with PSA doubling time of 3 months or longer (2.23 months; 95% CI, 0.81-3.66 months) but not among patients with prior docetaxel (-0.25 months; 95% CI, -2.59 to 2.09 months) or with PSA doubling time of less than 3 months (0.05 months; 95% CI, -1.05 to 1.15 months). Conclusions and Relevance: In this cohort study of patients with mCRPC, initiation of enzalutamide was associated with small but statistically significant improvements in OS, PCS, TTS, and TTR compared with initiation of abiraterone acetate. The improvements were more prominent in short-term outcomes, including TTS and TTR, and in patient subgroups without prior docetaxel or with PSA doubling time longer than 3 months.

Anterior dislocation of elbow in an adult female: A rare case report.

Elbow dislocations are among one of the most frequently dislocated joints, with an incidence of five to six cases annually per 100,000 persons in the United States. The vast majority of elbow dislocations occur posteriorly, secondary to a disruption in the anterior elbow- and posterior elbow-stabilizing structures. Anterior elbow dislocations are rare injuries in both children and adults, occurring as the proximal ulna is forced anterior to the distal humerus with or without the proximal radius. As of 2019, only 21 anterior elbow dislocations without fractures have been reported between 1922 and 2018. Here, we report the rare case of a patient that sustained a complex anterior elbow dislocation after being involved in a motor vehicle collision.

The performance status gap in immunotherapy for frail patients with advanced non-small cell lung cancer.

PURPOSE: In advanced non-small cell lung cancer (NSCLC), immune checkpoint inhibitor (ICI) monotherapy is often preferred over intensive ICI treatment for frail patients and those with poor performance status (PS). Among those with poor PS, the additional effect of frailty on treatment selection and mortality is unknown. METHODS: Patients in the veterans affairs national precision oncology program from 1/2019-12/2021 who received first-line ICI for advanced NSCLC were followed until death or study end 6/2022. Association of an electronic frailty index with treatment selection was examined using logistic regression stratified by PS. We also examined overall survival (OS) on intensive treatment using Cox regression stratified by PS. Intensive treatment was defined as concurrent use of platinum-doublet chemotherapy and/or dual checkpoint blockade and non-intensive as ICI monotherapy. RESULTS: Of 1547 patients receiving any ICI, 66.2% were frail, 33.8% had poor PS (≥ 2), and 25.8% were both. Frail patients received less intensive treatment than non-frail patients in both PS subgroups (Good PS: odds ratio [OR] 0.67, 95% confidence interval [CI] 0.51 - 0.88; Poor PS: OR 0.69, 95% CI 0.44 - 1.10). Among 731 patients receiving intensive treatment, frailty was associated with lower OS for those with good PS (hazard ratio [HR] 1.53, 95% CI 1.2 - 1.96), but no association was observed with poor PS (HR 1.03, 95% CI 0.67 - 1.58). CONCLUSION: Frail patients with both good and poor PS received less intensive treatment. However, frailty has a limited effect on survival among those with poor PS. These findings suggest that PS, not frailty, drives survival on intensive treatment.

Natural Language Processing Algorithm to Extract Multiple Myeloma Stage From Oncology Notes in the Veterans Affairs Healthcare System.

PURPOSE: Stage in multiple myeloma (MM) is an essential measure of disease risk, but its measurement in large databases is often lacking. We aimed to develop and validate a natural language processing (NLP) algorithm to extract oncologists' documentation of stage in the national Veterans Affairs (VA) Healthcare System. METHODS: Using nationwide electronic health record (EHR) and cancer registry data from the VA Corporate Data Warehouse, we developed and validated a rule-based NLP algorithm to extract oncologist-determined MM stage. To that end, a clinician annotated MM stage within over 5,000 short snippets of clinical notes, and annotated MM stage at MM treatment initiation for 200 patients. These were allocated into snippet- and patient-level development and validation sets. We developed MM stage extraction and roll-up algorithms within the development sets. After the algorithms were finalized, we validated them using standard measures in held-out validation sets. RESULTS: We developed algorithms for three different MM staging systems that have been in widespread use (Revised International Staging System [R-ISS], International Staging System [ISS], and Durie-Salmon [DS]) and for stage reported without a clearly defined system. Precision and recall were uniformly high for MM stage at the snippet level, ranging from 0.92 to 0.99 for the different MM staging systems. Performance in identifying for MM stage at treatment initiation at the patient level was also excellent, with precision of 0.92, 0.96, 0.90, and 0.86 and recall of 0.99, 0.98, 0.94, and 0.92 for R-ISS, ISS, DS, and unclear stage, respectively. CONCLUSION: Our MM stage extraction algorithm uses rule-based NLP and data aggregation to accurately measure MM stage documented in oncology notes and pathology reports in VA's national EHR system. It may be adapted to other systems where MM stage is recorded in clinical notes.

Racial and Ethnic Disparity for Cancer Mortality in General and Single-Payer Healthcare Systems in the United States.

BACKGROUND: It remains unclear what factors significantly drive racial disparity in cancer survival in the United States (US). We compared adjusted mortality outcomes in cancer patients from different racial and ethnic groups on a population level in the US and a single-payer healthcare system. PATIENTS AND METHODS: We selected adult patients with incident solid and hematologic malignancies from the Surveillance, Epidemiology, and End Results (SEER) 2011-2020 and Veteran Affairs national healthcare system (VA) 2011-2021. We classified the self-reported NIH race and ethnicity into non-Hispanic White (NHW), non-Hispanic Black (NHB), non-Hispanic Asian Pacific Islander (API), and Hispanic. Cox regression models for hazard ratio of racial and ethnic groups were built after adjusting confounders in each cohort. RESULTS: The study included 3,104,657 patients from SEER and 287,619 patients from VA. There were notable differences in baseline characteristics in the two cohorts. In SEER, adjusted HR for mortality was 1.12 (95% CI, 1.12-1.13), 1.03 (95% CI, 1.03-1.04), and 0.91 (95% CI, 0.90-0.92), for NHB, Hispanic, and API patients, respectively, vs. NHW. In VA, adjusted HR was 0.94 (95% CI, 0.92-0.95), 0.84 (95% CI, 0.82-0.87), and 0.96 (95% CI, 0.93-1.00) for NHB, Hispanic, and API, respectively, vs. NHW. Additional subgroup analyses by cancer types, age, and sex did not significantly change these associations. CONCLUSIONS: Racial disparity continues to persist on a population level in the US especially for NHB vs. NHW patients, where the adjusted mortality was 12% higher in the general population but 6% lower in the single-payer VA system.

Automating Clinical Trial Matches Via Natural Language Processing of Synthetic Electronic Health Records and Clinical Trial Eligibility Criteria.

Clinical trials are critical to many medical advances; however, recruiting patients remains a persistent obstacle. Automated clinical trial matching could expedite recruitment across all trial phases. We detail our initial efforts towards automating the matching process by linking realistic synthetic electronic health records to clinical trial eligibility criteria using natural language processing methods. We also demonstrate how the Sørensen-Dice Index can be adapted to quantify match quality between a patient and a clinical trial.

Antidepressant exposure and long-term dementia risk in a nationwide retrospective study on US veterans with midlife major depressive disorder.

INTRODUCTION: The use of antidepressants in major depressive disorder (MDD) has been reported to influence long-term risk of Alzheimer's disease (AD) and AD-related dementias (AD/ADRD), but studies are conflicting. METHODS: We used inverse probability weighted (IPW) Cox models with time-varying covariates in a retrospective cohort study among midlife veterans with MDD within the US Veterans Affairs healthcare system from January 1, 2000 to June 1, 2022. RESULTS: A total of 35,200 patients with MDD were identified. No associations were seen regarding the effect of being exposed to any antidepressant versus no exposure on AD/ADRD risk (events = 1,056, hazard ratio = 0.94, 95% confidence interval: 0.81 to 1.09) or the exposure to specific antidepressant classes versus no exposure. A risk reduction was observed for female patients in a stratified analysis; however, the number of cases was small. DISCUSSION: Our study suggests that antidepressant exposure has no effect on AD/ADRD risk. The association in female patients should be interpreted with caution and requires further attention. HIGHLIGHTS: We studied whether antidepressant use was associated with future dementia risk. We specifically focused on patients after their first-ever diagnosis of depression. We used IPW Cox models with time-varying covariates and a large observation window. Our study did not identify an effect of antidepressant use on dementia risk. A risk reduction was observed in female patients, but the number of cases was small.

Phenotyping Hepatic Immune-Related Adverse Events in the Setting of Immune Checkpoint Inhibitor Therapy.

PURPOSE: We present and validate a rule-based algorithm for the detection of moderate to severe liver-related immune-related adverse events (irAEs) in a real-world patient cohort. The algorithm can be applied to studies of irAEs in large data sets. METHODS: We developed a set of criteria to define hepatic irAEs. The criteria include: the temporality of elevated laboratory measurements in the first 2-14 weeks of immune checkpoint inhibitor (ICI) treatment, steroid intervention within 2 weeks of the onset of elevated laboratory measurements, and intervention with a duration of at least 2 weeks. These criteria are based on the kinetics of patients who experienced moderate to severe hepatotoxicity (Common Terminology Criteria for Adverse Events grades 2-4). We applied these criteria to a retrospective cohort of 682 patients diagnosed with hepatocellular carcinoma and treated with ICI. All patients were required to have baseline laboratory measurements before and after the initiation of ICI. RESULTS: A set of 63 equally sampled patients were reviewed by two blinded, clinical adjudicators. Disagreements were reviewed and consensus was taken to be the ground truth. Of these, 25 patients with irAEs were identified, 16 were determined to be hepatic irAEs, 36 patients were nonadverse events, and two patients were of indeterminant status. Reviewers agreed in 44 of 63 patients, including 19 patients with irAEs (0.70 concordance, Fleiss' kappa: 0.43). By comparison, the algorithm achieved a sensitivity and specificity of identifying hepatic irAEs of 0.63 and 0.81, respectively, with a test efficiency (percent correctly classified) of 0.78 and outcome-weighted F1 score of 0.74. CONCLUSION: The algorithm achieves greater concordance with the ground truth than either individual clinical adjudicator for the detection of irAEs.

Severe COVID-19 in Vaccinated Adults With Hematologic Cancers in the Veterans Health Administration.

Importance: With SARS-CoV-2 transforming into an endemic disease and with antiviral treatments available, it is important to establish which patients remain at risk of severe COVID-19 despite vaccination. Objective: To quantify the associations of clinical and demographic variables with odds of severe COVID-19 among patients with hematologic cancers. Design, Setting, and Participants: This case-control study included all patients with hematologic malignant neoplasms in the national Veterans Health Administration (VHA) who had documented SARS-CoV-2 infection after vaccination. Groups of patients with severe (cases) vs nonsevere (controls) COVID-19 were compared. Data were collected between January 1, 2020, and April 5, 2023, with data on infection collected between January 1, 2021, and September 30, 2022. All patients with diagnostic codes for hematologic malignant neoplasms who had documented vaccination followed by documented SARS-CoV-2 infection and for whom disease severity could be assessed were included. Data were analyzed from July 28 to December 30, 2023. Exposures: Clinical (comorbidities, predominant viral variant, treatment for malignant neoplasm, booster vaccination, and antiviral treatment) and demographic (age and sex) variables shown in prior studies to be associated with higher or lower rates of severe COVID-19. Comorbidities included Alzheimer disease or dementia, chronic kidney disease, chronic obstructive pulmonary disease, diabetes, heart failure, and peripheral vascular disease. Main Outcome and Measures: The main outcome was severe COVID-19 compared with nonsevere SARS-CoV-2 infection. Severe COVID-19 was defined as death within 28 days, mechanical ventilation, or hospitalization with use of dexamethasone or evidence of hypoxemia or use of supplemental oxygen. Multivariable logistic regression was used to estimate the associations of demographic and clinical variables with the odds of severe COVID-19, expressed as adjusted odds ratios (aORs) with 95% CIs. Results: Among 6122 patients (5844 [95.5%] male, mean [SD] age, 70.89 [11.57] years), 1301 (21.3%) had severe COVID-19. Age (aOR per 1-year increase, 1.05; 95% CI, 1.04-1.06), treatment with antineoplastic or immune-suppressive drugs (eg, in combination with glucocorticoids: aOR, 2.32; 95% CI, 1.93-2.80), and comorbidities (aOR per comorbidity, 1.35; 95% CI, 1.29-1.43) were associated with higher odds of severe disease, whereas booster vaccination was associated with lower odds (aOR, 0.73; 95% CI, 0.62-0.86). After oral antiviral drugs became widely used in March 2022, 20 of 538 patients (3.7%) with SARS-CoV-2 infection during this period had progression to severe COVID-19. Conclusions and Relevance: In this case-control study of patients with hematologic cancers, odds of severe COVID-19 remained high through mid-2022 despite vaccination, especially in patients requiring treatment.

An Intelligent Search & Retrieval System (IRIS) and Clinical and Research Repository for Decision Support Based on Machine Learning and Joint Kernel-based Supervised Hashing.

Large-scale, multi-site collaboration is becoming indispensable for a wide range of research and clinical activities in oncology. To facilitate the next generation of advances in cancer biology, precision oncology and the population sciences it will be necessary to develop and implement data management and analytic tools that empower investigators to reliably and objectively detect, characterize and chronicle the phenotypic and genomic changes that occur during the transformation from the benign to cancerous state and throughout the course of disease progression. To facilitate these efforts it is incumbent upon the informatics community to establish the workflows and architectures that automate the aggregation and organization of a growing range and number of clinical data types and modalities ranging from new molecular and laboratory tests to sophisticated diagnostic imaging studies. In an attempt to meet those challenges, leading health care centers across the country are making steep investments to establish enterprise-wide, data warehouses. A significant limitation of many data warehouses, however, is that they are designed to support only alphanumeric information. In contrast to those traditional designs, the system that we have developed supports automated collection and mining of multimodal data including genomics, digital pathology and radiology images. In this paper, our team describes the design, development and implementation of a multi-modal, Clinical & Research Data Warehouse (CRDW) that is tightly integrated with a suite of computational and machine-learning tools to provide actionable insight into the underlying characteristics of the tumor environment that would not be revealed using standard methods and tools. The System features a flexible Extract, Transform and Load (ETL) interface that enables it to adapt to aggregate data originating from different clinical and research sources depending on the specific EHR and other data sources utilized at a given deployment site.

Building Research Infrastructure to Develop Greater Learning Efficiencies (BRIDGE).

In this manuscript, we outline our developed version of a Learning Health System (LHS) in oncology implemented at the Department of Veterans Affairs (VA). Transferring healthcare into an LHS framework has been one of the spearpoints of VA's Central Office and given the general lack of evidence generated through randomized control clinical trials to guide medical decisions in oncology, this domain is one of the most suitable for this change. We describe our technical solution, which includes a large real-world data repository, a data science and algorithm development framework, and the mechanism by which results are brought back to the clinic and to the patient. Additionally, we propose the need for a bridging framework that requires collaboration between informatics specialists and medical professionals to integrate knowledge generation into the clinical workflow at the point of care.

Matching Patients to Accelerate Clinical Trials (MPACT): Enabling Technology for Oncology Clinical Trial Workflow.

Clinical trial enrollment is impeded by the significant time burden placed on research coordinators screening eligible patients. With 50,000 new cancer cases every year, the Veterans Health Administration (VHA) has made increased access for Veterans to high-quality clinical trials a priority. To aid in this effort, we worked with research coordinators to build the MPACT (Matching Patients to Accelerate Clinical Trials) platform with a goal of improving efficiency in the screening process. MPACT supports both a trial prescreening workflow and a screening workflow, employing Natural Language Processing and Data Science methods to produce reliable phenotypes of trial eligibility criteria. MPACT also has a functionality to track a patient's eligibility status over time. Qualitative feedback has been promising with users reporting a reduction in time spent on identifying eligible patients.

Transfer Learning for Mortality Prediction in Non-Small Cell Lung Cancer with Low-Resolution Histopathology Slide Snapshots.

High-resolution whole slide image scans of histopathology slides have been widely used in recent years for prediction in cancer. However, in some cases, clinical informatics practitioners may only have access to low-resolution snapshots of histopathology slides, not high-resolution scans. We evaluated strategies for training neural network prognostic models in non-small cell lung cancer (NSCLC) based on low-resolution snapshots, using data from the Veterans Affairs Precision Oncology Data Repository. We compared strategies without transfer learning, with transfer learning from general domain images, and with transfer learning from publicly available high-resolution histopathology scans. We found transfer learning from high-resolution scans achieved significantly better performance than other strategies. Our contribution provides a foundation for future development of prognostic models in NSCLC that incorporate data from low-resolution pathology slide snapshots alongside known clinical predictors.

Brain tumors in United States military veterans.

BACKGROUND: Comprehensive analysis of brain tumor incidence and survival in the Veteran population has been lacking. METHODS: Veteran data were obtained from the Veterans Health Administration (VHA) Medical Centers via VHA Corporate Data Warehouse. Brain tumor statistics on the overall US population were generated from the Central Brain Tumor Registry of the US data. Cases were individuals (≥18 years) with a primary brain tumor, diagnosed between 2004 and 2018. The average annual age-adjusted incidence rates (AAIR) and 95% confidence intervals were estimated per 100 000 population and Kaplan-Meier survival curves evaluated overall survival outcomes among Veterans. RESULTS: The Veteran population was primarily white (78%), male (93%), and between 60 and 64 years old (18%). Individuals with a primary brain tumor in the general US population were mainly female (59%) and between 18 and 49 years old (28%). The overall AAIR of primary brain tumors from 2004 to 2018 within the Veterans Affairs cancer registry was 11.6. Nonmalignant tumors were more common than malignant tumors (AAIR:7.19 vs 4.42). The most diagnosed tumors in Veterans were nonmalignant pituitary tumors (AAIR:2.96), nonmalignant meningioma (AAIR:2.62), and glioblastoma (AAIR:1.96). In the Veteran population, survival outcomes became worse with age and were lowest among individuals diagnosed with glioblastoma. CONCLUSIONS: Differences between Veteran and US populations can be broadly attributed to demographic composition differences of these groups. Prior to this, there have been no reports on national-level incidence rates and survival outcomes for Veterans. These data provide vital information that can drive efforts to understand disease burden and improve outcomes for individuals with primary brain tumors.

Risk of severe coronavirus disease 2019 despite vaccination in patients requiring treatment with immune-suppressive drugs: A nationwide cohort study of US Veterans.

BACKGROUND: Patients taking immune-suppressive drugs are at increased risk of severe coronavirus disease 2019 (COVID-19), not fully ameliorated by vaccination. We assessed the contributions of clinical and demographic factors to the risk of severe disease despite vaccination in patients taking immune-suppressive medications for solid organ transplantation (SOT), rheumatoid arthritis (RA), inflammatory bowel disease (IBD), or psoriasis. METHODS: Veterans Health Administration electronic health records were used to identify patients diagnosed with RA, IBD, psoriasis, or SOT who had been vaccinated against severe acute respiratory syndrome coronavirus 2, were subsequently infected, and had received immune-suppressive drugs within 3 months before infection. The association of severe (defined as hypoxemia, mechanical ventilation, dexamethasone use, or death) versus non-severe COVID-19 with the use of immune-suppressive and antiviral drugs and clinical covariates was assessed by multivariable logistic regression. RESULTS: Severe COVID-19 was more common in patients with SOT (230/1011, 22.7%) than RA (173/1355, 12.8%), IBD (51/742, 6.9%), or psoriasis (82/1125, 7.3%). Age was strongly associated with severe COVID-19, adjusted odds ratio (aOR) of 1.04 (CI 1.03-1.05) per year. Comorbidities indicating chronic brain, heart, lung, or kidney damage were also associated with severity, aOR 1.35-2.38. The use of glucocorticoids was associated with increased risk (aOR 1.66, CI 1.39-2.18). Treatment with antivirals was associated with reduced severity, for example, aOR 0.28 (CI 0.13-0.62) for nirmatrelvir/ritonavir. CONCLUSION: The risk of severe COVID-19 despite vaccination is substantial in patients taking immune-suppressive drugs, more so in patients with SOT than in patients with inflammatory diseases. Age and severe comorbidities contribute to risk, as in the general population. Oral antivirals were very beneficial but not widely used.

Prostate cancer patient stratification by molecular signatures in the Veterans Precision Oncology Data Commons.

Veterans are at an increased risk for prostate cancer, a disease with extraordinary clinical and molecular heterogeneity, compared with the general population. However, little is known about the underlying molecular heterogeneity within the veteran population and its impact on patient management and treatment. Using clinical and targeted tumor sequencing data from the National Veterans Affairs health system, we conducted a retrospective cohort study on 45 patients with advanced prostate cancer in the Veterans Precision Oncology Data Commons (VPODC), most of whom were metastatic castration-resistant. We characterized the mutational burden in this cohort and conducted unsupervised clustering analysis to stratify patients by molecular alterations. Veterans with prostate cancer exhibited a mutational landscape broadly similar to prior studies, including KMT2A and NOTCH1 mutations associated with neuroendocrine prostate cancer phenotype, previously reported to be enriched in veterans. We also identified several potential novel mutations in PTEN, MSH6, VHL, SMO, and ABL1 Hierarchical clustering analysis revealed two subgroups containing therapeutically targetable molecular features with novel mutational signatures distinct from those reported in the Catalogue of Somatic Mutations in Cancer database. The clustering approach presented in this study can potentially be used to clinically stratify patients based on their distinct mutational profiles and identify actionable somatic mutations for precision oncology.

Baseline correlates of frailty and its association with survival in United States veterans with acute myeloid leukemia.

Frailty is an important construct to measure in acute myeloid leukemia (AML). We used the Veterans Affairs Frailty Index (VA-FI) - calculated using readily available data within the VA's electronic health records - to measure frailty in U.S. veterans with AML. Of the 1166 newly diagnosed and treated veterans with AML between 2012 and 2022, 722 (62%) veterans with AML were classified as frail (VA-FI > 0.2). At a median follow-up of 252.5 days, moderate-severely frail veterans had significantly worse survival than mildly frail, and non-frail veterans (median survival 179 vs. 306 vs. 417 days, p < .001). Increasing VA-FI severity was associated with higher mortality. A model with VA-FI in addition to the European LeukemiaNet (ELN) risk classification and other covariates statistically outperformed a model containing the ELN risk and other covariates alone (p < .001). These findings support the VA-FI as a tool to expand frailty measurement in research and clinical practice for informing prognosis in veterans with AML.

Machine learning-based natural language processing to extract PD-L1 expression levels from clinical notes.

Introduction: PD-L1 expression is used to determine oncology patients' response to and eligibility for immunologic treatments; however, PD-L1 expression status often only exists in unstructured clinical notes, limiting ability to use it in population-level studies. Methods: We developed and evaluated a machine learning based natural language processing (NLP) tool to extract PD-L1 expression values from the nationwide Veterans Affairs electronic health record system. Results: The model demonstrated strong evaluation performance across multiple levels of label granularity. Mean precision of the overall PD-L1 positive label was 0.859 (sd, 0.039), recall 0.994 (sd, 0.013), and F1 0.921 (0.024). When a numeric PD-L1 value was identified, the mean absolute error of the value was 0.537 on a scale of 0 to 100. Conclusion: We presented an accurate NLP method for deriving PD-L1 status from clinical notes. By reducing the time and manual effort needed to review medical records, our work will enable future population-level studies in cancer immunotherapy.

More intensive therapy as more effective treatment for frail patients with multiple myeloma [corrected].

Although randomized controlled trial data suggest that the more intensive triplet bortezomib-lenalidomide-dexamethasone (VRd) is superior to the less intensive doublet lenalidomide-dexamethasone (Rd) in patients newly diagnosed with multiple myeloma (MM), guidelines have historically recommended Rd over VRd for patients who are frail and may not tolerate a triplet. We identified 2573 patients (median age, 69.7 years) newly diagnosed with MM who were initiated on VRd (990) or Rd (1583) in the national US Veterans Affairs health care System from 2004 to 2020. We measured frailty using the Veterans Affairs Frailty Index. To reduce imbalance in confounding, we matched patients for MM stage and 1:1 based on a propensity score. Patients who were moderate-severely frail had a higher prevalence of stage III MM and myeloma-related frailty deficits than patients who were not frail. VRd vs Rd was associated with lower mortality (hazard ratio [HR], 0.81; 95% confidence interval [CI], 0.70-0.94) in the overall matched population. Patients who were moderate-severely frail demonstrated the strongest association (HR 0.74; 95% CI, 0.56-0.97), whereas the association weakened in those who were mildly frail (HR, 0.80; 95% CI, 0.61-1.05) and nonfrail (HR, 0.86; 95% CI, 0.67-1.10). VRd vs Rd was associated with a modestly higher incidence of hospitalizations in the overall population, but this association weakened in patients who were moderate-severely frail. Our findings confirm the benefit of VRd over Rd in US veterans and further suggest that this benefit is strongest in patients with the highest levels of frailty, arguing that more intensive treatment of myeloma may be more effective treatment of frailty itself.