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.

Thrombosis risk prediction in lymphoma patients: A multi-institutional, retrospective model development and validation study.

Venous thromboembolism (VTE) poses a significant risk to cancer patients receiving systemic therapy. The generalizability of pan-cancer models to lymphomas is limited. Currently, there are no reliable risk prediction models for thrombosis in patients with lymphoma. Our objective was to create a risk assessment model (RAM) specifically for lymphomas. We performed a retrospective cohort study to develop Fine and Gray sub-distribution hazard model for VTE and pulmonary embolism (PE)/ lower extremity deep vein thrombosis (LE-DVT) respectively in adult lymphoma patients from the Veterans Affairs national healthcare system (VA). External validations were performed at the Harris Health System (HHS) and the MD Anderson Cancer Center (MDACC). Time-dependent c-statistic and calibration curves were used to assess discrimination and fit. There were 10,313 (VA), 854 (HHS), and 1858 (MDACC) patients in the derivation and validation cohorts with diverse baseline. At 6 months, the VTE incidence was 5.8% (VA), 8.2% (HHS), and 8.8% (MDACC), respectively. The corresponding estimates for PE/LE-DVT were 3.9% (VA), 4.5% (HHS), and 3.7% (MDACC), respectively. The variables in the final RAM included lymphoma histology, body mass index, therapy type, recent hospitalization, history of VTE, history of paralysis/immobilization, and time to treatment initiation. The RAM had c-statistics of 0.68 in the derivation and 0.69 and 0.72 in the two external validation cohorts. The two models achieved a clear differentiation in risk stratification in each cohort. Our findings suggest that easy-to-implement, clinical-based model could be used to predict personalized VTE risk for lymphoma patients.

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.

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.

Underuse of Antiviral Drugs to Prevent Progression to Severe COVID-19 - Veterans Health Administration, March-September 2022.

Antiviral drugs reduce the rate of progression to severe COVID-19 when given to patients with mild-to-moderate disease within 5 days of symptom onset. Despite being recommended for patients at high risk for progression to severe COVID-19 because of age or chronic conditions, reported antiviral use among the general adult population has been ≤35%. To ascertain reasons for underuse of antiviral medications to prevent severe COVID-19 and propose interventions accordingly, a detailed review was conducted of 110 Veterans Health Administration patients with mild-to-moderate infection at high risk for progression because of underlying conditions (organ transplantation or hematologic malignancies) who did not receive an antiviral drug. Among these 110 patients, all of whom had received COVID-19 vaccine, 22 (20.0%) were offered treatment but declined, and 88 (80.0%) were not offered treatment. Among the 88 patients not offered treatment, provider reasons included symptom duration of >5 days (22.7%), concern about possible drug interactions (5.7%), or absence of symptoms (22.7%); however, among nearly one half (43 of 88; 48.9%) of these patients, no reason other than mild symptoms was given. Among 24 (55.8%) of those 43 patients, follow-up was limited to telephone calls to report test results and inquire about symptom evolution, with no documentation of treatment being offered. These findings suggest that education of patients, providers, and medical personnel tasked with follow-up calls, combined with advance planning in the event of a positive test result, might improve the rate of recommended antiviral medication use to prevent severe COVID-19-associated illness, including death.

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.

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.

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.

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.

Epidemiology of Cancer-Associated Venous Thromboembolism in Patients With Solid and Hematologic Neoplasms in the Veterans Affairs Health Care System.

Importance: Identifying changes in epidemiologic patterns of the incidence and risk of cancer-associated thrombosis (CAT), particularly with evolving cancer-directed therapy, is essential for risk stratification. Objective: To assess the incidence of CAT over time and to determine pertinent patient-specific, cancer-specific, and treatment-specific factors associated with its risk. Design, Setting, and Participants: This longitudinal, retrospective cohort study was conducted from 2006 to 2021. Duration of follow-up was from the date of diagnosis until first venous thromboembolism (VTE) event, death, loss of follow-up (defined as a 90-day gap without clinical encounters), or administrative censoring on April 1, 2022. The study took place within the US Department of Veterans Affairs national health care system. Patients with newly diagnosed invasive solid tumors and hematologic neoplasms were included in the study. Data were analyzed from December 2022 to February 2023. Exposure: Newly diagnosed invasive solid tumors and hematologic neoplasms. Main Outcomes: Incidence of VTE was assessed using a combination of International Classification of Diseases, Ninth Revision, Clinical Modification and International Statistical Classification of Diseases, Tenth Revision, Clinical Modification and natural language processing confirmed outcomes. Cumulative incidence competing risk functions were used to estimate incidence of CAT. Multivariable Cox regression models were built to assess the association of baseline variables with CAT. Pertinent patient variables included demographics, region, rurality, area deprivation index, National Cancer Institute comorbidity index, cancer type, staging, first-line systemic treatment within 3 months (time-varying covariate), and other factors that could be associated with the risk of VTE. Results: A total of 434 203 patients (420 244 men [96.8%]; median [IQR] age, 67 [62-74] years; 7414 Asian or Pacific Islander patients [1.7%]; 20 193 Hispanic patients [4.7%]; 89 371 non-Hispanic Black patients [20.6%]; 313 157 non-Hispanic White patients [72.1%]) met the inclusion criteria. Overall incidence of CAT at 12 months was 4.5%, with yearly trends ranging stably from 4.2% to 4.7%. The risk of VTE was associated with cancer type and stage. In addition to confirming well-known risk distribution among patients with solid tumors, a higher risk of VTE was observed among patients with aggressive lymphoid neoplasms compared with patients with indolent lymphoid or myeloid hematologic neoplasms. Compared with no treatment, patients receiving first-line chemotherapy (hazard ratio [HR], 1.44; 95% CI, 1.40-1.49) and immune checkpoint inhibitors (HR, 1.49; 95% CI, 1.22-1.82) had a higher adjusted relative risk than patients receiving targeted therapy (HR, 1.21; 95% CI, 1.13-1.30) or endocrine therapy (HR, 1.20; 95% CI, 1.12-1.28). Finally, adjusted VTE risk was significantly higher among Non-Hispanic Black patients (HR, 1.23; 95% CI, 1.19-1.27) and significantly lower in Asian or Pacific Islander patients (HR, 0.84; 95% CI, 0.76-0.93) compared with Non-Hispanic White patients. Conclusions and Relevance: In this cohort study of patients with cancer, a high incidence of VTE was observed, with yearly trends that remained stable over the 16-year study period. Both novel and known factors associated with the risk of CAT were identified, providing valuable and applicable insights in this current treatment landscape.

Venous thromboembolism risk in cancer patients receiving first-line immune checkpoint inhibitor versus chemotherapy.

It remains unclear if immune checkpoint inhibitor (ICI) therapy is associated with higher rate of venous thromboembolism (VTE) compared with cytotoxic chemotherapy (chemo) in patients with comparable cancer type, staging, and comorbidities. Using the national Veterans Affairs healthcare system database from 2016 to 2021, we performed a propensity score (PS)-weighted retrospective cohort study to compare the incidence of VTE in patients with selected stage III/IV cancer receiving first-line ICI versus chemo. The PS model utilized overlap weights to balance age, sex, race, treatment year, VTE history, paralysis/immobilization, prolonged hospitalization, cancer type, staging, time between diagnosis and treatment, and National Cancer Institute comorbidity index. Weighted Cox regressions with robust standard error were used to assess the hazard ratio (HR) and 95% confidence interval (CI). We found that among comparable advanced cancers, first-line ICI (n = 1823) and first-line chemo (n = 6345) had similar rates of VTE (8.49% for ICI and 8.36% for chemo at 6 months). The weighted HR was 1.06 (95% CI 0.88-1.26) for ICI versus chemo. In a subgroup analysis restricted to lung cancers, first-line ICI/chemo (n = 828), ICI monotherapy (n = 428), and chemo monotherapy (n = 4371) had similar rates of VTE (9.60% for ICI/chemo, 10.04% for ICI, and 8.91% for chemo at 6 months). The weighted HR was 1.05 (95% CI 0.77-1.42) for ICI versus chemo, and 1.08 (95% CI 0.83-1.42) for ICI/chemo versus chemo. In conclusion, ICI as a systemic therapy has a similarly elevated risk as cytotoxic chemo for VTE occurrence in cancer patients. This finding can inform future prospective studies exploring thromboprophylaxis strategies.

Risk factors of SARS-CoV-2 infection and complications from COVID-19 in lung cancer patients.

BACKGROUND: Identifying lung cancer patients at an increased risk of getting SARS-CoV-2-related complications will facilitate tailored therapy to maximize the benefit of anti-cancer therapy, while decreasing the likelihood of COVID-19 complications. This analysis aimed to identify the characteristics of lung cancer patients that predict for increased risk of death or serious SARS-CoV-2 infection. PATIENTS AND METHODS: This was a retrospective cohort study of patients with lung cancer diagnosed October 1, 2015, and December 1, 2020, and a diagnosis of COVID-19 between February 2, 2020, and December 1, 2020, within the Veterans Health Administration. Serious SARS-CoV-2 infection was defined as hospitalization, ICU admission, or mechanical ventilation or intubation within 2 weeks of COVID-19 diagnosis. For categorical variables, differences were assessed using Χ2 tests, while Kruskal-Wallis rank-sum test was used for continuous variables. Multivariable logistic regression models were fit relative to onset of serious SARS-CoV-2 infection and death from SARS-CoV-2 infection. RESULTS: COVID-19 infection was diagnosed in 352 lung cancer patients. Of these, 61 patients (17.3%) died within four weeks of diagnosis with COVID-19, and 42 others (11.9%) experienced a severe infection. Patients who had fatal or severe infection were older and had lower hemoglobin levels than those with mild or moderate infection. Factors associated with death from SARS-CoV-2 infection included increasing age, immune checkpoint inhibitor therapy and low hemoglobin level. CONCLUSIONS:  The mortality of lung cancer patients from COVID-19 disease in the present cohort was less than previously reported in the literature. The identification of risk factors associated with severe or fatal outcomes informs management of patients with lung cancer who develop COVID-19 disease.

Derivation and Validation of a Clinical Risk Assessment Model for Cancer-Associated Thrombosis in Two Unique US Health Care Systems.

PURPOSE: Venous thromboembolism (VTE), especially pulmonary embolism (PE) and lower extremity deep vein thrombosis (LE-DVT), is a serious and potentially preventable complication for patients with cancer undergoing systemic therapy. METHODS: Using retrospective data from patients diagnosed with incident cancer from 2011-2020, we derived a parsimonious risk assessment model (RAM) using least absolute shrinkage and selection operator regression from the Harris Health System (HHS, n = 9,769) and externally validated it using the Veterans Affairs (VA) health care system (n = 79,517). Bootstrapped c statistics and calibration curves were used to assess external model discrimination and fit. Dichotomized risk strata using integer scores were created and compared against the Khorana score (KS). RESULTS: Incident VTE and PE/LE-DVT at 6 months occurred in 590 (6.2%) and 437 (4.6%) patients in HHS and 4,027 (5.1%) and 3,331 (4.2%) patients in the VA health care system. Assessed at the time of systemic therapy initiation, the new RAM included components of the KS with the modified cancer subtype, cancer staging, systemic therapy class, history of VTE, history of paralysis/immobility, recent hospitalization, and Asian/Pacific Islander race. The c statistic was 0.71 in HHS and 0.68 in the VA health care system (compared with 0.65 and 0.60, respectively, for KS). Furthermore, the new RAM appropriately reclassified 28% of patients and increased the proportion of VTEs in the high-risk group from 37% to 68% in the validation data set. CONCLUSION: The novel RAM stratified patients with cancer into a high-risk group with 8%-10% cumulative incidence of VTE and 7% PE/LE-DVT at 6 months (v 3% and 2%, respectively, in the low-risk group). The model had improved performance over the original KS and doubled the number of VTE events in the high-risk stratum. We encourage additional external validation from prospective studies.[Media: see text].

Sentiment analysis of medical record notes for lung cancer patients at the Department of Veterans Affairs.

Natural language processing of medical records offers tremendous potential to improve the patient experience. Sentiment analysis of clinical notes has been performed with mixed results, often highlighting the issue that dictionary ratings are not domain specific. Here, for the first time, we re-calibrate the labMT sentiment dictionary on 3.5M clinical notes describing 10,000 patients diagnosed with lung cancer at the Department of Veterans Affairs. The sentiment score of notes was calculated for two years after date of diagnosis and evaluated against a lab test (platelet count) and a combination of data points (treatments). We found that the oncology specific labMT dictionary, after re-calibration for the clinical oncology domain, produces a promising signal in notes that can be detected based on a comparative analysis to the aforementioned parameters.

Validation of algorithms to select patients with multiple myeloma and patients initiating myeloma treatment in the national Veterans Affairs Healthcare System.

BACKGROUND: We aimed to evaluate and compare the performance of multiple myeloma (MM) selection algorithms for use in Veterans Affairs (VA) research. METHODS: Using the VA Corporate Data Warehouse (CDW), the VA Cancer Registry (VACR), and VA pharmacy data, we randomly selected 500 patients from 01/01/1999 to 06/01/2021 who had (1) either one MM diagnostic code OR were listed in the VACR as having MM AND (2) at least one MM treatment code. A team reviewed oncology notes for each veteran to annotate details regarding MM diagnosis and initial treatment within VA. We evaluated inter-annotator agreement and compared the performance of four published algorithms (two developed and validated external to VA data and two used in VA data). RESULTS: A total of 859 patients were reviewed to obtain 500 patients who were annotated as having MM and initiating MM treatment in VA. Agreement was high among annotators for all variables: MM diagnosis (98.3% agreement, Kappa = 0.93); initial treatment in VA (91.8% agreement; Kappa = 0.77); and initial treatment classification (87.6% agreement; Kappa = 0.86). VA Algorithms were more specific and had higher PPVs than non-VA algorithms for both MM diagnosis and initial treatment in VA. We developed the "VA Recommended Algorithm," which had the highest PPV among all algorithms in identifying patients diagnosed with MM (PPV = 0.98, 95% CI = 0.95-0.99) and in identifying patients who initiated their MM treatment in VA (PPV = 0.93, 95% CI = 0.90-0.96). CONCLUSION: Our VA Recommended Algorithm optimizes sensitivity and PPV for cohort selection and treatment classification.

Factors Associated With Severe COVID-19 Among Vaccinated Adults Treated in US Veterans Affairs Hospitals.

Importance: With a large proportion of the US adult population vaccinated against SARS-CoV-2, it is important to identify who remains at risk of severe infection despite vaccination. Objective: To characterize risk factors for severe COVID-19 disease in a vaccinated population. Design, Setting, and Participants: This nationwide, retrospective cohort study included US veterans who received a SARS-CoV-2 vaccination series and later developed laboratory-confirmed SARS-CoV-2 infection and were treated at US Department of Veterans Affairs (VA) hospitals. Data were collected from December 15, 2020, through February 28, 2022. Exposures: Demographic characteristics, comorbidities, immunocompromised status, and vaccination-related variables. Main Outcomes and Measures: Development of severe vs nonsevere SARS-CoV-2 infection. Severe disease was defined as hospitalization within 14 days of a positive SARS-CoV-2 diagnostic test and either blood oxygen level of less than 94%, receipt of supplemental oxygen or dexamethasone, mechanical ventilation, or death within 28 days. Association between severe disease and exposures was estimated using logistic regression models. Results: Among 110 760 patients with infections following vaccination (97 614 [88.1%] men, mean [SD] age at vaccination, 60.8 [15.3] years; 26 953 [24.3%] Black, 11 259 [10.2%] Hispanic, and 71 665 [64.7%] White), 10 612 (9.6%) had severe COVID-19. The strongest association with risk of severe disease after vaccination was age, which increased among patients aged 50 years or older with an adjusted odds ratio (aOR) of 1.42 (CI, 1.40-1.44) per 5-year increase in age, such that patients aged 80 years or older had an aOR of 16.58 (CI, 13.49-20.37) relative to patients aged 45 to 50 years. Immunocompromising conditions, including receipt of different classes of immunosuppressive medications (eg, leukocyte inhibitor: aOR, 2.80; 95% CI, 2.39-3.28) or cytotoxic chemotherapy (aOR, 2.71; CI, 2.27-3.24) prior to breakthrough infection, or leukemias or lymphomas (aOR, 1.87; CI, 1.61-2.17) and chronic conditions associated with end-organ disease, such as heart failure (aOR, 1.74; CI, 1.61-1.88), dementia (aOR, 2.01; CI, 1.83-2.20), and chronic kidney disease (aOR, 1.59; CI, 1.49-1.69), were also associated with increased risk. Receipt of an additional (ie, booster) dose of vaccine was associated with reduced odds of severe disease (aOR, 0.50; CI, 0.44-0.57). Conclusions and Relevance: In this nationwide, retrospective cohort of predominantly male US Veterans, we identified risk factors associated with severe disease despite vaccination. Findings could be used to inform outreach efforts for booster vaccinations and to inform clinical decision-making about patients most likely to benefit from preexposure prophylaxis and antiviral therapy.

Associations of air pollution with peripheral inflammation and cardiac autonomic physiology in children.

Climate change-related disasters have drawn increased attention to the impact of air pollution on health. 122 children ages 9-11 years old, M(SD) = 9.91(.56), participated. Levels of particulate matter (PM2.5) near participants' homes were obtained from the Environmental Protection Agency. Cytokines were assayed from 100 child serum samples: IL-6, IL-8, IL-10, and TNFα. Autonomic physiology was indexed by pre-ejection period (PEP), respiratory sinus arrhythmia (RSA), cardiac autonomic regulation (CAR), and cardiac autonomic balance (CAB). IL-6 was positively related to daily PM2.5 (r = .26, p = .009). IL-8 was negatively associated with monthly PM2.5 (r = -.23, p = .02). PEP was positively related to daily (r = .29, p = .001) and monthly PM2.5 (r = .18, p = .044). CAR was negatively associated with daily PM2.5 (r = -.29, p = .001). IL-10, TNFα, RSA, and CAB were not associated with PM2.5. Air pollution may increase risk of inflammation in children.

Increased relative proportions of advanced melanoma among veterans: A comparative analysis with the Surveillance, Epidemiology, and End Results registry.

BACKGROUND: The Surveillance, Epidemiology, and End Results (SEER) program reflects a third of the population of the United States. However, SEER may not be generalizable to the veteran population. Because veterans comprise a high-risk population, this discrepancy may limit our understanding of the epidemiology of melanoma in such high-risk populations. OBJECTIVES: To assess differences in demographics, tumor characteristics, and melanoma-specific survival (MSS) in veterans compared to the general population. METHODS: Data were collected from the Veterans Affairs Cancer Registry (VACR) and SEER (18 registries) from 2009 to 2017. RESULTS: We identified 15,334 veterans and 166,265 SEER patients with melanoma. Veterans were more likely to present with regional or distant disease (17.5% vs 13.0% in SEER). In VACR relative to SEER, the 5-year MSS was lower across all ages, except those diagnosed at ≥80 years. From 2009 to 2017, MSS by stage was lower across all stages in VACR. However, for stage IV melanomas diagnosed in 2015 to 2017 compared to 2011-2014, 2-year MSS increased from 37.8% to 51.5% in VACR versus 36.4% to 44.8% in SEER. LIMITATIONS: Unique veteran demographics and missing data inherent to VACR. CONCLUSION: Compared to SEER, veterans with melanoma were diagnosed at later stages; however, both exhibited recent improvement in stage IV MSS.

Prognostic value of the veterans affairs frailty index in older patients with non-small cell lung cancer.

BACKGROUND: Older patients with non-small cell lung cancer (NSCLC) are a heterogeneous population with varying degrees of frailty. An electronic frailty index such as the Veterans Affairs Frailty Index (VA-FI) can potentially help identify vulnerable patients at high risk of poor outcomes. METHODS: NSCLC patients ≥65 years old and diagnosed in 2002-2017 were identified using the VA Central Cancer Registry. The VA-FI was calculated using administrative codes from VA electronic health records data linked with Medicare and Medicaid data. We assessed associations between the VA-FI and times to mortality, hospitalization, and emergency room (ER) visit following diagnosis by Kaplan-Meier analysis and multivariable stratified Cox models. We also evaluated the change in discrimination and calibration of reference prognostic models after adding VA-FI. RESULTS: We identified a cohort of 42,204 older NSCLC VA patients, in which 55.5% were classified as frail (VA-FI >0.2). After adjustment, there was a strong association between VA-FI and the risk of mortality (HR = 1.23 for an increase of four deficits or, equivalently, an increase of 0.129 on VA-FI, p < 0.001), hospitalization (HR = 1.16 for four deficits, p < 0.001), and ER visit (HR = 1.18 for four deficits, p < 0.001). Adding VA-FI to baseline prognostic models led to statistically significant improvements in time-dependent area under curves and did not have a strong impact on calibration. CONCLUSION: Older NSCLC patients with higher VA-FI have significantly elevated risks of mortality, hospitalizations, and ER visits following diagnosis. An electronic frailty index can serve as an accessible tool to identify patients with vulnerabilities to inform clinical care and research.