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Pancreatic cancer is an aggressive disease that typically presents late with poor outcomes, indicating a pronounced need for early detection. In this study, we applied artificial intelligence methods to clinical data from 6 million patients (24,000 pancreatic cancer cases) in Denmark (Danish National Patient Registry (DNPR)) and from 3 million patients (3,900 cases) in the United States (US Veterans Affairs (US-VA)). We trained machine learning models on the sequence of disease codes in clinical histories and tested prediction of cancer occurrence within incremental time windows (CancerRiskNet). For cancer occurrence within 36 months, the performance of the best DNPR model has area under the receiver operating characteristic (AUROC) curve = 0.88 and decreases to AUROC (3m) = 0.83 when disease events within 3 months before cancer diagnosis are excluded from training, with an estimated relative risk of 59 for 1,000 highest-risk patients older than age 50 years. Cross-application of the Danish model to US-VA data had lower performance (AUROC = 0.71), and retraining was needed to improve performance (AUROC = 0.78, AUROC (3m) = 0.76). These results improve the ability to design realistic surveillance programs for patients at elevated risk, potentially benefiting lifespan and quality of life by early detection of this aggressive cancer. A deep learning algorithm using electronic health records from two large cohorts of patients predicts the risk of pancreatic cancer from pre-cancer disease trajectories up to 3 years in advance, showing promising performance in retrospective validation.

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.

Patients 65 or older with hypertension who switched from hydrochlorothiazide to chlorthalidone did not have fewer major cardiovascular events or non–cancer-related deaths than those who continued receiving hydrochlorothiazide.

To describe the design and ongoing conduct of the Million Veteran Program (MVP), as an observational cohort study and mega-biobank in the Department of Veterans Affairs (VA) health care system. Data are being collected from participants using questionnaires, the VA electronic health record, and a blood sample for genomic and other testing. Several ongoing projects are linked to MVP, both as peer-reviewed research studies and as activities to help develop an infrastructure for future, broad-based research uses. Formal planning for MVP commenced in 2009; the protocol was approved in 2010, and enrollment began in 2011. As of August 3, 2015, and with a steady state of ≈50 recruiting sites nationwide, N = 397,104 veterans have been enrolled. Among N = 199,348 with currently available genotyping data, most participants (as expected) are male (92.0%) between the ages of 50 and 69 years (55.0%). On the basis of self-reported race, white (77.2%) and African American (13.5%) populations are well represented. By helping to promote the future integration of genetic testing in health care delivery, including clinical decision making, the MVP is designed to contribute to the development of precision medicine.

In this study, patients with type 2 diabetes, albuminuria, and mild-to-moderate renal dysfunction received losartan followed by lisinopril or placebo. The study was stopped early because of increased risks of hyperkalemia and acute kidney injury with combination therapy. Diabetic nephropathy is the leading cause of end-stage renal disease (ESRD) in the United States. 1 Persons with diabetes and proteinuria are at high risk for progression to ESRD. 2 Blockade of the renin–angiotensin system decreases the progression of proteinuric kidney disease, 3 – 5 and the degree of reduction in proteinuria correlates with the extent to which the decrease in the glomerular filtration rate (GFR) is slowed. 2 , 6 Given these observations, it has been hypothesized that interventions that further lower proteinuria will further reduce the risk of progression. 6 Combination therapy with an angiotensin-converting–enzyme (ACE) inhibitor and an angiotensin II–receptor blocker (ARB) results in . . .

Serum concentration of hepatic enzymes are linked to liver dysfunction, metabolic and cardiovascular diseases. We perform genetic analysis on serum levels of alanine transaminase (ALT), alkaline phosphatase (ALP) and gamma-glutamyl transferase (GGT) using data on 437,438 UK Biobank participants. Replication in 315,572 individuals from European descent from the Million Veteran Program, Rotterdam Study and Lifeline study confirms 517 liver enzyme SNPs. Genetic risk score analysis using the identified SNPs is strongly associated with serum activity of liver enzymes in two independent European descent studies (The Airwave Health Monitoring study and the Northern Finland Birth Cohort 1966). Gene-set enrichment analysis using the identified SNPs highlights involvement in liver development and function, lipid metabolism, insulin resistance, and vascular formation. Mendelian randomization analysis shows association of liver enzyme variants with coronary heart disease and ischemic stroke. Genetic risk score for elevated serum activity of liver enzymes is associated with higher fat percentage of body, trunk, and liver and body mass index. Our study highlights the role of molecular pathways regulated by the liver in metabolic disorders and cardiovascular disease. Plasma levels of liver enzymes provide insights into hepatic function and related diseases. Here, the authors perform a genome-wide association study on three liver enzymes, identifying genetic variants associated with their plasma concentration as well as links to metabolic and cardiovascular diseases.

Effective, low-cost therapeutics are needed to prevent and treat COVID-19. Severe COVID-19 disease is linked to excessive inflammation. Disulfiram is an approved oral drug used to treat alcohol use disorder that is a potent anti-inflammatory agent and an inhibitor of the viral proteases. We investigated the potential effects of disulfiram on SARS-CoV-2 infection and disease severity in an observational study using a large database of clinical records from the national US Veterans Affairs healthcare system. A multivariable Cox regression adjusted for demographic information and diagnosis of alcohol use disorder revealed a reduced risk of SARS-CoV-2 infection with disulfiram use at a hazard ratio of 0.66 (34% lower risk, 95% confidence interval 24–43%). There were no COVID-19 related deaths among the 188 SARS-CoV-2 positive patients treated with disulfiram, in contrast to 5–6 statistically expected deaths based on the untreated population (P = 0.03). Our epidemiological results suggest that disulfiram may contribute to the reduced incidence and severity of COVID-19. These results support carefully planned clinical trials to assess the potential therapeutic effects of disulfiram in COVID-19.

In a 48-week trial in patients with active rheumatoid arthritis despite treatment with methotrexate, adding sulfasalazine and hydroxychloroquine to methotrexate was not inferior to adding etanercept. The prognosis for patients with rheumatoid arthritis has improved dramatically over the past two decades. 1 , 2 The reasons for the improved prognosis include earlier diagnosis, treatment targeted to low disease activity or remission, the use of disease-modifying antirheumatic drugs (DMARDs) in combinations, and the availability of biologic therapies. 1 – 4 A substantial portion of patients who are diagnosed today will have a clinical remission with therapy. 1 , 2 , 5 , 6 Unfortunately, the cost of treating rheumatoid arthritis has also risen dramatically, and this disease is now more expensive to treat than diabetes, 7 largely as a consequence of the biologic therapies. Most clinicians . . .

PurposeIn 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.MethodsPatients 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.ResultsOf 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).ConclusionFrail 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.