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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.

Coronavirus disease 2019 (COVID-19) hospitalization definitions do not include a disease severity assessment. Thus, we sought to identify a simple and objective mechanism for identifying hospitalized severe cases and to measure the impact of vaccination on trends. All admissions to a Veterans' Affairs (VA) hospital, where routine inpatient screening is recommended, between March 1, 2020, and November 22, 2021, with laboratory-confirmed severe acute respiratory coronavirus virus 2 (SARS-CoV-2) were included. Moderate-to-severe COVID-19 was defined as any oxygen supplementation or any oxygen saturation (SpO ) <94% between 1 day before and 2 weeks after the positive SARS-CoV-2 test. Admissions with moderate-to-severe disease were divided by the total number of admissions, and the proportion of admissions with moderate-to-severe COVID-19 was modelled using a penalized spline in a Poisson regression and stratified by vaccination status. Dexamethasone receipt and its correlation with moderate-to-severe cases was also assessed. Among 67,025 admissions with SARS-CoV-2, the proportion with hypoxemia or supplemental oxygen fell from 64% prior to vaccine availability to 56% by November 2021, driven in part by lower rates in vaccinated patients (vaccinated, 52% versus unvaccinated, 58%). The proportion of cases of moderate-to-severe disease identified using SpO levels and oxygen supplementation was highly correlated with dexamethasone receipt (correlation coefficient, 0.95), and increased after July 1, 2021, concurrent with δ (delta) variant predominance. A simple and objective definition of COVID-19 hospitalizations using SpO levels and oxygen supplementation can be used to track pandemic severity. This metric could be used to identify risk factors for severe breakthrough infections, to guide clinical treatment algorithms, and to detect trends in changes in vaccine effectiveness over time and against new variants.

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.

Background The global COVID-19 pandemic is an opportunity to evaluate factors associated with high levels of adoption of different therapeutics in a real-world setting. The aim of this nationwide, retrospective cohort study was to evaluate the diffusion and adoption of novel therapeutics with an emerging evidence basis and to identify factors that influenced physicians’ treatment decisions. Methods Cohort creation : A cohort of Veteran patients with a microbiologically confirmed diagnosis of SARS-CoV2 were identified, and cases were classified by disease severity (outpatient, inpatient with mild and severe disease, intensive care unit ICU]). After classification of disease severity, the proportion of cases (outpatients) and admissions (inpatients) in each category receiving each type of medication were plotted as a function of time. Identification of milestones and guidance changes : Key medications used for the management of COVID-19 milestones in the release of primary research results in various forms (e.g. via press release, preprint or publication in a traditional medical journal), policy events and dates of key guidelines were identified and plotted as a timeline. After a timeline was created, time points were compared to changes in medication use, and factors potentially impacting the magnitude (i.e. proportion of patients who received the treatment) and the speed (i.e. the slope of the change in use) of practice changes were evaluated. Results Dexamethasone and remdesivir, the first two medications with clinical trial data to support their use, underwent the most rapid, complete and sustained diffusion and adoption; the majority of practice changes occurred after press releases and preprints were available and prior to guideline changes, although some additional uptake occurred following guideline updates. Medications that were not “first in class”, that were identified later in the pandemic, and that had higher perceived risk had slower and less complete uptake regardless of the strength and quality of the evidence supporting the intervention. Conclusions Our findings suggest that traditional and social media platforms and preprint releases were major catalysts of practice change, particularly prior to the identification of effective treatments. The “first available treatment in class” impact appeared to be the single most important factor determining the speed and scope of diffusion.

Objective: Active neutrophils are important contributors to Alzheimer’s disease (AD) pathology through the formation of capillary stalls that compromise cerebral blood flow (CBF) and through aberrant neutrophil signaling that advances disease progression. The neutrophil to lymphocyte ratio (NLR) is a proxy of neutrophil-mediated inflammation, and higher NLR is found in persons diagnosed with clinical AD. The objective of this study was to investigate whether increased NLR in older adults is independently associated with the risk of subsequent dementia. Methods: We examined associations of baseline NLR with incident dementia risk in the community-based Framingham Heart Study (FHS) longitudinal cohorts. The association between NLR and risk of dementia was evaluated using the cumulative incidence function (CIF) and inverse probability-weighted Cox proportional cause-specific hazards regression models, with adjustment for age, sex, body mass index (BMI), systolic and diastolic blood pressure, diabetes, current smoking status, low-density lipoprotein (LDH), high-density lipoprotein (LDL), total cholesterol, triglycerides, and history of cardiovascular disease (CVD). Random forest survival models were used to evaluate the relative predictive value of the model covariates on dementia risk. Results: The final study sample included 1,648 participants with FHS (average age, 69 years; 56% women). During follow-up (median, 5.9 years), we observed 51 cases of incident dementia, of which 41 were AD cases. Results from weighted models suggested that the NLR was independently associated with incident dementia, and it was preceded in predictive value only by age, history of CVD, and blood pressure at baseline. Conclusion: Our study shows that individuals with higher NLR are at a greater risk of subsequent dementia during a 5.9-year follow-up period. Further evaluating the role of neutrophil-mediated inflammation in AD progression may be warranted.

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.

Previous studies evaluated the SARS-CoV-2 vaccine safety or compared adverse events following vaccination to those from infection. Limited data about the impact of prior infection on post-vaccine adverse events are available. The objective of this study was to evaluate the impact of prior SARS-CoV-2 infection on outcomes shortly after vaccination using a longitudinal design. Nationwide, multicenter, retrospective cohort study of hospitalization, death, and pre-specified adverse event rates among Veterans who received mRNA vaccines within the Veterans Health Administration between 12/11/2020 and 8/31/2021. Daily incidence rates were compared before and after vaccine doses, stratified by history of microbiologically-confirmed SARS-CoV-2. 3,118,802 patients received a first dose and 2,979,326 a second, including 102,829 with a history of SARS-CoV-2 infection. Daily incident hospitalization rates were unchanged before and after the second dose among patients without previous infection (28.8/100,000 post-dose versus 28.6/100,000 pre-dose, p = 0.92). In previously-infected patients, the hospitalization rate increased above baseline one day following vaccination (158.2/100,000 after dose 2 versus 57.3/100,000 pre-dose, p < 0.001), then returned to baseline. Chart review indicated vaccine side effects, such as fever, constitutional symptoms, weakness, or falls, as the definite (39%) or possible (18%) cause of hospitalization. Affected patients had mean age 75, and 90% had at least one serious comorbidity. Hospitalizations were brief (median 2 days), with rapid return to baseline health. Worse baseline health among previously-infected patients prevented conclusions about mortality risk. Two-dose mRNA vaccine regimens are safe in a population with many comorbidities. Transient increased risks of hospitalization were identified among patients with prior SARS-CoV-2, absolute risk ∼1:1000. Findings support additional study regarding the optimal dosing schedule in this population. None.