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BACKGROUND: Programmed cell death protein 1 (PD-1) inhibitors prolong survival versus chemotherapy in recurrent/metastatic head and neck squamous cell carcinoma (R/M HNSCC), which often expresses cytotoxic T-lymphocyte-associated protein 4 (CTLA-4) and programmed cell death-ligand 1 (PD-L1), providing a rationale for combined PD-(L)1 and CTLA-4 blockade. We report a phase I, open-label study of the PD-L1 inhibitor durvalumab plus the CTLA-4 inhibitor tremelimumab (NCT02262741). METHODS: In dose exploration, two cohorts of previously treated patients received durvalumab 10 mg/kg plus tremelimumab 3 mg/kg, or durvalumab 20 mg/kg plus tremelimumab 1 mg/kg, for up to 12 months. Dose expansion comprised two cohorts of previously untreated patients with R/M HNSCC having baseline PD-L1 tumor cell (TC) expression ≥25% and

IntroductionCancer patients may have increased risk for adverse cardiac events, but our understanding of cardiovascular risk in thymic cancer patients is not clear. We sought to characterize baseline cardiometabolic risk factors before thymic cancer diagnosis and the potential association between cancer treatment and subsequent cardiac events.MethodsThis was a retrospective cohort study evaluating patients with thymic cancer from 2003 to 2020 compared to age- and sex-matched controls without cancer. Baseline cardiovascular risk factors, cancer characteristics, and incidence of cardiac events were collected from the health information exchange. Multivariable regression was used to examine the impact of cardiovascular risk factors and cancer therapies.ResultsWe compared 296 patients with pathology-confirmed thymic cancer to 2,960 noncancer controls. Prior to cancer diagnosis, thymic cancer patients (TCPs) had lower prevalence of hypertension, dyslipidemia, and diabetes mellitus and similar rates of obesity, tobacco use, and pre-existing cardiovascular disease (CVD) compared to controls. After diagnosis, high-risk TCPs (>2 cardiovascular risk factors or pre-existing CVD) had higher risk for cardiac events (HR 3.73, 95% CI 2.88–4.83, p

Rationale: Observational studies report a significant protective effect of antifibrotics on mortality among patients with idiopathic pulmonary fibrosis (IPF). Many of these studies, however, were subject to immortal time bias because of the mishandling of delayed antifibrotic initiation. Objectives: To evaluate the antifibrotic effect on mortality among patients with IPF using appropriate statistical methods that avoid immortal time bias. Methods: Using a large administrative database, we identified 10,289 patients with IPF, of whom 2,300 used antifibrotics. Treating delayed antifibrotic initiation as a time-dependent variable, three statistical methods were used to control baseline characteristics and avoid immortal time bias. Stratified analysis was performed for patients who initiated antifibrotics early and those who initiated treatment late. For comparison, methods that mishandle immortal time bias were performed. A simulation study was conducted to demonstrate the performance of these models in a wide range of scenarios. Results: All three statistical methods yielded nonsignificant results for the antifibrotic effect on mortality, with the stratified analysis for patients with early antifibrotic initiation suggesting evidence for reduced mortality risk (for all patients, hazard ratio, 0.89; 95% confidence interval, 0.79–1.01; P = 0.08; for patients who were 65 years or older, hazard ratio, 0.85; 95% confidence interval, 0.73–0.98; P = 0.03). Methods that mishandle immortal time bias demonstrated significantly lower mortality risk for antifibrotic users. Bias of these methods was evident in the simulation study, where appropriate methods performed well with little to no bias. Conclusions: Findings in this study did not confirm an association between antifibrotics and mortality, with a stratified analysis showing support for a potential treatment effect with early treatment initiation. [ABSTRACT FROM AUTHOR]

Abstract Background Many patients with atrial fibrillation (AF) remain undiagnosed despite availability of interventions to reduce stroke risk. Predictive models to date are limited by data requirements and theoretical usage. We aimed to develop a model for predicting the 2-year probability of AF diagnosis and implement it as proof-of-concept (POC) in a production electronic health record (EHR). Methods We used a nested case–control design using data from the Indiana Network for Patient Care. The development cohort came from 2016 to 2017 (outcome period) and 2014 to 2015 (baseline). A separate validation cohort used outcome and baseline periods shifted 2 years before respective development cohort times. Machine learning approaches were used to build predictive model. Patients ≥ 18 years, later restricted to age ≥ 40 years, with at least two encounters and no AF during baseline, were included. In the 6-week EHR prospective pilot, the model was silently implemented in the production system at a large safety-net urban hospital. Three new and two previous logistic regression models were evaluated using receiver-operating characteristics. Number, characteristics, and CHA2DS2-VASc scores of patients identified by the model in the pilot are presented. Results After restricting age to ≥ 40 years, 31,474 AF cases (mean age, 71.5 years; female 49%) and 22,078 controls (mean age, 59.5 years; female 61%) comprised the development cohort. A 10-variable model using age, acute heart disease, albumin, body mass index, chronic obstructive pulmonary disease, gender, heart failure, insurance, kidney disease, and shock yielded the best performance (C-statistic, 0.80 [95% CI 0.79–0.80]). The model performed well in the validation cohort (C-statistic, 0.81 [95% CI 0.8–0.81]). In the EHR pilot, 7916/22,272 (35.5%; mean age, 66 years; female 50%) were identified as higher risk for AF; 5582 (70%) had CHA2DS2-VASc score ≥ 2. Conclusions Using variables commonly available in the EHR, we created a predictive model to identify 2-year risk of developing AF in those previously without diagnosed AF. Successful POC implementation of the model in an EHR provided a practical strategy to identify patients who may benefit from interventions to reduce their stroke risk.

BackgroundData on initiation and utilization of direct-acting antiviral therapies for hepatitis C virus infection in the United States are limited. This study evaluated treatment initiation, time to treatment, and real-world effectiveness of direct-acting antiviral therapy in individuals with hepatitis C virus infection treated during the first 2 years of availability of all-oral direct-acting antiviral therapies.MethodsA retrospective cohort analysis was undertaken using electronic medical records and chart review abstraction of hepatitis C virus-infected individuals aged >18 years diagnosed with chronic hepatitis C virus infection between January 1, 2014, and December 31, 2015 from the Indiana University Health database.ResultsEight hundred thirty people initiated direct-acting antiviral therapy during the 2-year observation window. The estimated incidence of treatment initiation was 8.8%±0.34% at the end of year 1 and 15.0%±0.5% at the end of year 2. Median time to initiating therapy was 300 days. Using a Cox regression analysis, positive predictors of treatment initiation included age (hazard ratio, 1.008), prior hepatitis C virus treatment (1.74), cirrhosis (2.64), and history of liver transplant (1.5). History of drug abuse (0.43), high baseline alanine aminotransferase levels (0.79), hepatitis B virus infection (0.41), and self-pay (0.39) were negatively associated with treatment initiation. In the evaluable population (n = 423), 83.9% (95% confidence interval, 80.1-87.3%) of people achieved sustained virologic response.ConclusionIn the early years of the direct-acting antiviral era,