The burden of undertreatment and non-treatment among patients with non-valvular atrial fibrillation and elevated stroke risk: a systematic review.

OBJECTIVE: Global treatment guidelines recommend treatment with oral anticoagulants (OACs) for patients with non-valvular atrial fibrillation (NVAF) and an elevated stroke risk. However, not all patients with NVAF and an elevated stroke risk receive guideline-recommended therapy. A literature review and synthesis of observational studies were undertaken to identify the body of evidence on untreated and undertreated NVAF and the association with clinical and economic outcomes. METHODS: An extensive search (1/2010-4/2020) of MEDLINE, the Cochrane Library, conference proceedings, and health technology assessments (HTAs) was conducted. Studies must have evaluated rates of nontreatment or undertreatment in NVAF. Nontreatment was defined as absence of OACs (but with possible antiplatelet treatment), while undertreatment was defined as treatment with only antiplatelet agents. RESULTS: Sixteen studies met our inclusion criteria. Rates of nontreatment for patients with elevated stroke risk ranged from 2.0-51.1%, while rates of undertreatment ranged from 10.0-45.1%. The clinical benefits of anticoagulation were reported in the evaluated studies with reductions in stroke and mortality outcomes observed among patients treated with anticoagulants compared to untreated or undertreated patients. Adverse events associated with all bleeding types (i.e. hemorrhagic stroke, major bleeding or gastrointestinal hemorrhaging) were found to be higher for warfarin patients compared to untreated patients in real-world practice. Healthcare resource utilization was found to be lower among patients highly-adherent to warfarin compared to untreated patients. CONCLUSIONS: Rates of nontreatment and undertreatment among NVAF patients remain high and are associated with preventable cardiovascular events and death. Strategies to increase rates of treatment may improve clinical outcomes.

The IMPact of untReated nOn-Valvular atrial fibrillation on short-tErm clinical and economic outcomes in the US Medicare population: the IMPROVE-AF model.

BACKGROUND: Despite treatment guidelines recommending the use of oral anticoagulants (OACs) for patients with non-valvular atrial fibrillation (NVAF) and moderate to high risk of stroke (CHA2DS2-VASc score ≥1), many patients remain untreated. A study conducted among Medicare beneficiaries with AF and a CHA2DS2-VASc score of ≥2 found that 51% of patients were not prescribed an OAC despite being eligible for treatment. When left untreated, NVAF poses an enormous burden to society, as stroke events are estimated to cost the US healthcare system about $34 billion each year in both direct medical costs and indirect productivity losses. This research explored the short-term clinical implications and budget impact (BI) of increasing OAC use among Medicare beneficiaries with NVAF. METHODS: A decision-analytic model was developed from the payer and societal perspectives to estimate the impact of increasing treatment rates among Medicare-eligible NVAF patients with a moderate-to-high risk of stroke over 1 year. Results of the model compared (1) a base case scenario using literature-derived rates of OAC use, and (2) a hypothetical scenario assuming an absolute 5% increase in overall OAC use. Clinical outcomes included the incremental annual number of ischemic stroke, hemorrhagic stroke, and gastrointestinal bleeding events, and stroke-related deaths. Economic outcomes included incremental annual and per-member per-month (PMPM) direct medical costs for the payer perspective and the incremental sum of annual direct medical and indirect costs from productivity loss and caregiver burden for the societal perspective. RESULTS: In total, 1.95 million Medicare patients with NVAF were estimated to be treated with OACs in the base case (3.8% of beneficiaries). In the hypothetical scenario analysis, nearly 200,000 more patients were treated resulting in 3,705 fewer ischemic strokes, 14 fewer gastrointestinal bleeds, 141 more hemorrhagic strokes, and 175 fewer deaths. The total incremental BI was $399.16 million ($0.65 PMPM) from the payer perspective and $377.10 million from the societal perspective due to indirect cost savings ($22.06 million). CONCLUSION: Our findings suggest that increased overall OAC use has a positive clinical benefit on the annual number of ischemic stroke events and deaths avoided in the Medicare population, while maintaining a modest increase in the overall BI to the Medicare system.

Cost-utility analyses of targeted immunomodulators in rheumatoid arthritis: systematic review.

Aims: Cost-utility (CU) modeling is a common technique used to determine whether new treatments represent good value for money. As with any modeling exercise, findings are a direct result of methodology choices, which may vary widely. Several targeted immuno-modulators have been launched in recent years to treat moderate-to-severe rheumatoid arthritis (RA) which have been evaluated using CU methods. Our objectives were to identify common and innovative modeling choices in moderate-to-severe RA and to highlight their implications for future models in RA.Materials and methods: A systematic literature search was conducted to identify CU models in moderate-to-severe RA published from January 2013 to June 2019. Studies must have included an active comparator and used quality-adjusted life-years (QALYs) as the common measure of effectiveness. Modeling methods were characterized by stakeholder perspective, simulation type, mapping between parameters, and data sources.Results: Thirty-one published modeling studies were reviewed spanning 13 countries and 9 drugs, with common methodological choices and innovations observed among them. Over the evaluated time period, we observed common methods and assumptions that are becoming more prominent in the RA CU modeling landscape, including patient-level simulations, two-stage models combining trial results and real-world evidence, real-world treatment durations, long-term health consequences, and Health Assessment Questionnaire (HAQ)-related hospitalization costs. Models that consider the societal perspective are increasingly being developed as well.Limitations: This review did not consider studies that did not report QALYs as a utility measure, models published only as conference abstracts, or cost-consequence models that did not report an incremental CU ratio.Conclusions: CU modeling for RA increasingly reflects real-world conditions and patient experiences which are anticipated to provide better information in the assessment of health technologies. Future CU models in RA should consider applying the observed advances in modeling choices to optimize their CU predictions and simulation of real-world outcomes.

Integrating high-content screening and ligand-target prediction to identify mechanism of action.

High-content screening is transforming drug discovery by enabling simultaneous measurement of multiple features of cellular phenotype that are relevant to therapeutic and toxic activities of compounds. High-content screening studies typically generate immense datasets of image-based phenotypic information, and how best to mine relevant phenotypic data is an unsolved challenge. Here, we introduce factor analysis as a data-driven tool for defining cell phenotypes and profiling compound activities. This method allows a large data reduction while retaining relevant information, and the data-derived factors used to quantify phenotype have discernable biological meaning. We used factor analysis of cells stained with fluorescent markers of cell cycle state to profile a compound library and cluster the hits into seven phenotypic categories. We then compared phenotypic profiles, chemical similarity and predicted protein binding activities of active compounds. By integrating these different descriptors of measured and potential biological activity, we can effectively draw mechanism-of-action inferences.

A support vector machine classifier for recognizing mitotic subphases using high-content screening data.

High-content screening studies of mitotic checkpoints are important for identifying cancer targets and developing novel cancer-specific therapies. A crucial step in such a study is to determine the stage of cell cycle. Due to the overwhelming number of cells assayed in a high-content screening experiment and the multiple factors that need to be taken into consideration for accurate determination of mitotic subphases, an automated classifier is necessary. In this article, the authors describe in detail a support vector machine (SVM) classifier that they have implemented to recognize various mitotic subphases. In contrast to previous studies to recognize subcellular patterns, they used only low-resolution cell images and a few parameters that can be calculated inexpensively with off-the-shelf image-processing software. The performance of the SVM was evaluated with a cross-validation method and was shown to be comparable to that of a human expert.