Rationale: Despite effective treatments, a large proportion of patients with asthma do not achieve sustained asthma control. The “preventable” burden associated with lack of proper control is likely taking a high toll at the personal and population level.Objectives: We predicted the future excess health and economic burden associated with uncontrolled asthma among American adolescents and adults for the next 20 years.Methods: We built a probabilistic model that linked state-specific estimates of population growth, aging, asthma prevalence, and asthma control levels. We conducted several meta-analyses to estimate the adjusted differences in healthcare resource use, quality-adjusted life years (QALYs), and productivity loss across control levels. We projected, nationally and at the state level, total direct and indirect (due to productivity loss) costs (in 2018 dollars) and QALYs lost because of uncontrolled asthma from 2019 to 2038.Measurements and Main Results: Total 20-year direct costs associated with uncontrolled asthma are estimated to be $300.6 billion (95% confidence interval [CI], $190.1 billion–411.1 billion). When indirect costs are added, total economic burden will be $963.5 billion (95% CI, $664.1 billion–1,262.9 billion). American adolescents and adults will lose an estimated 15.46 million (95% CI, 12.77 million–18.14 million) QALYs over this period because of uncontrolled asthma. Across states, the average 20-year per capita costs due to uncontrolled asthma ranged from $2,209 (Arkansas) to $6,132 (Connecticut).Conclusions: The burden of uncontrolled asthma is substantial and will continue to grow. Given that a substantial fraction of this burden is preventable, better adherence to evidence-informed asthma management strategies by care providers and patients has the potential to substantially reduce costs and improve quality of life.

The growth in the publication of clinical prediction models (CPMs) has been exponential, largely as a result of an ever-increasingavailabilityofclinicaldata,inexpensivecomputational power, and an expanding tool kit for constructing predictive algorithms. Such an abundance of CPMs has led to an overcrowded, confusing landscape in which it is difficult to identify and select the best, most useful models. 1 Few models are externally validated by the same researchers who developed them, and even fewer by independentinvestigators.Only592(43.3%)of1366cardiovascular CPMs in the Tufts PACE Clinical Predictive Model Registry reported at least 1 validation. 2 The proportions of models in the Tufts registry that reported at least 2, 3, and 10 validations were 20.1%, 12.8%, and 2.9%, respectively. 2 A few select CPMs, such as the Framingham Risk Score and EuroSCORE, have had numerous validations.However, even these models are subject to modifications (eg, adding or removing a predictor variable), with the resulting modified model not revalidated externally. Fragmented efforts that assess only one model at a time do not allow for reliable ranking of the comparative performance of the many CPMs available for the same clinical application. A small number of VIEWPOINT

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]. COPD is a major burden globally. According to the Global Burden of Disease study, COPD caused 3.2 million deaths in 2015, accounting for 5% of all deaths worldwide, making it the third leading cause of death in the world [1]. The Global Initiative for Chronic Obstructive Lung Disease (GOLD) defines spirometrically confirmed COPD based on a forced expiratory volume during the first second (FEV 1) to a forced vital capacity (FVC) ratio smaller than 0.7 [2]. The severity of airflow obstruction is further defined through GOLD severity grades based on the ratio of FEV 1 to its predicted value, with GOLD 1, 2, 3 and 4 defined around cutoff points of 80%, 50%, and 30% [2]. While diagnostic and disease management decisions (e.g. therapeutic choices) demand definitions that create distinct categories, the physiological processes underlying COPD act on a continuous scale [3]. For example, it is recognised that patients fall on a continuous spectrum on the three major aspects of COPD: rate of lung function decline [4], frequency of acute COPD exacerbations [5] and symptom burden [6], with little correlation between the three. Categorising such a continuous process inevitably results in COPD phenotypes that are numerous, loosely defined, and not always mutually exclusive [7, 8].

This proof-of-concept study shows that adjuvant MSC therapy for intracranial aneurysms is feasible and may enhance histological improvement of coiled aneurysms at 4 weeks post-treatment.

Background: Asthma diagnosis in the community is often made without objective testing. Objective: The aim of this study was to evaluate the cost-effectiveness of implementing a stepwise objective diagnostic verification algorithm among patients with community-diagnosed asthma in the United States. Methods: We developed a probabilistic time-in-state cohort model that compared a stepwise asthma verification algorithm on the basis of spirometry testing and a methacholine challenge test against the current standard of care over 20 years. Model input parameters were informed from the literature and with original data analyses when required. The target population was US adults (> _15 years old) with physician-diagnosed asthma. The final outcomes were costs (in 2018 dollars) and quality-adjusted life years (QALYs), discounted at 3% annually. Deterministic and probabilistic analyses were undertaken to examine the effect of alternative assumptions and uncertainty in model parameters on the results. Results: In a simulated cohort of 10,000 adults with diagnosed asthma, the stepwise algorithm resulted in removal of the diagnosis of 3,366. This was projected to be associated with savings of $36.26 million in direct costs and a gain of 4,049.28 QALYs over 20 years. Extrapolating these results to the US population indicated an undiscounted potential savings of $56.48 billion over 20 years. The results were robust against alternative assumptions and plausible changes in values of input parameters. Conclusion: Implementation of a simple diagnostic testing algorithm to verify asthma diagnosis might result in substantial savings and improvement in patients' quality of life.