Creating a Policy for Coverage of Lymphatic Surgery: Addressing a Critical Unmet Need.

SUMMARY: This article describes the key stakeholders and process involved in developing an insurance policy in the United States to establish medical necessity criteria for lymphatic surgery procedures. Lymphedema is a chronic health issue that impacts over 1.2 million patients and is associated with lifelong health, economic, and psychosocial costs. Patients affected have been described as "medical nomads," as they often interface with multiple providers before receiving an accurate diagnosis and treatment. This underscores the lack of attention and understanding about this disease across all sectors of the medical system. Unlike nations including Sweden and the United Kingdom, which provide insurance coverage for treatment, the United States has lagged behind. As a country, we have neglected to fully recognize the consequences of inadequate treatment of lymphedema, including chronic morbidities such as loss of mobility, psychosocial sequelae, recurrent infections, and even death. Recently, the authors' lymphatic center had the unique opportunity to help develop a policy that merged their clinical experience, recently established lymphatic care center of excellence criteria, and third-party payer policy expertise. This experience spanned 1 year from June of 2018 to June of 2019. The authors identify how key partnerships helped fill evidentiary gaps that ultimately resulted in policy change.

Birth cohort hepatitis C antibody prevalence in real-world screening settings in Ontario.

Background: Widespread screening and treatment of hepatitis C virus (HCV) is required to decrease late-stage liver disease and liver cancer. Clinical practice guidelines and Canadian Task Force on Preventative Health Care recommendations differ on the value of one-time birth cohort (1945-75) HCV screening in Canada. To assess the utility of this approach, we conducted a real-world analysis of HCV antibody (Ab) prevalence among birth cohort individuals seen in different clinical contexts. Methods: Cross-sectional study of individuals born between 1945 and 1975 who completed HCV Ab testing at multiple participating centres in Ontario, Canada between January 2016 and December 2020. Differences in prevalence were compared by year of birth, gender, and setting. Results: Among 16,672 birth cohort individuals tested, HCV Ab prevalence was 3.2%. Prevalence was higher among younger individuals which increased from 0.9% among those born between 1945 and 1956 to 4.6% among those born between 1966 and 1975. Prevalence was higher among males (4.4%) compared with females (2.0%) and differed by test site. In primary care, the prevalence was 0.5%, whereas the prevalence was highest among those tested at drug treatment centres (28.7%) and through community outreach (14.0%). Conclusions: HCV Ab prevalence remains high in the 1945-1975 birth cohort. These data highlight the need to re-evaluate existing Canadian Preventative Task Force recommendations, to consider incorporating one-time birth cohort and/or other population-based approaches to HCV screening into the clinical workflow as a preventative health measure, and to increase training among community providers to screen for and treat HCV.

COVID-19 Mortality Prediction From Deep Learning in a Large Multistate Electronic Health Record and Laboratory Information System Data Set: Algorithm Development and Validation.

BACKGROUND: COVID-19 is caused by the SARS-CoV-2 virus and has strikingly heterogeneous clinical manifestations, with most individuals contracting mild disease but a substantial minority experiencing fulminant cardiopulmonary symptoms or death. The clinical covariates and the laboratory tests performed on a patient provide robust statistics to guide clinical treatment. Deep learning approaches on a data set of this nature enable patient stratification and provide methods to guide clinical treatment. OBJECTIVE: Here, we report on the development and prospective validation of a state-of-the-art machine learning model to provide mortality prediction shortly after confirmation of SARS-CoV-2 infection in the Mayo Clinic patient population. METHODS: We retrospectively constructed one of the largest reported and most geographically diverse laboratory information system and electronic health record of COVID-19 data sets in the published literature, which included 11,807 patients residing in 41 states of the United States of America and treated at medical sites across 5 states in 3 time zones. Traditional machine learning models were evaluated independently as well as in a stacked learner approach by using AutoGluon, and various recurrent neural network architectures were considered. The traditional machine learning models were implemented using the AutoGluon-Tabular framework, whereas the recurrent neural networks utilized the TensorFlow Keras framework. We trained these models to operate solely using routine laboratory measurements and clinical covariates available within 72 hours of a patient's first positive COVID-19 nucleic acid test result. RESULTS: The GRU-D recurrent neural network achieved peak cross-validation performance with 0.938 (SE 0.004) as the area under the receiver operating characteristic (AUROC) curve. This model retained strong performance by reducing the follow-up time to 12 hours (0.916 [SE 0.005] AUROC), and the leave-one-out feature importance analysis indicated that the most independently valuable features were age, Charlson comorbidity index, minimum oxygen saturation, fibrinogen level, and serum iron level. In the prospective testing cohort, this model provided an AUROC of 0.901 and a statistically significant difference in survival (P<.001, hazard ratio for those predicted to survive, 95% CI 0.043-0.106). CONCLUSIONS: Our deep learning approach using GRU-D provides an alert system to flag mortality for COVID-19-positive patients by using clinical covariates and laboratory values within a 72-hour window after the first positive nucleic acid test result.

Improving emergency department flow through Rapid Medical Evaluation unit.

The Toronto Western Hospital is an academic hospital in Toronto, Canada, with an annual Emergency Department (ED) volume of 64,000 patients. Despite increases in patient volumes of almost six percent per annum over the last decade, there have been no commensurate increases in resources, infrastructure, and staffing. This has led to substantial increase in patient wait times, most specifically for those patients with lower acuity presentations. Despite requiring only minimal care, these patients contribute disproportionately to ED congestion, which can adversely impact resource utilization and quality of care for all patients. We undertook a retrospective evaluation of a quality improvement initiative aimed at improving wait times experienced by patients with lower acuity presentations. A rapid improvement event was organized by frontline workers to rapidly overhaul processes of care, leading to the creation of the Rapid Medical Evaluation (RME) unit - a new pathway of care for patients with lower acuity presentations. The RME unit was designed by re-purposing existing resources and re-assigning one physician and one nurse towards the specific care of these patients. We evaluated the performance of the RME unit through measurement of physician initial assessment (PIA) times and total length of stay (LOS) times for multiple groups of patients assigned to various ED care pathways, during three periods lasting three months each. Weekly measurements of mean and 90th percentile of PIA and LOS times showed special cause variation in all targeted patient groups. Of note, the patients seen in the RME unit saw their median PIA and LOS times decrease from 98min to 70min and from 165min to 130min, respectively, from baseline. Despite ever-growing numbers of patient visits, wait times for all patients with lower acuity presentations remained low, and wait times of patients with higher acuity presentations assigned to other ED care pathways were not adversely affected. By specifically re-purposing a fraction of existing staff, resources, and infrastructure for patients with lower acuity presentations, we were able to streamline their care and decrease wait times in the ED. These results were achieved through the incremental improvements afforded by rapidly cycling through PDSA cycles, with strong frontline staff involvement and continuously eliciting feedback for improvement. We believe the model to be replicable in other academic medical centres.

A brain adaptation view of plasticity: is synaptic plasticity an overly limited concept?

A view that is emerging is that the brain has multiple forms of plasticity that must be governed, at least in part, by independent mechanisms. This view is illustrated by: (1) the apparent separate governance of some non-neural changes by activity, in contrast to synaptic changes driven by learning; (2) the apparent independence of different kinds of synaptic changes that occur in response to the learning aspects of training; (3) the occurrence of separate patterns of synaptic plasticity in the same system in response to different task demands; and (4) apparent dissociations between behaviorally induced synaptogenesis and LTP. The historical focus of research and theory in areas ranging from learning and memory to experiential modulation of brain development has been heavily upon synaptic plasticity since shortly after the discovery of the synapse. Based upon available data, it could be argued that: (1) synaptic, and even neuronal, plasticity is but a small fraction of the range of changes that occur in response to experience; and (2) we are just beginning to understand the importance of these other forms of brain plasticity. Appreciation of this aspect of the brain's adaptive process may allow us to better understand the capacity of the brain to tailor a particular set of changes to the demands of the specific experiences that generated them.