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Background Migraine is one of the most common neurological disorders in clinical practice and is a substantial cause of disability worldwide. Current approaches to therapy are primarily based on medication but are often limited by inadequate effectiveness and common side effects. Newer, more effective medications are expensive. Mindfulness-based stress reduction (MBSR), an 8-week classroom-based meditation intervention, is inexpensive, has no known side effects, and has demonstrated clinically meaningful effectiveness for several chronic-pain syndromes. In addition, MBSR has shown promising results for migraine therapy in a few small case studies and pilot studies. We present here the protocol for a two-arm randomized controlled pilot trial of MBSR for moderate-to-severe episodic migraine, which, if successful, will form the basis for a fully powered clinical trial. Methods/design This study, set in Northern California, is a two-arm parallel-comparison single-blinded randomized controlled pilot trial with the goal of recruiting approximately 60 participants with moderate-to-severe episodic migraine. The feasibility outcomes include ability and time required to recruit, adherence to the MBSR treatment, and ability to measure outcomes using 31-day headache diaries and patient-reported questionnaire data. The active treatment arm consists of an 8-week community-based MBSR class plus usual care, and the wait-list control group is usual care. Recruitment is underway and expected to be complete by the end of 2018. Discussion To our knowledge, this is the first pragmatic trial in the U.S. of MBSR for migraine using community-based classes, and if it proves viable, we plan to conduct a fully powered trial to determine the effectiveness of the intervention for reducing headache days for moderate-to-severe episodic migraineurs. Trial registration Clinicaltrials.gov, NCT02824250. Registered on 6 July 2016. Electronic supplementary material The online version of this article (10.1186/s13063-019-3355-y) contains supplementary material, which is available to authorized users.

Introduction: Several current guidelines assess sonographic features to guide management of thyroid nodules. The ACR uses an additive point system to assign the level of risk to various sonographic features, whereas the ATA groups sonographic features together to determine the level of risk. The purpose of this study is to compare the performance of the ATA guidelines and ACR TI-RADS at an urban endocrinology clinic in risk stratifying thyroid nodules by their specific sonographic features. Methods: This retrospective, chart-review study includes adult patients who met sonographic criteria for fine needle aspiration (FNA) biopsy based on ATA or ACR TI-RADS at an outpatient endocrinology practice in San Francisco, CA between December 2011 and August 2019. Patients with a prior history of thyroid malignancy (anaplastic and medullary thyroid carcinoma or thyroid lymphoma) were excluded. The reference standard for the diagnosis of malignancy was surgical pathology or FNA cytology Bethesda category V or VI when surgical pathology was unavailable. Analysis of guideline performances and specific sonographic features included: sensitivities (Sn), specificities (Sp), positive predictive values (PPV), negative predictive values (NPV), and area-under-the-curve (AUC) using Fisher’s exact test. Results: Two hundred seventy-five nodules among 195 adults (86.2% were women) were included in the analysis. Twelve nodules were malignant, with an associated malignancy rate of 4.4%. TI-RADS had higher accuracy based on AUC of 0.710 compared to 0.623 using ATA guidelines. TI-RADS also had a higher PPV of 21.4% among nodules with 9 points, versus 5.8% among nodules in the ATA “high suspicion” category. Ultrasound characteristics with the highest Sp, relative PPV and NPV were: microcalcifications (84.5%, 4.3%, 96.0%, respectively), taller-than-wide (81.7%, 7.1%, 96.7%), irregular margins (77.7%, 6.0%, 96.5%); the characteristic with the highest Sn was hypoechogenicity (83.3%), however this had relatively low Sp (25.4%) and PPV (4.5%). Conclusions: TI-RADS performed better with higher overall accuracy and PPV when applied to nodules classified as having the highest malignancy risk. Taller-than-wide shape, irregular margins, and microcalcifications were the characteristics most useful for malignancy risk stratification. Limitations of this study include: interobserver bias, small sample size, referred patient population (which may differ from other institutions), and inability in some cases to confirm malignant FNA cytology with surgical pathology.

Graphical abstract, Data quality is essential to the success of the most simple and the most complex analysis. In the context of the COVID-19 pandemic, large-scale data sharing across the US and around the world has played an important role in public health responses to the pandemic and has been crucial to understanding and predicting its likely course. In California, hospitals have been required to report a large volume of daily data related to COVID-19. In order to meet this need, electronic health records (EHRs) have played an important role, but the challenges of reporting high-quality data in real-time from EHR data sources have not been explored. We describe some of the challenges of utilizing EHR data for this purpose from the perspective of a large, integrated, mixed-payer health system in northern California, US. We emphasize some of the inadequacies inherent to EHR data using several specific examples, and explore the clinical-analytic gap that forms the basis for some of these inadequacies. We highlight the need for data and analytics to be incorporated into the early stages of clinical crisis planning in order to utilize EHR data to full advantage. We further propose that lessons learned from the COVID-19 pandemic can result in the formation of collaborative teams joining clinical operations, informatics, data analytics, and research, ultimately resulting in improved data quality to support effective crisis response.

Avoidable hospital readmissions not only contribute to the high costs of healthcare in the US, but also have an impact on the quality of care for patients. Large scale adoption of Electronic Health Records (EHR) has created the opportunity to proactively identify patients with high risk of hospital readmission, and apply effective interventions to mitigate that risk. To that end, in the past, numerous machine-learning models have been employed to predict the risk of 30-day hospital readmission. However, the need for an accurate and real-time predictive model, suitable for hospital setting applications still exists. Here, using data from more than 300,000 hospital stays in California from Sutter Health's EHR system, we built and tested an artificial neural network (NN) model based on Google's TensorFlow library. Through comparison with other traditional and non-traditional models, we demonstrated that neural networks are great candidates to capture the complexity and interdependency of various data fields in EHRs. LACE, the current industry standard, showed a precision (PPV) of 0.20 in identifying high-risk patients in our database. In contrast, our NN model yielded a PPV of 0.24, which is a 20% improvement over LACE. Additionally, we discussed the predictive power of Social Determinants of Health (SDoH) data, and presented a simple cost analysis to assist hospitalists in implementing helpful and cost-effective post-discharge interventions.