Linking Symptom Inventories using Semantic Textual Similarity

An extensive library of symptom inventories has been developed over time to measure clinical symptoms, but this variety has led to several long standing issues. Most notably, results drawn from different settings and studies are not comparable, which limits reproducibility. Here, we present an artificial intelligence (AI) approach using semantic textual similarity (STS) to link symptoms and scores across previously incongruous symptom inventories. We tested the ability of four pre-trained STS models to screen thousands of symptom description pairs for related content - a challenging task typically requiring expert panels. Models were tasked to predict symptom severity across four different inventories for 6,607 participants drawn from 16 international data sources. The STS approach achieved 74.8% accuracy across five tasks, outperforming other models tested. This work suggests that incorporating contextual, semantic information can assist expert decision-making processes, yielding gains for both general and disease-specific clinical assessment

Finishing the euchromatic sequence of the human genome

The sequence of the human genome encodes the genetic instructions for human physiology, as well as rich information about human evolution. In 2001, the International Human Genome Sequencing Consortium reported a draft sequence of the euchromatic portion of the human genome. Since then, the international collaboration has worked to convert this draft into a genome sequence with high accuracy and nearly complete coverage. Here, we report the result of this finishing process. The current genome sequence (Build 35) contains 2.85 billion nucleotides interrupted by only 341 gaps. It covers ∼99% of the euchromatic genome and is accurate to an error rate of ∼1 event per 100,000 bases. Many of the remaining euchromatic gaps are associated with segmental duplications and will require focused work with new methods. The near-complete sequence, the first for a vertebrate, greatly improves the precision of biological analyses of the human genome including studies of gene number, birth and death. Notably, the human enome seems to encode only 20,000-25,000 protein-coding genes. The genome sequence reported here should serve as a firm foundation for biomedical research in the decades ahead

Proceedings of the 3rd Biennial Conference of the Society for Implementation Research Collaboration (SIRC) 2015: advancing efficient methodologies through community partnerships and team science

It is well documented that the majority of adults, children and families in need of evidence-based behavioral health interventionsi do not receive them [1, 2] and that few robust empirically supported methods for implementing evidence-based practices (EBPs) exist. The Society for Implementation Research Collaboration (SIRC) represents a burgeoning effort to advance the innovation and rigor of implementation research and is uniquely focused on bringing together researchers and stakeholders committed to evaluating the implementation of complex evidence-based behavioral health interventions. Through its diverse activities and membership, SIRC aims to foster the promise of implementation research to better serve the behavioral health needs of the population by identifying rigorous, relevant, and efficient strategies that successfully transfer scientific evidence to clinical knowledge for use in real world settings [3]. SIRC began as a National Institute of Mental Health (NIMH)-funded conference series in 2010 (previously titled the “Seattle Implementation Research Conference”; $150,000 USD for 3 conferences in 2011, 2013, and 2015) with the recognition that there were multiple researchers and stakeholdersi working in parallel on innovative implementation science projects in behavioral health, but that formal channels for communicating and collaborating with one another were relatively unavailable. There was a significant need for a forum within which implementation researchers and stakeholders could learn from one another, refine approaches to science and practice, and develop an implementation research agenda using common measures, methods, and research principles to improve both the frequency and quality with which behavioral health treatment implementation is evaluated. SIRC’s membership growth is a testament to this identified need with more than 1000 members from 2011 to the present.ii SIRC’s primary objectives are to: (1) foster communication and collaboration across diverse groups, including implementation researchers, intermediariesi, as well as community stakeholders (SIRC uses the term “EBP champions” for these groups) – and to do so across multiple career levels (e.g., students, early career faculty, established investigators); and (2) enhance and disseminate rigorous measures and methodologies for implementing EBPs and evaluating EBP implementation efforts. These objectives are well aligned with Glasgow and colleagues’ [4] five core tenets deemed critical for advancing implementation science: collaboration, efficiency and speed, rigor and relevance, improved capacity, and cumulative knowledge. SIRC advances these objectives and tenets through in-person conferences, which bring together multidisciplinary implementation researchers and those implementing evidence-based behavioral health interventions in the community to share their work and create professional connections and collaborations

Factors Related to Physician Clinical Decision-Making for African-American and Hispanic Patients : a Qualitative Meta-Synthesis

Clinical decision-making may have a role in racial and ethnic disparities in healthcare but has not been evaluated systematically. The purpose of this study was to synthesize qualitative studies that explore various aspects of how a patient's African-American race or Hispanic ethnicity may factor into physician clinical decision-making. Using Ovid MEDLINE, Embase, and Cochrane Library, we identified 13 manuscripts that met inclusion criteria of usage of qualitative methods; addressed US physician clinical decision-making factors when caring for African-American, Hispanic, or Caucasian patients; and published between 2000 and 2017. We derived six fundamental themes that detail the role of patient race and ethnicity on physician decision-making, including importance of race, patient-level issues, system-level issues, bias and racism, patient values, and communication. In conclusion, a non-hierarchical system of intertwining themes influenced clinical decision-making among racial and ethnic minority patients. Future study should systematically intervene upon each theme in order to promote equitable clinical decision-making among diverse racial/ethnic patients

Contemporary patterns of lipoprotein(a) testing and associated clinical care and outcomes

Objective: Elevated lipoprotein(a) [Lp(a)] is associated with atherosclerotic cardiovascular disease, yet little is known about Lp(a) testing patterns in real-world practice. The objective of this analysis was to determine how Lp(a) testing is used in clinical practice in comparison with low density lipoprotein cholesterol (LDL-C) testing alone, and to determine whether elevated Lp(a) level is associated with subsequent initiation of lipid-lowering therapy (LLT) and incident cardiovascular (CV) events. Methods: This is an observational cohort study, based on lab tests administered between Jan 1, 2015 and Dec 31, 2019. We used electronic health record (EHR) data from 11 United States health systems participating in the National Patient-Centered Clinical Research Network (PCORnet). We created two cohorts for comparison: 1) the Lp(a) cohort, of adults with an Lp(a) test and 2) the LDL-C cohort, of 4:1 date- and site-matched adults with an LDL-C test, but no Lp(a) test. The primary exposure was the presence of an Lp(a) or LDL-C test result. In the Lp(a) cohort, we used logistic regression to assess the relationship between Lp(a) results in mass units ( 100mg/dL) and molar units ( 250nmol/L) and initiation of LLT within 3 months. We used multivariable adjusted Cox proportional hazards regression to evaluate these Lp(a) levels and time to composite CV hospitalization, including hospitalization for myocardial infarction, revascularization and ischemic stroke. Results: Overall, 20,551 patients had Lp(a) test results and 2,584,773 patients had LDL-C test results (82,204 included in the matched LDL-C cohort). Compared with the LDL-C cohort, the Lp(a) cohort more frequently had prevalent ASCVD (24.3% vs. 8.5%) and multiple prior CV events (8.6% vs. 2.6%). Elevated Lp(a) was associated with greater odds of subsequent LLT initiation. Elevated Lp(a) reported in mass units was also associated with subsequent composite CV hospitalization [aHR (95% CI): Lp(a) 50-100mg/dL 1.25 (1.02-1.53), p 100mg/dL 1.23 (1.08-1.40), p<0.01]. Conclusion: Lp(a) testing is relatively infrequent in health systems across the U.S. As new therapies for Lp(a) emerge, improved patient and provider education is needed to increase awareness of the utility of this risk marker