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 It is uncertain whether stroke risk of asymptomatic ambulatory atrial fibrillation (AA-AF) incidentally detected in primary care is comparable with other clinical AF presentations in primary care or hospital.

 The stoke risk of 22,035 patients with incident nonvalvular AF from the United Kingdom primary care Clinical Practice Research Datalink with linkage to hospitalization and mortality data was compared with 23,605 controls without AF (age- and sex-matched 51 to 5,409 AA-AF patients). Incident AF included 5,913 with symptomatic ambulatory AF (SA-AF); 4,989 with primary and 5,724 with nonprimary hospital AF discharge diagnosis (PH-AF and non-PH-AF); and 5,409 with AA-AF. Ischemic stroke adjusted subhazard ratios (aSHRs) within 3 years of AA-AF were compared with SA-AF, PH-AF, non-PH-AF, and no AF, accounting for mortality as competing risk and adjusted for ischemic stroke risk factors.

 There were 1,026 ischemic strokes in 49,544 person-years in patients with incident AF (crude incidence rate 2.1 iscphylaxis, to reduce the approximately 10% of ischemic strokes related to unrecognized AF.

 Considerable variation exists in platelet reactivity to stimulation among healthy individuals. Various metabolites and metabolic pathways influence platelet reactivity, but a comprehensive overview of these associations is missing. The gut microbiome has a strong influence on the plasma metabolome. Here, we investigated the association of platelet reactivity with results of untargeted plasma metabolomics and gut microbiome profiling.

 We used data from a cohort of 534 healthy adult Dutch volunteers (the 500 Functional Genomics study). Platelet activation and reactivity were measured by the expression of the alpha-granule protein P-selectin and the binding of fibrinogen to the activated integrin αIIbβ3, both in unstimulated blood and after ex vivo stimulation with platelet agonists. Plasma metabolome was measured using an untargeted metabolic profiling approach by quadrupole time-of-flight mass spectrometry. Gut microbiome data were measured by shotgun metagenomic sequencing from stool samples.

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My Diabetes Care (MDC) is a novel, multifaceted patient portal intervention designed to help patients better understand their diabetes health data and support self-management. MDC uses infographics to visualize and summarize patients’ diabetes health data, incorporates motivational strategies, and provides literacy level-appropriate educational resources.

We aimed to assess the usability, acceptability, perceptions, and potential impact of MDC.

We recruited 69 participants from four clinics affiliated with Vanderbilt University Medical Center. Participants were given 1 month of access to MDC and completed pre- and post-questionnaires including validated measures of usability and patient activation, and questions about user experience.

Sixty participants completed the study. Participants’ mean age was 58, 55% were females, 68% were Caucasians, and 48% had limited health literacy (HL). Most participants (80%) visited MDC three or more times and 50% spent a total of ≥15 minutes on MDC. Participants’ medint portals.

Participants, including those with limited HL, rated the usability of MDC above average, anticipated continued use, and identified key features that improved their understanding of diabetes health data. Patient activation improved over the study period. Our findings suggest MDC may be a beneficial addition to existing patient portals.

Smart infusion pumps affect workflows as they add alerts and alarms in an information-rich clinical environment where alarm fatigue is already a major concern. An analytic approach is needed to quantify the impact of these alerts and alarms on nursing workflows and patient safety.

To analyze a detailed infusion dataset from a smart infusion pump system and identify contributing factors for infusion programming alerts, operational alarms, and alarm resolution times.

We analyzed detailed infusion pump data across four hospitals in a health system for up to 1 year. The prevalence of alerts and alarms was grouped by infusion type and a selected list of 32 high-alert medications (HAMs). Logistic regression was used to explore the relationship between a set of risk factors and the occurrence of alerts and alarms. We used nonparametric tests to explore the relationship between alarm resolution times and a subset of predictor variables.

The study dataset included 745,641 unique infusions with a total of 3,231e infusion pump informatics to prioritize quality improvement and patient safety initiatives pertaining to infusion practices.

This article presents a comparative study of two Health Level Seven International (HL7) standards for clinical knowledge representation, the Arden Syntax and the Clinical Quality Language (CQL), regarding their expressiveness and utility to represent knowledge for clinical decision support (CDS) systems.

We compiled a concatenated set of features from both languages and made descriptive comparisons of 27 categories covering areas of language characteristics, data, control statements, and operators.

Both Arden and CQL have similar constructs that can be used for representing CDS knowledge but also have unique constructs that could support distinct use cases. They have constructs that fully or partially address several of the categories used in the comparison, except for data models and terminologies in Arden and event triggering and iteration statements in CQL.

These standards can facilitate the sharing, management, and reuse of computable knowledge, and permit knowledge to be represented with their languages and converted to a machine-friendly executable code that can be shared and reused by other systems. Alflutinib concentration Having support for standard data models and terminologies will continue to be a differential for adoption of a language. The HL7 working groups responsible for developing these standards can direct future development to enhance the functions of the standard and address the gaps identified in this study.

These standards can facilitate the sharing, management, and reuse of computable knowledge, and permit knowledge to be represented with their languages and converted to a machine-friendly executable code that can be shared and reused by other systems. Having support for standard data models and terminologies will continue to be a differential for adoption of a language. The HL7 working groups responsible for developing these standards can direct future development to enhance the functions of the standard and address the gaps identified in this study.