Urinary cystatin B differentiates progressive versus stable IRIS Stage 1 chronic kidney disease in dogs.

BACKGROUND: Early identification of dogs with progressive vs stable chronic kidney disease (CKD) might afford opportunity for interventions that would slow progression. However, currently no surrogate biomarker reliably predicts CKD progression. HYPOTHESIS/OBJECTIVES: Urinary cystatin B (uCysB), a novel kidney injury biomarker, predicts progressive disease in International Renal Interest Society (IRIS) CKD Stage 1. ANIMALS: Seventy-two dogs, including 20 dogs from 4 university centers with IRIS CKD Stage 1, with IDEXX symmetric dimethylarginine (SDMA) concentration up to 17 µg/dL and no systemic comorbidities, and 52 clinically healthy staff-owned dogs from a fifth university center. METHODS: A multicenter prospective longitudinal study was conducted between 2016 and 2021 to assess uCysB concentration in IRIS CKD Stage 1 and control dogs. Dogs were followed to a maximum of 3 years (control) or 25 months (CKD). Stage 1 IRIS CKD was classified as stable or progressive using the slope of 1/SDMA, calculated from 3 timepoints during the initial 90-day period. Dogs with slope above or below -0.0007 week × dL/µg were classified as stable or progressive, respectively. Mixed effects modeling was used to assess the association between uCysB and progression rate. RESULTS: Estimates of first visit uCysB results predictive of active ongoing kidney injury based on the mixed effects models were 17 ng/mL for control, 24 ng/mL for stable CKD, and 212 ng/mL for progressive CKD (P < .001). CONCLUSIONS AND CLINICAL IMPORTANCE: Urinary cystatin B differentiated stable vs progressive IRIS CKD Stage 1. Identification of dogs with progressive CKD may provide an opportunity for clinicians to intervene early and slow progression rate.

Differentiation of stable kidney function versus progressive dysfunction in dogs.

BACKGROUND: Circulating creatinine and symmetric dimethylarginine (SDMA) are biomarkers of kidney function that have been used variously to define stable vs progressive chronic kidney disease (CKD). Slope monitoring of inverse biomarker values (creatinine-1 or SDMA-1 ) has shown promise, but quantitative criteria to distinguish stable vs progressive CKD using this approach are lacking. OBJECTIVE: Assessment of creatinine-1 and SDMA-1 slope cutoffs to distinguish stable vs progressive CKD. ANIMALS: One hundred ten clinically healthy university staff-owned dogs and 29 male colony dogs with progressive X-linked hereditary nephropathy (XLHN). METHODS: Retrospective analysis combining 2 prospective observational studies, 1 tracking kidney function biomarkers in healthy dogs (HDs) to a maximum of 3 years, and 1 tracking kidney function biomarkers in male colony dogs with progressive XLHN to a maximum of 1 year. The minimum slope of creatinine-1 or SDMA-1 as measured using the IDEXX SDMA test from HD was assigned as the slope cutoff for stable kidney function. RESULTS: The stable vs progressive slope cutoff was -0.0119 week × dL/mg for creatinine-1 and -0.0007 week × dL/µg for SDMA-1 . CONCLUSIONS AND CLINICAL IMPORTANCE: In the studied CKD population, progressive dysfunction can be distinguished from stable kidney function by using the slope of creatinine-1 or SDMA-1 . These criteria may serve to characterize CKD in other cohorts of dogs and to establish guidelines for degrees of progression rate in dogs with naturally occurring CKD.

Day-1 Competencies for Veterinarians Specific to Health Informatics.

In 2015, the American Association of Veterinary Medical Colleges (AAVMC) developed the Competency-Based Veterinary Education (CBVE) framework to prepare practice-ready veterinarians through competency-based education, which is an outcomes-based approach to equipping students with the skills, knowledge, attitudes, values, and abilities to do their jobs. With increasing use of health informatics (HI: the use of information technology to deliver healthcare) by veterinarians, competencies in HI need to be developed. To reach consensus on a HI competency framework in this study, the Competency Framework Development (CFD) process was conducted using an online adaptation of Developing-A-Curriculum, an established methodology in veterinary medicine for reaching consensus among experts. The objectives of this study were to (1) create an HI competency framework for new veterinarians; (2) group the competency statements into common themes; (3) map the HI competency statements to the AAVMC competencies as illustrative sub-competencies; (4) provide insight into specific technologies that are currently relevant to new veterinary graduates; and (5) measure panelist satisfaction with the CFD process. The primary emphasis of the final HI competency framework was that veterinarians must be able to assess, select, and implement technology to optimize the client-patient experience, delivery of healthcare, and work-life balance for the veterinary team. Veterinarians must also continue their own education regarding technology by engaging relevant experts and opinion leaders.

A scoping review of 'big data', 'informatics', and 'bioinformatics' in the animal health and veterinary medical literature.

Research in big data, informatics, and bioinformatics has grown dramatically (Andreu-Perez J, et al., 2015, IEEE Journal of Biomedical and Health Informatics 19, 1193-1208). Advances in gene sequencing technologies, surveillance systems, and electronic medical records have increased the amount of health data available. Unconventional data sources such as social media, wearable sensors, and internet search engine activity have also contributed to the influx of health data. The purpose of this study was to describe how 'big data', 'informatics', and 'bioinformatics' have been used in the animal health and veterinary medical literature and to map and chart publications using these terms through time. A scoping review methodology was used. A literature search of the terms 'big data', 'informatics', and 'bioinformatics' was conducted in the context of animal health and veterinary medicine. Relevance screening on abstract and full-text was conducted sequentially. In order for articles to be relevant, they must have used the words 'big data', 'informatics', or 'bioinformatics' in the title or abstract and full-text and have dealt with one of the major animal species encountered in veterinary medicine. Data items collected for all relevant articles included species, geographic region, first author affiliation, and journal of publication. The study level, study type, and data sources were collected for primary studies. After relevance screening, 1093 were classified. While there was a steady increase in 'bioinformatics' articles between 1995 and the end of the study period, 'informatics' articles reached their peak in 2012, then declined. The first 'big data' publication in animal health and veterinary medicine was in 2012. While few articles used the term 'big data' (n = 14), recent growth in 'big data' articles was observed. All geographic regions produced publications in 'informatics' and 'bioinformatics' while only North America, Europe, Asia, and Australia/Oceania produced publications about 'big data'. 'Bioinformatics' primary studies tended to use genetic data and tended to be conducted at the genetic level. In contrast, 'informatics' primary studies tended to use non-genetic data sources and conducted at an organismal level. The rapidly evolving definition of 'big data' may lead to avoidance of the term.