Exercise-associated rhythm disturbances in poorly performing Thoroughbreds: risk factors and association with racing performance.

BACKGROUND: Exercise-associated cardiac rhythm disturbances are common, but there is a lack of evidence-based criteria on which to distinguish clinically relevant rhythm disturbances from those that are not. OBJECTIVES: To describe and characterise rhythm disturbances during clinical exercise testing; to explore potential risk factors for these rhythm disturbances and to determine whether they influenced future racing. STUDY DESIGN: Retrospective cohort using a convenience sample. METHODS: Medical records were reviewed from two clinical services to identify horses with poor performance and/or respiratory noise with both exercise endoscopy and electrocardiography results. Respiratory and ECG findings recorded by the attending clinicians were described, and for polymorphic ventricular rhythms (n = 12), a consensus team agreed the final rhythm characterisation. Several statistical models analysing risk factors were built and racing records were reviewed to compare horses with and without rhythm disturbance. RESULTS: Of 245 racehorses, 87 (35.5%) had no ectopic/re-entrant rhythms, 110 (44.9%) had isolated premature depolarisations during sinus rhythm and 48 (19.6%) horses had complex tachydysrrythmias. Rhythm disturbances were detected during warm-up in 20 horses (8.2%); during gallop in 61 horses (24.9%) and during recovery in 124 horses (50.6%). Most complex rhythm events occurred during recovery, but there was one horse with a single couplet during gallop and another with a triplet during gallop. Fifteen horses (one with frequent isolated premature depolarisations and 14 complex rhythms) were considered by clinicians to be potentially contributing to poor performance. Treadmill exercise tests, the presence of exercise-associated upper respiratory tract obstructions and National Hunt racehorses were associated with rhythm disturbances. The proportion of horses racing again after diagnosis (82%) was similar in all groups and univariable analysis revealed no significant associations between subsequent racing and the presence of any ectopic/re-entrant rhythm, or the various sub-groups based on phase of exercise in which this was detected. MAIN LIMITATIONS: Reliance on retrospective data collection from medical records with no control group. Exercise ECGs were collected using only 1 or 2 leads. Variables examined as risk factors could be considered to be inter-related and our sub-groups were small. CONCLUSIONS: This study confirms a high prevalence of cardiac rhythm disturbances, including complex ectopic/re-entrant rhythms, in poorly performing racehorses. Detection of rhythm disturbances may vary with exercise test conditions and exercise-associated upper respiratory tract obstructions increase the risk of rhythm disturbances.

Associations among echocardiography, cardiac biomarkers, insulin metabolism, morphology, and inflammation in cats with asymptomatic hypertrophic cardiomyopathy.

BACKGROUND: Insulin, insulin-like growth factor-1 (IGF-1), and inflammation possibly are involved in cats with asymptomatic hypertrophic cardiomyopathy (aHCM). OBJECTIVES: To evaluate echocardiography, morphology, cardiac and inflammatory markers, insulin and IGF-1 in cats with aHCM. ANIMALS: Fifty-one client-owned cats with aHCM. METHODS: Observational descriptive study. Variables (body weight [BW], body condition score [BCS], echocardiography, and serum concentrations of N-terminal pro-B-type natriuretic peptide [NT-proBNP], ultra-sensitive troponin-I [c-TnI], serum amyloid A [SAA], insulin, glucose and IGF-1) were evaluated for significant increases above echocardiography cutoff values and laboratory reference ranges, associations and effect of left atrial (LA) remodeling and generalized hypertrophy. RESULTS: Cats with aHCM had BCS ≥6/9 (P = .01) and insulin (P < .001), NT-proBNP (P = .001) and cTn-I (P < .001) above laboratory reference ranges. Associations were present between NT-proBNP and maximum end-diastolic interventricular septum thickness (IVSd; ρ = .32; P = .05), maximum end-diastolic left ventricular free wall thickness;(ρ = .41; P = .01), LA/Aorta (ρ = .52; P = .001) and LA diameter (LA-max; ρ = .32; P = .05); c-TnI and LA/Aorta (ρ = .49; P = .003) and LA-max (ρ = .28; P = .05); and SAA and number of IVSd regions ≥6 mm thickness (ρ = .28; P = .05). Body weight and BCS were associated with IGF-1 (r = 0.44; P = .001), and insulin (ρ = .33; P = .02), glucose (ρ = .29; P = .04) and IGF-1 (ρ = .32; P = .02), respectively. Concentrations of NT-proBNP (P = .02) and c-TnI (P = .01), and SAA (P = .02), were higher in cats with LA remodeling, and generalized hypertrophy, respectively. CONCLUSIONS AND CLINICAL IMPORTANCE: Results suggest potential implications of insulin, IGF-1, and inflammation in cats with aHCM, but it remains to be confirmed whether these findings represent a physiological process or a part of the pathogenesis and development of disease.

Exploring semantic deep learning for building reliable and reusable one health knowledge from PubMed systematic reviews and veterinary clinical notes.

BACKGROUND: Deep Learning opens up opportunities for routinely scanning large bodies of biomedical literature and clinical narratives to represent the meaning of biomedical and clinical terms. However, the validation and integration of this knowledge on a scale requires cross checking with ground truths (i.e. evidence-based resources) that are unavailable in an actionable or computable form. In this paper we explore how to turn information about diagnoses, prognoses, therapies and other clinical concepts into computable knowledge using free-text data about human and animal health. We used a Semantic Deep Learning approach that combines the Semantic Web technologies and Deep Learning to acquire and validate knowledge about 11 well-known medical conditions mined from two sets of unstructured free-text data: 300 K PubMed Systematic Review articles (the PMSB dataset) and 2.5 M veterinary clinical notes (the VetCN dataset). For each target condition we obtained 20 related clinical concepts using two deep learning methods applied separately on the two datasets, resulting in 880 term pairs (target term, candidate term). Each concept, represented by an n-gram, is mapped to UMLS using MetaMap; we also developed a bespoke method for mapping short forms (e.g. abbreviations and acronyms). Existing ontologies were used to formally represent associations. We also create ontological modules and illustrate how the extracted knowledge can be queried. The evaluation was performed using the content within BMJ Best Practice. RESULTS: MetaMap achieves an F measure of 88% (precision 85%, recall 91%) when applied directly to the total of 613 unique candidate terms for the 880 term pairs. When the processing of short forms is included, MetaMap achieves an F measure of 94% (precision 92%, recall 96%). Validation of the term pairs with BMJ Best Practice yields precision between 98 and 99%. CONCLUSIONS: The Semantic Deep Learning approach can transform neural embeddings built from unstructured free-text data into reliable and reusable One Health knowledge using ontologies and content from BMJ Best Practice.