Multi-parametric system for risk stratification in mitral regurgitation: A multi-task Gaussian prediction approach.

BACKGROUND: We hypothesized that a multi-parametric approach incorporating medical comorbidity information, electrocardiographic P-wave indices, echocardiographic assessment, neutrophil-to-lymphocyte ratio (NLR) and prognostic nutritional index (PNI) calculated from laboratory data can improve risk stratification in mitral regurgitation (MR). METHODS: Patients diagnosed with mitral regurgitation between 1 March 2005 and 30 October 2018 from a single centre were retrospectively analysed. Outcomes analysed were incident atrial fibrillation (AF), transient ischemic attack (TIA)/stroke and mortality. RESULTS: This study cohort included 706 patients, of whom 171 had normal inter-atrial conduction, 257 had inter-atrial block (IAB) and 266 had AF at baseline. Logistic regression analysis showed that age, hypertension and mean P-wave duration (PWD) were significant predictors of new-onset AF. Low left ventricular ejection fraction (LVEF), abnormal P-wave terminal force in V1 (PTFV1) predicted TIA/stroke. Age, smoking, hypertension, diabetes mellitus, hypercholesterolaemia, ischemic heart disease, secondary mitral regurgitation, urea, creatinine, NLR, PNI, left atrial diameter (LAD), left ventricular end-diastolic dimension, LVEF, pulmonary arterial systolic pressure, IAB, baseline AF and heart failure predicted all-cause mortality. A multi-task Gaussian process learning model demonstrated significant improvement in risk stratification compared to logistic regression and a decision tree method. CONCLUSIONS: A multi-parametric approach incorporating multi-modality clinical data improves risk stratification in mitral regurgitation. Multi-task machine learning can significantly improve overall risk stratification performance.

Ocean warming has a greater effect than acidification on the early life history development and swimming performance of a large circumglobal pelagic fish.

Ocean warming and acidification are serious threats to marine life; however, their individual and combined effects on large pelagic and predatory fishes are poorly understood. We determined the effects of projected future temperature and carbon dioxide (CO2 ) levels on survival, growth, morphological development and swimming performance on the early life stages of a large circumglobal pelagic fish, the yellowtail kingfish Seriola lalandi. Eggs, larvae and juveniles were reared in cross-factored treatments of temperature (21 and 25°C) and pCO2 (500 and 985 µatm) from fertilisation to 25 days post hatching (dph). Temperature had the greatest effect on survival, growth and development. Survivorship was lower, but growth and morphological development were faster at 25°C, with surviving fish larger and more developed at 1, 11 and 21 dph. Elevated pCO2 affected size at 1 dph, but not at 11 or 21 dph, and did not affect survival or morphological development. Elevated temperature and pCO2 had opposing effects on swimming performance at 21 dph. Critical swimming speed (Ucrit ) was increased by elevated temperature but reduced by elevated pCO2 . Additionally, elevated temperature increased the proportion of individuals that responded to a startle stimulus, reduced latency to respond and increased maximum escape speed, potentially due to the more advanced developmental stage of juveniles at 25°C. By contrast, elevated pCO2 reduced the distance moved and average speed in response to a startle stimulus. Our results show that higher temperature is likely to be the primary driver of global change impacts on kingfish early life history; however, elevated pCO2 could affect critical aspects of swimming performance in this pelagic species. Our findings will help parameterise and structure fisheries population dynamics models and improve projections of impacts to large pelagic fishes under climate change scenarios to better inform adaptation and mitigation responses.

Athletic trainers' viewpoints of patient-centered care: Preliminary findings.

The core competency of patient-centered care (PCC) states that for positive patient outcomes, the provider must respect the patient's views and recognize their experiences. The Athletic Training Strategic Alliance Research Agenda Task Force identified a profession-wide belief that examining the extent to which athletic trainers (ATs) provide PCC in their clinical practice would benefit the profession. To first address this line of inquiry, we must study the subjectivity of how ATs view PCC. This study used Q methodology which is a research design that collects data from participants from a quantitative and qualitative perspective. A total of 115 (males = 62, females = 53, age = 37±10 y, experience = 13±10 y) ATs dispersed between 11 job settings volunteered for this study. Participants were asked to pre-sort (agree, disagree, neutral) 36 validated statements representing the 8 dimensions of PCC, then completed a Q-sort where they dragged-and-dropped the pre-sorted statements based on perceived importance in providing PCC. The Q-sorts were analyzed using QMethod software. A principal component analysis was used to identify statement rankings and factors. Factors were determined by an Eigenvalue > 1 and analyzed using a scree plot. The 6 highest selected statements per factor were assessed to create the distinguishing viewpoints. Two distinguishing viewpoints emerged from the factor analysis of the Q-sorts: 1) the interpersonal connection that valued teamwork, open communication, and respectful care with varied populations; 2) the holistic gatekeeper that valued personal promotion for activities of daily living, self-care, and quality of life. Overall, ATs value patient's preferences and respect. However, a lack of importance was identified for incorporating the disablement model which is a core competency and adopted framework by the athletic training profession.