Preliminary Investigation of Cardiovascular-Renal Disorders in Dogs with Chronic Mitral Valve Disease.

BACKGROUND: Veterinary literature lacks data about cardiovascular-renal disorders (CvRD) and cardiorenal-anemia syndrome (CRAS) in dogs. HYPOTHESIS: A direct correlation exists between ACVIM class and IRIS stage; chronic kidney disease (CKD) complicates chronic mitral valve disease (CMVD) more often than does anemia in dogs. ANIMALS: One hundred and fifty-eight client-owned dogs with CMVD. METHODS: Signalment, physical examination findings, electrocardiography, thoracic radiographs, echocardiography, and blood analysis were retrospectively evaluated to assess the prevalence of CKD and anemia in dogs with CMVD and to investigate the relationships among ACVIM class, IRIS stage, and survival. RESULTS: The prevalence of CKD and anemia in dogs with CMVD was significantly higher than in the general population of dogs. Dogs being treated for heart failure had a significantly higher prevalence of CKD than did dogs that had not received treatment. A statistically significant direct correlation was found between ACVIM class and IRIS stage. Severe heart disease, severe renal disease or both, furosemide administration, and advanced age at diagnosis of heart disease were associated with shorter survival time. Survival time of dogs affected by CvRD was statistically shorter than survival time of dogs affected by CMVD alone. CONCLUSION AND CLINICAL RELEVANCE: Chronic mitral valve disease is associated with increased prevalence of CKD and anemia in dogs. Treatment for medical management of heart failure may play a role in inducing CKD. Class of heart disease and IRIS stage were directly correlated. Cardiovascular-renal disorders decrease survival time compared to the only presence of CMVD alone, whereas anemia does not play a central role in worsening heart function.

Confidence About Possible Explanations.

We revise the notion of confidence with which we estimate the parameters of a given distribution law in terms of their compatibility with the sample we have observed. This is a recent perspective that allows us to get a more intuitive feeling of the crucial concept of the confidence interval in parametric inference together with quick tools for exactly computing them even in conditions far from the common Gaussian framework where standard methods fail. The key artifact consists of working with a representation of the compatible parameters in terms of random variables without priors. This leads to new estimators that meet the most demanding requirements of the modern statistical inference in terms of learning algorithms. We support our methods with: a consistent theoretical framework, general-purpose estimation procedures, and a set of paradigmatic benchmarks.

Appreciation of medical treatments through confidence intervals.

The typical way of judging about either the efficacy of a new treatment or, on the contrary, the damage of a pollutant agent is through a test of hypothesis having its ineffectiveness as null hypothesis. This is the typical operational field of Kolmogorov's statistical framework where wastes of data (for instance non significant deaths in a polluted region) represent the main drawback. Instead, confidence intervals about treatment/pollution effectiveness are a way of exploiting all data, whatever their number is. We recently proposed a new statistical framework, called Algorithmic Inference, for overcoming crucial difficulties usually met when computing these intervals and abandoning general simplifying hypotheses such as errors' Gaussian distribution. When effectiveness is expressed in terms of regression curves between observed data we come to a learning problem that we solve by identifying a region where the whole curve lies with a given confidence. The approach to inference we propose is very suitable for identifying these regions with great accuracy, even in the case of nonlinear regression models and/or a limited size of the observed sample, provided that a normally powered computing station is available. In the paper we discuss this new way of extracting functions from the experimental data and drawing conclusions about the treatments originating them. From an operational perspective, we give the general layout of the procedure for computing confidence regions as well as some applications on real data.