Multiple retrospective analysis of survival and evaluation of cardiac death predictors in a population of dogs affected by degenerative mitral valve disease in ACVIM class C treated with different therapeutic protocols.

Clinical records of dogs with spontaneous degenerative mitral valve disease (DMVD) with clinical signs related to congestive heart failure (CHF) recruited during routine clinical practice between 2001 and 2018 at the Cardiology Unit of the Veterinary Teaching Hospital (University of Milan) were included in this retrospective cohort study. Baseline echocardiographic data were evaluated. Median survival time (MST) was calculated. Data on therapeutic treatment, ISACHC (International Small Animal Cardiac Health Council) or ACVIM (American College of Veterinary Internal Medicine) classes were reviewed based on the inclusion period and type of endpoint (i.e. cardiac death or death for other causes). A univocal classification was needed, and the patients classified in ISACHC classes II, IIIa and IIIb, visited before 2009, were reallocated to ACVIM class C. The main goal of this data review was to retrospectively evaluate 259 clinical records of subjects belonging to ACVIM C class examined between 2001 to 2018 and 202 dogs examined between 2010 to 2018. In this way, in the second group, the bias of the reclassification was avoided. The MST (median survival time) of these subjects was 531 d (2001-2018) and 335.5 d (2010-2018), respectively. Univariate survival regression analysis for subjects included from 2010 to 2018 showed as significantly related to cardiac death (CD): left atrium to aorta ratio (LA/Ao) (HR 2.754, p=0.000), E wave (HR 2.961, p=0.000), E/A ratio (HR 1.372, p=0.000), end-diastolic (HR 1.007, p=0.000) (EDVI) and end-systolic (HR 1.012, p=0.026) (ESVI) volume indexes, allometric diastolic (HR 4.018, p=0.000) (LVIDdN) and systolic (HR 2.674, p=0.049) (LVIDsN) left ventricular internal diameters, age (HR 1.006, p=0.009) and pulmonary hypertension severity (HR=1.309, p=0.012) (PH). Multivariate analysis, adjusted for age, showed that the only variable that determined a statistically significant difference in MST was PH severity (HR 1.334, p=0.033). The type of therapeutic treatment within this class was not significant for the MST of the subjects.

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.