Better transplant patient outcomes are achieved not only due to the advanced pre-operative management, improvements in surgical techniques, sophisticated post-operative intensive cardiac care and more precise immunosuppression, but also because the early rehabilitation protocols have become a must part of the multidisciplinary care. The latter engages various assessment domains and corresponding interventions, with special focus on the most vulnerable systems, as it was with the respiratory, muscle and physical function after the ECMO decannulation in the re-transplantation case (1). Apparently, general evidence-based conclusions and pediatric cardiac transplantation rehabilitation guidelines will need to be outlined. Albeit, currently hospitals use internal protocols typically based on personal experience, center policies, adoptions of recommendations for adults and literature data. This is why sharing even case reports on this topic, especially with challenging scenarios, is valuable and requires utmost attention.

The results of the multidisciplinary approach conducted by the authors and their team cannot be underestimated. Their rehabilitation design and the success of its implementation will be especially useful to those centers who lack their own protocols for this fragile category of patients.

The paper describes the medical data personalization problem by determining the individual characteristics needed to predict the number of days a patient spends in a hospital. The mathematical problem of patient information analysis is formalized, which will help identify critical personal characteristics based on conditioned space analysis. The condition space is given in cube form as a reflection of the functional relationship of the general parameters to the studied object. The dataset consists of 51 instances, and ten parameters are processed using different clustering and regression models. Days in hospital is the target variable. A condition space cube is formed based on clustering analysis and features selection. In this manner, a hierarchical predictor based on clustering and an ensemble of weak regressors is built. The quality of the developed hierarchical predictor for Root Mean Squared Error metric is 1.47 times better than the best weak predictor (perceptron with 12 units in a single hidden layer).

Introduction:
Studies on age differences of arterial trauma (AT) carry significant methodological differences in terms of selection of the most appropriate age classification.

Aim:
This study aims to verify the most optimal age classification when comparing clinical patterns of the civil AT.

Material and methods:
222 AT patients were identified from the Lviv Clinical Regional Hospital. In each case the following clinical patterns were identified: patient age, etiology, mechanism, AT type, topography, diagnostics mode, treatment type. Patients were distributed using six age classifications (Erikson 1950, UN 1989, Quinn 1994, Craig 2000, WHO physical activity recommendations 2010, by decades of life). Generalized linear models (GLMs) were created, with age distributions as predictors and clinical patterns as dependent factors. Akaike information criterion (AIK) was used to compare the quality of statistical sets.

Results and discussion:
Six GLMs were obtained, in each age of them age classifications were compared using the AIK. Rating list of age classifications was developed (starting with the most appropriate and ending with the least appropriate): E. Erikson (1950) → V. Quinn (1994) → G. Craig (2000) → UN (1989) → Decades → WHO (2010).

Conclusions:
Human development classifications may be preferable in assessing the age differences of AT in patients of wide range.