Based on the growing problem of heart diseases, their efficient diagnosis is of great importance to the modern world. Statistical inference is the tool that most physicians use for diagnosis, though in many cases it does not appear powerful enough. Clustering of patient instances allows finding out groups for which statistical models can be built more efficiently. However, the performance of such an approach depends on the features used as clustering attributes. In this paper, the methodology that consists of combining unsupervised feature selection and grouping to improve the performance of statistical analysis is considered. We assume that the set of attributes used in clustering and statistical analysis phases should be different and not correlated. Thus, the method consisting of selecting reversed correlated features as attributes of cluster analysis is considered. The proposed methodology has been verified by experiments done on three real datasets of cardiovascular cases. The obtained effects have been evaluated regarding the number of detected dependencies between parameters. Experiment results showed the advantage of the presented approach compared to other feature selection methods and without using clustering to support statistical inference.

In children with primary arterial hypertension, with the use of tissue Doppler echocardiography there are significantly lower values of diastolic and systolic parameters observed, which may be a sign of myocardial function deterioration.

SummaryIntroductionIntra-uterine growth restriction (IUGR) is present in about 3–10% of live-born newborns and it is as high as 20–30% in developing countries. Since the 1990s, it has been known that abnormalities during foetal growth may result in cardiovascular disease, including hypertension in adulthood.MethodsThis study evaluated blood pressure parameters (using ambulatory blood pressure monitoring) in children aged six to 10 years old, born as small for gestational age (SGA), and compared them to their healthy peers born as appropriate for gestational age (AGA).ResultsIn the SGA group, an abnormal blood pressure level (prehypertension or hypertension) was present significantly more often than in the AGA group (50 vs 16%, p < 0.01). This relationship also occurred in association with the type of IUGR (asymmetric p < 0.01, symmetric p < 0.05).ConclusionIn SGA children, abnormal blood pressure values occurred more frequently than in AGA children.

IntroductionDiagnosis of contrast induced-nephropathy (CIN) by a classic renal biomarker such as creatinine concentration can be delayed because of various factors that can influence this marker. Changes in new biomarkers such as neutrophil-gelatinase associated lipocalin (NGAL) and cystatin C are postulated to be more sensitive for recognizing patients prone to CIN-acute kidney injury (AKI).AimTo investigate the role of NGAL and cystatin C as early biomarkers in the diagnosis of kidney injury after cardiac catheterisation.Material and methodsThe study group consisted of 50 patients with congenital heart malformation admitted for scheduled cardiac catheterisation. The biomarkers serum creatinine, serum NGAL and serum cystatin C were tested at 5 time-points sequentially from start to 48 h after the procedure.ResultsSignificant changes were noted during the research in the serum creatinine concentration (p < 0.001) and serum NGAL concentration (p < 0.001). CIN-AKI, diagnosed by the modified Schwartz formula, occurred in 16 (32%) patients after 24 h and in 8 (16%) after 48 h. Subsequent analysis showed that serum creatinine significantly rose in the first 2 h of the study with simultaneous reduction in the eGFR. Maximum growth in serum NGAL occurred at 6 h after contrast administration and then returned to the baseline values at 24 h. Serum cystatin C level did not significantly change during the study.ConclusionsWe observed a transient decrease in eGFR and a rise of serum NGAL after 2 h but NGAL was most pronounced at 6 h after the procedure. The potential role of cystatin C as a biomarker of CIN-AKI was not proved.

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