Sudden death in infants due to primary cardiac tumors is extremely rare. Herein we describe a case of an 8-month-old male infant, without any previous medical history, who died in a hospital in the city of Medellín-Antioquia, Colombia. The family stated that approximately 15 minutes after he received a bottle, the baby became cyanotic and subsequently lost consciousness. He was taken to the hospital immediately; however, he arrived lifeless. As this was a sudden death case, the child was referred to the Institute of Legal Medicine and Forensic Sciences in the city of Medellín to clarify the cause, manner, and mechanism of death. The forensic autopsy revealed a eutrophic infant with central and peripheral cyanosis, without signs of trauma, and the internal examination found a single cardiac tumor in the anterior wall of the left ventricle. The mass was white and whorled; histological evaluation diagnosed a fibroma. The manner of death was natural due to a cardiogenic shock caused by a primary tumor.

Abstract-Sudden Cardiac Death (SCD) associated with ventricular tachyarrhythmia, is one of the main causes of death worldwide. The Left Ventricle Ejection Fraction (LVEF) is the predictive parameter used in clinical practice for the stratification of risk of SCD. However, this has a poor predictive value. Several authors have proposed methods seeking to estimate characteristics that can be used as predictors of SCD from an analysis of the nonlinear dynamics of the signal of heart rate variability (HRV). These authors have shown the great potential of fractal analysis for this purpose. In this article, we worked under the hypothesis that if there is an underlying non-linear dynamic to the HRV signal, this dynamic should be best described by the multifractal analysis, which by fractal indices. Therefore, a comparison of fractal and multifractal features for SCD prediction was made and implemented a classifier that will combine this type of characteristics. The results show that hfluctuacion multifractal index shows a 94.44% sensitivity and 87.50% of specificity compared to the exponents of scale with Detrend Fluctuation Analysis (DFA) an 88.89% and 87.50% respectively. Combining these two features as input for a Support Vector Machine (SVM) is achieved an accuracy of the 97.06%, 100% sensitivity and specificity 94.44%, surpassing the results that are reported in the literature so far.

La Tetralogía de Fallot es la principal causa de cardiopatía congénita cianosante, que genera una gran mortalidad y que en algunos casos es susceptible de tratamiento quirúrgico. Reportamos un caso de una niña de 7 días de nacida con esta malformación acompañada de ausencia de las válvulas pulmonares, que fallece como consecuencia de la severidad de la misma. En el momento del examen interno se encuentra un corazón de tamaño normal con las anomalías morfológicas características de la Tetralogía de Fallot con ausencia de las válvulas sigmoideas pulmonares.