Univariate Analysis of Short-Chain Fatty Acids Related to Sudden Infant Death Syndrome

Machine Learning Model Based on Lipidomic Profile Information to Predict Sudden Infant Death Syndrome

Sudden infant death syndrome (SIDS) represents the leading cause of death in under one year of age in developing countries. Even in our century, its etiology is not clear, and there is no biomarker that is discriminative enough to predict the risk of suffering from it. Therefore, in this work, taking a public dataset on the lipidomic profile of babies who died from this syndrome compared to a control group, a univariate analysis was performed using the Mann–Whitney U test, with the aim of identifying the characteristics that enable discriminating between both groups. Those characteristics with a p-value less than or equal to 0.05 were taken; once these characteristics were obtained, classification models were implemented (random forests (RF), logistic regression (LR), support vector machine (SVM) and naive Bayes (NB)). We used seventy percent of the data for model training, subjecting it to a cross-validation (k = 5) and later submitting to validation in a blind test with 30% of the remaining data, which allows simulating the scenario in real life—that is, with an unknown population for the model. The model with the best performance was RF, since in the blind test, it obtained an AUC of 0.9, specificity of 1, and sensitivity of 0.8. The proposed model provides the basis for the construction of a SIDS risk prediction computer tool, which will contribute to prevention, and proposes lines of research to deal with this pathology.

Indeterminacy of the Diagnosis of Sudden Infant Death Syndrome Leading to Problems with the Validity of Data

The validity of infant mortality data is essential in assessing health care quality and in the setting of preventive measures. This study explores different diagnostic procedures used to determine the cause of death across forensic settings and thus the issue of the reduced validity of data. All records from three forensic medical departments that conducted autopsies on children aged 12 months or younger (n = 204) who died during the years 2007–2016 in Moravia were included. Differences in diagnostic procedures were found to be statistically significant. Each department works with a different set of risk factors and places different emphasis on different types of examination. The most significant differences could be observed in sudden infant death syndrome and suffocation diagnosis frequency. The validity of statistical data on the causes of infant mortality is thus significantly reduced. Therefore, the possibilities of public health and social policy interventions toward preventing sudden and unexpected infant death are extraordinarily complicated by this lack of data validity.

Automatic Evaluation of Heart Condition According to the Sounds Emitted and Implementing Six Classification Methods

The main cause of death in Mexico and the world is heart disease, and it will continue to lead the death rate in the next decade according to data from the World Health Organization (WHO) and the National Institute of Statistics and Geography (INEGI). Therefore, the objective of this work is to implement, compare and evaluate machine learning algorithms that are capable of classifying normal and abnormal heart sounds. Three different sounds were analyzed in this study; normal heart sounds, heart murmur sounds and extra systolic sounds, which were labeled as healthy sounds (normal sounds) and unhealthy sounds (murmur and extra systolic sounds). From these sounds, fifty-two features were calculated to create a numerical dataset; thirty-six statistical features, eight Linear Predictive Coding (LPC) coefficients and eight Cepstral Frequency-Mel Coefficients (MFCC). From this dataset two more were created; one normalized and one standardized. These datasets were analyzed with six classifiers: k-Nearest Neighbors, Naive Bayes, Decision Trees, Logistic Regression, Support Vector Machine and Artificial Neural Networks, all of them were evaluated with six metrics: accuracy, specificity, sensitivity, ROC curve, precision and F1-score, respectively. The performances of all the models were statistically significant, but the models that performed best for this problem were logistic regression for the standardized data set, with a specificity of 0.7500 and a ROC curve of 0.8405, logistic regression for the normalized data set, with a specificity of 0.7083 and a ROC curve of 0.8407, and Support Vector Machine with a lineal kernel for the non-normalized data; with a specificity of 0.6842 and a ROC curve of 0.7703. Both of these metrics are of utmost importance in evaluating the performance of computer-assisted diagnostic systems.

Sudden Infant Death Syndrome: Beyond Risk Factors

Sudden infant death syndrome (SIDS) is defined as "the sudden death of an infant under 1 year of age which remains unexplained after thorough investigation including a complete autopsy, death scene investigation, and detailed clinical and pathological review". A significant decrease of SIDS deaths occurred in the last decades in most countries after the beginning of national campaigns, mainly as a consequence of the implementation of risk reduction action mostly concentrating on the improvement of sleep conditions. Nevertheless, infant mortality from SIDS still remains unacceptably high. There is an urgent need to get insight into previously unexplored aspects of the brain system with a special focus on high-risk groups. SIDS pathogenesis is associated with a multifactorial condition that comprehends genetic, environmental and sociocultural factors. Effective prevention of SIDS requires multiple interventions from different fields. Developing brain susceptibility, intrinsic vulnerability and early identification of infants with high risk of SIDS represents a challenge. Progress in SIDS research appears to be fundamental to the ultimate aim of eradicating SIDS deaths. A complex model that combines different risk factor data from biomarkers and omic analysis may represent a tool to identify a SIDS risk profile in newborn settings. If high risk is detected, the infant may be referred for further investigations and follow ups. This review aims to illustrate the most recent discoveries from different fields, analyzing the neuroanatomical, genetic, metabolic, proteomic, environmental and sociocultural aspects related to SIDS.