Sudden Infant Death Syndrome: Beyond Risk Factors

Perrone, Serafina; Lembo, Chiara; Moretti, Silvia; Prezioso, Giovanni; Buonocore, Giuseppe; Toscani, Giorgia; Marinelli, Francesca; Marzano, Francesco Nonnis; Esposito, Susanna

doi:10.3390/life11030184

Cited by 15 publications

(17 citation statements)

References 124 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…6 There is emerging consensus that SUDP represents a heterogeneous grouping of rare and undiagnosed diseases presenting with death and sometimes involve genetic mechanisms. 7,8 Genetic contributions to SUDP are supported by studies of families whose children died from SIDS during the "safe sleep" era, which showed an increased risk for recurrence in subsequent siblings (odds ratio, [OR], 4.2) and within 3 generations (OR, 9.3). 9 The prevailing etiologic model of SUDP postulates that modest extrinsic threats become fatal in infants and children who harbor intrinsic vulnerabilities.…”

Section: Introductionmentioning

confidence: 99%

Genetic Determinants of Sudden Unexpected Death in Pediatrics

Koh

Haghighi

Keywan

et al. 2022

Genetics in Medicine

View full text Add to dashboard Cite

This study aimed to evaluate genetic contributions to sudden unexpected death in pediatrics (SUDP). Methods: We phenotyped and performed exome sequencing for 352 SUDP cases. We analyzed variants in 294 "SUDP genes" with mechanisms plausibly related to sudden death. In a subset of 73 cases with parental data (trios), we performed exome-wide analyses and conducted cohortwide burden analyses. Results: In total, we identified likely contributory variants in 37 of 352 probands (11%). Analysis of SUDP genes identified pathogenic/likely pathogenic variants in 12 of 352 cases (SCN1A, DEPDC5 [2], GABRG2, SCN5A [2], TTN [2], MYBPC3, PLN, TNNI3, and PDHA1) and variants of unknown significance-favor-pathogenic in 17 of 352 cases. Exome-wide analyses of the 73 cases with family data additionally identified 4 de novo pathogenic/likely pathogenic variants (SCN1A [2], ANKRD1, and BRPF1) and 4 de novo variants of unknown significance-favor-pathogenic. Comparing cases with controls, we demonstrated an excess burden of rare damaging SUDP gene variants (odds ratio, 2.94; 95% confidence interval, 2.37-4.21) and of exome-wide de novo variants in the subset of 73 with trio data (odds ratio, 3.13; 95% confidence interval, 1.91-5.16). Conclusion:We provide strong evidence for a role of genetic factors in SUDP, involving both candidate genes and novel genes for SUDP and expanding phenotypes of disease genes not previously associated with sudden death.

show abstract

Section: Introductionmentioning

confidence: 99%

Genetic Determinants of Sudden Unexpected Death in Pediatrics

Koh

Haghighi

Keywan

et al. 2022

Genetics in Medicine

View full text Add to dashboard Cite

show abstract

“…There is no definitive diagnosis of SIDS, and the underlying cause is not clear [ 8 , 47 ]. SIDS is a diagnosis of exclusion—as such, it is an unexpected death in terms of a previous disease, not explained by a detailed and thorough autopsy or the results of laboratory tests and accompanied by knowledge of all the circumstances from the site of death [ 48 ].…”

Section: Literature Reviewmentioning

confidence: 99%

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

et al. 2022

View full text Add to dashboard Cite

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.

show abstract

“…Omics technologies provide a large amount of data, which, when used in a univariate or multivariate manner, could contribute to the detection of the risk profile of the patient who dies from SIDS [ 14 ]. The large amount of data, which is produced with new technologies, benefits from the analysis with machine learning techniques.…”

Section: Introductionmentioning

confidence: 99%

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

Villagrana-Bañuelos

Galván-Tejada

et al. 2022

Healthcare

View full text Add to dashboard Cite

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.

show abstract

Sudden Infant Death Syndrome: Beyond Risk Factors

Cited by 15 publications

References 124 publications

Genetic Determinants of Sudden Unexpected Death in Pediatrics

Genetic Determinants of Sudden Unexpected Death in Pediatrics

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

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

Contact Info

Product

Resources

About