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Complex signals bioinformatics: evaluation of heart rate characteristics monitoring as a novel risk marker for neonatal sepsis.

ABSTRACT: Heart rate characteristics monitoring for early detection of late-onset neonatal sepsis was first described in 2003. This technique, which uses mathematical methods to report the fold-increase in the risk of imminent neonatal sepsis, adds independent information to laboratory tests and clinical findings, and, in a large randomized trial, reduced NICU mortality of very low birth weight infants. Through re-analysis and new secondary analyses of published studies, we have systematically evaluated the utility of this new risk marker for screening the growing population of premature infants.We followed the guidelines proposed by Hlatky et al. (Circulation, 119:2408-2416, 2009), reviewed past works, and re-analyzed data from 1,489 patients receiving conventional monitoring alone, 348 of whom had 488 episodes of proven sepsis, in the large randomized trial.Heart rate characteristics monitoring passed all phases of risk marker development from proof of concept to improvement of clinical outcomes. The predictiveness curve affirmed good calibration, and addition of the heart rate characteristics index to predictive models using standard risk factors favorably impacted the receiver operating characteristic curve area (increase of 0.030), continuous net reclassification index (0.389) and the integrated discrimination index (0.008), and compares well to other modern risk factors.Heart rate characteristics monitoring is a validated risk marker for sepsis in the NICU.

SUBMITTER: Lake DE

PROVIDER: S-EPMC4026344 | biostudies-literature | 2014 Aug

REPOSITORIES: biostudies-literature

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Complex signals bioinformatics: evaluation of heart rate characteristics monitoring as a novel risk marker for neonatal sepsis.

Lake Douglas E DE Fairchild Karen D KD Moorman J Randall JR

Journal of clinical monitoring and computing 20131119 4

<h4>Purposes</h4>Heart rate characteristics monitoring for early detection of late-onset neonatal sepsis was first described in 2003. This technique, which uses mathematical methods to report the fold-increase in the risk of imminent neonatal sepsis, adds independent information to laboratory tests and clinical findings, and, in a large randomized trial, reduced NICU mortality of very low birth weight infants. Through re-analysis and new secondary analyses of published studies, we have systemati ...[more]

PMID: 24248424

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Project description:BackgroundNeonatal sepsis, a perilous medical situation, is typified by the malfunction of organs and serves as the primary reason for neonatal mortality. Nevertheless, the mechanisms underlying newborn sepsis remain ambiguous. Programmed cell death (PCD) has a connection with numerous infectious illnesses and holds a significant function in newborn sepsis, potentially serving as a marker for diagnosing the condition.MethodsFrom the GEO public repository, we selected two groups, which we referred to as the training and validation sets, for our analysis of neonatal sepsis. We obtained PCD-related genes from 12 different patterns, including databases and published literature. We first obtained differential expressed genes (DEGs) for neonatal sepsis and controls. Three advanced machine learning techniques, namely LASSO, SVM-RFE, and RF, were employed to identify potential genes connected to PCD. To further validate the results, PPI networks were constructed, artificial neural networks and consensus clustering were used. Subsequently, a neonatal sepsis diagnostic prediction model was developed and evaluated. We conducted an analysis of immune cell infiltration to examine immune cell dysregulation in neonatal sepsis, and we established a ceRNA network based on the identified marker genes.ResultsWithin the context of neonatal sepsis, a total of 49 genes exhibited an intersection between the differentially expressed genes (DEGs) and those associated with programmed cell death (PCD). Utilizing three distinct machine learning techniques, six genes were identified as common to both DEGs and PCD-associated genes. A diagnostic model was subsequently constructed by integrating differential expression profiles, and subsequently validated by conducting artificial neural networks and consensus clustering. Receiver operating characteristic (ROC) curves were employed to assess the diagnostic merit of the model, which yielded promising results. The immune infiltration analysis revealed notable disparities in patients diagnosed with neonatal sepsis. Furthermore, based on the identified marker genes, the ceRNA network revealed an intricate regulatory interplay.ConclusionIn our investigation, we methodically identified six marker genes (AP3B2, STAT3, TSPO, S100A9, GNS, and CX3CR1). An effective diagnostic prediction model emerged from an exhaustive analysis within the training group (AUC 0.930, 95%CI 0.887-0.965) and the validation group (AUC 0.977, 95%CI 0.935-1.000).

Dataset Information

Complex signals bioinformatics: evaluation of heart rate characteristics monitoring as a novel risk marker for neonatal sepsis.

Publications

Complex signals bioinformatics: evaluation of heart rate characteristics monitoring as a novel risk marker for neonatal sepsis.

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