Dataset Information

Prediction for late-onset sepsis in preterm infants based on data from East China.

ABSTRACT:

Aim

To construct a prediction model based on the data of premature infants and to apply the data in our study as external validation to the prediction model proposed by Yuejun Huang et al. to evaluate the predictive ability of both models.

Methods

In total, 397 premature infants were randomly divided into the training set (n = 278) and the testing set (n = 119). Univariate and multivariate logistic analyses were applied to identify potential predictors, and the prediction model was constructed based on the predictors. The area under the curve (AUC) value, the receiver operator characteristic (ROC) curves, and the calibration curves were used to evaluate the predictive performances of prediction models. The data in our study were used in the prediction model proposed by Yuejun Huang et al. as external validation.

Results

In the current study, endotracheal intubation [odds ratio (OR) = 10.553, 95% confidence interval (CI): 4.959-22.458], mechanical ventilation (OR = 10.243, 95% CI: 4.811-21.806), asphyxia (OR = 2.614, 95% CI: 1.536-4.447), and antibiotics use (OR = 3.362, 95% CI: 1.454-7.775) were risk factors for late-onset sepsis in preterm infants. The higher birth weight of infants (OR = 0.312, 95% CI: 0.165-0.588) and gestational age were protective factors for late-onset sepsis in preterm infants. The training set was applied for the construction of the models, and the testing set was used to test the diagnostic efficiency of the model. The AUC values of the prediction model were 0.760 in the training set and 0.796 in the testing set.

Conclusion

The prediction model showed a good predictive ability for late-onset sepsis in preterm infants.

SUBMITTER: Shuai X

PROVIDER: S-EPMC9515484 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature

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Publications

Prediction for late-onset sepsis in preterm infants based on data from East China.

Shuai Xianghua X Li Xiaoxia X Wu Yiling Y

Frontiers in pediatrics 20220914

<h4>Aim</h4>To construct a prediction model based on the data of premature infants and to apply the data in our study as external validation to the prediction model proposed by Yuejun Huang et al. to evaluate the predictive ability of both models.<h4>Methods</h4>In total, 397 premature infants were randomly divided into the training set (<i>n</i> = 278) and the testing set (<i>n</i> = 119). Univariate and multivariate logistic analyses were applied to identify potential predictors, and the predi ...[more]

PMID: 36186643

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Project description:ObjectivesPrediction of late-onset sepsis (onset beyond day 3 of life) in preterm infants, based on multiple patient monitoring signals 24 hours before onset.DesignContinuous high-resolution electrocardiogram and respiration (chest impedance) data from the monitoring signals were extracted and used to create time-interval features representing heart rate variability, respiration, and body motion. For each infant with a blood culture-proven late-onset sepsis, a Cultures, Resuscitation, and Antibiotics Started Here moment was defined. The Cultures, Resuscitation, and Antibiotics Started Here moment served as an anchor point for the prediction analysis. In the group with controls (C), an "equivalent crash moment" was calculated as anchor point, based on comparable gestational and postnatal age. Three common machine learning approaches (logistic regressor, naive Bayes, and nearest mean classifier) were used to binary classify samples of late-onset sepsis from C. For training and evaluation of the three classifiers, a leave-k-subjects-out cross-validation was used.SettingLevel III neonatal ICU.PatientsThe patient population consisted of 32 premature infants with sepsis and 32 age-matched control patients.InterventionsNo interventions were performed.Measurements and main resultsFor the interval features representing heart rate variability, respiration, and body motion, differences between late-onset sepsis and C were visible up to 5 hours preceding the Cultures, Resuscitation, and Antibiotics Started Here moment. Using a combination of all features, classification of late-onset sepsis and C showed a mean accuracy of 0.79 ± 0.12 and mean precision rate of 0.82 ± 0.18 3 hours before the onset of sepsis.ConclusionsInformation from routine patient monitoring can be used to predict sepsis. Specifically, this study shows that a combination of electrocardiogram-based, respiration-based, and motion-based features enables the prediction of late-onset sepsis hours before the clinical crash moment.

Project description:BACKGROUND:Host immune responses during late-onset sepsis (LOS) in very preterm infants are poorly characterised due to a complex and dynamic pathophysiology and challenges in working with small available blood volumes. We present here an unbiased transcriptomic analysis of whole peripheral blood from very preterm infants at the time of LOS. METHODS:RNA-Seq was performed on peripheral blood samples (6-29 days postnatal age) taken at the time of suspected LOS from very preterm infants <30 weeks gestational age. Infants were classified based on blood culture positivity and elevated C-reactive protein concentrations as having confirmed LOS (n = 5), possible LOS (n = 4) or no LOS (n = 9). Bioinformatics and statistical analyses performed included pathway over-representation and protein-protein interaction network analyses. Plasma cytokine immunoassays were performed to validate differentially expressed cytokine pathways. RESULTS:The blood leukocyte transcriptional responses of infants with confirmed LOS differed significantly from infants without LOS (1,317 differentially expressed genes). However, infants with possible LOS could not be distinguished from infants with no LOS or confirmed LOS. Transcriptional alterations associated with LOS included genes involved in pathogen recognition (mainly TLR pathways), cytokine signalling (both pro-inflammatory and inhibitory responses), immune and haematological regulation (including cell death pathways), and metabolism (altered cholesterol biosynthesis). At the transcriptional-level cytokine responses during LOS were characterised by over-representation of IFN-α/β, IFN-γ, IL-1 and IL-6 signalling pathways and up-regulation of genes for inflammatory responses. Infants with confirmed LOS had significantly higher levels of IL-1α and IL-6 in their plasma. CONCLUSIONS:Blood responses in very preterm infants with LOS are characterised by altered host immune responses that appear to reflect unbalanced immuno-metabolic homeostasis.

Dataset Information

Prediction for late-onset sepsis in preterm infants based on data from East China.

Aim

Methods

Results

Conclusion

Publications

Prediction for late-onset sepsis in preterm infants based on data from East China.

Similar Datasets

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