Development and validation of a nomogram for predicting late-onset sepsis in preterm infants on the basis of thyroid function and other risk factors: Mixed retrospective and prospective cohort study

Huang, Yuejun; Yu, Xiaochan; Li, Weidong; Li, Yuewa; Yang, Jianhui; Hu, Zhimei; Wang, Yanli; Chen, Peishan; Li, Weizhong; Chen, Yunbin

doi:10.1016/j.jare.2020.02.005

Cited by 10 publications

(21 citation statements)

References 22 publications

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“…Majority (five studies) of the included articles were performed in United State of America [24,26,29,30], while the remaining each were from Israel, China, Mexico, United Kingdom and Canada [22,23,27,28,31]. The highest sample size of all the included studies was 4794, while the minimal is 36, which are illustrated in Fig.…”

Section: Study Characteristicsmentioning

confidence: 99%

“…Neonatal sepsis detection was the primary outcome of considerable number of studies (five studies) [27][28][29][30][31]. Earlyonset and late-onset sepsis detection were encountered as the main outcome in three and two different studies, respectively [22][23][24][25][26]. Sheer single study had stratification of the earlyonset neonatal sepsis patients based on the degree of illness as the defined outcome [25].…”

Section: Study Characteristicsmentioning

confidence: 99%

“…Half of the included studies had utilized 2:1 ratio, i.e. split method of derivation and validation set for evaluating the accuracy, precision and validity of model [22,24,25,29,30]; meanwhile, mere one study had used the external validation procedure of conducting the same study in different set of samples at different period of time [23]. 70:15:15 (70 derivation set, 15 validations set, 15 testing set) ratio was applied for validation of model developed by maternal and neonatal risk factor for identification of neonates with sepsis.…”

Section: Validation Measuresmentioning

confidence: 99%

“…Included articles in our study had either utilised clinical/laboratory parameters, neonatal/maternal risk factors, or physiological events of the neonates for building the prediction model [22][23][24][25][26][27][28][29][30][31]. Birth weight characteristics, gestational age features, role of setting, sampling strategy and concomitant interventions were some of the primary predictors assessed for late-onset neonatal sepsis by routine neonatal screening [34].…”

Section: Predictors Assessed Compared With Other Studiesmentioning

confidence: 99%

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Prediction modelling in the early detection of neonatal sepsis

et al. 2022

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Background Prediction modelling can greatly assist the health-care professionals in the management of diseases, thus sparking interest in neonatal sepsis diagnosis. The main objective of the study was to provide a complete picture of performance of prediction models for early detection of neonatal sepsis. Methods PubMed, Scopus, CINAHL databases were searched and articles which used various prediction modelling measures for the early detection of neonatal sepsis were comprehended. Data extraction was carried out based on Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies checklist. Extricate data consisted of objective, study design, patient characteristics, type of statistical model, predictors, outcome, sample size and location. Prediction model Risk of Bias Assessment Tool was applied to gauge the risk of bias of the articles. Results An aggregate of ten studies were included in the review among which eight studies had applied logistic regression to build a prediction model, while the remaining two had applied artificial intelligence. Potential predictors like neonatal fever, birth weight, foetal morbidity and gender, cervicovaginitis and maternal age were identified for the early detection of neonatal sepsis. Moreover, birth weight, endotracheal intubation, thyroid hypofunction and umbilical venous catheter were promising factors for predicting late-onset sepsis; while gestational age, intrapartum temperature and antibiotics treatment were utilised as budding prognosticators for early-onset sepsis detection. Conclusion Prediction modelling approaches were able to recognise promising maternal, neonatal and laboratory predictors in the rapid detection of early and late neonatal sepsis and thus, can be considered as a novel way for clinician decision-making towards the disease diagnosis if not used alone, in the years to come.

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Section: Study Characteristicsmentioning

confidence: 99%

Section: Study Characteristicsmentioning

confidence: 99%

Section: Validation Measuresmentioning

confidence: 99%

Section: Predictors Assessed Compared With Other Studiesmentioning

confidence: 99%

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Prediction modelling in the early detection of neonatal sepsis

et al. 2022

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“…Pharmacological intervention with dopamine in the treatment of hypotension can further suppress T 4 concentrations in preterm infants (144). A recent study suggests that thyroid hypofunction is indicative of an at-risk baby who may be susceptible to development of LOI (145). Thyroid hormone concentrations are significantly lower in sick babies as described below (79,80,(146)(147)(148).…”

Section: Hypothalamic-pituitary-thyroidal Axismentioning

confidence: 99%

Influence of innate immune activation on endocrine and metabolic pathways in infancy

O’Connor

Ashoori

Dias

et al. 2021

American Journal of Physiology-Endocrinology and Metabolism

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Prematurity is the leading cause of neonatal morbidity and mortality worldwide. Premature infants often require extended hospital stays, with increased risk of developing infection compared with term infants. A picture is emerging of wide-ranging deleterious consequences resulting from innate immune system activation in the newborn infant. Those who survive infection have been exposed to a stimulus that can impose long-lasting alterations into later life. In this review, we discuss sepsis-driven alterations in integrated neuroendocrine and metabolic pathways and highlight current knowledge gaps in respect of neonatal sepsis. We review established biomarkers for sepsis and extend the discussion to examine emerging findings from human and animal models of neonatal sepsis that propose novel biomarkers for early identification of sepsis. Future research in this area is required to establish a greater understanding of the distinct neonatal signature of early and late-stage infection, to improve diagnosis, curtail inappropriate antibiotic use and promote precision medicine through a biomarker-guided empirical and adjunctive treatment approach for neonatal sepsis. There is an unmet clinical need to decrease sepsis-induced morbidity in neonates, to limit and prevent adverse consequences in later life and decrease mortality.

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