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Strunk T, Molloy EJ, Mishra A, Bhutta ZA. Neonatal bacterial sepsis. Lancet 2024; 404:277-293. [PMID: 38944044 DOI: 10.1016/s0140-6736(24)00495-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/02/2022] [Revised: 02/06/2024] [Accepted: 03/07/2024] [Indexed: 07/01/2024]
Abstract
Neonatal sepsis remains one of the key challenges of neonatal medicine, and together with preterm birth, causes almost 50% of all deaths globally for children younger than 5 years. Compared with advances achieved for other serious neonatal and early childhood conditions globally, progress in reducing neonatal sepsis has been much slower, especially in low-resource settings that have the highest burden of neonatal sepsis morbidity and mortality. By contrast to sepsis in older patients, there is no universally accepted neonatal sepsis definition. This poses substantial challenges in clinical practice, research, and health-care management, and has direct practical implications, such as diagnostic inconsistency, heterogeneous data collection and surveillance, and inappropriate treatment, health-resource allocation, and education. As the clinical manifestation of neonatal sepsis is frequently non-specific and the current diagnostic standard blood culture has performance limitations, new improved diagnostic techniques are required to guide appropriate and warranted antimicrobial treatment. Although antimicrobial therapy and supportive care continue as principal components of neonatal sepsis therapy, refining basic neonatal care to prevent sepsis through education and quality improvement initiatives remains paramount.
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Affiliation(s)
- Tobias Strunk
- Neonatal Directorate, King Edward Memorial Hospital, Child and Adolescent Health Service, Perth, WA, Australia; Wesfarmers Centre for Vaccines and Infectious Diseases, Telethon Kids Institute, University of Western Australia, Perth, WA, Australia.
| | - Eleanor J Molloy
- Discipline of Paediatrics, Trinity College, University of Dublin and Trinity Research in Childhood Centre, Dublin, Ireland; Children's Health Hospital at Tallaght, Tallaght University Hospital, Dublin, Ireland; Trinity Translational Medicine Institute, St James Hospital, Dublin, Ireland; Neonatology, Children's Health Hospital at Crumlin, Dublin, Ireland; Paediatrics, Coombe Women's and Infant's University Hospital, Dublin, Ireland
| | - Archita Mishra
- Wesfarmers Centre for Vaccines and Infectious Diseases, Telethon Kids Institute, University of Western Australia, Perth, WA, Australia
| | - Zulfiqar A Bhutta
- Centre for Global Child Health, Hospital for Sick Children, Toronto, ON, Canada; Institute for Global Health and Development, The Aga Khan University South-Central Asia, Karachi, Pakistan
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Jiang Z, Luo Y, Wei L, Gu R, Zhang X, Zhou Y, Zhang S. Bioinformatic Analysis and Machine Learning Methods in Neonatal Sepsis: Identification of Biomarkers and Immune Infiltration. Biomedicines 2023; 11:1853. [PMID: 37509492 PMCID: PMC10377054 DOI: 10.3390/biomedicines11071853] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2023] [Revised: 06/18/2023] [Accepted: 06/26/2023] [Indexed: 07/30/2023] Open
Abstract
The disease neonatal sepsis (NS) poses a serious threat to life, and its pathogenesis remains unclear. Using the Gene Expression Omnibus (GEO) database, differentially expressed genes (DEGs) were identified and functional enrichment analyses were conducted. Three machine learning algorithms containing the least absolute shrinkage and selection operator (LASSO), support vector machine recursive feature elimination (SVM-RFE), and random forest (RF) were applied to identify the optimal feature genes (OFGs). This study conducted CIBERSORT to present the abundance of immune infiltrates between septic and control neonates and assessed the relationship between OFGs and immune cells. In total, 44 DEGs were discovered between the septic and control newborns. Throughout the enrichment analysis, DEGs were primarily related to inflammatory signaling pathways and immune responses. The OFGs derived from machine learning algorithms were intersected to yield four biomarkers, namely Hexokinase 3 (HK3), Cystatin 7 (CST7), Resistin (RETN), and Glycogenin 1 (GYG1). The potential biomarkers were validated in other datasets and LPS-stimulated HEUVCs. Septic infants showed a higher proportion of neutrophils (p < 0.001), M0 macrophages (p < 0.001), and regulatory T cells (p = 0.004). HK3, CST7, RETN, and GYG1 showed significant correlations with immune cells. Overall, the biomarkers offered promising insights into the molecular mechanisms of immune regulation for the prediction and treatment of NS.
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Affiliation(s)
- Zhou Jiang
- Department of NICU, Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, No. 368 Xiasha Road, Qiantang District, Hangzhou 310016, China
| | - Yujia Luo
- Department of NICU, Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, No. 368 Xiasha Road, Qiantang District, Hangzhou 310016, China
| | - Li Wei
- Department of NICU, Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, No. 368 Xiasha Road, Qiantang District, Hangzhou 310016, China
| | - Rui Gu
- Department of NICU, Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, No. 368 Xiasha Road, Qiantang District, Hangzhou 310016, China
| | - Xuandong Zhang
- Department of NICU, Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, No. 368 Xiasha Road, Qiantang District, Hangzhou 310016, China
| | - Yuanyuan Zhou
- Department of Reproductive Endocrinology, Women's Hospital, School of Medicine, Zhejiang University, Hangzhou 310006, China
| | - Songying Zhang
- Department of Obstetrics and Gynecology, Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, No. 3 Qingchun East Road, Shangcheng District, Hangzhou 310016, China
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Rallis D, Giapros V, Serbis A, Kosmeri C, Baltogianni M. Fighting Antimicrobial Resistance in Neonatal Intensive Care Units: Rational Use of Antibiotics in Neonatal Sepsis. Antibiotics (Basel) 2023; 12:antibiotics12030508. [PMID: 36978375 PMCID: PMC10044400 DOI: 10.3390/antibiotics12030508] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2023] [Revised: 02/26/2023] [Accepted: 02/28/2023] [Indexed: 03/06/2023] Open
Abstract
Antibiotics are the most frequently prescribed drugs in neonatal intensive care units (NICUs) due to the severity of complications accompanying neonatal sepsis. However, antimicrobial drugs are often used inappropriately due to the difficulties in diagnosing sepsis in the neonatal population. The reckless use of antibiotics leads to the development of resistant strains, rendering multidrug-resistant pathogens a serious problem in NICUs and a global threat to public health. The aim of this narrative review is to provide a brief overview of neonatal sepsis and an update on the data regarding indications for antimicrobial therapy initiation, current guidance in the empirical antimicrobial selection and duration of therapy, and indications for early discontinuation.
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Affiliation(s)
- Dimitrios Rallis
- Neonatal Intensive Care Unit, School of Medicine, University of Ioannina, 451 10 Ioannina, Greece
| | - Vasileios Giapros
- Neonatal Intensive Care Unit, School of Medicine, University of Ioannina, 451 10 Ioannina, Greece
- Correspondence: ; Tel.: +30-(26)-51099326
| | - Anastasios Serbis
- Department of Paediatrics, School of Medicine, University of Ioannina, 451 10 Ioannina, Greece
| | - Chrysoula Kosmeri
- Department of Paediatrics, School of Medicine, University of Ioannina, 451 10 Ioannina, Greece
| | - Maria Baltogianni
- Neonatal Intensive Care Unit, School of Medicine, University of Ioannina, 451 10 Ioannina, Greece
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Bai Y, Zhao N, Zhang Z, Jia Y, Zhang G, Dong G. Identification and validation of a novel four-gene diagnostic model for neonatal early-onset sepsis with bacterial infection. Eur J Pediatr 2023; 182:977-985. [PMID: 36527479 PMCID: PMC10023633 DOI: 10.1007/s00431-022-04753-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/02/2022] [Revised: 11/25/2022] [Accepted: 12/03/2022] [Indexed: 12/23/2022]
Abstract
Neonatal early-onset sepsis (EOS) has unfortunately been the third leading cause of neonatal death worldwide. The current study is aimed at discovering reliable biomarkers for the diagnosis of neonatal EOS through transcriptomic analysis of publicly available datasets. Whole blood mRNA expression profiling of neonatal EOS patients in the GSE25504 dataset was downloaded and analyzed. The binomial LASSO model was constructed to select genes that most accurately predicted neonatal EOS. Then, ROC curves were generated to assess the performance of the predictive features in differentiating between neonatal EOS and normal infants. Finally, the miRNA-mRNA network was established to explore the potential biological mechanisms of genes within the model. Four genes (CST7, CD3G, CD247, and ANKRD22) were identified that most accurately predicted neonatal EOS and were subsequently used to construct a diagnostic model. ROC analysis revealed that this diagnostic model performed well in differentiating between neonatal EOS and normal infants in both the GSE25504 dataset and our clinical cohort. Finally, the miRNA-mRNA network consisting of the four genes and potential target miRNAs was constructed. Through bioinformatics analysis, a diagnostic four-gene model that can accurately distinguish neonatal EOS in newborns with bacterial infection was constructed, which can be used as an auxiliary test for diagnosing neonatal EOS with bacterial infection in the future. CONCLUSION In the current study, we analyzed gene expression profiles of neonatal EOS patients from public databases to develop a genetic model for predicting sepsis, which could provide insight into early molecular changes and biological mechanisms of neonatal EOS. WHAT IS KNOWN • Infants with suspected EOS usually receive empiric antibiotic therapy directly after birth. • When blood cultures are negative after 48 to 72 hours, empirical antibiotic treatment is often halted. Needless to say, this is not a short time. Additionally, because of the concern for inadequate clinical sepsis production and the limited sensitivity of blood cultures, the duration of antibiotic therapy for the kid is typically extended. WHAT IS NEW • We established a 4-gene diagnostic model of neonatal EOS with bacterial infection by bioinformatics analysis method. The model has better diagnostic performance compared with conventional inflammatory indicators such as CRP, Hb, NEU%, and PCT.
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Affiliation(s)
- Yong Bai
- Children's Hospital Affiliated to Zhengzhou University, Zhengzhou Key Laboratory of Children's Infection and Immunity, Zhengzhou, China
| | - Na Zhao
- Department of Pathology, Zhengzhou Central Hospital Affiliated to Zhengzhou University, Zhengzhou, China
| | - Zhenhua Zhang
- Children's Hospital Affiliated to Zhengzhou University, Zhengzhou Key Laboratory of Children's Infection and Immunity, Zhengzhou, China
| | - Yangjie Jia
- Department of Pathology, Zhengzhou Central Hospital Affiliated to Zhengzhou University, Zhengzhou, China
| | - Genhao Zhang
- Department of Blood Transfusion, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Geng Dong
- Children's Hospital Affiliated to Zhengzhou University, Zhengzhou Key Laboratory of Children's Infection and Immunity, Zhengzhou, China.
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Monitoring of heart rate characteristics to detect neonatal sepsis. Pediatr Res 2022; 92:1070-1074. [PMID: 34916625 DOI: 10.1038/s41390-021-01913-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/08/2021] [Revised: 12/02/2021] [Accepted: 12/04/2021] [Indexed: 01/22/2023]
Abstract
BACKGROUND Monitoring of heart rate characteristics (HRC) index may improve outcomes of late-onset neonatal sepsis (LOS) through early detection. We aimed at describing the association between LOS and elevated HRC index. METHODS This single-center retrospective case-control study included neonates who presented with blood culture-proven hospital-acquired LOS. Controls were matched to cases (ratio 1:2) based on gestational age, postnatal age, and birthweight. We compared the highest HRC indexes in the 48 h preceding blood culture sampling in LOS cases to the highest HRC indexes at the same postnatal days in controls. RESULTS In 59 LOS cases and 123 controls, an HRC index > 2 was associated with LOS (OR 7.1, 95% CI 2.6-19.0). Sensitivity and specificity of an HRC index > 2 to predict LOS were 53% (32/59) and 79% (98/123). Sensitivity increased from 25% in infants born > 32 weeks to 76% in infants born < 28 weeks. Specificity decreased from 97% in infants > 32 weeks to 63% in those born < 28 weeks. CONCLUSIONS An increase of HRC index > 2 has a significant association with the diagnosis of LOS, supporting the use of HRC monitoring to assist early detection of LOS. Clinicians using HRC monitoring should be aware of its diagnostic accuracy and limitations in different gestational age groups. IMPACT There is a paucity of data regarding the predictive value of heart rate characteristics (HRC) monitoring for early diagnosis of late-onset neonatal sepsis (LOS) in daily clinical practice. Monitoring of heart rate characteristics provides valuable information to assist the early diagnosis of LOS across all gestational age groups. However, the strong influence of gestational age on positive and negative predictive values adds complexity to the interpretation of HRC indexes.
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Abstract
Neonatal sepsis is a major cause of morbidity and mortality in neonates and is challenging to diagnose. Infants manifest nonspecific clinical signs in response to sepsis; these signs may be caused by noninfectious conditions. Time to antibiotics affects neonatal sepsis outcome, so clinicians need to identify and treat neonates with sepsis expeditiously. Clinicians use serum biomarkers to measure inflammation and infection and assess the infant's risk of sepsis. However, current biomarkers lack sufficient sensitivity or specificity to be consider useful diagnostic tools. Continued research to identify novel biomarkers as well as novel ways of measuring them is sorely needed.
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Affiliation(s)
- Joseph B Cantey
- Department of Pediatrics, Division of Allergy, Immunology, and Infectious Diseases, University of Texas Health San Antonio, 7703 Floyd Curl Drive, San Antonio, TX 78229, USA.
| | - John H Lee
- Department of Pediatrics, University of Texas Health San Antonio, 7703 Floyd Curl Drive, San Antonio, TX 78229, USA
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