Novel biomarker combination improves the diagnosis of serious bacterial infections in Malawian children

Irwin, Adam; Marriage, Fiona; Mankhambo, Limangeni; Jeffers, Graham; Kolamunnage‐Dona, Ruwanthi; Guiver, Malcolm; Denis, Brigitte; Molyneux, Elizabeth; Molyneux, Malcolm E.; Day, Philip J.; Carrol, Enitan D.

doi:10.1186/1755-8794-5-13

Cited by 19 publications

(15 citation statements)

References 41 publications

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“…Overall, 16 studies containing 17 different cohorts were included (Table 1a , b). These 16 studies include expression profiles from both adult 15 , 17 , 19 , 43 – 52 and pediatric 48 , 53 – 56 cohorts. In these cases, the gene expression data were publicly available.…”

Section: Methodsmentioning

confidence: 99%

A community approach to mortality prediction in sepsis via gene expression analysis

et al. 2018

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Improved risk stratification and prognosis prediction in sepsis is a critical unmet need. Clinical severity scores and available assays such as blood lactate reflect global illness severity with suboptimal performance, and do not specifically reveal the underlying dysregulation of sepsis. Here, we present prognostic models for 30-day mortality generated independently by three scientific groups by using 12 discovery cohorts containing transcriptomic data collected from primarily community-onset sepsis patients. Predictive performance is validated in five cohorts of community-onset sepsis patients in which the models show summary AUROCs ranging from 0.765–0.89. Similar performance is observed in four cohorts of hospital-acquired sepsis. Combining the new gene-expression-based prognostic models with prior clinical severity scores leads to significant improvement in prediction of 30-day mortality as measured via AUROC and net reclassification improvement index These models provide an opportunity to develop molecular bedside tests that may improve risk stratification and mortality prediction in patients with sepsis.

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Section: Methodsmentioning

confidence: 99%

A community approach to mortality prediction in sepsis via gene expression analysis

et al. 2018

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“…16 We have previously reported the combined performance of procalcitonin (PCT), resistin, and neutrophil gelatinase-associated lipocalin (NGAL) in Malawian children. 17 Diagnostic accuracy studies of febrile children have so far failed to impact clinical practice. Restrictive inclusion criteria (such as age, temperature, or clinical syndrome 18 ) have limited their external validity, and few have progressed to validation in external populations.…”

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confidence: 99%

Predicting Risk of Serious Bacterial Infections in Febrile Children in the Emergency Department

Irwin¹,

Grant

Williams

et al. 2017

Pediatrics

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BACKGROUND: Improving the diagnosis of serious bacterial infections (SBIs) in the children’s emergency department is a clinical priority. Early recognition reduces morbidity and mortality, and supporting clinicians in ruling out SBIs may limit unnecessary admissions and antibiotic use. METHODS: A prospective, diagnostic accuracy study of clinical and biomarker variables in the diagnosis of SBIs (pneumonia or other SBI) in febrile children <16 years old. A diagnostic model was derived by using multinomial logistic regression and internally validated. External validation of a published model was undertaken, followed by model updating and extension by the inclusion of procalcitonin and resistin. RESULTS: There were 1101 children studied, of whom 264 had an SBI. A diagnostic model discriminated well between pneumonia and no SBI (concordance statistic 0.84, 95% confidence interval 0.78–0.90) and between other SBIs and no SBI (0.77, 95% confidence interval 0.71–0.83) on internal validation. A published model discriminated well on external validation. Model updating yielded good calibration with good performance at both high-risk (positive likelihood ratios: 6.46 and 5.13 for pneumonia and other SBI, respectively) and low-risk (negative likelihood ratios: 0.16 and 0.13, respectively) thresholds. Extending the model with procalcitonin and resistin yielded improvements in discrimination. CONCLUSIONS: Diagnostic models discriminated well between pneumonia, other SBIs, and no SBI in febrile children in the emergency department. Improvements in the classification of nonevents have the potential to reduce unnecessary hospital admissions and improve antibiotic prescribing. The benefits of this improved risk prediction should be further evaluated in robust impact studies.

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“…The diagnostics of SBI are still challenging although there are new tests and clinical scores available. The diagnosis of SBI is complicated by its non-specific clinical symptoms, and their variability; failure to diagnose and promptly treat SBI results in significant morbidity and mortality [12]. In addition, the complex pathophysiology and great patient-to-patient variability in SBIs needs to be considered.…”

Section: Discussionmentioning

confidence: 99%

Inflammatory Cytokine and Chemokine Patterns in Paediatric Patients with Suspected Serious Bacterial Infection

et al. 2019

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Background and objectives: In children, acute infection is the most common cause of visits to the emergency department. Although most of them are self-limiting, mortality due to severe bacterial infections (SBI) in developed countries is still high. When the risk of serious bacterial infection is too high to ignore, yet too low to justify admission and hospital observation, clinicians try to improve diagnostic accuracy by performing various laboratory tests. The aim of the study was to investigate whether an early inflammatory cytokine and chemokine panel can add information in diagnostics of SBI and assessment of efficacy of early therapies in hospitalized children with fever. Methods: This study included 51 children with febrile infections that were admitted to the emergency department (ED). Clinical examination and microbiological and radiological tests were used as reference standards for the definition of SBI. Study population was categorized into two groups: (1) patients with SBI (n = 21); (2) patients without SBI (n = 30). Inflammatory cytokine and chemokine panels were analyzed from the first routine blood samples at hospital admission and after 24 h. Results: Out of 12 cytokines and chemokines, only Eotaxin and granulocyte colony-stimulating factor (G-CSF) had statistically significant differences between groups at the time of inclusion. Receiver operator characteristic analysis to predict SBI showed an area under the curve (AUC) of 0.679 for G-CSF. Conclusions: Analysis of inflammatory cytokine profiles may provide additional information in early diagnostics of SBI.

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Novel biomarker combination improves the diagnosis of serious bacterial infections in Malawian children

Cited by 19 publications

References 41 publications

A community approach to mortality prediction in sepsis via gene expression analysis

A community approach to mortality prediction in sepsis via gene expression analysis

Predicting Risk of Serious Bacterial Infections in Febrile Children in the Emergency Department

Inflammatory Cytokine and Chemokine Patterns in Paediatric Patients with Suspected Serious Bacterial Infection

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