Dataset Information

Validation of the Sepsis MetaScore for Diagnosis of Neonatal Sepsis.

ABSTRACT: What’s Known on This Subject:Neonates are at increased risk for developing sepsis, but this population often exhibits ambiguous clinical signs that complicate the diagnosis of infection. No biomarker has yet shown enough diagnostic accuracy to rule out sepsis at the time of clinical suspicion. What This Study Adds:We show that a gene-expression-based signature is an accurate objective measure of the risk of sepsis in a neonate or preterm infant, and it substantially improves diagnostic accuracy over that of commonly used laboratory-based testing. Implementation might decrease inappropriate antibiotic use. Background:Neonatal sepsis can have devastating consequences, but accurate diagnosis is difficult. As a result, up to 200 neonates with suspected sepsis are treated with empiric antibiotics for every 1 case of microbiologically confirmed sepsis. These unnecessary antibiotics enhance bacterial antibiotic resistance, increase economic costs, and alter gut microbiota composition. We recently reported an 11-gene diagnostic test for sepsis (Sepsis MetaScore) based on host whole-blood gene expression in children and adults, but this test has not been evaluated in neonates. Methods:We identified existing gene expression microarray-based cohorts of neonates with sepsis. We then tested the accuracy of the Sepsis MetaScore both alone and in combination with standard diagnostic laboratory tests in diagnosing sepsis. Results:We found 3 cohorts with a total of 213 samples from control neonates and neonates with sepsis. The Sepsis MetaScore had an area under the receiver operating characteristic curve of 0.92-0.93 in all 3 cohorts. We also found that, as a diagnostic test for sepsis, it outperformed standard laboratory measurements alone and, when used in combination with another test(s), resulted in a significant net reclassification index (0.3-0.69) in 5 of 6 comparisons. The mean point estimates for sensitivity and specificity were 95% and 60%, respectively, which, if confirmed prospectively and applied in a high-risk cohort, could reduce inappropriate antibiotic usage substantially. Conclusions:The Sepsis MetaScore had excellent diagnostic accuracy across 3 separate cohorts of neonates from 3 different countries. Further prospective targeted study will be needed before clinical application.

SUBMITTER: Sweeney TE

PROVIDER: S-EPMC5954302 | biostudies-literature | 2018 May

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Validation of the Sepsis MetaScore for Diagnosis of Neonatal Sepsis.

Sweeney Timothy E TE Wynn James L JL Cernada María M Serna Eva E Wong Hector R HR Baker Henry V HV Vento Máximo M Khatri Purvesh P

Journal of the Pediatric Infectious Diseases Society 20180501 2

<h4>What’s known on this subject</h4>Neonates are at increased risk for developing sepsis, but this population often exhibits ambiguous clinical signs that complicate the diagnosis of infection. No biomarker has yet shown enough diagnostic accuracy to rule out sepsis at the time of clinical suspicion.<h4>What this study adds</h4>We show that a gene-expression-based signature is an accurate objective measure of the risk of sepsis in a neonate or preterm infant, and it substantially improves diagn ...[more]

PMID: 28419265

Similar Datasets

Project description:Given the long-life expectancy of the newborn, research aimed at improving sepsis diagnosis and management in this population has been recognized as cost-effective, which at early stages continues to be a tremendous challenge. Despite there is not an ideal-specific biomarker, the simultaneous detection of biomarkers with different behavior during an infection such as procalcitonin (PCT) as high specificity biomarker with one of the earliest biomarkers in sepsis as interleukin-6 (IL-6) increases diagnostic performance. This is not only due to their high positive predictive value but also, since it can also help the clinician to rule out infection and thus avoid the use of antibiotics, due to their high negative predictive value. To this end, we explore a cutting-edge micromotor (MM)-based OFF-ON dual aptassay for simultaneous determination of both biomarkers in 15 min using just 2 μL of sample from low-birth-weight neonates with gestational age less than 32 weeks and birthweight below 1000 g with clinical suspicion of late-onset sepsis. The approach reached the high sensitivities demanded in the clinical scenario (LODPCT = 0.003 ng/mL, LODIL6 = 0.15 pg/mL) with excellent correlation performance (r > 0.9990, p < 0.05) of the MM-based approach with the Hospital method for both biomarkers during the analysis of diagnosed samples and reliability (Er < 6% for PCT, and Er < 4% for IL-6). The proposed approach also encompasses distinctive technical attributes in a clinical scenario since its minimal sample volume requirements and expeditious results compatible with few easy-to-obtain drops of heel stick blood samples from newborns admitted to the neonatal intensive care unit. This would enable the monitoring of both sepsis biomarkers within the initial hours after the manifestation of symptoms in high-risk neonates as a valuable tool in facilitating prompt and well-informed decisions about the initiation of antibiotic therapy.These results revealed the asset behind micromotor technology for multiplexing analysis in diagnosing neonatal sepsis, opening new avenues in low sample volume-based diagnostics.

Project description:BackgroundCurrently, there is a lack of clear definition for neonatal sepsis. The Pediatric Committee of the European Medicines Agency (EMA) developed consensus criteria to ensure a standardization for neonatal sepsis definition. However, there is no evidence supporting the accuracy of the EMA sepsis criteria in neonatal sepsis diagnosis. The main objective of this study was to evaluate the diagnostic accuracy of EMA sepsis criteria for proven neonatal sepsis.MethodsA multicenter prospective cohort study was conducted from October 2015 to November 2018. Infants with a gestational age over 34th weeks, diagnosed with clinical sepsis and received antibiotics according to the EMA criteria or experienced neonatologists' opinion were included. Blood culture or multiplex real time-PCR or 16S-rRNA positive infants were accepted as "proven sepsis". The predictive performance of EMA criteria for proven sepsis was evaluated by sensitivity, specificity, accuracy, and area under the curve measures of receiver operator characteristic curves. Data-mining methods were used for further analysis.ResultsAmong the 245 included infants, the EMA criteria were positive in 97 infants (39.6%), while proven sepsis was diagnosed in 113 infants (46.1%). The sensitivity, specificity, and accuracy of the EMA criteria for proven sepsis were 44.2% (95%CI: 34.9-53.9), 64.4% (95%CI: 55.6-72.5), 55.1% (95%CI: 46.6-59.4) respectively. None of the clinical and laboratory parameters had sufficient performance individually in terms of sensitivity, specificity and accuracy measures. The diagnostic performance was similar when different clinical findings were added to the EMA sepsis criteria or assessment of the score was interpreted in different ways.ConclusionsResults highlighted that clinician opinion and standard laboratory tests are limited in the neonatal sepsis diagnosis. The EMA criteria also did not efficiently meet the diagnostic accuracy measures for neonatal sepsis. A predictive sepsis definition and rapid bedside point-of care tests are urgently needed.

Project description:BackgroundHeart rate characteristics aid early detection of late-onset sepsis (LOS), but respiratory data contain additional signatures of illness due to infection. Predictive models using cardiorespiratory data may improve early sepsis detection. We hypothesized that heart rate (HR) and oxygenation (SpO2) data contain signatures that improve sepsis risk prediction over HR or demographics alone.MethodsWe analyzed cardiorespiratory data from very low birth weight (VLBW, <1500 g) infants admitted to three NICUs. We developed and externally validated four machine learning models to predict LOS using features calculated every 10 m: mean, standard deviation, skewness, kurtosis of HR and SpO2, and cross-correlation. We compared feature importance, discrimination, calibration, and dynamic prediction across models and cohorts. We built models of demographics and HR or SpO2 features alone for comparison with HR-SpO2 models.ResultsPerformance, feature importance, and calibration were similar among modeling methods. All models had favorable external validation performance. The HR-SpO2 model performed better than models using either HR or SpO2 alone. Demographics improved the discrimination of all physiologic data models but dampened dynamic performance.ConclusionsCardiorespiratory signatures detect LOS in VLBW infants at 3 NICUs. Demographics risk-stratify, but predictive modeling with both HR and SpO2 features provides the best dynamic risk prediction.ImpactHeart rate characteristics aid early detection of late-onset sepsis, but respiratory data contain signatures of illness due to infection. Predictive models using both heart rate and respiratory data may improve early sepsis detection. A cardiorespiratory early warning score, analyzing heart rate from electrocardiogram or pulse oximetry with SpO2, predicts late-onset sepsis within 24 h across multiple NICUs and detects sepsis better than heart rate characteristics or demographics alone. Demographics risk-stratify, but predictive modeling with both HR and SpO2 features provides the best dynamic risk prediction. The results increase understanding of physiologic signatures of neonatal sepsis.

Dataset Information

Validation of the Sepsis MetaScore for Diagnosis of Neonatal Sepsis.

Publications

Validation of the Sepsis MetaScore for Diagnosis of Neonatal Sepsis.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets