<clinical_study>
  <!-- This xml conforms to an XML Schema at:
    https://clinicaltrials.gov/ct2/html/images/info/public.xsd -->
  <required_header>
    <download_date>ClinicalTrials.gov processed this data on September 20, 2023</download_date>
    <link_text>Link to the current ClinicalTrials.gov record.</link_text>
    <url>https://clinicaltrials.gov/ct2/show/NCT03777670</url>
  </required_header>
  <id_info>
    <org_study_id>248404</org_study_id>
    <nct_id>NCT03777670</nct_id>
  </id_info>
  <brief_title>nSeP: Detecting Neonatal Sepsis by Immune-Metabolic Network Analysis</brief_title>
  <acronym>nSeP</acronym>
  <official_title>Detecting Neonatal Sepsis by Immune-Metabolic Network Analysis</official_title>
  <sponsors>
    <lead_sponsor>
      <agency>Cardiff and Vale University Health Board</agency>
      <agency_class>Other</agency_class>
    </lead_sponsor>
    <collaborator>
      <agency>Cardiff University</agency>
      <agency_class>Other</agency_class>
    </collaborator>
  </sponsors>
  <source>Cardiff and Vale University Health Board</source>
  <oversight_info>
    <is_fda_regulated_drug>No</is_fda_regulated_drug>
    <is_fda_regulated_device>No</is_fda_regulated_device>
  </oversight_info>
  <brief_summary>
    <textblock>
      Diagnosis of neonatal sepsis remains a challenge due to non-specific signs and diagnostic&#xD;
      inaccuracies. Studies have shown that this could lead to overdiagnosis and overuse of&#xD;
      antibiotic treatment, with potential long-term adverse effects.&#xD;
&#xD;
      A systems approach towards diagnosing neonatal sepsis has been shown to have high accuracy in&#xD;
      initial studies. This study aims to recruit a large validation cohort to confirm findings.&#xD;
    </textblock>
  </brief_summary>
  <detailed_description>
    <textblock>
      Several studies have shown that changes in host gene expression may occur pre-&#xD;
      symptomatically in response to infection in any part of the body, with the continuous&#xD;
      interaction between blood and tissue allowing blood cells to act as biosensors for the&#xD;
      changes (Manger and Relman, 2000, Liew et al., 2006). Genome wide analysis reveals coordinate&#xD;
      expression that develop networks causally linked through pathways.&#xD;
&#xD;
      Earlier studies from our group investigating optimal methods for the sampling and extraction&#xD;
      of neonatal whole transcription products (RNA) demonstrated the first feasibility studies for&#xD;
      using genome wide RNA analysis as a methodological approach for identifying host biomarkers&#xD;
      of infection and vaccination in early life (Smith et al., 2007, Flanagan et al., 2013). The&#xD;
      sampling methods were further refined in 2015 with the development of a single drop&#xD;
      methodology that has been extensively tested in a wide range of settings including the&#xD;
      collection of neonatal samples in the home, at the point of Guthrie testing by mid-wives.&#xD;
      Details of these methods have been recently submitted to the Nature Protocols journal.&#xD;
      Further, we conducted early on virtual clinical trials using a super computing framework that&#xD;
      simulated several 100,000 neonatal whole blood samples for predicting infection (Khondoker et&#xD;
      al., 2010). Those investigations showed the requirement for multiple markers and ideally in&#xD;
      discrete biological pathways underpinning causality (Khondoker et al., 2010, Watterson and&#xD;
      Ghazal, 2010). Those investigations provided a strong foundation for initiating a&#xD;
      case-control of neonatal sepsis (Dickinson et al., 2015). Accordingly, we were the first&#xD;
      group to publish studies investigating the systemic immune response in neonates to sepsis by&#xD;
      measuring the activity of all known human genes (Smith et al Nature comm. 2014). These&#xD;
      computationally intensive investigations led to uncovering, for the first time, the pathway&#xD;
      biology underlying neonatal sepsis with blood samples taken at the first clinical signs of&#xD;
      suspecting an infection. A combination of machine learning, statistical and deep pathway&#xD;
      biology analyses led to the identification of a 52-gene panel of biologically connected&#xD;
      network modules. The modules comprise three central pathways, innate-immune or inflammatory,&#xD;
      adaptive-immune and unexpectedly metabolic. The expression levels of particular combinations&#xD;
      of biomarkers, and specifically those of a pathway previously unconnected to immune&#xD;
      responses, gives an unusually high diagnostic quality. Despite patient heterogeneity, the&#xD;
      52-node dual biomarker network had greater than 99% accuracy for detecting bacterial&#xD;
      infection with 100% sensitivity showing superior performance to previously characterised&#xD;
      markers. Furthermore, these specific combinations of biomarkers allowed the detection of&#xD;
      neonatal sepsis in samples which had displayed blood-culture negative results, illustrating&#xD;
      the specific diagnostic benefits of the particular combinations of biomarkers. The&#xD;
      unexpectedly high accuracy and sensitivity values could not have resulted from the&#xD;
      investigation of any of the individual biomarkers alone, nor could they have been predicted.&#xD;
      A critical part of these findings is the requirement of metabolic pathways for increasing&#xD;
      both sensitivity and specificity. A subset of the metabolic markers encompass ligands&#xD;
      (specifically small and medium chain fatty acids) that are derived from microbial metabolism,&#xD;
      in particular from commensals and which are reflected in the faecal microbiome. To date these&#xD;
      studies provide a proof of concept but need independent confirmatory studies as well as&#xD;
      investigating specificity against non-bacterial (viral and fungal) infections and sterile&#xD;
      inflammation. The urgent unmet medical question is whether predictive host pathways of&#xD;
      infection can be used to first identify whether a patient is infected at or before clinical&#xD;
      presentation and, to further discriminate between the type of infection (in particular&#xD;
      bacterial or viral) and predictability of sepsis.&#xD;
    </textblock>
  </detailed_description>
  <overall_status>Recruiting</overall_status>
  <start_date type="Actual">February 26, 2020</start_date>
  <completion_date type="Anticipated">February 28, 2024</completion_date>
  <primary_completion_date type="Anticipated">February 28, 2023</primary_completion_date>
  <study_type>Observational</study_type>
  <has_expanded_access>No</has_expanded_access>
  <study_design_info>
    <observational_model>Case-Control</observational_model>
    <time_perspective>Prospective</time_perspective>
  </study_design_info>
  <primary_outcome>
    <measure>Sepsis</measure>
    <time_frame>5-years</time_frame>
    <description>Immune signature of sepsis</description>
  </primary_outcome>
  <number_of_groups>2</number_of_groups>
  <enrollment type="Anticipated">1000</enrollment>
  <condition>Sepsis</condition>
  <arm_group>
    <arm_group_label>Cases</arm_group_label>
    <description>Infants with suspected sepsis</description>
  </arm_group>
  <arm_group>
    <arm_group_label>Controls</arm_group_label>
    <description>Infants with no suspicion of sepsis</description>
  </arm_group>
  <intervention>
    <intervention_type>Diagnostic Test</intervention_type>
    <intervention_name>Blood test</intervention_name>
    <description>Drop of blood for RNA and metabolic analysis</description>
    <arm_group_label>Cases</arm_group_label>
    <arm_group_label>Controls</arm_group_label>
  </intervention>
  <biospec_retention>Samples Without DNA</biospec_retention>
  <biospec_descr>
    <textblock>
      RNA&#xD;
    </textblock>
  </biospec_descr>
  <eligibility>
    <study_pop>
      <textblock>
        Newborn infants in the neonatal unit with a suspicion of sepsis&#xD;
      </textblock>
    </study_pop>
    <sampling_method>Probability Sample</sampling_method>
    <criteria>
      <textblock>
        Inclusion Criteria:&#xD;
&#xD;
          -  Screened with traditional tests (full-blood count [FBC], inflammatory markers like&#xD;
             C-reactive protein [CRP], and blood-culture) for suspected sepsis (including&#xD;
             non-infective inflammatory conditions) and started on antibiotics - potential cases.&#xD;
&#xD;
          -  Being sampled for non-septic conditions (bloods sampling for routine monitoring,&#xD;
             jaundice, hypoglycaemia, etc.) - controls.&#xD;
&#xD;
          -  Informed consent from parents to use blood and stool samples (initial sample and&#xD;
             24-hour sample) and clinical data for study.&#xD;
&#xD;
        Exclusion Criteria:&#xD;
&#xD;
          -  Language and communication issues, which will make it difficult to explain study and&#xD;
             request informed consent.&#xD;
&#xD;
          -  When an infant has a high chance of mortality in the next 24-hours.&#xD;
      </textblock>
    </criteria>
    <gender>All</gender>
    <minimum_age>N/A</minimum_age>
    <maximum_age>4 Months</maximum_age>
  </eligibility>
  <overall_contact>
    <last_name>Peter Ghazal, PhD</last_name>
    <phone>29207 44721</phone>
    <email>ghazalp@cardiff.ac.uk</email>
  </overall_contact>
  <location>
    <facility>
      <name>University Hospital of Wales</name>
      <address>
        <city>Cardiff</city>
        <state>South Glamorgan</state>
        <zip>CF14 4XN</zip>
        <country>United Kingdom</country>
      </address>
    </facility>
    <status>Recruiting</status>
    <contact>
      <last_name>Mallinath Chakraborty, PhD</last_name>
      <phone>+44 2920 742276</phone>
      <email>chakrabortym@cardiff.ac.uk</email>
    </contact>
  </location>
  <location_countries>
    <country>United Kingdom</country>
  </location_countries>
  <verification_date>April 2020</verification_date>
  <study_first_submitted>December 13, 2018</study_first_submitted>
  <study_first_submitted_qc>December 14, 2018</study_first_submitted_qc>
  <study_first_posted type="Actual">December 17, 2018</study_first_posted>
  <last_update_submitted>April 4, 2020</last_update_submitted>
  <last_update_submitted_qc>April 4, 2020</last_update_submitted_qc>
  <last_update_posted type="Actual">April 7, 2020</last_update_posted>
  <responsible_party>
    <responsible_party_type>Principal Investigator</responsible_party_type>
    <investigator_affiliation>Cardiff and Vale University Health Board</investigator_affiliation>
    <investigator_full_name>Mallinath Chakraborty</investigator_full_name>
    <investigator_title>Consultant in Neonatal Medicine</investigator_title>
  </responsible_party>
  <keyword>Newborn</keyword>
  <condition_browse>
    <!-- CAUTION:  The following MeSH terms are assigned with an imperfect algorithm            -->
    <mesh_term>Sepsis</mesh_term>
    <mesh_term>Toxemia</mesh_term>
    <mesh_term>Neonatal Sepsis</mesh_term>
  </condition_browse>
  <!-- Results have not yet been posted for this study                                          -->
</clinical_study>


download date: ClinicalTrials.gov processed this data on September 20, 2023
link text: Link to the current ClinicalTrials.gov record.
url: https://clinicaltrials.gov/ct2/show/NCT03777670
org study id: 248404
nct id: NCT03777670
lead sponsor: 
collaborator: 
is fda regulated drug: No
is fda regulated device: No
Diagnosis of neonatal sepsis remains a challenge due to non-specific signs and diagnostic
      inaccuracies. Studies have shown that this could lead to overdiagnosis and overuse of
      antibiotic treatment, with potential long-term adverse effects.

      A systems approach towards diagnosing neonatal sepsis has been shown to have high accuracy in
      initial studies. This study aims to recruit a large validation cohort to confirm findings.
Several studies have shown that changes in host gene expression may occur pre-
      symptomatically in response to infection in any part of the body, with the continuous
      interaction between blood and tissue allowing blood cells to act as biosensors for the
      changes (Manger and Relman, 2000, Liew et al., 2006). Genome wide analysis reveals coordinate
      expression that develop networks causally linked through pathways.

      Earlier studies from our group investigating optimal methods for the sampling and extraction
      of neonatal whole transcription products (RNA) demonstrated the first feasibility studies for
      using genome wide RNA analysis as a methodological approach for identifying host biomarkers
      of infection and vaccination in early life (Smith et al., 2007, Flanagan et al., 2013). The
      sampling methods were further refined in 2015 with the development of a single drop
      methodology that has been extensively tested in a wide range of settings including the
      collection of neonatal samples in the home, at the point of Guthrie testing by mid-wives.
      Details of these methods have been recently submitted to the Nature Protocols journal.
      Further, we conducted early on virtual clinical trials using a super computing framework that
      simulated several 100,000 neonatal whole blood samples for predicting infection (Khondoker et
      al., 2010). Those investigations showed the requirement for multiple markers and ideally in
      discrete biological pathways underpinning causality (Khondoker et al., 2010, Watterson and
      Ghazal, 2010). Those investigations provided a strong foundation for initiating a
      case-control of neonatal sepsis (Dickinson et al., 2015). Accordingly, we were the first
      group to publish studies investigating the systemic immune response in neonates to sepsis by
      measuring the activity of all known human genes (Smith et al Nature comm. 2014). These
      computationally intensive investigations led to uncovering, for the first time, the pathway
      biology underlying neonatal sepsis with blood samples taken at the first clinical signs of
      suspecting an infection. A combination of machine learning, statistical and deep pathway
      biology analyses led to the identification of a 52-gene panel of biologically connected
      network modules. The modules comprise three central pathways, innate-immune or inflammatory,
      adaptive-immune and unexpectedly metabolic. The expression levels of particular combinations
      of biomarkers, and specifically those of a pathway previously unconnected to immune
      responses, gives an unusually high diagnostic quality. Despite patient heterogeneity, the
      52-node dual biomarker network had greater than 99% accuracy for detecting bacterial
      infection with 100% sensitivity showing superior performance to previously characterised
      markers. Furthermore, these specific combinations of biomarkers allowed the detection of
      neonatal sepsis in samples which had displayed blood-culture negative results, illustrating
      the specific diagnostic benefits of the particular combinations of biomarkers. The
      unexpectedly high accuracy and sensitivity values could not have resulted from the
      investigation of any of the individual biomarkers alone, nor could they have been predicted.
      A critical part of these findings is the requirement of metabolic pathways for increasing
      both sensitivity and specificity. A subset of the metabolic markers encompass ligands
      (specifically small and medium chain fatty acids) that are derived from microbial metabolism,
      in particular from commensals and which are reflected in the faecal microbiome. To date these
      studies provide a proof of concept but need independent confirmatory studies as well as
      investigating specificity against non-bacterial (viral and fungal) infections and sterile
      inflammation. The urgent unmet medical question is whether predictive host pathways of
      infection can be used to first identify whether a patient is infected at or before clinical
      presentation and, to further discriminate between the type of infection (in particular
      bacterial or viral) and predictability of sepsis.
observational model: Case-Control
time perspective: Prospective
measure: Sepsis
time frame: 5-years
description: Immune signature of sepsis
arm group label: Cases
description: Infants with suspected sepsis
arm group label: Controls
description: Infants with no suspicion of sepsis
intervention type: Diagnostic Test
intervention name: Blood test
description: Drop of blood for RNA and metabolic analysis
arm group label: Cases
arm group label: Controls
RNA
study pop: 
sampling method: Probability Sample
criteria: 
gender: All
minimum age: N/A
maximum age: 4 Months
last name: Peter Ghazal, PhD
phone: 29207 44721
email: ghazalp@cardiff.ac.uk
facility: 
status: Recruiting
contact: 
country: United Kingdom
responsible party type: Principal Investigator
investigator affiliation: Cardiff and Vale University Health Board
investigator full name: Mallinath Chakraborty
investigator title: Consultant in Neonatal Medicine
mesh term: Sepsis
mesh term: Toxemia
mesh term: Neonatal Sepsis