<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/NCT04473105</url>
  </required_header>
  <id_info>
    <org_study_id>20GA039</org_study_id>
    <nct_id>NCT04473105</nct_id>
  </id_info>
  <brief_title>Optimising Resource Allocation Via Prediction of Outcomes for Covid-19</brief_title>
  <official_title>Optimising Resource Allocation Via Prediction of Outcomes for Suspected and Proven Covid-19</official_title>
  <sponsors>
    <lead_sponsor>
      <agency>Nottingham University Hospitals NHS Trust</agency>
      <agency_class>Other</agency_class>
    </lead_sponsor>
    <collaborator>
      <agency>East Midlands Academic Health Sciences Network</agency>
      <agency_class>Other</agency_class>
    </collaborator>
  </sponsors>
  <source>Nottingham University Hospitals NHS Trust</source>
  <oversight_info>
    <has_dmc>No</has_dmc>
    <is_fda_regulated_drug>No</is_fda_regulated_drug>
    <is_fda_regulated_device>No</is_fda_regulated_device>
  </oversight_info>
  <brief_summary>
    <textblock>
      The investigators plan to use all of the information available within their local NHS&#xD;
      hospitals Trust to work out what happens to people admitted with both suspected and proven&#xD;
      Covid-19 infections. The investigators will use all of the information that they can to&#xD;
      provide the most evidence possible to use in their investigation as this will make the&#xD;
      results more accurate. This will include information on existing health conditions (e.g. by&#xD;
      looking at previous discharge letters, GP summaries), clinical observations recorded in the&#xD;
      hospital (e.g. temperature, blood pressure, pulse, oxygen levels) and laboratory measures&#xD;
      (e.g. blood markers of infection). The investigators experienced team will then analyse all&#xD;
      of this together with information about whether the person has Covid-19 to help work out what&#xD;
      any new patients' risk will be. To do this the investigators need to use individual patients'&#xD;
      information, however once removed from the hospital records system it will not be&#xD;
      identifiable and will be held securely within the hospital at all times. As a result of this&#xD;
      work the investigators plan to be able to do two things:&#xD;
&#xD;
        1. When a patient is admitted to hospital with possible or confirmed Covid-19 the&#xD;
           investigators will be able to make a highly accurate prediction of what is likely to&#xD;
           happen to them (e.g. being admitted to high dependency or intensive care, dying or&#xD;
           surviving to discharge) which will help health care professional make decisions about&#xD;
           their care.&#xD;
&#xD;
        2. By knowing what is likely to happen to a patient the investigators are able to make&#xD;
           informed decisions about how to distribute healthcare resources e.g. which areas are&#xD;
           likely to need more ventilators (machines to help with breathing), need for intensive&#xD;
           care beds, discharge planning.&#xD;
    </textblock>
  </brief_summary>
  <detailed_description>
    <textblock>
      Background.&#xD;
&#xD;
      Relatively simple clinical risk scores based upon easily available clinical information can&#xD;
      greatly aid in the triaging of patients to early discharge or more rapid and intensive&#xD;
      intervention. One example of this is in upper gastrointestinal bleeding where several such&#xD;
      scores (https://www.mdcalc.com/glasgow-blatchford-bleeding-score-gbs4 5;&#xD;
      https://www.mdcalc.com/rockall-score-upper-gi-bleeding-complete6) have permitted safe early&#xD;
      discharge thereby relieving pressure on hospitals. The investigators believe that similar&#xD;
      results might potentially be achievable from data routinely collected in our trust on&#xD;
      patients with proven or suspected Covid-19.&#xD;
&#xD;
      Objectives&#xD;
&#xD;
      The investigators aim to answer the following questions&#xD;
&#xD;
        1. What pattern of clinical history and symptoms, observations, blood and other&#xD;
           investigative markers best predicts that a patient suspected of Covid-19 or proven to&#xD;
           have it will progress to requiring ventilation?&#xD;
&#xD;
        2. What pattern of clinical history and symptoms, observations, blood and other&#xD;
           investigative markers best predicts that a patient suspected of Covid-19 or proven to&#xD;
           have it will die during their illness?&#xD;
&#xD;
        3. What pattern of clinical history and symptoms, observations, blood and other&#xD;
           investigative markers best predicts that a patient suspected of Covid-19 or proven to&#xD;
           have it will make a full recovery without requiring supplemental oxygen?&#xD;
&#xD;
      Methods&#xD;
&#xD;
      The investigators propose to two main approaches to the first two of the aims above.&#xD;
&#xD;
      A standard approach assessing baseline characteristics to predict poor outcome, similar to&#xD;
      other ongoing studies like ISARIC and PRIEST, but benefitting from an unbiased sample of&#xD;
      patients as no additional samples or data are to be collected and so the investigators&#xD;
      believe that it will be ethically acceptable subject to due attention to confidentiality to&#xD;
      analyse all patients presenting and not only those willing and able to consent.&#xD;
&#xD;
      A multi-level modelling approach that uses the rich repeated daily laboratory and clinical&#xD;
      measurements to predict the daily risk of a subsequent deterioration.&#xD;
&#xD;
      This second approach complements other ongoing studies like ISARIC and PRIEST, as it can only&#xD;
      feasibly be delivered within a single institution like NUH as it requires detailed&#xD;
      longitudinal linked electronic health data. Furthermore, it is an efficient, low cost study&#xD;
      design that does not require manual data collection.&#xD;
&#xD;
      The investigators will utilize the data held in the electronic systems of Nottingham&#xD;
      University Hospitals (NUH) NHS Trust, to identify all patients either suspected of having&#xD;
      Covid-19 infection or in whom the diagnosis is eventually confirmed. This will be performed&#xD;
      from the start of the pandemic retrospectively and then in a rolling program prospectively to&#xD;
      maximise available data.&#xD;
&#xD;
      For these patients the investigators will gather all relevant data on diagnoses&#xD;
      (comorbidities), daily clinical observations and daily laboratory parameters both at&#xD;
      presentation and on a rolling basis going forward from that until the point of death or&#xD;
      discharge. These data will include repeated measures of, temperature, pulse, blood oxygen&#xD;
      saturation, inspired oxygen concentration, respiratory rate, C reactive protein, and white&#xD;
      cell count among others.&#xD;
&#xD;
      Data will be analysed, and outcomes modelled within the Nottingham University Hospitals Trust&#xD;
      network to ensure that confidentiality is maintained.&#xD;
&#xD;
      Analysis&#xD;
&#xD;
      Characteristics of patients who go on to intubation The investigators will assess the&#xD;
      association of any baseline parameters and demographics of patients who require ventilation&#xD;
      compared to those who do not by cross tabulating baseline parameters with ventilation, and&#xD;
      calculating adjusted associations using a logistic regression model. The investigators will&#xD;
      also examine daily risk of intubation using a random effects model with the repeated daily&#xD;
      measurements. In order to examine the potential for an easily calculable clinical risk score,&#xD;
      logistic models will be prepared, but retaining continuous variables as continuous (to&#xD;
      maximise the retention of useful predictive data) and reducing them to categorical data (for&#xD;
      ease of clinical calculation). The investigators will select predictors for the model using&#xD;
      backward elimination with the Akaike information criterion and alpha = 0.05. For candidate&#xD;
      models the investigators will calculate the C-index and receiver operating curves and assess&#xD;
      calibration using the Hosmer-Lemeshow test. Bootstrapping and cross validation will be used&#xD;
      to avoid overfitting and assess model performance.&#xD;
&#xD;
      Time trends analysis to identify markers leading to intubation The investigators will examine&#xD;
      the data for trends over time in repeated measurements where available and describe these&#xD;
      using appropriate summary statistics. If there are enough repeated measurements, then each&#xD;
      covariate will be assessed with a JoinPoint analysis to assess if there is an obvious&#xD;
      inflexion in time to indicate a change in the clinical picture which marks a decline towards&#xD;
      ventilation. This process will feed into the rational choice of categories for categorical&#xD;
      representation of the data.&#xD;
&#xD;
      Predictors at intubation suggesting death The investigators will assess the association of&#xD;
      any parameters and demographics of ventilated patients who die compared to those who do not&#xD;
      by cross tabulating parameters at point of ventilation with death, and calculating adjusted&#xD;
      associations using a logistic regression model. The investigators will also examine daily&#xD;
      risk of death using a random effects model with the repeated daily measurements. In order to&#xD;
      examine the potential for an easily calculable clinical risk score, logistic models will be&#xD;
      prepared, but retaining continuous variables as continuous (to maximise the retention of&#xD;
      useful predictive data) and reducing them to categorical data (for ease of clinical&#xD;
      calculation). The investigators will select predictors for the model using backward&#xD;
      elimination with the Akaike information criterion and alpha = 0.05. For candidate models the&#xD;
      investigators will calculate the c index and receiver operating curves and assess calibration&#xD;
      using the Hosmer-Lemeshow test. Bootstrapping and cross validation will be used to avoid&#xD;
      overfitting and assess model performance.&#xD;
&#xD;
      Time trends analysis to identify markers leading to death We will examine the data for trends&#xD;
      over time in repeated measurements where available and describe these using appropriate&#xD;
      summary statistics. If there are enough repeated measurements, then each covariate will be&#xD;
      assessed with a JoinPoint analysis to assess if there is an obvious inflexion in time to&#xD;
      indicate a change in the clinical picture which marks a decline towards death Baseline&#xD;
      characteristics of patients discharged without supplemental oxygen We will assess the&#xD;
      association of any baseline parameters and demographics of patients who are discharged&#xD;
      without supplemental oxygen compared to those who are not by cross tabulating baseline&#xD;
      parameters with ventilation, and calculating adjusted associations using a logistic&#xD;
      regression model. In order to examine the potential for an easily calculable clinical risk&#xD;
      score, logistic models will be prepared, but retaining continuous variables as continuous (to&#xD;
      maximise the retention of useful predictive data) and reducing them to categorical data (for&#xD;
      ease of clinical calculation). The investigators will select predictors for the model using&#xD;
      backward elimination with the Akaike information criterion and alpha = 0.05. For candidate&#xD;
      models the investigators will calculate the c index and receiver operating curves and assess&#xD;
      calibration using the Hosmer-Lemeshow test. Each of these steps will be repeated in 10&#xD;
      randomly selected 20% subsamples of the dataset.&#xD;
&#xD;
      Stratified supplemental analyses To identify different patient groups at risk of poor&#xD;
      outcomes, interactions with each risk model will be assessed to identify if stratification is&#xD;
      needed by age bands, sex, prior co-morbidities and baseline factors such as lymphopaenia, CRP&#xD;
      etc.&#xD;
&#xD;
      All analyses will be repeated for groups of PCR proven Covid-19 patients and those only&#xD;
      suspected.&#xD;
&#xD;
      For the analysis of discharge without supplemental oxygen the investigators will examine the&#xD;
      risk of readmission for those so discharged and repeat the analysis only for those not&#xD;
      subsequently readmitted.&#xD;
&#xD;
      Eligibility Criteria&#xD;
&#xD;
      Inclusion criteria • All patients admitted to Nottingham University Hospitals Trust either&#xD;
      suspected of having Covid-19 infection or in whom the diagnosis is eventually confirmed and&#xD;
      who are over the age of 18 years&#xD;
&#xD;
      Exclusion criteria&#xD;
&#xD;
      • Aged under 18.&#xD;
    </textblock>
  </detailed_description>
  <overall_status>Unknown status</overall_status>
  <last_known_status>Recruiting</last_known_status>
  <start_date type="Actual">May 1, 2020</start_date>
  <completion_date type="Anticipated">January 31, 2021</completion_date>
  <primary_completion_date type="Anticipated">December 31, 2020</primary_completion_date>
  <study_type>Observational</study_type>
  <has_expanded_access>No</has_expanded_access>
  <study_design_info>
    <observational_model>Other</observational_model>
    <time_perspective>Prospective</time_perspective>
  </study_design_info>
  <primary_outcome>
    <measure>Ventilation</measure>
    <time_frame>During admission up to 3 months</time_frame>
    <description>The need to ventilate the patient</description>
  </primary_outcome>
  <secondary_outcome>
    <measure>Death</measure>
    <time_frame>During admission or within 30 days</time_frame>
    <description>Death of the patient</description>
  </secondary_outcome>
  <enrollment type="Anticipated">500</enrollment>
  <condition>COVID19</condition>
  <eligibility>
    <study_pop>
      <textblock>
        All patients meeting above criteria during the study period and prior to it for whom follow&#xD;
        up to 30 days can be completed during the period (i.e. admissions until 30 days prior to&#xD;
        study closes)&#xD;
      </textblock>
    </study_pop>
    <sampling_method>Non-Probability Sample</sampling_method>
    <criteria>
      <textblock>
        Inclusion Criteria:&#xD;
&#xD;
        • All patients admitted to Nottingham University Hospitals Trust either suspected of having&#xD;
        Covid-19 infection or in whom the diagnosis is eventually confirmed and who are over the&#xD;
        age of 18 years&#xD;
&#xD;
        Exclusion Criteria:&#xD;
&#xD;
        • Aged under 18.&#xD;
      </textblock>
    </criteria>
    <gender>All</gender>
    <minimum_age>18 Years</minimum_age>
    <maximum_age>N/A</maximum_age>
    <healthy_volunteers>No</healthy_volunteers>
  </eligibility>
  <overall_official>
    <last_name>Timothy R Card, FRCP, PhD</last_name>
    <role>Principal Investigator</role>
    <affiliation>University of Nottingham</affiliation>
  </overall_official>
  <overall_contact>
    <last_name>Timothy R Card, FRCP, PhD</last_name>
    <phone>0115 8231346</phone>
    <email>tim.card@nottingham.ac.uk</email>
  </overall_contact>
  <overall_contact_backup>
    <last_name>Colin Crooks, MRCP, PhD</last_name>
    <phone>0115 823 1058</phone>
    <email>colin.crooks@nottingham.ac.uk</email>
  </overall_contact_backup>
  <location>
    <facility>
      <name>Nottingham University Hospitals NHS Trust</name>
      <address>
        <city>Nottingham</city>
        <zip>NG7 2UH</zip>
        <country>United Kingdom</country>
      </address>
    </facility>
    <status>Recruiting</status>
  </location>
  <location_countries>
    <country>United Kingdom</country>
  </location_countries>
  <verification_date>October 2020</verification_date>
  <study_first_submitted>May 20, 2020</study_first_submitted>
  <study_first_submitted_qc>July 15, 2020</study_first_submitted_qc>
  <study_first_posted type="Actual">July 16, 2020</study_first_posted>
  <last_update_submitted>October 7, 2020</last_update_submitted>
  <last_update_submitted_qc>October 7, 2020</last_update_submitted_qc>
  <last_update_posted type="Actual">October 8, 2020</last_update_posted>
  <responsible_party>
    <responsible_party_type>Sponsor</responsible_party_type>
  </responsible_party>
  <keyword>prognostic score</keyword>
  <keyword>ventilation</keyword>
  <keyword>mortality</keyword>
  <keyword>early discharge</keyword>
  <condition_browse>
    <!-- CAUTION:  The following MeSH terms are assigned with an imperfect algorithm            -->
    <mesh_term>COVID-19</mesh_term>
  </condition_browse>
  <patient_data>
    <sharing_ipd>No</sharing_ipd>
  </patient_data>
  <!-- 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/NCT04473105
org study id: 20GA039
nct id: NCT04473105
lead sponsor: 
collaborator: 
has dmc: No
is fda regulated drug: No
is fda regulated device: No
The investigators plan to use all of the information available within their local NHS
      hospitals Trust to work out what happens to people admitted with both suspected and proven
      Covid-19 infections. The investigators will use all of the information that they can to
      provide the most evidence possible to use in their investigation as this will make the
      results more accurate. This will include information on existing health conditions (e.g. by
      looking at previous discharge letters, GP summaries), clinical observations recorded in the
      hospital (e.g. temperature, blood pressure, pulse, oxygen levels) and laboratory measures
      (e.g. blood markers of infection). The investigators experienced team will then analyse all
      of this together with information about whether the person has Covid-19 to help work out what
      any new patients' risk will be. To do this the investigators need to use individual patients'
      information, however once removed from the hospital records system it will not be
      identifiable and will be held securely within the hospital at all times. As a result of this
      work the investigators plan to be able to do two things:

        1. When a patient is admitted to hospital with possible or confirmed Covid-19 the
           investigators will be able to make a highly accurate prediction of what is likely to
           happen to them (e.g. being admitted to high dependency or intensive care, dying or
           surviving to discharge) which will help health care professional make decisions about
           their care.

        2. By knowing what is likely to happen to a patient the investigators are able to make
           informed decisions about how to distribute healthcare resources e.g. which areas are
           likely to need more ventilators (machines to help with breathing), need for intensive
           care beds, discharge planning.
Background.

      Relatively simple clinical risk scores based upon easily available clinical information can
      greatly aid in the triaging of patients to early discharge or more rapid and intensive
      intervention. One example of this is in upper gastrointestinal bleeding where several such
      scores (https://www.mdcalc.com/glasgow-blatchford-bleeding-score-gbs4 5;
      https://www.mdcalc.com/rockall-score-upper-gi-bleeding-complete6) have permitted safe early
      discharge thereby relieving pressure on hospitals. The investigators believe that similar
      results might potentially be achievable from data routinely collected in our trust on
      patients with proven or suspected Covid-19.

      Objectives

      The investigators aim to answer the following questions

        1. What pattern of clinical history and symptoms, observations, blood and other
           investigative markers best predicts that a patient suspected of Covid-19 or proven to
           have it will progress to requiring ventilation?

        2. What pattern of clinical history and symptoms, observations, blood and other
           investigative markers best predicts that a patient suspected of Covid-19 or proven to
           have it will die during their illness?

        3. What pattern of clinical history and symptoms, observations, blood and other
           investigative markers best predicts that a patient suspected of Covid-19 or proven to
           have it will make a full recovery without requiring supplemental oxygen?

      Methods

      The investigators propose to two main approaches to the first two of the aims above.

      A standard approach assessing baseline characteristics to predict poor outcome, similar to
      other ongoing studies like ISARIC and PRIEST, but benefitting from an unbiased sample of
      patients as no additional samples or data are to be collected and so the investigators
      believe that it will be ethically acceptable subject to due attention to confidentiality to
      analyse all patients presenting and not only those willing and able to consent.

      A multi-level modelling approach that uses the rich repeated daily laboratory and clinical
      measurements to predict the daily risk of a subsequent deterioration.

      This second approach complements other ongoing studies like ISARIC and PRIEST, as it can only
      feasibly be delivered within a single institution like NUH as it requires detailed
      longitudinal linked electronic health data. Furthermore, it is an efficient, low cost study
      design that does not require manual data collection.

      The investigators will utilize the data held in the electronic systems of Nottingham
      University Hospitals (NUH) NHS Trust, to identify all patients either suspected of having
      Covid-19 infection or in whom the diagnosis is eventually confirmed. This will be performed
      from the start of the pandemic retrospectively and then in a rolling program prospectively to
      maximise available data.

      For these patients the investigators will gather all relevant data on diagnoses
      (comorbidities), daily clinical observations and daily laboratory parameters both at
      presentation and on a rolling basis going forward from that until the point of death or
      discharge. These data will include repeated measures of, temperature, pulse, blood oxygen
      saturation, inspired oxygen concentration, respiratory rate, C reactive protein, and white
      cell count among others.

      Data will be analysed, and outcomes modelled within the Nottingham University Hospitals Trust
      network to ensure that confidentiality is maintained.

      Analysis

      Characteristics of patients who go on to intubation The investigators will assess the
      association of any baseline parameters and demographics of patients who require ventilation
      compared to those who do not by cross tabulating baseline parameters with ventilation, and
      calculating adjusted associations using a logistic regression model. The investigators will
      also examine daily risk of intubation using a random effects model with the repeated daily
      measurements. In order to examine the potential for an easily calculable clinical risk score,
      logistic models will be prepared, but retaining continuous variables as continuous (to
      maximise the retention of useful predictive data) and reducing them to categorical data (for
      ease of clinical calculation). The investigators will select predictors for the model using
      backward elimination with the Akaike information criterion and alpha = 0.05. For candidate
      models the investigators will calculate the C-index and receiver operating curves and assess
      calibration using the Hosmer-Lemeshow test. Bootstrapping and cross validation will be used
      to avoid overfitting and assess model performance.

      Time trends analysis to identify markers leading to intubation The investigators will examine
      the data for trends over time in repeated measurements where available and describe these
      using appropriate summary statistics. If there are enough repeated measurements, then each
      covariate will be assessed with a JoinPoint analysis to assess if there is an obvious
      inflexion in time to indicate a change in the clinical picture which marks a decline towards
      ventilation. This process will feed into the rational choice of categories for categorical
      representation of the data.

      Predictors at intubation suggesting death The investigators will assess the association of
      any parameters and demographics of ventilated patients who die compared to those who do not
      by cross tabulating parameters at point of ventilation with death, and calculating adjusted
      associations using a logistic regression model. The investigators will also examine daily
      risk of death using a random effects model with the repeated daily measurements. In order to
      examine the potential for an easily calculable clinical risk score, logistic models will be
      prepared, but retaining continuous variables as continuous (to maximise the retention of
      useful predictive data) and reducing them to categorical data (for ease of clinical
      calculation). The investigators will select predictors for the model using backward
      elimination with the Akaike information criterion and alpha = 0.05. For candidate models the
      investigators will calculate the c index and receiver operating curves and assess calibration
      using the Hosmer-Lemeshow test. Bootstrapping and cross validation will be used to avoid
      overfitting and assess model performance.

      Time trends analysis to identify markers leading to death We will examine the data for trends
      over time in repeated measurements where available and describe these using appropriate
      summary statistics. If there are enough repeated measurements, then each covariate will be
      assessed with a JoinPoint analysis to assess if there is an obvious inflexion in time to
      indicate a change in the clinical picture which marks a decline towards death Baseline
      characteristics of patients discharged without supplemental oxygen We will assess the
      association of any baseline parameters and demographics of patients who are discharged
      without supplemental oxygen compared to those who are not by cross tabulating baseline
      parameters with ventilation, and calculating adjusted associations using a logistic
      regression model. In order to examine the potential for an easily calculable clinical risk
      score, logistic models will be prepared, but retaining continuous variables as continuous (to
      maximise the retention of useful predictive data) and reducing them to categorical data (for
      ease of clinical calculation). The investigators will select predictors for the model using
      backward elimination with the Akaike information criterion and alpha = 0.05. For candidate
      models the investigators will calculate the c index and receiver operating curves and assess
      calibration using the Hosmer-Lemeshow test. Each of these steps will be repeated in 10
      randomly selected 20% subsamples of the dataset.

      Stratified supplemental analyses To identify different patient groups at risk of poor
      outcomes, interactions with each risk model will be assessed to identify if stratification is
      needed by age bands, sex, prior co-morbidities and baseline factors such as lymphopaenia, CRP
      etc.

      All analyses will be repeated for groups of PCR proven Covid-19 patients and those only
      suspected.

      For the analysis of discharge without supplemental oxygen the investigators will examine the
      risk of readmission for those so discharged and repeat the analysis only for those not
      subsequently readmitted.

      Eligibility Criteria

      Inclusion criteria • All patients admitted to Nottingham University Hospitals Trust either
      suspected of having Covid-19 infection or in whom the diagnosis is eventually confirmed and
      who are over the age of 18 years

      Exclusion criteria

      • Aged under 18.
observational model: Other
time perspective: Prospective
measure: Ventilation
time frame: During admission up to 3 months
description: The need to ventilate the patient
measure: Death
time frame: During admission or within 30 days
description: Death of the patient
study pop: 
sampling method: Non-Probability Sample
criteria: 
gender: All
minimum age: 18 Years
maximum age: N/A
healthy volunteers: No
last name: Timothy R Card, FRCP, PhD
role: Principal Investigator
affiliation: University of Nottingham
last name: Timothy R Card, FRCP, PhD
phone: 0115 8231346
email: tim.card@nottingham.ac.uk
last name: Colin Crooks, MRCP, PhD
phone: 0115 823 1058
email: colin.crooks@nottingham.ac.uk
facility: 
status: Recruiting
country: United Kingdom
responsible party type: Sponsor
mesh term: COVID-19
sharing ipd: No