<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/NCT01706497</url>
  </required_header>
  <id_info>
    <org_study_id>KUL-S54474</org_study_id>
    <nct_id>NCT01706497</nct_id>
  </id_info>
  <brief_title>Predictive Value of the FORE-SIGHT™ Monitor for Hemodynamic Deterioration</brief_title>
  <official_title>Validation of the Predictive Value of the FORE-SIGHT™ Monitor for Early Detection of Hemodynamic Deterioration After Pediatric Cardiac Surgery. A Data-mining Study.</official_title>
  <sponsors>
    <lead_sponsor>
      <agency>KU Leuven</agency>
      <agency_class>Other</agency_class>
    </lead_sponsor>
    <collaborator>
      <agency>CAS Medical Systems, Inc.</agency>
      <agency_class>Industry</agency_class>
    </collaborator>
  </sponsors>
  <source>KU Leuven</source>
  <oversight_info>
    <has_dmc>No</has_dmc>
  </oversight_info>
  <brief_summary>
    <textblock>
      The postoperative period after congenital heart surgery in children can be a very critical&#xD;
      episode, where it is of utmost importance to closely monitor the circulation in these&#xD;
      patients. Invasive hemodynamic monitoring tools available in the adult population, are often&#xD;
      not suitable to use in small children.&#xD;
&#xD;
      The Fore-Sight(TM) is a non-invasive monitor for brain tissue oxygenation (SctO2), by&#xD;
      projecting harmless near-infrared light trough the skin, skull, and brain via a disposable&#xD;
      sensor that is applied on the forehead of patients. In many centres, the Fore-Sight (TM) is&#xD;
      part of the routine monitoring of children during cardio-pulmonary bypass for congenital&#xD;
      heart surgery. Although the monitor has not been tested for this purpose, it is often&#xD;
      continued in the postoperative phase in the intensive care unit (ICU), where it is used to&#xD;
      monitor the hemodynamic situation of the patient.&#xD;
&#xD;
      The purpose of the present study is to examine and validate the use of the Fore-Sight monitor&#xD;
      for hemodynamic monitoring of children in the postoperative phase after cardiac surgery.&#xD;
&#xD;
      The study hypothesis is whether SctO2 desaturations are predictive for future hemodynamic&#xD;
      deterioration of the patient, and whether these SctO2 desaturations are predictive for the&#xD;
      outcome of these patients.&#xD;
    </textblock>
  </brief_summary>
  <detailed_description>
    <textblock>
      1. Background&#xD;
&#xD;
           Accurate hemodynamic monitoring often requires the use of invasive tools or catheters.&#xD;
           Many of these tools, available for adults, cannot be used for critically ill infants and&#xD;
           children, because of their size, and because these invasive techniques carry a high risk&#xD;
           of complications. Non-invasive monitoring techniques that allow for detecting critical&#xD;
           hemodynamic states are of high interest in this population.&#xD;
&#xD;
           FORE-SIGHT™ is a non-invasive tool to measure cerebral tissue oxygen saturation.&#xD;
           Cerebral tissue oxygen saturation (SctO2) values are important to clinicians because&#xD;
           cerebral hypoxia (lack of oxygen supply to brain tissue) is one of the leading causes of&#xD;
           brain injuries that occur in many surgical and clinical situations. The FORE-SIGHT™&#xD;
           Cerebral Oximeter utilizes optically-based Near Infra-Red Spectroscopy (NIRS) technology&#xD;
           to monitor absolute SctO2. It works by projecting harmless near infra-red light through&#xD;
           the scalp and skull and into the brain via a disposable sensor on the patient's&#xD;
           forehead. The device measures the light that is returned to detectors on the sensor and&#xD;
           analyzes this information utilizing patented algorithms to determine absolute cerebral&#xD;
           tissue oxygen saturation levels. The FORE-SIGHT™ is designed to monitor SctO2 in a&#xD;
           continuous way, and provides clinicians an opportunity to intervene before damage to the&#xD;
           brain occurs. The monitor is currently part of the standard monitoring for children&#xD;
           undergoing cardiopulmonary bypass, in the operating theatre, and is often continued in&#xD;
           the postoperative phase in the pediatric intensive care unit (PICU) of the university&#xD;
           hospitals Leuven.&#xD;
&#xD;
           Several studies have pointed to the potential advantages of SctO2 monitoring in the&#xD;
           peri-operative phase after cardiac or abdominal surgery in adults [1,2,3,4], as well as&#xD;
           in children undergoing surgical corrections of congenital cardiac defects [5,6,7]. Low&#xD;
           SctO2 values are associated with worse outcome, and correspond with data from other&#xD;
           monitors indicating that the oxygen content of the brain at that time is insufficient.&#xD;
           In particular, the duration of SctO2 desaturations below 55%, 60%, and 65%, is&#xD;
           predictive for postoperative complications [8]. These studies, however, have only&#xD;
           analyzed the use of NIRS as a tool to monitor the brain at times when it is at the&#xD;
           highest risk, during complex surgery with a compromised circulation. Although there is&#xD;
           no evidence that has demonstrated that decision making based on NIRS data is favorable&#xD;
           for patient outcome, many centers use NIRS as a monitoring tool outside of the surgical&#xD;
           environment.&#xD;
&#xD;
           Because the brain is very sensitive to changes in oxygenation, monitoring of SctO2 might&#xD;
           provide an indication of critical changes in the hemodynamic state of the patient, and&#xD;
           thus serve as a monitor for the general hemodynamic status of the patient. The present&#xD;
           study wishes to examine the added value of SctO2 monitoring over the routine monitoring&#xD;
           of pediatric patients in PICU, in the postoperative phase after cardiac surgery.&#xD;
           Ideally, in order to be of added value, SctO2 changes should precede an episode of&#xD;
           hemodynamic deteriorations.&#xD;
&#xD;
           Prediction is at the heart of intensive care medicine, where physicians make use of&#xD;
           their medical knowledge and all the patient-related data in order to foresee changes in&#xD;
           the patient's condition, and administer the appropriate (preventive) treatments. An&#xD;
           intensive care unit (ICU) is a very data-rich environment with several information&#xD;
           sources, such as admission records, medical history, laboratory analyses of samples,&#xD;
           medication and treatment records, and monitoring of vital signals. A Patient Data&#xD;
           Management System (PDMS) (MetaVision®, iMD-Soft®, Boston, MA) was installed in our ICU&#xD;
           starting in February 2006. This PDMS software automatically collects and integrates the&#xD;
           data from the multiple information sources. One of the benefits of having all&#xD;
           patient-related data in an integrated format is that it can be readily analyzed through&#xD;
           the use of computational techniques in general and data mining in particular. These&#xD;
           techniques make use of the information in large databases to automatically generate&#xD;
           models that can be used for prediction (e.g. to predict a patient's probability of&#xD;
           survival in ICU). In previous studies our research group has shown the high predictive&#xD;
           performances than can be obtained with data mining models in the intensive care domain.&#xD;
&#xD;
           In this study we will make use of data mining techniques to assess the predictive power&#xD;
           of SctO2-monitoring for future hemodynamic deterioration of the PICU patient after&#xD;
           cardiac surgery, by analyzing data collected in the PDMS, including the FORE-SIGHT™&#xD;
           NIRS.&#xD;
&#xD;
        2. Aims of the project&#xD;
&#xD;
           • To assess the independent predictive power of the FORE-SIGHT™ NIRS signal for future&#xD;
           hemodynamic deterioration of critically ill infants and children, in the postoperative&#xD;
           phase after cardiac surgery.&#xD;
&#xD;
           • To assess the predictive power of the FORE-SIGHT™ NIRS signal for outcome in&#xD;
           critically ill infants and children, in the postoperative phase after cardiac surgery.&#xD;
&#xD;
        3. Study design&#xD;
&#xD;
      Prospective, observational, non-interventional study. All eligible children will be monitored&#xD;
      with the FORE-SIGHT™, from admission until they are weaned off mechanical ventilation.&#xD;
      Typically, patients admitted after cardiac surgery in the PICU of the university hospitals&#xD;
      Leuven are mechanically ventilated between 12 hours and two weeks [8].&#xD;
&#xD;
      The FORE-SIGHTTM SctO2 signal will be blinded to the bedside clinician and will be stored in&#xD;
      the PDMS system for analysis. The first 20 minutes of data after arrival to the ICU will not&#xD;
      be used for the analysis, since during this time the patient typically stabilizes to the new&#xD;
      environment after transport, and the data are therefore not characteristic of the ICU stay.&#xD;
&#xD;
      Data from these patients will be used for analysis, to build predictive models. A copy of the&#xD;
      PDMS database, after removal of all data that refer to the identity of the patients, will be&#xD;
      used for data analysis. Data mining models, such as simple logistic regression models, but&#xD;
      also more advanced machine learning techniques (models automatically learned by a computer&#xD;
      algorithm, such as Decision Tree models [9], Bayesian Networks, Gaussian Processes, Support&#xD;
      Vector Machines, …), will assess the independent predictive power of the FORE-SIGHT™ signal&#xD;
      over the routinely monitored data.&#xD;
&#xD;
      1. FORE-SIGHT™ for early detection of future hemodynamic instability.&#xD;
&#xD;
      First, models will be built to assess the independent predictive power of the FORE-SIGHT™&#xD;
      signal to detect episodes of hemodynamic instability 10 minutes in advance.&#xD;
&#xD;
      Since cardiac output is not directly measured invasively in children, such an episode is&#xD;
      evidenced by indirect signs. If one of more of the following criteria is met, this is&#xD;
      considered to be an episode of hemodynamic instability.&#xD;
&#xD;
        1. Hemodynamic monitoring • Heart Rate &gt;160 or &lt;90, for at least 5 consecutive minutes •&#xD;
           Systolic Blood Pressure &lt; 55 (infants) or &lt; 65 (children) , for at least 5 consecutive&#xD;
           minutes&#xD;
&#xD;
        2. Point of care laboratory analysis&#xD;
&#xD;
           • Venous Saturation (SvO2) &lt;55 (when cyanogenic cardiopathy is present), or &lt;65 (in all&#xD;
           other patients)&#xD;
&#xD;
           • Lactate &gt; 2 mmol/L on arterial blood gas sampling&#xD;
&#xD;
        3. Clinical observation • Urine Output rate &lt; normal rate of 0.5 ml/kg/h over 2 consecutive&#xD;
           hours&#xD;
&#xD;
      2. FORE-SIGHT™ for prediction of outcome. Second, models will be built to assess whether the&#xD;
      FORE-SIGHT™ signal is predictive for hospital and ICU Length of Stay (LOS ), mortality, and&#xD;
      duration of (invasive or non-invasive) mechanical ventilator support.&#xD;
&#xD;
      In order to assess predictive performance for the 2 predictive tasks, the following&#xD;
      statistics will be used: the positive predictive value, the area under the ROC curve (AUROC),&#xD;
      the Hosmer-Lemeshow statistic, calibration-in-the-large, calibration-slope and Brier scores.&#xD;
&#xD;
      The positive predictive value is the proportion of positive instances that are correctly&#xD;
      classified. It reflects the probability that a positive result corresponds to the underlying&#xD;
      condition being tested for.&#xD;
&#xD;
      The AUROC is a measure of the model's ability to discriminate between positive and negative&#xD;
      instances. Usually AUROC as evaluation is preferred above accuracy as it allows to trade off&#xD;
      the possibly different costs of incorrectly classifying a negative instance as positive, or&#xD;
      equivalently to tradeoff between the model's sensitivity and specificity.&#xD;
&#xD;
      The Hosmer-Lemeshow statistic, calibration-in-the-large and the calibration-slope are used to&#xD;
      determine whether a model is well calibrated. These test assess whether the observed event&#xD;
      rates match expected event rates in subgroups of the population. Models for which expected&#xD;
      and observed event rates in subgroups are similar are said to be well calibrated.&#xD;
&#xD;
      Overall model performance will be assessed with the Brier Score and its normalized version,&#xD;
      the Brier Scaled Score. Accurate models have a low overall prediction error and therefore a&#xD;
      low Brier Score (below the base case value for each task).&#xD;
&#xD;
      4 Study population&#xD;
&#xD;
      All children younger than 12 years of age, admitted to the PICU of the Leuven University&#xD;
      hospitals Leuven after cardiac surgery are eligible for the study, if they meet the following&#xD;
      inclusion criteria:&#xD;
&#xD;
        -  Mechanically ventilated upon ICU admission or intubated after admission&#xD;
&#xD;
        -  Arterial line in place.&#xD;
&#xD;
        -  Expected to stay at least 24h in the PICU.&#xD;
&#xD;
      Patients with actual or potential brain damage, such as traumatic brain injury patients,&#xD;
      patients with brain tumors, or patients after cardiopulmonary resuscitation (CPR) are&#xD;
      excluded. Patients with a condition or a wound that prohibits the placement of a forehead&#xD;
      sensor are also excluded.&#xD;
&#xD;
      The study will collect data for a period of 1,5 year. 300 critically ill children will be&#xD;
      recruited. Approximately 20% of the PICU patients exhibit clinical deterioration events, and&#xD;
      with a median (IQR) length of stay of 3 (2-7) days [8] and at least one event per day, we&#xD;
      expect a minimum of 180 events of clinical deterioration in the yearly population. From&#xD;
      previous studies with a similar event distribution [15,16] we expect our models to result in&#xD;
      a sensitivity (and specificity) of at least 0.8, with an alpha error of 5% and a statistical&#xD;
      power of 80%.&#xD;
&#xD;
      The study will therefore have the required statistical power to detect the potential of the&#xD;
      FORE-SIGHT™ NIRS signal in predicting clinical deterioration.&#xD;
    </textblock>
  </detailed_description>
  <overall_status>Completed</overall_status>
  <start_date>October 2012</start_date>
  <completion_date type="Actual">January 2016</completion_date>
  <primary_completion_date type="Actual">January 2016</primary_completion_date>
  <study_type>Observational</study_type>
  <has_expanded_access>No</has_expanded_access>
  <study_design_info>
    <observational_model>Cohort</observational_model>
    <time_perspective>Prospective</time_perspective>
  </study_design_info>
  <primary_outcome>
    <measure>The Accuracy to Predict Acute Kidney Injury (AKI) Per Patient, 6 Hours Before This Clinical Event (AKI) Occurs</measure>
    <time_frame>Predictive window of 6 hours before AKI occurence</time_frame>
    <description>Defined according to the Kidney Disease: Improving Global Outcome criteria (AKI stage 2 or 3)&#xD;
serum creatinine (SCr) level ≥ 2 times the baseline level, or&#xD;
urine output (UO) &lt; 0.5 ml/kg/hour for ≥ 12 hours, or&#xD;
provision of dialysis</description>
  </primary_outcome>
  <secondary_outcome>
    <measure>Hospital Length of Stay</measure>
    <time_frame>Hospital discharge</time_frame>
    <description>participants will be followed for the duration of hospital stay, an expected average of 1-2 weeks</description>
  </secondary_outcome>
  <secondary_outcome>
    <measure>Intensive Care Unit Length of Stay</measure>
    <time_frame>Intensive care unit discharge</time_frame>
    <description>participants will be followed for the duration of hospital stay, an expected average of 1-2 weeks</description>
  </secondary_outcome>
  <secondary_outcome>
    <measure>Duration of Mechanical Ventilation</measure>
    <time_frame>ICU discharge</time_frame>
    <description>participants will be followed for the duration of hospital stay, an expected average of 1-2 weeks</description>
  </secondary_outcome>
  <secondary_outcome>
    <measure>Hospital Mortality</measure>
    <time_frame>Hospital discharge</time_frame>
    <description>participants will be followed for the duration of hospital stay, an expected average of 1-2 weeks</description>
  </secondary_outcome>
  <enrollment type="Actual">300</enrollment>
  <condition>Pediatric Congenital Heart Surgery</condition>
  <intervention>
    <intervention_type>Device</intervention_type>
    <intervention_name>Cerebral tissue oxygen saturation monitoring, blinded</intervention_name>
    <other_name>Fore-Sight(TM) NIRS</other_name>
  </intervention>
  <eligibility>
    <study_pop>
      <textblock>
        All children younger than 12 years, admitted after cardiac surgery in the pediatric&#xD;
        intensive care unit (PICU) of the university hospitals Leuven, Belgium, on mechanical&#xD;
        ventilation or intubated after admission. Children are monitored with the FORE-SIGHT™, from&#xD;
        admission until they are weaned off mechanical ventilation (typically, most of these&#xD;
        patients are mechanically ventilated between 12 hours and two weeks after ICU admission).&#xD;
      </textblock>
    </study_pop>
    <sampling_method>Probability Sample</sampling_method>
    <criteria>
      <textblock>
        Inclusion Criteria:&#xD;
&#xD;
          -  younger than 12 years of age&#xD;
&#xD;
          -  Mechanically ventilated upon ICU admission or intubated after admission&#xD;
&#xD;
          -  arterial line in place&#xD;
&#xD;
          -  expected to stay at least 24 hous in the PICU&#xD;
&#xD;
        Exclusion Criteria:&#xD;
&#xD;
          -  actual or potential brain damage (such as traumatic brain injury, brain tumors, or&#xD;
             patients after cardiopulmonary resuscitation (CPR), ...).&#xD;
&#xD;
          -  patients with a condition or a wound that prohibits the placement of a forehead sensor&#xD;
             are also excluded.&#xD;
      </textblock>
    </criteria>
    <gender>All</gender>
    <minimum_age>N/A</minimum_age>
    <maximum_age>12 Years</maximum_age>
    <healthy_volunteers>No</healthy_volunteers>
  </eligibility>
  <overall_official>
    <last_name>Geert JP Meyfroidt, MD, PhD</last_name>
    <role>Principal Investigator</role>
    <affiliation>Department of Intensive Care Medicine, University Hospitals Leuven, Belgium and Laboratory of intensive care medicine, department of cellular and molecular medicine, Biomedical sciences group, KULeuven - University, Belgium</affiliation>
  </overall_official>
  <location>
    <facility>
      <name>Department of intensive care medicine, university hospitals Leuven</name>
      <address>
        <city>Leuven</city>
        <zip>3000</zip>
        <country>Belgium</country>
      </address>
    </facility>
  </location>
  <location_countries>
    <country>Belgium</country>
  </location_countries>
  <link>
    <url>http://www.kuleuven.be/licm/</url>
    <description>Laboratory of intensive care medicine, KULeuven - University</description>
  </link>
  <reference>
    <citation>Pennekamp CW, Bots ML, Kappelle LJ, Moll FL, de Borst GJ. The value of near-infrared spectroscopy measured cerebral oximetry during carotid endarterectomy in perioperative stroke prevention. A review. Eur J Vasc Endovasc Surg. 2009 Nov;38(5):539-45. doi: 10.1016/j.ejvs.2009.07.008. Epub 2009 Aug 7.</citation>
    <PMID>19665397</PMID>
  </reference>
  <reference>
    <citation>Casati A, Fanelli G, Pietropaoli P, Proietti R, Tufano R, Danelli G, Fierro G, De Cosmo G, Servillo G; Collaborative Italian Study Group on Anesthesia in Elderly Patients. Continuous monitoring of cerebral oxygen saturation in elderly patients undergoing major abdominal surgery minimizes brain exposure to potential hypoxia. Anesth Analg. 2005 Sep;101(3):740-747. doi: 10.1213/01.ane.0000166974.96219.cd. Erratum In: Anesth Analg. 2006 Jun;102(6):1645. Fierro, Giovanni [corrected to Fierro, Giuseppe].</citation>
    <PMID>16115985</PMID>
  </reference>
  <reference>
    <citation>Murkin JM, Adams SJ, Novick RJ, Quantz M, Bainbridge D, Iglesias I, Cleland A, Schaefer B, Irwin B, Fox S. Monitoring brain oxygen saturation during coronary bypass surgery: a randomized, prospective study. Anesth Analg. 2007 Jan;104(1):51-8. doi: 10.1213/01.ane.0000246814.29362.f4.</citation>
    <PMID>17179242</PMID>
  </reference>
  <reference>
    <citation>Slater JP, Guarino T, Stack J, Vinod K, Bustami RT, Brown JM 3rd, Rodriguez AL, Magovern CJ, Zaubler T, Freundlich K, Parr GV. Cerebral oxygen desaturation predicts cognitive decline and longer hospital stay after cardiac surgery. Ann Thorac Surg. 2009 Jan;87(1):36-44; discussion 44-5. doi: 10.1016/j.athoracsur.2008.08.070.</citation>
    <PMID>19101265</PMID>
  </reference>
  <reference>
    <citation>Phelps HM, Mahle WT, Kim D, Simsic JM, Kirshbom PM, Kanter KR, Maher KO. Postoperative cerebral oxygenation in hypoplastic left heart syndrome after the Norwood procedure. Ann Thorac Surg. 2009 May;87(5):1490-4. doi: 10.1016/j.athoracsur.2009.01.071.</citation>
    <PMID>19379890</PMID>
  </reference>
  <reference>
    <citation>Hirsch JC, Charpie JR, Ohye RG, Gurney JG. Near-infrared spectroscopy: what we know and what we need to know--a systematic review of the congenital heart disease literature. J Thorac Cardiovasc Surg. 2009 Jan;137(1):154-9, 159e1-12. doi: 10.1016/j.jtcvs.2008.08.005. Epub 2008 Sep 24.</citation>
    <PMID>19154918</PMID>
  </reference>
  <reference>
    <citation>Uebing A, Furck AK, Hansen JH, Nufer E, Scheewe J, Dutschke P, Jung O, Kramer HH. Perioperative cerebral and somatic oxygenation in neonates with hypoplastic left heart syndrome or transposition of the great arteries. J Thorac Cardiovasc Surg. 2011 Sep;142(3):523-30. doi: 10.1016/j.jtcvs.2011.01.036. Epub 2011 Mar 29.</citation>
    <PMID>21450312</PMID>
  </reference>
  <reference>
    <citation>Vlasselaers D, Milants I, Desmet L, Wouters PJ, Vanhorebeek I, van den Heuvel I, Mesotten D, Casaer MP, Meyfroidt G, Ingels C, Muller J, Van Cromphaut S, Schetz M, Van den Berghe G. Intensive insulin therapy for patients in paediatric intensive care: a prospective, randomised controlled study. Lancet. 2009 Feb 14;373(9663):547-56. doi: 10.1016/S0140-6736(09)60044-1. Epub 2009 Jan 26.</citation>
    <PMID>19176240</PMID>
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    <citation>Tsien CL, Kohane IS, McIntosh N. Multiple signal integration by decision tree induction to detect artifacts in the neonatal intensive care unit. Artif Intell Med. 2000 Jul;19(3):189-202. doi: 10.1016/s0933-3657(00)00045-2.</citation>
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    <citation>Noble WS. What is a support vector machine? Nat Biotechnol. 2006 Dec;24(12):1565-7. doi: 10.1038/nbt1206-1565.</citation>
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    <citation>Faul S, Gregorcic G, Boylan G, Marnane W, Lightbody G, Connolly S. Gaussian process modeling of EEG for the detection of neonatal seizures. IEEE Trans Biomed Eng. 2007 Dec;54(12):2151-62. doi: 10.1109/tbme.2007.895745.</citation>
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  <verification_date>May 2019</verification_date>
  <study_first_submitted>October 10, 2012</study_first_submitted>
  <study_first_submitted_qc>October 12, 2012</study_first_submitted_qc>
  <study_first_posted type="Estimate">October 15, 2012</study_first_posted>
  <results_first_submitted>June 21, 2018</results_first_submitted>
  <results_first_submitted_qc>May 14, 2019</results_first_submitted_qc>
  <results_first_posted type="Actual">May 15, 2019</results_first_posted>
  <last_update_submitted>May 14, 2019</last_update_submitted>
  <last_update_submitted_qc>May 14, 2019</last_update_submitted_qc>
  <last_update_posted type="Actual">May 15, 2019</last_update_posted>
  <responsible_party>
    <responsible_party_type>Principal Investigator</responsible_party_type>
    <investigator_affiliation>KU Leuven</investigator_affiliation>
    <investigator_full_name>Geert Meyfroidt, MD, PhD</investigator_full_name>
    <investigator_title>Assistant Professor</investigator_title>
  </responsible_party>
  <keyword>cerebral tissue oxygen saturation</keyword>
  <keyword>congenital cardiac surgery</keyword>
  <keyword>pediatric intensive care unit</keyword>
  <keyword>Near Infra-Red Spectroscopy</keyword>
  <keyword>early warning monitor</keyword>
  <keyword>outcome prediction</keyword>

  <clinical_results>

    <participant_flow>
      <pre_assignment_details>In this prospective observational cohort study, we had aimed at including 300 patients with continuous NIRS monitoring in place. In only 177 patients we could retrieve a continuous near-infrared spectroscopy monitoring signal of sufficient quality for inclusion in the study.</pre_assignment_details>
      <group_list>
        <group group_id="P1">
          <title>Post-cardiac Surgery PICU Admissions</title>
          <description>Children after cardiac surgery, younger than 12 years old, with an arterial catheter in place, mechanically ventilated upon PICU admission or intubated after admission, and expected to stay at least 24 hours in the PICU, were included in the study.</description>
        </group>
      </group_list>
      <period_list>
        <period>
          <title>Overall Study</title>
          <milestone_list>
            <milestone>
              <title>STARTED</title>
              <participants_list>
                <participants group_id="P1" count="177"/>
              </participants_list>
            </milestone>
            <milestone>
              <title>COMPLETED</title>
              <participants_list>
                <participants group_id="P1" count="177"/>
              </participants_list>
            </milestone>
            <milestone>
              <title>NOT COMPLETED</title>
              <participants_list>
                <participants group_id="P1" count="0"/>
              </participants_list>
            </milestone>
          </milestone_list>
        </period>
      </period_list>
    </participant_flow>

    <baseline>
      <group_list>
        <group group_id="B1">
          <title>Post-cardiac Surgery PICU Admissions</title>
          <description>Children after cardiac surgery, younger than 12 years old, with an arterial catheter in place, mechanically ventilated upon PICU admission or intubated after admission, and expected to stay at least 24 hours in the PICU, were included in the study.</description>
        </group>
      </group_list>
      <analyzed_list>
        <analyzed>
          <units>Participants</units>
          <scope>Overall</scope>
          <count_list>
            <count group_id="B1" value="177"/>
          </count_list>
        </analyzed>
      </analyzed_list>
      <measure_list>
        <measure>
          <title>Age</title>
          <units>Months</units>
          <param>Median</param>
          <dispersion>Inter-Quartile Range</dispersion>
          <class_list>
            <class>
              <analyzed_list>
                <analyzed>
                  <units>Participants</units>
                  <scope>Class</scope>
                  <count_list>
                    <count group_id="B1" value="177"/>
                  </count_list>
                </analyzed>
              </analyzed_list>
              <category_list>
                <category>
                  <measurement_list>
                    <measurement group_id="B1" value="4" lower_limit="1" upper_limit="14"/>
                  </measurement_list>
                </category>
              </category_list>
            </class>
          </class_list>
        </measure>
        <measure>
          <title>Sex: Female, Male</title>
          <units>Participants</units>
          <param>Count of Participants</param>
          <class_list>
            <class>
              <analyzed_list>
                <analyzed>
                  <units>Participants</units>
                  <scope>Class</scope>
                  <count_list>
                    <count group_id="B1" value="177"/>
                  </count_list>
                </analyzed>
              </analyzed_list>
              <category_list>
                <category>
                  <title>Female</title>
                  <measurement_list>
                    <measurement group_id="B1" value="70"/>
                  </measurement_list>
                </category>
                <category>
                  <title>Male</title>
                  <measurement_list>
                    <measurement group_id="B1" value="107"/>
                  </measurement_list>
                </category>
              </category_list>
            </class>
          </class_list>
        </measure>
        <measure>
          <title>Race and Ethnicity Not Collected</title>
          <population>Race and Ethnicity were not collected from any participant.</population>
          <units>Participants</units>
          <param>Count of Participants</param>
          <class_list>
            <class>
              <analyzed_list>
                <analyzed>
                  <units>Participants</units>
                  <scope>Class</scope>
                  <count_list>
                    <count group_id="B1" value="0"/>
                  </count_list>
                </analyzed>
              </analyzed_list>
            </class>
          </class_list>
        </measure>
        <measure>
          <title>Region of Enrollment</title>
          <units>participants</units>
          <param>Number</param>
          <class_list>
            <class>
              <title>Belgium</title>
              <analyzed_list>
                <analyzed>
                  <units>Participants</units>
                  <scope>Class</scope>
                  <count_list>
                    <count group_id="B1" value="177"/>
                  </count_list>
                </analyzed>
              </analyzed_list>
              <category_list>
                <category>
                  <measurement_list>
                    <measurement group_id="B1" value="177"/>
                  </measurement_list>
                </category>
              </category_list>
            </class>
          </class_list>
        </measure>
        <measure>
          <title>Weight</title>
          <units>Kilogram</units>
          <param>Median</param>
          <dispersion>Inter-Quartile Range</dispersion>
          <class_list>
            <class>
              <analyzed_list>
                <analyzed>
                  <units>Participants</units>
                  <scope>Class</scope>
                  <count_list>
                    <count group_id="B1" value="177"/>
                  </count_list>
                </analyzed>
              </analyzed_list>
              <category_list>
                <category>
                  <measurement_list>
                    <measurement group_id="B1" value="5.2" lower_limit="3.8" upper_limit="8.0"/>
                  </measurement_list>
                </category>
              </category_list>
            </class>
          </class_list>
        </measure>
        <measure>
          <title>Cyanotic Heart Defect Post Surgery</title>
          <units>Participants</units>
          <param>Count of Participants</param>
          <class_list>
            <class>
              <analyzed_list>
                <analyzed>
                  <units>Participants</units>
                  <scope>Class</scope>
                  <count_list>
                    <count group_id="B1" value="177"/>
                  </count_list>
                </analyzed>
              </analyzed_list>
              <category_list>
                <category>
                  <measurement_list>
                    <measurement group_id="B1" value="70"/>
                  </measurement_list>
                </category>
              </category_list>
            </class>
          </class_list>
        </measure>
        <measure>
          <title>Number of patients on extracorporeal membrane oxygenation (ECMO)</title>
          <units>Participants</units>
          <param>Count of Participants</param>
          <class_list>
            <class>
              <analyzed_list>
                <analyzed>
                  <units>Participants</units>
                  <scope>Class</scope>
                  <count_list>
                    <count group_id="B1" value="177"/>
                  </count_list>
                </analyzed>
              </analyzed_list>
              <category_list>
                <category>
                  <measurement_list>
                    <measurement group_id="B1" value="8"/>
                  </measurement_list>
                </category>
              </category_list>
            </class>
          </class_list>
        </measure>
        <measure>
          <title>CPB duration</title>
          <units>Minutes</units>
          <param>Median</param>
          <dispersion>Inter-Quartile Range</dispersion>
          <class_list>
            <class>
              <analyzed_list>
                <analyzed>
                  <units>Participants</units>
                  <scope>Class</scope>
                  <count_list>
                    <count group_id="B1" value="177"/>
                  </count_list>
                </analyzed>
              </analyzed_list>
              <category_list>
                <category>
                  <measurement_list>
                    <measurement group_id="B1" value="84.5" lower_limit="56.3" upper_limit="113.8"/>
                  </measurement_list>
                </category>
              </category_list>
            </class>
          </class_list>
        </measure>
        <measure>
          <title>Delay between PICU admission and NIRS monitoring start</title>
          <units>Minutes</units>
          <param>Median</param>
          <dispersion>Inter-Quartile Range</dispersion>
          <class_list>
            <class>
              <analyzed_list>
                <analyzed>
                  <units>Participants</units>
                  <scope>Class</scope>
                  <count_list>
                    <count group_id="B1" value="177"/>
                  </count_list>
                </analyzed>
              </analyzed_list>
              <category_list>
                <category>
                  <measurement_list>
                    <measurement group_id="B1" value="1.1" lower_limit="0.5" upper_limit="3.8"/>
                  </measurement_list>
                </category>
              </category_list>
            </class>
          </class_list>
        </measure>
      </measure_list>
    </baseline>

    <outcome_list>
      <outcome>
        <type>Primary</type>
        <title>The Accuracy to Predict Acute Kidney Injury (AKI) Per Patient, 6 Hours Before This Clinical Event (AKI) Occurs</title>
        <description>Defined according to the Kidney Disease: Improving Global Outcome criteria (AKI stage 2 or 3)&#xD;
serum creatinine (SCr) level ≥ 2 times the baseline level, or&#xD;
urine output (UO) &lt; 0.5 ml/kg/hour for ≥ 12 hours, or&#xD;
provision of dialysis</description>
        <time_frame>Predictive window of 6 hours before AKI occurence</time_frame>
        <population>Of the 177 patients included, 21 patients were excluded:&#xD;
4 had AKI within 6 hours after admission&#xD;
16 were monitored with NIRS after AKI onset&#xD;
NIRS monitoring was initiated later than 72 hours in 1 patient, which prevented the fit of prediction time for this patient</population>
        <group_list>
          <group group_id="O1">
            <title>Post-cardiac Surgery PICU Admissions</title>
            <description>Children after cardiac surgery, younger than 12 years old, with an arterial catheter in place, mechanically ventilated upon PICU admission or intubated after admission, and expected to stay at least 24 hours in the PICU, were included in the study.</description>
          </group>
        </group_list>
        <measure>
          <title>The Accuracy to Predict Acute Kidney Injury (AKI) Per Patient, 6 Hours Before This Clinical Event (AKI) Occurs</title>
          <description>Defined according to the Kidney Disease: Improving Global Outcome criteria (AKI stage 2 or 3)&#xD;
serum creatinine (SCr) level ≥ 2 times the baseline level, or&#xD;
urine output (UO) &lt; 0.5 ml/kg/hour for ≥ 12 hours, or&#xD;
provision of dialysis</description>
          <population>Of the 177 patients included, 21 patients were excluded:&#xD;
4 had AKI within 6 hours after admission&#xD;
16 were monitored with NIRS after AKI onset&#xD;
NIRS monitoring was initiated later than 72 hours in 1 patient, which prevented the fit of prediction time for this patient</population>
          <units>Participants</units>
          <param>Count of Participants</param>
          <analyzed_list>
            <analyzed>
              <units>Participants</units>
              <scope>Measure</scope>
              <count_list>
                <count group_id="O1" value="156"/>
              </count_list>
            </analyzed>
          </analyzed_list>
          <class_list>
            <class>
              <category_list>
                <category>
                  <measurement_list>
                    <measurement group_id="O1" value="55"/>
                  </measurement_list>
                </category>
              </category_list>
            </class>
          </class_list>
        </measure>
      </outcome>
      <outcome>
        <type>Secondary</type>
        <title>Hospital Length of Stay</title>
        <description>participants will be followed for the duration of hospital stay, an expected average of 1-2 weeks</description>
        <time_frame>Hospital discharge</time_frame>
        <group_list>
          <group group_id="O1">
            <title>Post-cardiac Surgery PICU Admissions</title>
            <description>Children after cardiac surgery, younger than 12 years old, with an arterial catheter in place, mechanically ventilated upon PICU admission or intubated after admission, and expected to stay at least 24 hours in the PICU, were included in the study.</description>
          </group>
        </group_list>
        <measure>
          <title>Hospital Length of Stay</title>
          <description>participants will be followed for the duration of hospital stay, an expected average of 1-2 weeks</description>
          <units>days</units>
          <param>Median</param>
          <dispersion>Inter-Quartile Range</dispersion>
          <analyzed_list>
            <analyzed>
              <units>Participants</units>
              <scope>Measure</scope>
              <count_list>
                <count group_id="O1" value="177"/>
              </count_list>
            </analyzed>
          </analyzed_list>
          <class_list>
            <class>
              <category_list>
                <category>
                  <measurement_list>
                    <measurement group_id="O1" value="10" lower_limit="6" upper_limit="21"/>
                  </measurement_list>
                </category>
              </category_list>
            </class>
          </class_list>
        </measure>
      </outcome>
      <outcome>
        <type>Secondary</type>
        <title>Intensive Care Unit Length of Stay</title>
        <description>participants will be followed for the duration of hospital stay, an expected average of 1-2 weeks</description>
        <time_frame>Intensive care unit discharge</time_frame>
        <group_list>
          <group group_id="O1">
            <title>Post-cardiac Surgery PICU Admissions</title>
            <description>Children after cardiac surgery, younger than 12 years old, with an arterial catheter in place, mechanically ventilated upon PICU admission or intubated after admission, and expected to stay at least 24 hours in the PICU, were included in the study.</description>
          </group>
        </group_list>
        <measure>
          <title>Intensive Care Unit Length of Stay</title>
          <description>participants will be followed for the duration of hospital stay, an expected average of 1-2 weeks</description>
          <units>days</units>
          <param>Median</param>
          <dispersion>Inter-Quartile Range</dispersion>
          <analyzed_list>
            <analyzed>
              <units>Participants</units>
              <scope>Measure</scope>
              <count_list>
                <count group_id="O1" value="177"/>
              </count_list>
            </analyzed>
          </analyzed_list>
          <class_list>
            <class>
              <category_list>
                <category>
                  <measurement_list>
                    <measurement group_id="O1" value="4" lower_limit="3" upper_limit="8"/>
                  </measurement_list>
                </category>
              </category_list>
            </class>
          </class_list>
        </measure>
      </outcome>
      <outcome>
        <type>Secondary</type>
        <title>Duration of Mechanical Ventilation</title>
        <description>participants will be followed for the duration of hospital stay, an expected average of 1-2 weeks</description>
        <time_frame>ICU discharge</time_frame>
        <group_list>
          <group group_id="O1">
            <title>Post-cardiac Surgery PICU Admissions</title>
            <description>Children after cardiac surgery, younger than 12 years old, with an arterial catheter in place, mechanically ventilated upon PICU admission or intubated after admission, and expected to stay at least 24 hours in the PICU, were included in the study.</description>
          </group>
        </group_list>
        <measure>
          <title>Duration of Mechanical Ventilation</title>
          <description>participants will be followed for the duration of hospital stay, an expected average of 1-2 weeks</description>
          <units>hours</units>
          <param>Median</param>
          <dispersion>Inter-Quartile Range</dispersion>
          <analyzed_list>
            <analyzed>
              <units>Participants</units>
              <scope>Measure</scope>
              <count_list>
                <count group_id="O1" value="177"/>
              </count_list>
            </analyzed>
          </analyzed_list>
          <class_list>
            <class>
              <category_list>
                <category>
                  <measurement_list>
                    <measurement group_id="O1" value="111.3" lower_limit="69.3" upper_limit="190.4"/>
                  </measurement_list>
                </category>
              </category_list>
            </class>
          </class_list>
        </measure>
      </outcome>
      <outcome>
        <type>Secondary</type>
        <title>Hospital Mortality</title>
        <description>participants will be followed for the duration of hospital stay, an expected average of 1-2 weeks</description>
        <time_frame>Hospital discharge</time_frame>
        <group_list>
          <group group_id="O1">
            <title>Post-cardiac Surgery PICU Admissions</title>
            <description>Children after cardiac surgery, younger than 12 years old, with an arterial catheter in place, mechanically ventilated upon PICU admission or intubated after admission, and expected to stay at least 24 hours in the PICU, were included in the study.</description>
          </group>
        </group_list>
        <measure>
          <title>Hospital Mortality</title>
          <description>participants will be followed for the duration of hospital stay, an expected average of 1-2 weeks</description>
          <units>Participants</units>
          <param>Count of Participants</param>
          <analyzed_list>
            <analyzed>
              <units>Participants</units>
              <scope>Measure</scope>
              <count_list>
                <count group_id="O1" value="177"/>
              </count_list>
            </analyzed>
          </analyzed_list>
          <class_list>
            <class>
              <category_list>
                <category>
                  <measurement_list>
                    <measurement group_id="O1" value="9"/>
                  </measurement_list>
                </category>
              </category_list>
            </class>
          </class_list>
        </measure>
      </outcome>
    </outcome_list>

    <reported_events>
      <group_list>
        <group group_id="E1">
          <title>Post-cardiac Surgery PICU Admissions</title>
          <description>Children after cardiac surgery, younger than 12 years old, with an arterial catheter in place, mechanically ventilated upon PICU admission or intubated after admission, and expected to stay at least 24 hours in the PICU, were included in the study.</description>
        </group>
      </group_list>
      <serious_events>
        <category_list>
          <category>
            <title>Total</title>
            <event_list>
              <event>
                <sub_title>Total, all-cause mortality</sub_title>
                <counts group_id="E1" subjects_affected="9" subjects_at_risk="177"/>
              </event>
              <event>
                <sub_title>Total, serious adverse events</sub_title>
                <counts group_id="E1" subjects_affected="0" subjects_at_risk="177"/>
              </event>
            </event_list>
          </category>
        </category_list>
      </serious_events>
      <other_events>
        <frequency_threshold>0</frequency_threshold>
        <category_list>
          <category>
            <title>Total</title>
            <event_list>
              <event>
                <sub_title>Total, other adverse events</sub_title>
                <counts group_id="E1" subjects_affected="0" subjects_at_risk="177"/>
              </event>
            </event_list>
          </category>
        </category_list>
      </other_events>
    </reported_events>

    <certain_agreements>
      <pi_employee>All Principal Investigators ARE employed by the organization sponsoring the study.</pi_employee>
      <restrictive_agreement>There is NOT an agreement between Principal Investigators and the Sponsor (or its agents) that restricts the PI's rights to discuss or publish trial results after the trial is completed. </restrictive_agreement>
    </certain_agreements>
    <point_of_contact>
      <name_or_title>Prof Dr Geert Meyfroidt, Associate Professor</name_or_title>
      <organization>University Hospitals Leuven (UZ Leuven) and KU Leuven</organization>
      <phone>0032 16 344021</phone>
      <email>geert.meyfroidt@uzleuven.be</email>
    </point_of_contact>
  </clinical_results>
</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/NCT01706497
org study id: KUL-S54474
nct id: NCT01706497
lead sponsor: 
collaborator: 
has dmc: No
The postoperative period after congenital heart surgery in children can be a very critical
      episode, where it is of utmost importance to closely monitor the circulation in these
      patients. Invasive hemodynamic monitoring tools available in the adult population, are often
      not suitable to use in small children.

      The Fore-Sight(TM) is a non-invasive monitor for brain tissue oxygenation (SctO2), by
      projecting harmless near-infrared light trough the skin, skull, and brain via a disposable
      sensor that is applied on the forehead of patients. In many centres, the Fore-Sight (TM) is
      part of the routine monitoring of children during cardio-pulmonary bypass for congenital
      heart surgery. Although the monitor has not been tested for this purpose, it is often
      continued in the postoperative phase in the intensive care unit (ICU), where it is used to
      monitor the hemodynamic situation of the patient.

      The purpose of the present study is to examine and validate the use of the Fore-Sight monitor
      for hemodynamic monitoring of children in the postoperative phase after cardiac surgery.

      The study hypothesis is whether SctO2 desaturations are predictive for future hemodynamic
      deterioration of the patient, and whether these SctO2 desaturations are predictive for the
      outcome of these patients.
1. Background

           Accurate hemodynamic monitoring often requires the use of invasive tools or catheters.
           Many of these tools, available for adults, cannot be used for critically ill infants and
           children, because of their size, and because these invasive techniques carry a high risk
           of complications. Non-invasive monitoring techniques that allow for detecting critical
           hemodynamic states are of high interest in this population.

           FORE-SIGHT™ is a non-invasive tool to measure cerebral tissue oxygen saturation.
           Cerebral tissue oxygen saturation (SctO2) values are important to clinicians because
           cerebral hypoxia (lack of oxygen supply to brain tissue) is one of the leading causes of
           brain injuries that occur in many surgical and clinical situations. The FORE-SIGHT™
           Cerebral Oximeter utilizes optically-based Near Infra-Red Spectroscopy (NIRS) technology
           to monitor absolute SctO2. It works by projecting harmless near infra-red light through
           the scalp and skull and into the brain via a disposable sensor on the patient's
           forehead. The device measures the light that is returned to detectors on the sensor and
           analyzes this information utilizing patented algorithms to determine absolute cerebral
           tissue oxygen saturation levels. The FORE-SIGHT™ is designed to monitor SctO2 in a
           continuous way, and provides clinicians an opportunity to intervene before damage to the
           brain occurs. The monitor is currently part of the standard monitoring for children
           undergoing cardiopulmonary bypass, in the operating theatre, and is often continued in
           the postoperative phase in the pediatric intensive care unit (PICU) of the university
           hospitals Leuven.

           Several studies have pointed to the potential advantages of SctO2 monitoring in the
           peri-operative phase after cardiac or abdominal surgery in adults [1,2,3,4], as well as
           in children undergoing surgical corrections of congenital cardiac defects [5,6,7]. Low
           SctO2 values are associated with worse outcome, and correspond with data from other
           monitors indicating that the oxygen content of the brain at that time is insufficient.
           In particular, the duration of SctO2 desaturations below 55%, 60%, and 65%, is
           predictive for postoperative complications [8]. These studies, however, have only
           analyzed the use of NIRS as a tool to monitor the brain at times when it is at the
           highest risk, during complex surgery with a compromised circulation. Although there is
           no evidence that has demonstrated that decision making based on NIRS data is favorable
           for patient outcome, many centers use NIRS as a monitoring tool outside of the surgical
           environment.

           Because the brain is very sensitive to changes in oxygenation, monitoring of SctO2 might
           provide an indication of critical changes in the hemodynamic state of the patient, and
           thus serve as a monitor for the general hemodynamic status of the patient. The present
           study wishes to examine the added value of SctO2 monitoring over the routine monitoring
           of pediatric patients in PICU, in the postoperative phase after cardiac surgery.
           Ideally, in order to be of added value, SctO2 changes should precede an episode of
           hemodynamic deteriorations.

           Prediction is at the heart of intensive care medicine, where physicians make use of
           their medical knowledge and all the patient-related data in order to foresee changes in
           the patient's condition, and administer the appropriate (preventive) treatments. An
           intensive care unit (ICU) is a very data-rich environment with several information
           sources, such as admission records, medical history, laboratory analyses of samples,
           medication and treatment records, and monitoring of vital signals. A Patient Data
           Management System (PDMS) (MetaVision®, iMD-Soft®, Boston, MA) was installed in our ICU
           starting in February 2006. This PDMS software automatically collects and integrates the
           data from the multiple information sources. One of the benefits of having all
           patient-related data in an integrated format is that it can be readily analyzed through
           the use of computational techniques in general and data mining in particular. These
           techniques make use of the information in large databases to automatically generate
           models that can be used for prediction (e.g. to predict a patient's probability of
           survival in ICU). In previous studies our research group has shown the high predictive
           performances than can be obtained with data mining models in the intensive care domain.

           In this study we will make use of data mining techniques to assess the predictive power
           of SctO2-monitoring for future hemodynamic deterioration of the PICU patient after
           cardiac surgery, by analyzing data collected in the PDMS, including the FORE-SIGHT™
           NIRS.

        2. Aims of the project

           • To assess the independent predictive power of the FORE-SIGHT™ NIRS signal for future
           hemodynamic deterioration of critically ill infants and children, in the postoperative
           phase after cardiac surgery.

           • To assess the predictive power of the FORE-SIGHT™ NIRS signal for outcome in
           critically ill infants and children, in the postoperative phase after cardiac surgery.

        3. Study design

      Prospective, observational, non-interventional study. All eligible children will be monitored
      with the FORE-SIGHT™, from admission until they are weaned off mechanical ventilation.
      Typically, patients admitted after cardiac surgery in the PICU of the university hospitals
      Leuven are mechanically ventilated between 12 hours and two weeks [8].

      The FORE-SIGHTTM SctO2 signal will be blinded to the bedside clinician and will be stored in
      the PDMS system for analysis. The first 20 minutes of data after arrival to the ICU will not
      be used for the analysis, since during this time the patient typically stabilizes to the new
      environment after transport, and the data are therefore not characteristic of the ICU stay.

      Data from these patients will be used for analysis, to build predictive models. A copy of the
      PDMS database, after removal of all data that refer to the identity of the patients, will be
      used for data analysis. Data mining models, such as simple logistic regression models, but
      also more advanced machine learning techniques (models automatically learned by a computer
      algorithm, such as Decision Tree models [9], Bayesian Networks, Gaussian Processes, Support
      Vector Machines, …), will assess the independent predictive power of the FORE-SIGHT™ signal
      over the routinely monitored data.

      1. FORE-SIGHT™ for early detection of future hemodynamic instability.

      First, models will be built to assess the independent predictive power of the FORE-SIGHT™
      signal to detect episodes of hemodynamic instability 10 minutes in advance.

      Since cardiac output is not directly measured invasively in children, such an episode is
      evidenced by indirect signs. If one of more of the following criteria is met, this is
      considered to be an episode of hemodynamic instability.

        1. Hemodynamic monitoring • Heart Rate >160 or <90, for at least 5 consecutive minutes •
           Systolic Blood Pressure < 55 (infants) or < 65 (children) , for at least 5 consecutive
           minutes

        2. Point of care laboratory analysis

           • Venous Saturation (SvO2) <55 (when cyanogenic cardiopathy is present), or <65 (in all
           other patients)

           • Lactate > 2 mmol/L on arterial blood gas sampling

        3. Clinical observation • Urine Output rate < normal rate of 0.5 ml/kg/h over 2 consecutive
           hours

      2. FORE-SIGHT™ for prediction of outcome. Second, models will be built to assess whether the
      FORE-SIGHT™ signal is predictive for hospital and ICU Length of Stay (LOS ), mortality, and
      duration of (invasive or non-invasive) mechanical ventilator support.

      In order to assess predictive performance for the 2 predictive tasks, the following
      statistics will be used: the positive predictive value, the area under the ROC curve (AUROC),
      the Hosmer-Lemeshow statistic, calibration-in-the-large, calibration-slope and Brier scores.

      The positive predictive value is the proportion of positive instances that are correctly
      classified. It reflects the probability that a positive result corresponds to the underlying
      condition being tested for.

      The AUROC is a measure of the model's ability to discriminate between positive and negative
      instances. Usually AUROC as evaluation is preferred above accuracy as it allows to trade off
      the possibly different costs of incorrectly classifying a negative instance as positive, or
      equivalently to tradeoff between the model's sensitivity and specificity.

      The Hosmer-Lemeshow statistic, calibration-in-the-large and the calibration-slope are used to
      determine whether a model is well calibrated. These test assess whether the observed event
      rates match expected event rates in subgroups of the population. Models for which expected
      and observed event rates in subgroups are similar are said to be well calibrated.

      Overall model performance will be assessed with the Brier Score and its normalized version,
      the Brier Scaled Score. Accurate models have a low overall prediction error and therefore a
      low Brier Score (below the base case value for each task).

      4 Study population

      All children younger than 12 years of age, admitted to the PICU of the Leuven University
      hospitals Leuven after cardiac surgery are eligible for the study, if they meet the following
      inclusion criteria:

        -  Mechanically ventilated upon ICU admission or intubated after admission

        -  Arterial line in place.

        -  Expected to stay at least 24h in the PICU.

      Patients with actual or potential brain damage, such as traumatic brain injury patients,
      patients with brain tumors, or patients after cardiopulmonary resuscitation (CPR) are
      excluded. Patients with a condition or a wound that prohibits the placement of a forehead
      sensor are also excluded.

      The study will collect data for a period of 1,5 year. 300 critically ill children will be
      recruited. Approximately 20% of the PICU patients exhibit clinical deterioration events, and
      with a median (IQR) length of stay of 3 (2-7) days [8] and at least one event per day, we
      expect a minimum of 180 events of clinical deterioration in the yearly population. From
      previous studies with a similar event distribution [15,16] we expect our models to result in
      a sensitivity (and specificity) of at least 0.8, with an alpha error of 5% and a statistical
      power of 80%.

      The study will therefore have the required statistical power to detect the potential of the
      FORE-SIGHT™ NIRS signal in predicting clinical deterioration.
observational model: Cohort
time perspective: Prospective
measure: The Accuracy to Predict Acute Kidney Injury (AKI) Per Patient, 6 Hours Before This Clinical Event (AKI) Occurs
time frame: Predictive window of 6 hours before AKI occurence
description: Defined according to the Kidney Disease: Improving Global Outcome criteria (AKI stage 2 or 3)
serum creatinine (SCr) level ≥ 2 times the baseline level, or
urine output (UO) < 0.5 ml/kg/hour for ≥ 12 hours, or
provision of dialysis
measure: Hospital Length of Stay
time frame: Hospital discharge
description: participants will be followed for the duration of hospital stay, an expected average of 1-2 weeks
measure: Intensive Care Unit Length of Stay
time frame: Intensive care unit discharge
description: participants will be followed for the duration of hospital stay, an expected average of 1-2 weeks
measure: Duration of Mechanical Ventilation
time frame: ICU discharge
description: participants will be followed for the duration of hospital stay, an expected average of 1-2 weeks
measure: Hospital Mortality
time frame: Hospital discharge
description: participants will be followed for the duration of hospital stay, an expected average of 1-2 weeks
intervention type: Device
intervention name: Cerebral tissue oxygen saturation monitoring, blinded
other name: Fore-Sight(TM) NIRS
study pop: 
sampling method: Probability Sample
criteria: 
gender: All
minimum age: N/A
maximum age: 12 Years
healthy volunteers: No
last name: Geert JP Meyfroidt, MD, PhD
role: Principal Investigator
affiliation: Department of Intensive Care Medicine, University Hospitals Leuven, Belgium and Laboratory of intensive care medicine, department of cellular and molecular medicine, Biomedical sciences group, KULeuven - University, Belgium
facility: 
country: Belgium
url: http://www.kuleuven.be/licm/
description: Laboratory of intensive care medicine, KULeuven - University
citation: Pennekamp CW, Bots ML, Kappelle LJ, Moll FL, de Borst GJ. The value of near-infrared spectroscopy measured cerebral oximetry during carotid endarterectomy in perioperative stroke prevention. A review. Eur J Vasc Endovasc Surg. 2009 Nov;38(5):539-45. doi: 10.1016/j.ejvs.2009.07.008. Epub 2009 Aug 7.
PMID: 19665397
citation: Casati A, Fanelli G, Pietropaoli P, Proietti R, Tufano R, Danelli G, Fierro G, De Cosmo G, Servillo G; Collaborative Italian Study Group on Anesthesia in Elderly Patients. Continuous monitoring of cerebral oxygen saturation in elderly patients undergoing major abdominal surgery minimizes brain exposure to potential hypoxia. Anesth Analg. 2005 Sep;101(3):740-747. doi: 10.1213/01.ane.0000166974.96219.cd. Erratum In: Anesth Analg. 2006 Jun;102(6):1645. Fierro, Giovanni [corrected to Fierro, Giuseppe].
PMID: 16115985
citation: Murkin JM, Adams SJ, Novick RJ, Quantz M, Bainbridge D, Iglesias I, Cleland A, Schaefer B, Irwin B, Fox S. Monitoring brain oxygen saturation during coronary bypass surgery: a randomized, prospective study. Anesth Analg. 2007 Jan;104(1):51-8. doi: 10.1213/01.ane.0000246814.29362.f4.
PMID: 17179242
citation: Slater JP, Guarino T, Stack J, Vinod K, Bustami RT, Brown JM 3rd, Rodriguez AL, Magovern CJ, Zaubler T, Freundlich K, Parr GV. Cerebral oxygen desaturation predicts cognitive decline and longer hospital stay after cardiac surgery. Ann Thorac Surg. 2009 Jan;87(1):36-44; discussion 44-5. doi: 10.1016/j.athoracsur.2008.08.070.
PMID: 19101265
citation: Phelps HM, Mahle WT, Kim D, Simsic JM, Kirshbom PM, Kanter KR, Maher KO. Postoperative cerebral oxygenation in hypoplastic left heart syndrome after the Norwood procedure. Ann Thorac Surg. 2009 May;87(5):1490-4. doi: 10.1016/j.athoracsur.2009.01.071.
PMID: 19379890
citation: Hirsch JC, Charpie JR, Ohye RG, Gurney JG. Near-infrared spectroscopy: what we know and what we need to know--a systematic review of the congenital heart disease literature. J Thorac Cardiovasc Surg. 2009 Jan;137(1):154-9, 159e1-12. doi: 10.1016/j.jtcvs.2008.08.005. Epub 2008 Sep 24.
PMID: 19154918
citation: Uebing A, Furck AK, Hansen JH, Nufer E, Scheewe J, Dutschke P, Jung O, Kramer HH. Perioperative cerebral and somatic oxygenation in neonates with hypoplastic left heart syndrome or transposition of the great arteries. J Thorac Cardiovasc Surg. 2011 Sep;142(3):523-30. doi: 10.1016/j.jtcvs.2011.01.036. Epub 2011 Mar 29.
PMID: 21450312
citation: Vlasselaers D, Milants I, Desmet L, Wouters PJ, Vanhorebeek I, van den Heuvel I, Mesotten D, Casaer MP, Meyfroidt G, Ingels C, Muller J, Van Cromphaut S, Schetz M, Van den Berghe G. Intensive insulin therapy for patients in paediatric intensive care: a prospective, randomised controlled study. Lancet. 2009 Feb 14;373(9663):547-56. doi: 10.1016/S0140-6736(09)60044-1. Epub 2009 Jan 26.
PMID: 19176240
citation: Tsien CL, Kohane IS, McIntosh N. Multiple signal integration by decision tree induction to detect artifacts in the neonatal intensive care unit. Artif Intell Med. 2000 Jul;19(3):189-202. doi: 10.1016/s0933-3657(00)00045-2.
PMID: 10906612
citation: Noble WS. What is a support vector machine? Nat Biotechnol. 2006 Dec;24(12):1565-7. doi: 10.1038/nbt1206-1565.
PMID: 17160063
citation: Faul S, Gregorcic G, Boylan G, Marnane W, Lightbody G, Connolly S. Gaussian process modeling of EEG for the detection of neonatal seizures. IEEE Trans Biomed Eng. 2007 Dec;54(12):2151-62. doi: 10.1109/tbme.2007.895745.
PMID: 18075031
responsible party type: Principal Investigator
investigator affiliation: KU Leuven
investigator full name: Geert Meyfroidt, MD, PhD
investigator title: Assistant Professor
participant flow: 
baseline: 
outcome list: 
reported events: 
certain agreements: 
point of contact: 