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Bedside Resources to Gauge Intravascular Volume Status

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ClinicalTrials.gov Identifier: NCT03915587
Recruitment Status : Recruiting
First Posted : April 16, 2019
Last Update Posted : April 16, 2019
Sponsor:
Information provided by (Responsible Party):
Derderian, Sarkis, University of Colorado, Aurora

Brief Summary:
The goal if this study is to employ the CardioQ-Esophageal Aortic Doppler probe to define fluid responders from non-responders among infants undergoing cranial vault reconstruction for craniosynostosis. After defining these two groups in this single arm prospective trial, the investigators will compare the predictive utility of non-invasive devices such as the CipherOx-Compensatory Reserve Index (CipherOx-CRI) and Inferior Vena Cava Collapsibility Index (IVC CI) to currently employed indices (heart rate, systolic blood pressure, urine output and pulse pressure variability) to gauge the need for additional fluid and ongoing resuscitation. If the CipherOx-CRI or IVC CI proved to be as predictive or better at predicting fluid responders, the investigators hope to replace invasive arterial lines with non-invasive tools to guide resuscitation.

Condition or disease Intervention/treatment Phase
Hypovolemia Craniosynostoses Device: CardioQ-EDM and CipherOx-CRI Not Applicable

  Show Detailed Description

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Study Type : Interventional  (Clinical Trial)
Estimated Enrollment : 30 participants
Intervention Model: Single Group Assignment
Intervention Model Description: Single arm prospective trial which will define two groups
Masking: None (Open Label)
Masking Description: As these devices are experimental in this patient population, anesthesiologists will be blinded to hemodynamic data generated by the CardioQ-EDM, bedside ultrasound, and CipherOx CRI. If data is needed in an emergent setting, the subject will be excluded from analysis. Although recorded measurements from the Cardio-Q EDM monitor will be visible to the study team, IVC measurements will be stored and calculated post-hoc by a co-investigator blinded to whether or not the subject is or is not fluid responsive. Additionally, a trained statistician not involved in data collection will be paid for analysis. It should also be noted at Dr. Steven Moulton is a paid officer of CipherOx but will not be involved in the data collection and analysis.
Primary Purpose: Diagnostic
Official Title: Bedside Resources to Gauge Intravascular Volume Status in Hypovolemic Infants in the Operating Room
Actual Study Start Date : April 8, 2019
Estimated Primary Completion Date : April 8, 2020
Estimated Study Completion Date : April 8, 2020


Arm Intervention/treatment
Fluid Challenge
After defining fluid responders from non-responders in this single arm prospective trial, we will compare the predictive utility of non-invasive devices such as the CipherOx-CRI and IVC CI to currently employed indices (heart rate, systolic blood pressure, urine output and pulse pressure variability) to gauge the need for additional fluid and ongoing resuscitation.
Device: CardioQ-EDM and CipherOx-CRI
A CardioQ-EDM probe will be placed on the day of surgery after induction of general anesthesia. The anesthesiologist will inform the investigator of plans to provide a fluid or blood bolus per clinical judgement in addition to the protocolized 10 ml/kg bolus provided after induction. While the anesthesiologist is preparing to administer volume expansion, a co-investigator will collect pre-fluid bolus data. Measurements will be recorded for data analysis at the completion of the trial. Additionally, a CipherOx-CRI probe will be placed on the patient's index finger (recorded data will be interpreted post hoc) and a bedside ultrasound will be performed by either the principal investigator (PI) or one of two co-investigators to measure the IVC CI. Ultrasound cine-loops will be recorded, and CI will be calculated post-hoc. Data will be recorded on the Data Collection Form for each fluid bolus administered. The PI and co-investigators will manage all aspects of investigational devices.




Primary Outcome Measures :
  1. Utility of Compensatory Reserve Index (CRI) at predicting fluid responders from non-responders [ Time Frame: Through study completion (3-4 hours) ]
    Using a delta peak aortic velocity threshold of 10% (measured using the CardioQ-EDM) before and after a bolus to define fluid responders (=/>10%) from non-responders (<10%), the investigators will determine the performance of pre-bolus CRI reading to predict fluid responders from non-responders. Measurements will be recorded three times with one minute between measurements and then averaged.

  2. Utility of Inferior Vena Cava Collapsibility Index (IVC CI) at predicting fluid responders from non-responders [ Time Frame: Through study completion (3-4 hours) ]
    Using a delta peak aortic velocity threshold of 10% (measured using the CardioQ-EDM) before and after a bolus to define fluid responders (=/>10%) from non-responders (<10%), the investigators will determine the performance of pre-bolus IVC CI measurements to predict fluid responders from non-responders. Measurements will be recorded three times with one minute between measurements and then averaged.

  3. Utility of systolic blood pressure at predicting fluid responders from non-responders [ Time Frame: Through study completion (3-4 hours) ]
    Using a delta peak aortic velocity threshold of 10% (measured using the CardioQ-EDM) before and after a bolus to define fluid responders (=/>10%) from non-responders (<10%), the investigators will determine the performance of pre-bolus systolic blood pressure measurements to predict fluid responders from non-responders. Measurements will be recorded three times with one minute between measurements and then averaged.

  4. Utility of mean arterial pressure at predicting fluid responders from non-responders [ Time Frame: Through study completion (3-4 hours) ]
    Using a delta peak aortic velocity threshold of 10% (measured using the CardioQ-EDM) before and after a bolus to define fluid responders (=/>10%) from non-responders (<10%), the investigators will determine the performance of pre-bolus mean arterial pressure measurements to predict fluid responders from non-responders. Measurements will be recorded three times with one minute between measurements and then averaged.

  5. Utility of end-tidal carbon dioxide at predicting fluid responders from non-responders [ Time Frame: Through study completion (3-4 hours) ]
    Using a delta peak aortic velocity threshold of 10% (measured using the CardioQ-EDM) before and after a bolus to define fluid responders (=/>10%) from non-responders (<10%), the investigators will determine the performance of pre-bolus end-tidal carbon dioxide measurements to predict fluid responders from non-responders. Measurements will be recorded three times with one minute between measurements and then averaged.

  6. Utility of pulse pressure variability at predicting fluid responders from non-responders [ Time Frame: Through study completion (3-4 hours) ]
    Using a delta peak aortic velocity threshold of 10% (measured using the CardioQ-EDM) before and after a bolus to define fluid responders (=/>10%) from non-responders (<10%), the investigators will determine the performance of pre-bolus pulse pressure variability to predict fluid responders from non-responders. Measurements will be recorded three times with one minute between measurements and then averaged.


Secondary Outcome Measures :
  1. Evaluate whether sex confounds the accuracy of each primary outcome in distinguishing fluid responders from non-responders. [ Time Frame: Through study completion (3-4 hours) ]
    Area under the curve will be calculated for the primary outcome variables using multiple logistic regression models including sex. Confounders will be identified for inclusion in the multiple logistic regression models by calculating the univariate association with the gold standard (using a p<0.10 threshold). These adjusted models will be used to verify thresholds for classification using the primary outcomes which will then be applied to calculate classification summary measures (e.g., positive predictive value, negative predictive value, sensitivity, and specificity).

  2. Evaluate whether race confounds the accuracy of each primary outcome in distinguishing fluid responders from non-responders. [ Time Frame: Through study completion (3-4 hours) ]
    Area under the curve will be calculated for the primary outcome variables using multiple logistic regression models including race. Confounders will be identified for inclusion in the multiple logistic regression models by calculating the univariate association with the gold standard (using a p<0.10 threshold). These adjusted models will be used to verify thresholds for classification using the primary outcomes which will then be applied to calculate classification summary measures (e.g., positive predictive value, negative predictive value, sensitivity, and specificity).

  3. Evaluate whether ethnicity confounds the accuracy of each primary outcome in distinguishing fluid responders from non-responders. [ Time Frame: Through study completion (3-4 hours) ]
    Area under the curve will be calculated for the primary outcome variables using multiple logistic regression models including ethnicity. Confounders will be identified for inclusion in the multiple logistic regression models by calculating the univariate association with the gold standard (using a p<0.10 threshold). These adjusted models will be used to verify thresholds for classification using the primary outcomes which will then be applied to calculate classification summary measures (e.g., positive predictive value, negative predictive value, sensitivity, and specificity).

  4. Evaluate whether weight in kilograms confounds the accuracy of each primary outcome in distinguishing fluid responders from non-responders. [ Time Frame: Through study completion (3-4 hours) ]
    Area under the curve will be calculated for the primary outcome variables using multiple logistic regression models including weight. Confounders will be identified for inclusion in the multiple logistic regression models by calculating the univariate association with the gold standard (using a p<0.10 threshold). These adjusted models will be used to verify thresholds for classification using the primary outcomes which will then be applied to calculate classification summary measures (e.g., positive predictive value, negative predictive value, sensitivity, and specificity).

  5. Evaluate whether height in centimeters confounds the accuracy of each primary outcome in distinguishing fluid responders from non-responders. [ Time Frame: Through study completion (3-4 hours) ]
    Area under the curve will be calculated for the primary outcome variables using multiple logistic regression models including heigh. Confounders will be identified for inclusion in the multiple logistic regression models by calculating the univariate association with the gold standard (using a p<0.10 threshold). These adjusted models will be used to verify thresholds for classification using the primary outcomes which will then be applied to calculate classification summary measures (e.g., positive predictive value, negative predictive value, sensitivity, and specificity).

  6. Evaluate whether tidal volume in milliliters per kilogram confounds the accuracy of each primary outcome in distinguishing fluid responders from non-responders. [ Time Frame: Through study completion (3-4 hours) ]
    Area under the curve will be calculated for the primary outcome variables using multiple logistic regression models including tidal volume. Confounders will be identified for inclusion in the multiple logistic regression models by calculating the univariate association with the gold standard (using a p<0.10 threshold). These adjusted models will be used to verify thresholds for classification using the primary outcomes which will then be applied to calculate classification summary measures (e.g., positive predictive value, negative predictive value, sensitivity, and specificity).

  7. Evaluate whether peak inspiratory pressure measured in centimeters of water confounds the accuracy of each primary outcome in distinguishing fluid responders from non-responders. [ Time Frame: Through study completion (3-4 hours) ]
    Area under the curve will be calculated for the primary outcome variables using multiple logistic regression models including peak inspiratory pressure. Confounders will be identified for inclusion in the multiple logistic regression models by calculating the univariate association with the gold standard (using a p<0.10 threshold). These adjusted models will be used to verify thresholds for classification using the primary outcomes which will then be applied to calculate classification summary measures (e.g., positive predictive value, negative predictive value, sensitivity, and specificity).

  8. Evaluate whether peak end-expiratory pressure measured in centimeters of water confounds the accuracy of each primary outcome in distinguishing fluid responders from non-responders. [ Time Frame: Through study completion (3-4 hours) ]
    Area under the curve will be calculated for the primary outcome variables using multiple logistic regression models including peak end-expiratory pressure. Confounders will be identified for inclusion in the multiple logistic regression models by calculating the univariate association with the gold standard (using a p<0.10 threshold). These adjusted models will be used to verify thresholds for classification using the primary outcomes which will then be applied to calculate classification summary measures (e.g., positive predictive value, negative predictive value, sensitivity, and specificity).

  9. Evaluate whether respiratory rate measured in breaths per minute confounds the accuracy of each primary outcome in distinguishing fluid responders from non-responders. [ Time Frame: Through study completion (3-4 hours) ]
    Area under the curve will be calculated for the primary outcome variables using multiple logistic regression models including respiratory rate. Confounders will be identified for inclusion in the multiple logistic regression models by calculating the univariate association with the gold standard (using a p<0.10 threshold). These adjusted models will be used to verify thresholds for classification using the primary outcomes which will then be applied to calculate classification summary measures (e.g., positive predictive value, negative predictive value, sensitivity, and specificity).



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Ages Eligible for Study:   3 Months to 2 Years   (Child)
Sexes Eligible for Study:   All
Accepts Healthy Volunteers:   No
Criteria

Inclusion Criteria:

  • Children with craniosynostosis undergoing cranial vault reconstruction

Exclusion Criteria:

  • Children with known underlying cardiac anomalies or cardiac arrhythmias
  • Weight less than 3 kg
  • Children who have vasopressors adjusted during a fluid bolus

Information from the National Library of Medicine

To learn more about this study, you or your doctor may contact the study research staff using the contact information provided by the sponsor.

Please refer to this study by its ClinicalTrials.gov identifier (NCT number): NCT03915587


Contacts
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Contact: Sarkis C Derderian, MD 8034668100 s.derderian@ucdenver.edu

Locations
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United States, Colorado
Children's Hospital Colorado Recruiting
Aurora, Colorado, United States, 80045
Contact: Sarkis C Derderian, MD    803-466-8100    s.derderian@ucdenver.edu   
Sponsors and Collaborators
University of Colorado, Aurora

Publications:

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Responsible Party: Derderian, Sarkis, Trainee, Department of Pediatric Surgery, Principal Investigator, Surgical Fellow, University of Colorado, Aurora
ClinicalTrials.gov Identifier: NCT03915587     History of Changes
Other Study ID Numbers: 18-2513
First Posted: April 16, 2019    Key Record Dates
Last Update Posted: April 16, 2019
Last Verified: April 2019
Individual Participant Data (IPD) Sharing Statement:
Plan to Share IPD: No
Plan Description: No plan to share individual participant data with other researchers is planned

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Studies a U.S. FDA-regulated Drug Product: No
Studies a U.S. FDA-regulated Device Product: Yes
Device Product Not Approved or Cleared by U.S. FDA: No
Pediatric Postmarket Surveillance of a Device Product: No
Product Manufactured in and Exported from the U.S.: No

Additional relevant MeSH terms:
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Hypovolemia
Craniosynostoses
Pathologic Processes
Synostosis
Dysostoses
Bone Diseases, Developmental
Bone Diseases
Musculoskeletal Diseases
Craniofacial Abnormalities
Musculoskeletal Abnormalities
Congenital Abnormalities