In the first phase of the experiment, 20 healthy controls will test the VO device to determine whether the camera with a CCD or CMOS lens is the most appropriate for use in ICP patients and to synchronize the VO, ECG, PPG, IOP and respiratory signals.

Subjects in the second phase of the experiment (70 subjects total) will be randomized to either Group A or Group B. We anticipate that 25 adult and 10 pediatric (ages 4-17) patients will participate in each group. Individuals in Group A will have two inter-leaved examinations (1-14 days apart). Data from the first examination will serve for SVP-ICP transfer function estimation and data from the second examination will serve for the intra-group verification for the estimated transfer function.

Individuals in Group B will undergo one examination. Data from Group B participants will serve as the inter-group re-test verification of the estimated transfer function.

Intracranial Pressure Increase

Use of a custom video ophthalmoscope with either a CCD or CMOS camera lens. The healthy controls group (first 20 subjects) will provide data to help determine which lens might be best for measuring ICP patients (Groups A and B) in the second phase of the study.

Video ophthalmoscope

University of Minnesota

Non-Invasive and Non-Contact Intracranial Pressure Waveform Recording Using Dynamic Video Ophthalmoscopy

This study will help determine whether there could be a less invasive method of monitoring intracranial pressure. It is thought that the use of a video-ophthalmoscope recordings could be utilized to measure intracranial pressure. The outcome measures will be provided as z-scores.

In the first phase of the experiment, a total of 20 healthy controls will test the VO device to determine which camera is the most appropriate for ICP patients and to synchronize the VO, electrocardiogram (ECG), photoplethysmography (PPG), intraocular pressure (IOP) and respiratory signals.
The second phase of the experiment will enroll a total of 70 subjects who are undergoing ICP monitoring with a surgically placed probe will be randomly assigned into one of two groups. Each group will consist of 25 adult and 10 pediatric (aged 4-17) participants. Group A will undergo two inter-leaved examinations (up to 14 days apart). Data from the first exam will be used for SVP-ICP transfer function estimation and data from the second exam will serve for intra-group verification of the estimated transfer function. Group B participants will undergo only one examination and their data will serve as an inter-group re-test verification of the estimated transfer function.

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.

Hypovolemia

Craniosynostoses

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.

CardioQ-EDM and CipherOx-CRI

Children's Hospital Colorado

Bedside Resources to Gauge Intravascular Volume Status

Bedside Resources to Gauge Intravascular Volume Status in Hypovolemic Infants in the Operating Room

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.

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.

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.

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.

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.

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.

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).

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).

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).

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).

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).

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).

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).

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).

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).

No plan to share individual participant data with other researchers is planned

Derderian, Sarkis

Trainee, Department of Pediatric Surgery, Principal Investigator, Surgical Fellow

University of Colorado, Denver

Single arm prospective trial which will define two groups

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.

craniosynostoses/edema

Non-Invasive and Non-Contact Intracranial Pressure Waveform Recording Using Dynamic Video Ophthalmoscopy

Bedside Resources to Gauge Intravascular Volume Status

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