In the lab with Oliver C. Radke: turning the OR into a research lab
This “In the Lab” post is based on the article “Spontaneous Breathing during General Anesthesia Prevents the Ventral Redistribution of Ventilation as Detected by Electrical Impedance Tomography: A Randomized Trial,” authored by Dr. Oliver C. Radke (Assistant Clinical Professor, San Francisco General Hospital, Department of Anesthesia & Perioperative Care, University of California San Francisco, San Francisco, California, and Senior Attending Anesthesiologist, Klinik und Poliklinik für Anästhesiologie und Intensivtherapie, Fetscherstr, Dresden, Germany) and colleagues, which was published in the June issue of Anesthesiology.
Most interesting research questions arise from discussions with residents about what we do every day. When we talked about patients with laryngeal mask airways (LMAs), we wondered how the different ways to ventilate (spontaneous breathing, pressure control or pressure support) might change the way air is distributed in the lungs.
One possible way to find the answer to this question would be to perform an animal study in the lab. However, animal studies are always artificial to some extent, and we wanted to know what actually happens during the anesthesia we perform daily in the OR. Despite deciding against doing an animal study, the trip we took our pulmonary research group to in the animal lab was invaluable, because they had a device that we could borrow for our own research in the operating room.
Defining the primary outcome parameter
The device we found uses a method called “electrical impedance tomography,” otherwise known as EIT. It is a noninvasive method to visualize the distribution of impedances in a cross-section of the thorax. Air causes high impedance while blood and tissue cause low impedance. During ventilation, the air content of the lungs has the highest impact on changes in the impedances; thus, the EIT device allows us to assess the changes in air distribution in real time and without radiation.
In short, our study plan was to compare the influence of spontaneous breathing, pressure-controlled ventilation and pressure support ventilation on the dorsoventral distribution of air in the lungs during general anesthesia in patients with LMAs. While writing the study protocol, we drafted a standardized anesthesia regimen that would work with all three modes of ventilation.
After finding this method to assess our primary outcome and drafting a study protocol, we had to find a way to turn our operating room into a research lab. To reduce confounding factors, we had to make sure that we could control for as many variables as possible. Additionally, we had to find a way to collect and analyze all the relevant data.
One of the anesthesia machines we routinely use in clinical practice is the ZEUS® (Dräger Medical, Lübeck, Germany). A unique feature of the ZEUS is its ventilator design: The ZEUS is capable of running as a completely closed system. In a closed system, instead of dialing a fresh gas flow and opening up the vapor to mix in the volatile anesthetic, we set the desired inspired oxygen concentration (FiO2) and the desired end-tidal volatile fraction. The machine measures these concentrations and by means of a feedback algorithm it adjusts the gas concentrations as necessary. As a result, we achieve completely stable gas concentrations throughout the case, an ideal situation for our clinical study where we want to control as many independent variables as possible.
Another advantage of the ZEUS is that it is completely computerized and all of the relevant parameters such as airway pressure, CO2 levels, flow rates and even waveforms can be extracted from the machine via a standardized interface port (MEDIBUS).
The patient monitors we use in our operating rooms (Philips MP70, Philips Deutschland GmbH, Hamburg, Germany) do not offer a direct means to download the data. However, all the monitors are connected to a central server, and this server allows all the data to be displayed in a web browser. The data in the web browser’s window can easily be copied into a spreadsheet or database program. All the measured vital signs (heart rate, blood pressure, oxygen saturation) and even the BIS values are available to us.
The EIT device (EIT Evaluation Kit 2, Dräger Medical) is capable of recording real-time (20 Hz) two-dimensional images for extended periods of time. The raw data (several gigabytes) can be extracted by using a USB storage device for offline processing.
The EIT device uses a flexible electrode belt positioned around the patient’s chest. Because we didn’t want to get in the surgeon’s way, we chose knee, foot and ankle surgery cases for our study. The EIT device doesn’t take much time to set up, so integrating the EIT device into the clinical workflow was easy.
EIT data analysis
The EIT device provides some algorithms to analyze the EIT data, but it does not allow custom algorithms. However, the data format of the raw data stream is documented, so we decided to write our own software to analyze the data.
The screenshot shows the raw data in the top left corner and two different functional images below. The functional images basically map the impedance change across a transverse cross- section of the patient’s chest. Simply put, the lighter the colors, the more air has moved into that part of the lung.
The top graph shows the raw total impedance. The second graph shows several continuously calculated values. On the bottom are histograms of the dorsoventral distribution of the tidal variation.
All of the calculated values end up in a large table (partially seen in the lower right corner). Our primary outcome parameter was the center of ventilation (COV). This table can be exported from the EIT device and then imported into a database program.
Putting it all together
We created a database in Microsoft Access with several tables. One table holds the patient demographics, a second table contains all the measured parameters, and a third table holds all the results that were produced by our analyzer software. The database application allows us to filter, group and aggregate the data and prepare neatly aligned tables for statistical analysis.
The tables from Access were imported into the statistical software program SPSS. SPSS allows us to analyze the data and compare the groups in numerous ways. The results of these tests allowed us to differentiate with reasonable confidence between findings that were related to the difference in ventilation and findings that were caused by random chance.
Conducting a clinical study in a busy orthopedic operating room is quite demanding. It took us more than half a year to successfully recruit 30 patients, but owing to a great research team, a meticulous study plan, and invaluable help from our nurses, all patients who were enrolled successfully completed the study. Since we used the standard anesthesia equipment for data acquisition (except for the EIT device), turning the operating room into a research lab was accomplished in a matter of minutes. Thus we avoided delays, didn’t need much additional space, and kept our orthopedic surgeons happy.
Oliver C. Radke, MD, PhD, DEAA
Assistant Clinical Professor, San Francisco General Hospital, Department of Anesthesia & Perioperative Care University of California San Francisco, 1001 Potrero Ave, San Francisco, CA 94110
Senior Attending Anesthesiologist, Klinik und Poliklinik für Anästhesiologie und Intensivtherapie, Fetscherstr. 74, 01307 Dresden, Germany