Josef Brunner

Respiratory mechanics by least squares fitting in mechanically ventilated patients: Applications during paralysis and during pressure support ventilation

ObjectiveTo evaluate a least squares fitting technique for the purpose of measuring total respiratory compliance (Crs) and resistance (Rrs) in patients submitted to partial ventilatory support, without the need for esophageal pressure measurement.DesignProspective, randomized study.SettingA general ICU of a University Hospital.Patients11 patients in acute respiratory failure, intubated and assisted by pressure support ventilation (PSV).InterventionsPatients were ventilated at 4 different levels of pressure support. At the end of the study, they were paralyzed for diagnostic reasons and submitted to volume controlled ventilation (CMV).Measurements and resultsA least squares fitting (LSF) method was applied to measure Crs and Rrs at different levels of pressure support as well as in CMV. Crs and Rrs calculated by the LSF method were compared to reference values which were obtained in PSV by measurement of esophageal pressure, and in CMV by the application of the constant flow, end-inspiratory occlusion method. Inspiratory activity was measured by P0.1. In CMV, Crs and Rrs measured by the LSF method are close to quasistatic compliance (−1.5±1.5 ml/cmH2O) and to the mean value of minimum and maximum end-inspiratory resistance (+0.9±2.5 cmH2O/(l/s)). Applied during PSV, the LSF method leads to gross underestimation of Rrs (−10.4±2.3 cmH2O/(l/s)) and overestimation of Crs (+35.2±33 ml/cmH2O) whenever the set pressure support level is low and the activity of the respiratory muscles is high (P0.1 was 4.6±3.1 cmH2O). However, satisfactory estimations of Crs and Rrs by the LSF method were obtained at increased pressure support levels, resulting in a mean error of −0.4±6 ml/cmH2O and −2.8±1.5 cmH2O/(l/s), respectively. This condition was coincident with a P0.1 of 1.6±0.7 cmH2O.ConclusionThe LSF method allows non-invasive evaluation of respiratory mechanics during PSV, provided that a near-relaxation condition is obtained by means of an adequately increased pressure support level. The measurement of P0.1 may be helpful for titrating the pressure support in order to obtain the condition of near-relaxation.

Unfavorable mechanical effects of heat and moisture exchangers in ventilated patients

Objective: To investigate the mechanical effects of artificial noses. Setting: A general intensive care unit of a university hospital. Patients: 10 patients in pressure support ventilation for acute respir

Automatic selection of tidal volume, respiratory frequency and minute ventilation in intubated ICU patients as startup procedure for closed-loop controlled ventilation

Objective:Before a patient can be connected to a mechanical ventilator, the controls of the apparatus need to be set up appropriately. Today, this is done by the intensive care professional. With the advent of closed loop controlled mechanical ventilation, methods will be needed to select appropriate startup settings automatically. The objective of our study was to test such a computerized method which could eventually be used as a start-up procedure (first 5–10 minutes of ventilation) for closed-loop controlled ventilation.Design:Prospective Study.Settings:ICUs in two adult and one childrens hospital.Patients:25 critically ill adult patients (age≥15 y) and 17 critically ill children selected at random were studied.Interventions:To simulate ‘initial connection’, the patients were disconnected from their ventilator and transiently connected to a modified Hamilton AMADEUS ventilator for maximally one minute. During that time they were ventilated with a fixed and standardized breath pattern (Test Breaths) based on pressure controlled synchronized intermittent mandatory ventilation (PCSIMV).Measurements and main results:Measurements of airway flow, airway pressure and instantaneous CO2 concentration using a mainstream CO2 analyzer were made at the mouth during application of the Test-Breaths. Test-Breaths were analyzed in terms of tidal volume, expiratory time constant and series dead space. Using this data an initial ventilation pattern consisting of respiratory frequency and tidal volume was calculated. This ventilation pattern was compared to the one measured prior to the onset of the study using a two-tailed paired t-test. Additionally, it was compared to a conventional method for setting up ventilators. The computer-proposed ventilation pattern did not differ significantly from the actual pattern (p>0.05), while the conventional method did. However the scatter was large and in 6 cases deviations in the minute ventilation of more than 50% were observed.Conclusions:The analysis of standardized Test Breaths allows automatic determination of an initial ventilation pattern for intubated ICU patients. While this pattern does not seem to be superior to the one chosen by the conventional method, it is derived fully automatically and without need for manual patient data entry such as weight or height. This makes the method potentially useful as a startup procedure for closed-loop controlled ventilation.

Closed-loop control of airway occlusion pressure at 0.1 second (P0.1) applied to pressure-support ventilation: algorithm and application in intubated patients.

OBJECTIVE Airway occlusion pressure at 0.1 sec (P0.1) is an index of respiratory center output. During pressure-support ventilation, P0.1 correlates with the mechanical output of the inspiratory muscles and has an inverse relationship with the amount of pressure-support ventilation. Based on these observations, we designed a closed-loop control which, by automatically adjusting pressure-support ventilation, stabilizes P0.1, and hence patient inspiratory activity, at a desired target. The purpose of the study was to demonstrate the feasibility of the method, rather than its efficacy or even its influence on patient outcome. DESIGN Prospective, randomized trial. SETTING A general intensive care unit of a university hospital in Italy. PATIENTS Eight stable patients intubated and ventilated with pressure-support ventilation for acute respiratory failure. INTERVENTIONS Patients were transiently connected to a computer-controlled ventilator on which the algorithm for closed-loop control was implemented. The closed-loop control was based on breath by breath measurement of P0.1, and on comparison with a target set by the user. When actual P0.1 proved to be higher than the target value, the P0.1 controller automatically increased pressure-support ventilation, and decreased it when P0.1 proved to be lower than the target value. For safety, a volume controller was also implemented. Four P0.1 targets (1.5, 2.5, 3.5, and 4.5 cm H2O) were applied at random for 15 mins each. MEASUREMENTS AND MAIN RESULTS The closed-loop algorithm was able to control P0.1, with a difference from the set targets of 0.59 +/- 0.27 (SD) cm H2O. CONCLUSIONS The study shows that P0.1 can be automatically controlled by pressure-support ventilation adjustments with a computer. Inspiratory activity can thus be stabilized at a level prescribed by the physician.

The automatic selection of ventilation parameters during the initial phase of mechanical ventilation.

ObjectiveTo test a method that allows automatic set-up of the ventilator controls at the onset of ventilation.DesignProspective randomized crossover study.SettingICUs in one adult and one childrens hospital in Switzerland.PatientsThirty intubated stable, critically ill patients (20 adults and 10 children).InterventionsThe patients were ventilated during two 20-min periods using a modified Hamilton AMADEUS ventilator. During the control period the ventilator settings were chosen immediately prior to the study. During the other period individual settings were automatically determined by the ventilator (AutoInit).Measurements and resultsPressure, flow, and instantaneous CO2 concentration were measured at the airway opening. From these measurements, series dead space (VDS), expiratory time constant (RC), tidal volume (VT), total respiratory frequency (ftot), minute ventilation (MV), and maximal and mean airway pressure (Paw, max andPaw, mean) were calculated. Arterial blood gases were analyzed at the end of each period.Paw, max was significantly less with the AutoInit ventilator settings whileftot was significantly greater (P<0.05). The other values were not statistically significant.ConclusionsThe AutoInit ventilator settings, which were automatically derived, were acceptable for all patients for a period of 20 min and were not found to be inferior to the control ventilator settings. This makes the AutoInit method potentially useful as an automatic startup procedure for mechanical ventilation.

The Utah Anesthesia Workstation

A prototype anesthesia workstation has been developed to demonstrate the feasibility of a computer-assisted anesthesia workplace. The workstation provides a central display of information and aids the user in controlling and monitoring the anesthesia delivery system. The anesthesiologist interacts with the workstation through a Macintosh computer, which is easy for the clinician to understand and to use. Seventeen sensors and monitors transmit information from the anesthesia delivery system to the computer. The computer monitors this information using a set of rules, evaluated once each breath, to detect changes in the delivery system. If an event is detected, the computer alerts the anesthesiologist with a diagram, a text message, and an audible warning. A laboratory test of the monitoring system was performed to see if it properly identified 26 different critical events during simulated low flow and closed circuit anesthesia. Five hundred and eight-three of 660 simulated critical events (88%) were identified with the unique and correct message. On 35 occasions, multiple messages were displayed, including the correct one. Critical events were misidentified or not detected 42 times. Eight false positive alarms occurred during the 20 h of testing; all occurred as a result of baseline drift in a single transducer. These results demonstrate that a sophisticated monitoring system can reliably diagnose specific anesthesia machine failures.

Prototype ventilator and alarm algorithm for the NASA space station.

An alarm algorithm was developed to monitor the ventilator on the National Aeronautics and Space Administration space station. The algorithm automatically identifies and interprets critical events so that an untrained user can manage the mechanical ventilation of a critically injured crew member. The algorithm was tested in two healthy volunteers by simulating 260 critical events in each volunteer while the volunteer breathed via the ventilator. Thirteen critical events were induced eight times in random order, for the five different modes of ventilation. These events included various ventilator tubing disconnects, leaks, and occlusions, as well as power and gas supply failures. The algorithm identified the critical events and generated alarms in response to 99.2% (516 of 520, total) of the events. The alarm textual messages were correct 98% (505 of 516 messages) of the time. The alarm algorithm is an improvement over current alarms found on most ventilators because its alarm messages specifically identify failures in the patient breathing circuit or ventilator. The system may improve patient care by helping critical care personnel respond more rapidly and correctly to critical events.

Assessment of pulmonary mechanics in mechanical ventilation : Effects of imprecise breath detection, phase shift and noise

Objective. In mechanical ventilation, the assessment of pulmonary mechanics, mainly of total compliance (Crs), total resistance (Rrs), and intrinsic positive end-expiratory pressure (PEEPint), is clinically important. By using airway pressure (Paw) and flow (V′aw), the least squares fit (LSF) method allows the continuous calculation of these parameters. The objective of this work was to study the influence of imprecise breath detection, phase shift between airway pressure and flow signals, and noise on the determination of Crs, Rrs, and PEEPint. Methods. Paw and V′aw were mathematically simulated as well as recorded in mechanically ventilated patients. Crs, Rrs, and PEEPint were computed off-line using the LSF method. The boundaries of the breath data window and the phase relationship between Paw and V′aw signals were manipulated and noise was superimposed. Results. Both simulated and patient data gave similar results. Crs and Rrs were not sensitive to imprecise breath detection. If the first portion of the breath was missed, the mean relative error on PEEPint was 20% or 53% when the exact beginning of inspiration was missed by 0.1 or 0.3 sec, respectively. Paw lag of 66 ms with respect to V′aw yielded a relative error of −15 ± 4% (mean ± SD) for Rrs, −5 ± 2% for Crs, and +13 ± 16% for PEEPint. Paw lead of 66 ms with respect to V′aw yielded a relative error of +5 ± 4% for Rrs, +7 ± 3% for Crs, and +14 ± 18% for PEEPint. Noise had very little impact on the accuracy of Crs, Rrs, and PEEPint. Conclusions. We conclude that the LSF method allows the assessment of Crs, Rrs, and PEEPint even with high levels of noise in patients with normal lungs provided that Paw and V′aw signals are precisely synchronised and a reliable breath detection algorithm is used.

Computerized system for mechanical ventilation.

Drs. Tehrani and Roum recently described a computerized system for mechanical ventilation [1]. In this paper, the authors allege that ASV ‘‘... is a mode of a patented technology under license of US Patent No. 4,986,268’’, and further ‘‘...ASV closed-loop system which was originally described in 1991’’ [1]. We would like to clarify that ASV was invented by Hamilton Medical and not by Dr. Tehrani. Adaptive Support Ventilation (ASV) was developed by a team of medical doctors together with Hamilton Medical and was introduced 1997 in a commercially available device. A couple of years later, Dr. Tehrani brought a patent infringement suit against Hamilton Medical alleging that ASV would infringe on her U.S. Patent no. 4,986,268. We were and still are convinced that ASV does not infringe on the patent. However, a District Court found Hamilton Medical guilty of infringement. Later, the Court of Appeals vacated this ruling finding numerous errors in the District Court’s claim construction [2]. The Court of Appeals decided that another trial should be held to investigate a few open questions and to again decide on possible infringement. To avoid the high litigation cost, Hamilton Medical decided to enter into negotiations with Dr. Tehrani to find a settlement out of court. Such settlement was achieved. While the settlement Agreement is confidential, the parties are allowed to disclose that Hamilton Medical is a licensee of the U.S. Patent no. 4,986,268. However, we would like to point out that the settlement does not imply that ASV is based on US Patent No. 4,986,268. While it is correct that ASV and the patent both describe methods to automate certain aspects of ventilation, they have little in common. The reader is referred to the literature [3, 4] to decide independently. The settlement agreement was made to end the dispute between Hamilton Medical and Dr. Tehrani and does not imply that ASV is based on Dr. Tehrani’s patent. Drs. Tehrani and Roum further contend that FLEX incorporates the features of ASV [1]. Based on the description given in their paper, we disagree for the following reasons: Tehrani and Roum clearly state that ‘‘Unlike most previous systems, this one is designed for use in a wide range of ventilatory modes, including weaning’’, and further into the text ‘‘The minimum acceptable breathing rate is 45% of the optimal rate in the assist mode and 75% of that rate in the pressure support mode’’ [1]. In contrast, ASV is a Brunner JX, Iotti GA. Letter to the editor. J Clin Monit Comput 2008; 22:385–386

A ventilator and ventilation supervisor for the NASA Space Station

An expert ventilator alarm system was developed for the NASA Space Station. The alarm system identifies critical events and delivers diagnostic textual messages in real time. Using simulation, 520 critical events were created. The expert system correctly identified 98% of the events. By providing specific alarm text messages to identify specific events, the system can improve patient care and help the patient care personnel respond more correctly to critical events.<<ETX>>