Early extubation after coronary artery bypass graft surgery.
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Coronary Artery Bypass↗
Biomedical subjects
R T Geer
Publications and source records attributed to R T Geer.
A portable, low cost ventilation system for transportation of patients with severe acute respiratory failure.
As part of the development of a life support stretcher for transportation of critically ill patients, a portable ventilation system was developed. This system was used successfully during transportation of 6 of 11 patients who required ventilatory assistance and who were being considered for extracorporeal membrane oxygenator support. Immediately after transportation, PaCO2 values were significantly lower (p less than 0.05) in patients ventilated with this system, when compared to PaCO2 values of the remaining 5 patients in whom ventilation was assisted with a 2-liter anesthesia bag (PaCO2 = 58.7 +/- 3.6). This system offers significant advantages over other presently manufactured systems, including low cost, portability, and efficiency in terms of oxygen utilization. (Manual ventilation is supplied so that no auxiliary electrical power supply is necessary.) Positive end-expiratory pressure (PEEP) can be varied by 2.5 cm H2O increments using a commercially available, weighted ball valve. In addition, it has been useful for transporting patients with acute respiratory failure within the hospital for therapeutic maneuvers or diagnostic studies.
Carbon Dioxide↗
A comparison of human and machine-based predictions of successful weaning from mechanical ventilation.
PURPOSE: To evaluate the ability of an appropriately trained neural network to correctly interpret a set of weaning parameters to predict the liberation of a patient from mechanical ventilation, and to contrast these predictions with those of human experts restricted to the same limited set of physiologic data. METHODS: For each set of weaning parameters, a prediction was made by multiple realizations of a neural network and six expert volunteers. RESULTS: The percentage of correct predictions made by the neural network when the decision threshold was set to 0.5 (range 0-1) was 83.3 +/- 4.2 (mean +/- SD) and that for the experts was 83.3 +/- 4.7. Predictions by the network when the threshold was 0.5 had a sensitivity of 0.83 and a specificity of 0.84, compared with 0.90 and 0.77, respectively, for the experts. However, sensitivity and specificity comparable to those of the human experts could be obtained by adjusting the decision threshold of the network predictor so that only the most clearly ventilator-dependent patients would not be given a trial of extubation. CONCLUSION: When both are restricted to the same limited set of patient data, appropriately trained neural networks can be as effective as human experts in predicting whether weaning from mechanical ventilation will be successful.
Decision Making, Computer-Assisted↗