Ken B. Johnson
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Research Interests
Ken B. Johnson has a total of 9 co-authors. Here are the top co-authors by number of shared publications:
Cris D. LaPierre
Departments of Anesthesiology and Biomedical Engineering, University of Utah, Salt Laksse City, Utah, United States
1 shared publicationDhanesh K. Gupta
† Department of Anesthesiology, The Feinberg School of Medicine, Northwestern University, Chicago, Illinois, United States
1 shared publicationDiane Tyler
Departments of Anesthesiology and Biomedical Engineering, University of Utah, Salt Laksse City, Utah, United States
1 shared publicationDwayne R. Westenskow
Departments of Anesthesiology and Biomedical Engineering, University of Utah, Salt Laksse City, Utah, United States
1 shared publicationJulia L. White
Departments of Anesthesiology and Biomedical Engineering, University of Utah, Salt Laksse City, Utah, United States
1 shared publicationNathan L. Pace
Departments of Anesthesiology and Biomedical Engineering, University of Utah, Salt Laksse City, Utah, United States
1 shared publication
Latest Publications
- 1 August 2010
- Published by Wolters Kluwer Health in Anesthesia & Analgesia
- Vol. 111 (2) , 387-394
An Evaluation of Remifentanil-Sevoflurane Response Surface Models in Patients Emerging from Anesthesia
E from anesthesia and a response to tibial pressure when patients required analgesics in the recovery room. We hypothesized that model predictions would be consistent with observed responses. We also hypothesized that under non-steady-state conditions, accounting for the lag time between sevoflurane effect-site concentration (Ce) and end-tidal (ET) concentration would improve predictions. METHODS: Twenty patients received a sevoflurane, remifentanil, and fentanyl anesthetic. Two model predictions of responsiveness were recorded at emergence: an ET-based and a Ce-based prediction. Similarly, 2 predictions of a response to noxious stimuli were recorded when patients first required analgesics in the recovery room. Model predictions were compared with observations with graphical and temporal analyses. RESULTS: While patients were anesthetized, model predictions indicated a high likelihood that patients would be unresponsive (≥99%). However, after termination of the anesthetic, models exhibited a wide range of predictions at emergence (1%–97%). Although wide, the Ce-based predictions of responsiveness were better distributed over a percentage ranking of observations than the ET-based predictions. For the ET-based model, 45% of the patients awoke within 2 min of the 50% model predicted probability of unresponsiveness and 65% awoke within 4 min. For the Ce-based model, 45% of the patients awoke within 1 min of the 50% model predicted probability of unresponsiveness and 85% awoke within 3.2 min. Predictions of a response to a painful stimulus in the recovery room were similar for the Ce- and ET-based models. DISCUSSION: Results confirmed, in part, our study hypothesis; accounting for the lag time between Ce and ET sevoflurane concentrations improved model predictions of responsiveness but had no effect on predicting a response to a noxious stimulus in the recovery room. These models may be useful in predicting events of clinical interest but large-scale evaluations with numerous patients are needed to better characterize model performance....
- 1 August 2010
- Published by Wolters Kluwer Health in Anesthesia & Analgesia
- Vol. 111 (2) , 387-394
An Evaluation of Remifentanil-Sevoflurane Response Surface Models in Patients Emerging from Anesthesia
E from anesthesia and a response to tibial pressure when patients required analgesics in the recovery room. We hypothesized that model predictions would be consistent with observed responses. We also hypothesized that under non-steady-state conditions, accounting for the lag time between sevoflurane effect-site concentration (Ce) and end-tidal (ET) concentration would improve predictions. METHODS: Twenty patients received a sevoflurane, remifentanil, and fentanyl anesthetic. Two model predictions of responsiveness were recorded at emergence: an ET-based and a Ce-based prediction. Similarly, 2 predictions of a response to noxious stimuli were recorded when patients first required analgesics in the recovery room. Model predictions were compared with observations with graphical and temporal analyses. RESULTS: While patients were anesthetized, model predictions indicated a high likelihood that patients would be unresponsive (≥99%). However, after termination of the anesthetic, models exhibited a wide range of predictions at emergence (1%–97%). Although wide, the Ce-based predictions of responsiveness were better distributed over a percentage ranking of observations than the ET-based predictions. For the ET-based model, 45% of the patients awoke within 2 min of the 50% model predicted probability of unresponsiveness and 65% awoke within 4 min. For the Ce-based model, 45% of the patients awoke within 1 min of the 50% model predicted probability of unresponsiveness and 85% awoke within 3.2 min. Predictions of a response to a painful stimulus in the recovery room were similar for the Ce- and ET-based models. DISCUSSION: Results confirmed, in part, our study hypothesis; accounting for the lag time between Ce and ET sevoflurane concentrations improved model predictions of responsiveness but had no effect on predicting a response to a noxious stimulus in the recovery room. These models may be useful in predicting events of clinical interest but large-scale evaluations with numerous patients are needed to better characterize model performance....