Albert H. Tsai

All Publications

• Preparing for the Adult Cardiac Anesthesiology Subspecialty Certification: Recognition of Expertise in Cardiac Anesthesiology. Journal of cardiothoracic and vascular anesthesia Tsai, A., Faloye, A., Bodmer, N., Madhok, J., Nunes, S., Shook, D., Linganna, R. 2023

  View details for DOI 10.1053/j.jvca.2023.08.152

  View details for PubMedID 37805336

• Enhancing Telemedicine Perioperative Simulations Using Augmented Reality. The journal of education in perioperative medicine : JEPM Rama, A., Tsai, A. H., Caruso, T. J. 2023; 25 (3): E711

  View details for DOI 10.46374/volxxv_issue3_Rama

  View details for PubMedID 37720372

• Participant Perceptions of Augmented Reality Simulation for Cardiac Anesthesiology Training: A Prospective, Mixed-Methods Study. The journal of education in perioperative medicine : JEPM Tsai, A., Bodmer, N., Hong, T., Frackman, A., Hess, O., Khoury, M., Jackson, C., Caruso, T. J. 2023; 25 (3): E712

  Abstract

  Background: Simulations are a critical component of anesthesia education, and ways to broaden their delivery and accessibility should be studied. The primary aim was to characterize anesthesiology resident, fellow, and faculty experience with augmented reality (AR) simulations. The secondary aim was to explore the feasibility of quantifying performance using integrated eye-tracking technology.Methods: This was a prospective, mixed-methods study using qualitative thematic analysis of user feedback and quantitative analysis of gaze patterns. The study was conducted at a large academic medical center in Northern California. Participants included 7 anesthesiology residents, 6 cardiac anesthesiology fellows, and 5 cardiac anesthesiology attendings. Each subject participated in an AR simulation involving resuscitation of a patient with pericardial tamponade. Postsimulation interviews elicited user feedback, and eye-tracking data were analyzed for gaze duration and latency.Results: Thematic analysis revealed 5 domains of user experience: global assessment, spectrum of immersion, comparative assessment, operational potential, and human-technology interface. Participants reported a positive learning experience and cited AR technology's portability, flexibility, and cost-efficiency as qualities that may expand access to simulation training. Exploratory analyses of gaze patterns suggested that trainees had increased gaze duration of vital signs and gaze latency of malignant arrythmias compared with attendings. Limitations of the study include lack of a control group and underpowered statistical analyses of gaze data.Conclusions: This study suggests positive user perception of AR as a novel modality for medical simulation training. AR technology may increase exposure to simulation education and offer eye-tracking analyses of learner performance.

  View details for DOI 10.46374/volxxv_issue3_Tsai

  View details for PubMedID 37720369

• The Year in Graduate Medical Education: Selected Highlights From 2022. Journal of cardiothoracic and vascular anesthesia Patel, S. J., Notarianni, A. P., Martin, A. K., Tsai, A., Pulton, D. A., Linganna, R., Patel, P. A., Waldron, N. H., Nimma, S. R., Bodmer, N. J., Kothari, P., Jackson, E., Gupta, R. G., Roberts, M. L., Feinman, J. W. 2023

  View details for DOI 10.1053/j.jvca.2023.04.040

  View details for PubMedID 37210326

• It Takes a Village: A Narrative Review of Anesthesiology Mentorship. Anesthesiology clinics Tsai, A. H., Bodmer, N. J., Gupta, K., Caruso, T. J. 2022; 40 (2): 301-313

  Abstract

  Mentorships play a critical role in the development of physician careers and should be tailored within a structured, evidence-based mentoring program to ensure mutual benefit and avoidance of pitfalls. We offer a narrative review of the current literature and commentary on mentoring at the medical student, GME trainee, and early career faculty levels within anesthesiology, and propose a framework on which an effective mentoring program can be implemented.

  View details for DOI 10.1016/j.anclin.2022.01.005

  View details for PubMedID 35659402

• The Year in Graduate Medical Education: Selected Highlights From 2021. Journal of cardiothoracic and vascular anesthesia Patel, S. J., Patel, P. A., Martin, A. K., Tsai, A., Linganna, R. E., Ghofaily, L. A., Notarianni, A. P., Allen, W. L., Buric, D. M., Bodmer, N. J., Kothari, P., Jackson, E., Feinman, J. W. 2022

  Abstract

  This special article is the first in a planned annual series for the Journal of Cardiothoracic and Vascular Anesthesia that will highlight significant literature from the world of graduate medical education (GME) that was published over the past year. The major themes selected for this inaugural review are the educational value of simulation and training workshops, the expanding role of social media and other information technologies in GME and recruitment, the state of residency and fellowship training before the COVID-19 pandemic, and the inevitable effects COVID-19 has had on graduate medical education. The authors would like to thank the editorial board for allowing us to shine a light on a small subset of the writing and research produced in this field, so that educators may understand how best to educate and train the next generation of anesthesiologists.

  View details for DOI 10.1053/j.jvca.2022.05.013

  View details for PubMedID 35662516

• First lung and kidney multi-organ transplant following COVID-19 Infection. The Journal of heart and lung transplantation : the official publication of the International Society for Heart Transplantation Guenthart, B. A., Krishnan, A., Alassar, A., Madhok, J., Kakol, M., Miller, S., Cole, S. P., Rao, V. K., Acero, N. M., Hill, C. C., Cheung, C., Jackson, E. C., Feinstein, I., Tsai, A. H., Mooney, J. J., Pham, T., Elliott, I. A., Liou, D. Z., La Francesca, S., Shudo, Y., Hiesinger, W., MacArthur, J. W., Brar, N., Berry, G. J., McCarra, M. B., Desai, T. J., Dhillon, G. S., Woo, Y. J. 2021

  Abstract

  As the world responds to the global crisis of the COVID-19 pandemic an increasing number of patients are experiencing increased morbidity as a result of multi-organ involvement. Of these, a small proportion will progress to end-stage lung disease, become dialysis dependent, or both. Herein, we describe the first reported case of a successful combined lung and kidney transplantation in a patient with COVID-19. Lung transplantation, isolated or combined with other organs, is feasible and should be considered for select patients impacted by this deadly disease.

  View details for DOI 10.1016/j.healun.2021.02.015

  View details for PubMedID 34059432

• Management of Patients on Mechanical Circulatory Assist Devices During Noncardiac Surgery. International anesthesiology clinics Rao, V. K., Tsai, A. 2018; 56 (4): e1–e27

  View details for DOI 10.1097/AIA.0000000000000205

  View details for PubMedID 30204602

• Perioperative Management of Permanent Pacemakers (PPMs) and Automatic Implantable Cardioverter-Defibrillators (AICDs) Critical Events in Anesthesiology Tsai, A. edited by Pai Cole, S. Medscape Anesthesiology. 2017
• Irregular Respiration as a Marker of Wakefulness During Titration of CPAP SLEEP Ayappa, I., Norman, R. G., Whiting, D., Tsai, A. W., Anderson, F., Donnely, E., Silberstein, D. J., Rapoport, D. M. 2009; 32 (1): 99–104

  Abstract

  Regularity of respiration is characteristic of stable sleep without sleep disordered breathing. Appearance of respiratory irregularity may indicate onset of wakefulness. The present study examines whether one can detect transitions from sleep to wakefulness using only the CPAP flow signal and automate this recognition.Prospective study with blinded analysisSleep disorder center, academic institution.74 subjects with obstructive sleep apnealhypopnea syndrome (OSAHS) INTERVENTIONS: n/a.74 CPAP titration polysomnograms in patients with OSAHS were examined. First we visually identified characteristic patterns of ventilatory irregularity on the airflow signal and tested their relation to conventional detection of EEG defined wake or arousal. To automate recognition of sleep-wake transitions we then developed an artificial neural network (ANN) whose inputs were parameters derived exclusively from the airflow signal. This ANN was trained to identify the visually detected ventilatory irregularities. Finally, we prospectively determined the accuracy of the ANN detection of wake or arousal against EEG sleep/wake transitions. A visually identified irregular respiratory pattern (IrREG) was highly predictive of appearance of EEG wakefulness (Positive Predictive Value [PPV] = 0.89 to 0.98 across subjects). Furthermore, we were able to automate identification of this irregularity with an ANN which was highly predictive for wakefulness by EEG (PPV 0.66 to 0.86).Despite not detecting all wakefulness, the high positive predictive value suggests that analysis of the respiration signal alone may be a useful indicator of CNS state with potential utility in the control of CPAP in OSAHS. The present study demonstrates the feasibility of automating the detection of IrREG.

  View details for Web of Science ID 000262075600016

  View details for PubMedID 19189784

  View details for PubMedCentralID PMC2625330