Bio

Clinical Focus


  • Anesthesia
  • Cardiothoracic Anesthesia

Academic Appointments


  • Clinical Assistant Professor, Anesthesiology, Perioperative and Pain Medicine

Administrative Appointments


  • Clerkship Director, ANES 307A - Stanford Hospital Cardiovascular Anesthesia Clerkship (SUMC) (2019 - Present)

Honors & Awards


  • Fellowship Clinical Teaching Award, Adult Cardiothoracic Anesthesiology Fellowship (2019)
  • Chief Resident, Department of Anesthesiology and Critical Care, University of Pennsylvania Health System (2016)

Boards, Advisory Committees, Professional Organizations


  • Member, Society of Cardiac Anesthesiologists (2016 - Present)

Professional Education


  • Fellowship, Stanford University Hospital, Cardiothoracic Anesthesiology (2017)
  • Residency, Hospital of the University of Pennsylvania, Anesthesiology (2016)
  • MD, University of Pennsylvania School of Medicine (2012)
  • BA, University of Pennsylvania (2008)

Publications

All Publications


  • 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

  • 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

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