Tom A.D. Stone

All Publications

• Intraoperative Electroencephalogram Alpha Power Associated with Mortality: Reply. Anesthesiology Mather, R. V., Nipp, R. D., Balanza, G., Stone, T. A., Gutierrez, R., Raje, P., Higuchi, M., Liu, R., Mercado, L. A., Bittner, E. A., Kunitake, H., Purdon, P. L. 2025; 143 (5): 1425-1427

  View details for DOI 10.1097/ALN.0000000000005676

  View details for PubMedID 41085317

• The Effects of Timing of Intraoperative Opioid Administration on Postoperative Pain and Opioid Use Outcomes Liu, R., Gutierrez, R., Mather, R., Stone, T., Bittner, E., Purdon, P. LIPPINCOTT WILLIAMS & WILKINS. 2025: 734-738

  View details for Web of Science ID 001551889100285

• Intraoperative Frontal EEG Alpha Power is Associated with Postoperative Mortality and Other Adverse Outcomes Mather, R., Villegas, G., Stone, T., Gutierrez, R., Raje, P., Higuchi, M., Liu, R., Mercado, L., Bittner, E., Nipp, R., Kunitake, H., Purdon, P. LIPPINCOTT WILLIAMS & WILKINS. 2025: 554-558

  View details for Web of Science ID 001551889100215

• Electroencephalographic (EEG) Characteristics in Children Undergoing Sevoflurane Anesthesia: Comparing EEG-Guided Anesthesia vs Standard Care Bong, C., Villegas, G., Stone, T., Gutierrez, R., Liang, S., Allen, J., Purdon, P. LIPPINCOTT WILLIAMS & WILKINS. 2024: 677

  View details for Web of Science ID 001349531300264

• Development and multicentre validation of the FLEX score: personalised preoperative surgical risk prediction using attention-based ICD-10 and Current Procedural Terminology set embeddings. British journal of anaesthesia Liu, R., Stone, T. A., Raje, P., Mather, R. V., Santa Cruz Mercado, L. A., Bharadwaj, K., Johnson, J., Higuchi, M., Nipp, R. D., Kunitake, H., Purdon, P. L. 2024

  Abstract

  BACKGROUND: Preoperative knowledge of surgical risks can improve perioperative care and patient outcomes. However, assessments requiring clinician examination of patients or manual chart review can be too burdensome for routine use.METHODS: We conducted a multicentre retrospective study of 243 479 adult noncardiac surgical patients at four hospitals within the Mass General Brigham (MGB) system in the USA. We developed a machine learning method using routinely collected coding and patient characteristics data from the electronic health record which predicts 30-day mortality, 30-day readmission, discharge to long-term care, and hospital length of stay.RESULTS: Our method, the Flexible Surgical Set Embedding (FLEX) score, achieved state-of-the-art performance to identify comorbidities that significantly contribute to the risk of each adverse outcome. The contributions of comorbidities are weighted based on patient-specific context, yielding personalised risk predictions. Understanding the significant drivers of risk of adverse outcomes for each patient can inform clinicians of potential targets for intervention.CONCLUSIONS: FLEX utilises information from a wider range of medical diagnostic and procedural codes than previously possible and can adapt to different coding practices to accurately predict adverse postoperative outcomes.

  View details for DOI 10.1016/j.bja.2023.11.039

  View details for PubMedID 38184474

• Development and prospective validation of postoperative pain prediction from preoperative EHR data using attention-based set embeddings. NPJ digital medicine Liu, R., Gutiérrez, R., Mather, R. V., Stone, T. A., Santa Cruz Mercado, L. A., Bharadwaj, K., Johnson, J., Das, P., Balanza, G., Uwanaka, E., Sydloski, J., Chen, A., Hagood, M., Bittner, E. A., Purdon, P. L. 2023; 6 (1): 209

  Abstract

  Preoperative knowledge of expected postoperative pain can help guide perioperative pain management and focus interventions on patients with the greatest risk of acute pain. However, current methods for predicting postoperative pain require patient and clinician input or laborious manual chart review and often do not achieve sufficient performance. We use routinely collected electronic health record data from a multicenter dataset of 234,274 adult non-cardiac surgical patients to develop a machine learning method which predicts maximum pain scores on the day of surgery and four subsequent days and validate this method in a prospective cohort. Our method, POPS, is fully automated and relies only on data available prior to surgery, allowing application in all patients scheduled for or considering surgery. Here we report that POPS achieves state-of-the-art performance and outperforms clinician predictions on all postoperative days when predicting maximum pain on the 0-10 NRS in prospective validation, though with degraded calibration. POPS is interpretable, identifying comorbidities that significantly contribute to postoperative pain based on patient-specific context, which can assist clinicians in mitigating cases of acute pain.

  View details for DOI 10.1038/s41746-023-00947-z

  View details for PubMedID 37973817

  View details for PubMedCentralID 8369227

• Association of Intraoperative Opioid Administration With Postoperative Pain and Opioid Use JAMA SURGERY Mercado, L. A., Liu, R., Bharadwaj, K. M., Johnson, J. J., Gutierrez, R., Das, P., Balanza, G., Deng, H., Pandit, A., Stone, T. A. D., Macdonald, T., Horgan, C., Tou, S., Houle, T. T., Bittner, E. A., Purdon, P. L. 2023

  View details for DOI 10.1001/jamasurg.2023.2009

  View details for Web of Science ID 001012129000006