US healthcare is undergoing a rapid evolution with a renewed focus on improving healthcare at a reduced cost. Surgery, always a high-risk undertaking, has been at the nexus of this evolution. Even though the cost of surgical complications in the United States are estimated to be $87 BN dollars, current surgical risk assessment is complicated and non-uniform: each patient is unique, and identifying all of the factors that may affect surgical outcome is difficult. Established methods are limited in scope and require large patient populations as well as significant time to execute. Moreover, current risk-assessment methods incorporate the subjective assessments of clinicians, cannot be applied to individual patients, and do not take into account the dynamic nature of changing patient populations and practice patterns in health care systems. Recently, data collection during surgery has proven to be a rich resource to begin systematic evaluation of factors that influence the outcome of non-cardiac surgery, allowing researchers and clinicians to map complex physiological factors over the perioperative period and identify common trajectories taken by patients from admission to outcome.
Technology Description
A novel profiling system, the Triple Variable Index (TVI), will allow clinicians to predict outcomes and stage timely interventions by integrating data representing cardiovascular and neurologic system functions moment-to-moment for individual patients as they respond to anesthetics and surgery. The TVI system uses mean arterial pressure (MAP), Bispectral Index (BIS), and minimum alveolar concentration (MAC) to reveal a distinct pattern of organ system function. These measurements show the tightly regulated functions of multiple organ systems that work in concert to maintain homeostatic balance, and mapping their function over time yields connections to postoperative outcomes. The TVI method provides rapid analysis on an individual basis for a wide population of surgical patients. Surgical cases can be separated into distinct clusters combining Triple Variable Indexing and K-means cluster analysis based on one of three possible physiological states during surgery represented by an elevated, mixed, or depressed TVI value; TVI depression has been shown to correlate with postoperative mortality. Clinicians will be able to identify points of clinical interventions in a timely manner, decreasing patient risk of postoperative death as well as reducing costs for patients and care providers alike.
Advantages
* MAP, AMC, and BIS data are easy to access and are measured via non-invasive procedures
* A TVI profile can be generated for any patient where MAP, MAC, and BIS data are available; BIS values may be inferred
* TVI is mapped moment-to-moment, allowing real-time analysis and ability to stage time-sensitive surgical interventions
* Does not rely on the subjective assessment of clinicians and is patient-specific
Applications
* Real-time risk prediction for perioperative outcomes in patients undergoing general anesthesia
* Allows clinicians to stage timely, effective interventions post-surgery
Stage of Development
Prototype
IP Status
US 2019-0046122 A1
Relevant publications
The triple variable index combines information generated over time from common monitoring variables to identify patients expressing distinct patterns of intraoperative physiology.A. Murat Kaynar, MD, MPH
Professor, Departments of Critical Care Medicine, Anesthesiology, and Perioperative Medicine; Program Director, Anesthesiology Critical Care medicine Fellowship; Co-Medical Director, Neurovascular Intensive Care Unit, UPMC Presbyterian
Dr. Kaynar is interested in clinical care medicine and long-term outcomes following sepsis.
Education
* MA, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Critical Care Medicine Fellowship
* MA, Brigham and Women's Hospital, Harvard Medical School, Boston Anesthesiology Residency
* Cardiothoracic Surgery Residency, University of Istanbul
* Internship in Surgery, Columbia University, New York, NY
* MD, Cerrahpaşa Medical School, University of Istanbul
Publications
* The triple variable index combines information generated over time from common monitoring variables to identify patients expressing distinct patterns of intraoperative physiology. Schnetz MP, Hochheiser HS, Danks DJ, Landsittel DP, Vogt KM, Ibinson JW, Whitehurst SL, McDermott SP, Duque MG, Kaynar AM. BMC Med Res Methodol. 2019 Jan 14;19(1):17. doi: 10.1186/s12874-019-0660-9.
* The Basic Science and Molecular Mechanisms of Lung Injury and Acute Respiratory Distress Syndrome. Aranda-Valderrama P, Kaynar AM. Int Anesthesiol Clin. 2018 Winter;56(1):1-25. doi: 10.1097/AIA.0000000000000177. Review. No abstract available. PMID: 29227309
* Using Supervised Machine Learning to Classify Real Alerts and Artifact in Online Multisignal Vital Sign Monitoring Data. Chen L, Dubrawski A, Wang D, Fiterau M, Guillame-Bert M, Bose E, Kaynar AM, Wallace DJ, Guttendorf J, Clermont G, Pinsky MR, Hravnak M. Crit Care Med. 2016 Jul;44(7):e456-63. doi: 10.1097/CCM.0000000000001660. PMID:26992068
Michael Schnetz, MD, PhD
PGY-4 Resident, T32 Postdoctoral Scholar, University of Pittsburgh
Dr. Schnetz’s abstract was recently selected as a Kosaka Best of Meeting Top Finalist at the 2019 International Anesthesia Research Society Annual meeting and International Science Symposium in addition to winning an Association of University Anesthesiologists Resident Travel Award.
Education
* MD, Case Western Reserve University School of Medicine
* T32 Postdoctoral Research Fellowship, University of Pittsburgh Department of Anesthesiology and Perioperative Medicine
Publications
* CHD7 targets active gene enhancer elements to modulate ES cell-specific gene expression. Schnetz MP, Handoko L, Akhtar-Zaidi B, Bartels CF, Pereira CF, Fisher AG, Adams DJ, Flicek P, Crawford GE, Laframboise T, Tesar P, Wei CL, Scacheri PC. PLoS Genet. 2010 Jul 15;6(7):e1001023. doi: 10.1371/journal.pgen.1001023. PMID: 20657823
* CHD7 functions in the nucleolus as a positive regulator of ribosomal RNA biogenesis. Zentner GE, Hurd EA, Schnetz MP, Handoko L, Wang C, Wang Z, Wei C, Tesar PJ, Hatzoglou M, Martin DM, Scacheri PC. Hum Mol Genet. 2010 Sep 15;19(18):3491-501. doi: 10.1093/hmg/ddq265. Epub 2010 Jun 29. PMID: 20591827
* Phospholipase C-gamma1 is involved in signaling the activation by high NaCl of the osmoprotective transcription factor TonEBP/OREBP. Irarrazabal CE, Gallazzini M, Schnetz MP, Kunin M, Simons BL, Williams CK, Burg MB, Ferraris JD. Proc Natl Acad Sci U S A. 2010 Jan 12;107(2):906-11. doi: 10.1073/pnas.0913415107. Epub 2009 Dec 22. PMID: 20080774
