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POSH Program Research

POSH Program Research. CALM. NOSH. STEPS. DREAM. PROMISE. IOOO. Mitchell T. Heflin, MD, MHS Sandhya Lagoo-Deenadayalan, MD, PhD Shelley McDonald, DO, PhD November 30, 2018. Project CALM Confusion Avoidance Led by Music.

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POSH Program Research

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  1. POSH ProgramResearch CALM NOSH STEPS DREAM PROMISE IOOO Mitchell T. Heflin, MD, MHS Sandhya Lagoo-Deenadayalan, MD, PhD Shelley McDonald, DO, PhD November 30, 2018

  2. Project CALM Confusion Avoidance Led by Music • An inexpensive, patient-centered tool to optimize postoperative pain and anxiety. • An intervention that can be easily replicated at a variety of hospital systems. Neema Sharda, MD Bach to the Basics: Implementation and Impact of a Postoperative, Inpatient Personalized Music Program for Older Adults, Neema Sharda et al; Journal of PeriAnesthesia Nursing, 2018

  3. Nutritional (High Protein) Perihabilitation in Older Veterans Undergoing Surgery (CDA-2) Aim 1: Within VA POSH population: Select appropriate nutrition screening and assessment tools Employ these tools to characterize malnutrition and prevalence and severity Establish cut-off values associated with malnutrition Aim 2: In malnourished VA POSH patients anticipating abdominal surgery we will: Conduct a 6-week pilot trial of enhanced protein supplementation Kathryn Starr, PhD, RD Durham VA Medical Center

  4. STEPSPhysical Activity Trackers: Promising Tools to Promote Resilience in Older Surgical Patients Miriam C. Morey, PhD and Kenneth Manning. Morey, MC et al, Journal of Surgery , 2018

  5. Use of the Duke Anesthesia Resistance Scale (DARS) to Predict Postoperative Delirium among Older Adults Undergoing Elective Surgery PI- Miles Berger, MD PhD,

  6. Preoperative Cognitive Impairment As a Predictor of Postoperative Outcomes in a Collaborative Care Model Zietlow, Kahli, et al. Journal of the American Geriatrics Society (2018). Kahli Zietlow., MD

  7. The Correlation of Cognitive Performance and Capacity to Consent for Elective Surgery • Describe prevalence of cognitive impairment, incapacity in older adults presenting for elective surgery • Explore relationship between cognitive performance and capacity • Identify brief screening tools that can be used clinically to identify patients at highest risk for incapacity

  8. Exploring Reasons for Postponement of Surgery

  9. POSH-DREAMOptimized Collection of Delirium Risk Prediction & Management Data Aim: Systematic electronic point-of-contact pre-surgical and post-operative data capture and cognitive screening to improve delirium risk prediction and detection. Current Receivables & Outcomes: 1.) Proper delirium screening methods now operational in Durham VAMC SICU (e.g., 3D-CAM or CAM-ICU). 2.) Shift from SLUMS to more MoCA cognitive screen with administration via paper or iPad. 3.) Improved patient-reported symptoms and functional limitations via NIH PROMIS system. 4.) Completion of POSH-DREAM REDCap database with iPad point-of-contact pre-/post-op data entry. Jeff Browndyke, Ph.D Mitch Heflin, MD Funded through GRECC Medical Care (160) Atilio Barbeito, MD.

  10. POSH-DREAM Data Collection Points • Demographics / RN Data • Baseline Frailty Variables • Baseline Physiological & Sensory Variables • Postsurgical Cognition & Delirium • 3D-CAM (or CAM-ICU) • NIH Flanker Task • Patient Symptom & Functioning Self-report • PROMIS Questionnaires • Presurgical CognitionMoCA, • 3D-CAM Inattention • NIH Flanker Task Fully integrated REDCap Database w/ iPad Point-of-Contact Data Capture

  11. PROMISE Perioperative  Risk  Optimization with  Machine learning for an  Improved  Surgical  Experience Elizabeth Lorenzi, Kristin Corey, Mark Sendak, SehjKashyap, Krista Whalen, Shelley McDonald, MadhavSwaminathan, Annemarie Thompson, Katherine Heller, Mitch Heflin and Sandhya Lagoo-Deenadayalan Funded by Duke Institute for Health innovation (DIHI)

  12. Model Performance C-statistics (calculated on a held-out test set of 10,000 encounters) Both models achieved strong predictive parameters. • PROMISE Model: 0.78-0.90 • POSH Model: 0.68-0.87 PYTHIA: An Automated Surgical Outcomes Data Pipeline and Prediction Engine Using Machine Learning on EHR Data Repository to Identify High-Risk Surgical Patients., Kristin Corey et al, Plos Med, 2018

  13. 1000 PATIENT PROJECT • Cross-departmental initiative for comprehensive biospecimen collection in patients undergoing surgery at DUMC focused on perioperative biology • Goals • Collect pilot data that will support hypothesis-generation and will drive: • Future directions for discovery • Future opportunities for funding • foster intra- and inter-departmental collaboration, partnerships Shelley McDonald, DO, PhD Shelley Hwang, MD

  14. Questions? • Mitch Heflin mitchell.heflin@va.gov or mitchell.heflin@duke.edu • Sandhya Lagoo-Deenadayalan sandhya.lagoodeenada@duke.edu • Shelley McDonald shelley.mcdonald@duke.edu

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