Utility of Point of Care Ultrasound to evaluate gastric contents in patients taking glucagon-like peptide 1 (GLP-1) agonists
Faculty Information
Name:
Dr. Meghan Brennan
Email
mbrennan@anest.ufl.edu
Phone
(352) 273-6575
Faculty Department/Division
Anesthesiology
Project Information
This project is primarily:
Clinical
Research Project Description:
Background: Glucagon-like peptide 1 (GLP-1) is produced and released from the proglucagon cells in the small bowel and works by stimulating glucose- dependent insulin release from the pancreatic islets cells. Glucagon-like peptide 1 (GLP-1) agonists are a drug class that has been approved by the USA Food and Drug Administration (FDA) for medical management of diabetes and weight loss. This class of medications also slows gastric emptying and inhibits inappropriate post-meal glucagon release and the synthetic agonists also have a long half-life. As these medications have been recently FDA approved data is limited however a delay in gastric emptying may be associated with increased risk of aspiration with induction of general anesthesia. The American Society of Anesthesiologists have recently come out with guidelines for use of this medication in the perioperative period and have recommended gastric ultrasound as a method to evaluate stomach contents if these medications were not held preprocedure. Delayed gastric emptying may occur before a scheduled or emergent surgical procedure due to a number of factors including anxiety, pain, and medication administration.
Hypothesis/aim: The aim of our study is to evaluate gastric contents of patients taking a GLP-1 agonist who has been fasting for at least 8 hrs and compare it to an age matched control patient who has been fasting for 8hrs presenting for an elective surgical procedure.
Methods: We will plan to complete a point of care gastric ultrasound on patients taking GLP-1 agonists in outpatient clinics and compare gastric contents (full/empty) to a group of age-matched control patients who are presenting for surgical procedures.
Role of medical student: Medical students participating in the project will learn point of care gastric ultrasound, after completing necessary HIPAA and IRB training be responsible for patient enrollment, point of care gastric ultrasounds, data collection, data analysis, abstract preparation.
Does this project have an international component or travel?
No
Characterizing Intraoperative Blood Pressure Data Streams and Examining Relationship with Surgical Outcomes: Results from a Study of Adults 65 Years and Older Electing Surgery
Name:
Dr. John Heinbockel
Email
jheinbockel@anest.ufl.edu
Phone
(352) 299-8580
Faculty Department/Division
Anesthesiology
This project is primarily:
Clinical
Research Project Description:
Introduction:
Intraoperative hypotension (IOH) has been associated with adverse post-operative outcomes, including increased hospital length of stay (LOS).1-6 However, to date, there is no widely accepted definition of either IOH nor what defines an individual or population-based baseline blood pressure.7 Additionally, there has been no systematic characterization of perioperative blood pressure with a subsequent investigation of relationships with surgical outcomes. Recommendations on avoiding mean arterial pressures (MAP) less than 65 mmHg have been widely circulated and adopted.8 The widespread adoption of electronic medical records (EMR) in the last decade and subsequent capture of perioperative measures on a large scale makes a systematic study of hemodynamic data streams possible. Our objectives for the study are to: 1) characterize blood pressure (BP) data streams, 2) explore the relationship of blood pressure characteristics with surgical outcomes (LOS and one-year mortality), and 3) examine how hemodynamics change in critical intraoperative periods. We hypothesize that blood pressure characteristics are related to surgical outcomes and that they change between critical intraoperative periods.
Methods:
This IRB approved observational study will use de-identified patient data provided via an honest broker over two years (Jan 2018, through Dec 2022) to characterize the features of multiple blood pressure readings taken on each individual patient not influenced by outliers such as measurement artifacts.9 Intraoperative NIBP blood pressure data streams for each patient (i.e., MAP, systolic BP (SBP), diastolic BP (DBP)) will be characterized with quartiles (Q1-lower, Q2-median, Q3-upper) and IQR (interquartile range – a measure of variability). The proportion of NIBP blood pressure MAP values falling below 55, 65, and 85 mmHg will also be calculated. We will use the VARCLUS procedure in SAS version 9.4 (Cary, NC) to identify variable clusters and identify a set of oblique (i.e., non-collinear) BP characteristics. Surgical outcomes will be modelled as a function of blood pressure characteristics and preoperative patient characteristics using regression modelling. We will also use machine learning (ML) techniques to study the relationship of intraoperative blood pressure data streams and surgical outcomes.
Role of Medical Student:
- Assist with scoping review of intraoperative blood pressure management
- Learn and assist with statistical modeling and machine learning approaches to achieve study objectives.
Does this project have an international component or travel?
No
Investigation of Intraoperative Bleeding in Vascular Surgery Patients
Name:
Dr. Keith Howell
Email
khowell@anest.ufl.edu
Phone
(804) 399-3988
Faculty Department/Division
Anesthesiology
This project is primarily:
Clinical
Research Project Description:
Bleeding is an intraoperative and postoperative complication of surgery of concern. Management of acute coagulopathy and blood loss during major vascular procedures poses a significant challenge to anesthesiologists. The acute coagulopathy is multifactorial in origin with tissue injury and hypotension as the precipitating factors, followed by dilution, hypothermia, acidemia, hyperfibrinolysis and systemic inflammatory response, all acting as a self-perpetuating spiral of events. The problem is confounded by the high prevalence of antithrombotic agent use in these patients and intraoperative heparin administration [1]. A major issue impeding clinical studies investigating topical hemostatic agents was the lack of standardized definitions for intraoperative bleeding. To address the issue, Lewis et al. developed the Validated Intraoperative Bleeding Scale (VIBe Scale) [2]. The intent of the scale was to reduce patient risk, generate labeling claims, and allow comparisons among study results. The VIBe scale fulfilled criteria for a clinician-reported scale and may be useful for evaluating the efficacy of untested intraoperative hemostatic agents and for comparing the relative efficacy of two or more analogous agents [3]. VIBe has been validated in populations of both hepatopancreatobiliary and spine surgeons [4, 5]. However, there is a need to modify and validate VIBe in a population of vascular surgeons and anesthesiologists. As the first step in this line of investigation, it is desirable to characterize and quantify risk profiles for bleeding/transfusion events in vascular surgery patients. The study hypothesis is that vascular patient preoperative characteristics will be associated with intraoperative bleeding events and need for transfusion.
Methods:
With IRB approval, de-identified patient data provided via an honest broker) will be used characterize and quantify risk profiles for bleeding/transfusion events in vascular surgery via regression modeling and machine learning (ML) techniques. Planning the study to validate the VIBe Scale in a population of vascular surgeons and anesthesiologists will follow the methods of Tan et al. [5].
Role of Medical Student:
1) Assist with scoping review of factors associated with intraoperative bleeding in vascular surgery patients.
2) Learn and assist with statistical modeling and machine learning approaches to characterize and quantify risk profiles for bleeding/transfusion events in vascular surgery patients.
3) Participate in the design of a study to validate the Validated Intraoperative Bleeding Scale (VIBe Scale) in a population of vascular surgeons and anesthesiologists.
References
[1] Chee, Y.E., Liu, S.E. and Irwin, M.G., 2016. Management of bleeding in vascular surgery. BJA: British Journal of Anaesthesia, 117(suppl_2), pp.ii85-ii94.
[2] Lewis, K.M., Li, Q., Jones, D.S., Corrales, J.D., Du, H., Spiess, P.E., Menzo, E.L. and DeAnda Jr, A., 2017. Development and validation of an intraoperative bleeding severity scale for use in clinical studies of hemostatic agents. Surgery, 161(3), pp.771-781.
[3] Ramia, J.M., Aparicio-López, D., Asencio-Pascual, J.M., Blanco-Fernández, G., Cugat-Andorrá, E., Gómez-Bravo, M.Á., López-Ben, S., Martín-Pérez, E., Sabater, L. and Serradilla-Martín, M., 2022.
[4] Applicability and reproducibility of the validated intraoperative bleeding severity scale (VIBe scale) in liver surgery: A multicenter study. Surgery, 172(4), pp.1141-1146.
Sciubba, D.M., Khanna, N., Pennington, Z. and Singh, R.K., 2022. VIBe Scale: Validation of the Intraoperative Bleeding Severity Scale by Spine Surgeons. International Journal of Spine Surgery, 16(4), pp.740-747.
[5] Tan, E.K., Mayya, R., Kruger, D., Siriwardena, A.K. and Goh, B.K., 2023. Validation of VIBe bleeding scale amongst hepatopancreatobiliary surgeons: results from an IHPBA survey. HPB.
Does this project have an international component or travel?
No
Developing a graph model to predict patient outcomes after surgery
Faculty Information
Name:
Dr. Patrick Tighe
Email
ptighe@ufl.edu
Phone
(352) 273-7844
Faculty Department/Division
Anesthesiology
This project is primarily:
CQI
Research Project Description:
Background
The project is grounded in the clinical need to predict post-surgical complications, which remain a significant challenge in healthcare. Traditional prediction models may not fully capture the complex, non-linear interdependencies between patient characteristics and outcomes. Graph models offer a promising alternative by representing and analyzing the intricate relationships within patient data.
Hypothesis
The central hypothesis is that a graph-based model can more accurately predict patient outcomes after surgery compared to traditional statistical models. This is based on the assumption that graph models are better suited to capture the multifaceted relationships and interactions between patient demographics, medical history, and provider information.
Methods
The methodology involves several distinct phases:
Data Collection and Preparation: Utilizing an 11-year dataset of operative patients, the data will be formatted to suit graph model requirements.
Graph Model Construction: Applying machine learning techniques, including feature engineering and graph neural networks, to construct the predictive model.
Model Training and Validation: Training the model on a portion of the data and validating its predictive power on a separate test set.
Model Deployment: Implementing the validated model in a test environment to predict real-world patient outcomes post-surgery.
Role of Medical Student
A medical student involved in this project would likely assist in data preparation, participate in the model development process, and could be instrumental in interpreting the medical relevance of the model’s findings. They may also play a role in the clinical implementation and evaluation of the model.
Relevant Publications
The foundational literature includes works by Zhou (2018) and Lu & Uddin (2023), which review the methods and applications of graph neural networks and discuss disease prediction using graph machine learning from electronic health data.
Potential Impact
The successful development of a graph model for predicting post-operative outcomes could significantly enhance patient safety by identifying high-risk individuals and informing personalized care plans to mitigate potential complications.
Additional Considerations
Key challenges include ensuring high data quality, achieving model interpretability for clinical use, and ensuring the model’s generalizability across diverse patient populations and clinical settings.
Conclusion
The initiative has the potential to transform patient care by providing a more nuanced understanding of post-operative risks, thereby enabling more effective and targeted interventions to improve surgical outcomes.
Zhou, J. (2018, December 20). Graph Neural Networks: A Review of Methods and Applications. arXiv.org. https://arxiv.org/abs/1812.08434
Lu, H., & Uddin, S. (2023). Disease Prediction Using Graph Machine Learning Based on Electronic Health data: A review of Approaches and Trends. Healthcare, 11(7), 1031. https://doi.org/10.3390/healthcare11071031
Does this project have an international component or travel?
No