Modification of Automated Cardiac Function Prediction Algorithms from ‘Non-Expert’ Echocardiographers

in Pediatric Patients

Alex Stolz, Steffanie Fisher, Rachel Bates, Matthias Görges, Brendan Smith, Kandice Mah, Katherine Taylor, Simon Whyte, Heng Gan

Purpose: Cardiac output (CO) is the volume of blood pumped by the heart per minute. It is an important consideration during anesthesia care, with low-CO states being associated with adverse health outcomes if improperly treated. Ejection fraction (EF) measures how efficiently your heart pumps blood out, and can be used to identify the cause of low CO thus dictating treatment. Echocardiography (ECHO) is a technique where ultrasound images of heart structures are taken to visualize its function, and analyzed to determine CO and EF. ECHO is typically performed only by specialist cardiologists, decreasing its accessibility. There is increasing demand on anesthesiologists to intra-operatively perform ECHOs to guide clinical decision-making. The assessment of CO and EF using ECHO is difficult however, especially for practitioners with limited experience in it.

Recently the ECHONet-Dynamic algorithm, a machine-learning model for automatically assessing CO and EF using uploaded ECHO images/videos, was developed. This offers the potential to be a clinical tool for anesthesiologists, but has not been clinically validated in pediatric populations using ECHO taken by practitioners inexperienced with ECHO.

Objective: To investigate the clinical usability of the Prediction Algorithm for measuring CO and EF in pediatric patients under anesthesia, given ECHO images taken by practitioners without ECHO expertise. Mean absolute error under 15% and maximum error under 30% between model and cardiologist estimates are established performance goals.

Methods: This cohort study will recruit from a convenience sample of patients 0-18 years undergoing general anesthesia, without hemodynamically significant heart defects. Recruited providers will take transthoracic ECHO images after anesthetic induction. These providers will come from a pool of doctors with limited ECHO experience. Images will be uploaded to the algorithm and resulting CO and EF measurements will be compared to cardiologist estimates, which are considered the Gold Standard. Model performance will be stratified along various patient metrics such as age.

Future Direction: If validated, the ECHONet-Dynamic Algorithm offers clinicians a new tool for identifying low-CO states and EF during general anesthesia, ultimately improving clinical decision-making. If performance thresholds are not met, the model will be refined by applying its machine-learning capabilities to the dataset.

Congratulations to Alexander on receiving a BC Children’s Hospital Research Institute Summer Studentship

SESSION #9 Poster #79

Zoe Kortje

Undergraduate Student, Queen’s University | Supervisor: Donna Lang

Watch In-Person:

Thursday, July 27 | 1:30 - 3:00 pm

Chieng Family Atrium, BCCHR

Assessing cardiovascular health via retinal imaging in chronically treated schizophrenia patients

Zoe Kortje, Donna Lang, Ava Grier

Background: Persons with schizophrenia spectrum disorders (SSD) are disproportionately impacted by cardiovascular disease (CVD), likely due to genetic, pharmacological and lifestyle factors. Most studies find that CVD mortality in this population reaches frequencies of 40-50%, which is significantly higher than the general population. Despite this, cardiovascular screening in patients with SSD is not sufficiently prioritized or standardized.

Retinal fundus imaging allows for the direct and non-invasive assessment of the cerebral vasculature; the retinal and cerebral small vessels share physiological properties and embryological origins. With the assistance of semi-automated computer programs, retinal images can be analyzed for isolated vascular markers (eg. microaneurysm) and early changes in blood vessel patency, diameter, and tortuosity. A wide range of these microvascular signs have been correlated with worsened outcomes in cardio- and cerebrovascular health. Retinal fundus imaging may therefore have the potential to act as a screening tool for cardiovascular risk in vulnerable individuals with SSD.

Methods: Data are currently being collected from inpatients with chronic schizophrenia and from matched healthy control volunteers. Data collection entails retinal imaging with a non-mydriatic fundus camera, a fitness assessment, standard bloodwork, and a neurocognitive assessment battery. Individual retinal deficits in each image are identified by an ophthalmologist and documented by research assistants using a qualitative rating scale. VAMPIRE, a vessel segmentation program, allows for quantitative data to be collected from the images.

Anticipated Results: Our team expects to expose an increased volume of retinal deficits in the schizophrenia patient group. Secondary to this, it is expected that increased retinal deficit scores will be positively correlated with BMI and blood pressure.

Implications: Results from this pilot study may facilitate the development of a rapid and tolerable method for detecting the risk of adverse cardio- and cerebrovascular events in a vulnerable patient population. Further, this study may provide more insight on the retinal vasculature and its relationship to cognition in individuals with SSD. Lastly, this study will allow for the practical implementation of our team’s qualitative retinal rating scale, which has been an ongoing project since 2022.

Congratulations to Zoe on receiving a BC Mental Health and Substance Use Services Summer Studentship

SESSION #9 Poster #80

Kyle Ma

Watch In-Person:

Thursday, July 27 | 1:30 - 3:00 pm

Chieng Family Atrium, BCCHR

Undergraduate Student, University of British Columbia | Supervisor: Jehannine Austin