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American Heart Association

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Final ID: 4144283

A Novel EMR-Based Algorithm with the Virtual Echocardiography Screening Tool (VEST) to Screen Patients for Pulmonary Arterial Hypertension

Abstract Body (Do not enter title and authors here): Introduction
Pulmonary arterial hypertension (PAH) remains an underrecognized, fatal disease. Limited awareness, non-specific symptoms, and late referral to accredited PH centers all contribute to an overall poor prognosis. The previously validated Virtual Echocardiography Screening Tool (VEST) uses 3 routine transthoracic echocardiogram (TTE) parameters (left atrial size, transmitral E:e’ and systolic interventricular septal flattening) to recognize a high PAH likelihood. A positive VEST score has been shown to have 80% sensitivity and 76% specificity for PAH hemodynamics, while a VEST score of +3 has 92.7% specificity for PAH hemodynamics with a positive predictive value of 88.0%.

Aim
We aimed to implement a novel algorithm via our electronic medical record (EMR) as an automated VEST calculator to identify patients with a high likelihood of PAH.

Methods
An automated EMR VEST calculator was applied retrospectively to 4,952 patients who underwent TTE with TR velocity >/= 2.9 m/s at an accredited PH center from 12/2021-8/2023. Automated EMR VEST scores were validated by comparison to 60 manually scored echocardiograms. Those with VEST score of +3 (highest risk for PAH) underwent chart review to identify whether they were seen by a PH specialist.

Results
There was 100% correlation between the automated EMR VEST scores and the manual results.
Of the 4,952 patients, 1,655 had a positive automated EMR VEST score, and 355 had a score of +3, predicting the highest likelihood of PAH and warranting urgent referral to an accredited PH center. Of those patients with a +3 score, 103 (29.0%) were never seen by a PH specialist (Fig 1).

Conclusion
VEST is a validated, noninvasive and accessible screening tool for identification of patients with a high likelihood of PAH likely to benefit from early referral to a PH center. We present a novel, accurate, and automated EMR algorithm for determination of the VEST score to prompt urgent referral for PH expert evaluation and timely initiation of complex medical therapies. These findings highlight the potential of future artificial intelligence and machine-learning applications for improved recognition of life-threatening PAH.
  • Narowska, Gabriela  ( Temple University Hospital , Philadelphia , Pennsylvania , United States )
  • Anand, Suneesh  ( Temple University Hospital , Philadelphia , Pennsylvania , United States )
  • Gangireddy, Chethan  ( Temple University Hospital , Philadelphia , Pennsylvania , United States )
  • Enevoldsen, John  ( Temple University Hospital , Philadelphia , Pennsylvania , United States )
  • Keane, Martin  ( Temple University Hospital , Philadelphia , Pennsylvania , United States )
  • Edmundowicz, Daniel  ( Temple University Hospital , Philadelphia , Pennsylvania , United States )
  • Forfia, Paul  ( Temple University Hospital , Philadelphia , Pennsylvania , United States )
  • Vaidya, Anjali  ( Temple University Hospital , Philadelphia , Pennsylvania , United States )
  • Author Disclosures:
    Gabriela Narowska: DO NOT have relevant financial relationships | suneesh Anand: DO NOT have relevant financial relationships | Chethan Gangireddy: DO NOT have relevant financial relationships | John Enevoldsen: DO NOT have relevant financial relationships | Martin Keane: DO NOT have relevant financial relationships | Daniel Edmundowicz: No Answer | Paul Forfia: No Answer | Anjali Vaidya: DO NOT have relevant financial relationships
Meeting Info:

Scientific Sessions 2024

2024

Chicago, Illinois

Session Info:

Beyond Stethoscopes: AI’s Impact on Cardiovascular Screening

Saturday, 11/16/2024 , 03:15PM - 04:30PM

Abstract Oral Session

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