1
|
Dadon Z, Rav Acha M, Orlev A, Carasso S, Glikson M, Gottlieb S, Alpert EA. Artificial Intelligence-Based Left Ventricular Ejection Fraction by Medical Students for Mortality and Readmission Prediction. Diagnostics (Basel) 2024; 14:767. [PMID: 38611680 PMCID: PMC11011323 DOI: 10.3390/diagnostics14070767] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2024] [Revised: 03/28/2024] [Accepted: 03/31/2024] [Indexed: 04/14/2024] Open
Abstract
INTRODUCTION Point-of-care ultrasound has become a universal practice, employed by physicians across various disciplines, contributing to diagnostic processes and decision-making. AIM To assess the association of reduced (<50%) left-ventricular ejection fraction (LVEF) based on prospective point-of-care ultrasound operated by medical students using an artificial intelligence (AI) tool and 1-year primary composite outcome, including mortality and readmission for cardiovascular-related causes. METHODS Eight trained medical students used a hand-held ultrasound device (HUD) equipped with an AI-based tool for automatic evaluation of the LVEF of non-selected patients hospitalized in a cardiology department from March 2019 through March 2020. RESULTS The study included 82 patients (72 males aged 58.5 ± 16.8 years), of whom 34 (41.5%) were diagnosed with AI-based reduced LVEF. The rates of the composite outcome were higher among patients with reduced systolic function compared to those with preserved LVEF (41.2% vs. 16.7%, p = 0.014). Adjusting for pertinent variables, reduced LVEF independently predicted the composite outcome (HR 2.717, 95% CI 1.083-6.817, p = 0.033). As compared to those with LVEF ≥ 50%, patients with reduced LVEF had a longer length of stay and higher rates of the secondary composite outcome, including in-hospital death, advanced ventilatory support, shock, and acute decompensated heart failure. CONCLUSION AI-based assessment of reduced systolic function in the hands of medical students, independently predicted 1-year mortality and cardiovascular-related readmission and was associated with unfavorable in-hospital outcomes. AI utilization by novice users may be an important tool for risk stratification for hospitalized patients.
Collapse
Affiliation(s)
- Ziv Dadon
- Jesselson Integrated Heart Center, Eisenberg R&D Authority, Shaare Zedek Medical Center, Jerusalem 9103102, Israel
- Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem 9112102, Israel
| | - Moshe Rav Acha
- Jesselson Integrated Heart Center, Eisenberg R&D Authority, Shaare Zedek Medical Center, Jerusalem 9103102, Israel
- Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem 9112102, Israel
| | - Amir Orlev
- Jesselson Integrated Heart Center, Eisenberg R&D Authority, Shaare Zedek Medical Center, Jerusalem 9103102, Israel
- Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem 9112102, Israel
| | - Shemy Carasso
- Jesselson Integrated Heart Center, Eisenberg R&D Authority, Shaare Zedek Medical Center, Jerusalem 9103102, Israel
- Azrieli Faculty of Medicine, Bar-Ilan University, Safed 1311502, Israel
| | - Michael Glikson
- Jesselson Integrated Heart Center, Eisenberg R&D Authority, Shaare Zedek Medical Center, Jerusalem 9103102, Israel
- Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem 9112102, Israel
| | - Shmuel Gottlieb
- Jesselson Integrated Heart Center, Eisenberg R&D Authority, Shaare Zedek Medical Center, Jerusalem 9103102, Israel
- Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv 6997801, Israel
| | - Evan Avraham Alpert
- Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem 9112102, Israel
- Department of Emergency Medicine, Hadassah Medical Center—Ein Kerem, Jerusalem 9112001, Israel
| |
Collapse
|
2
|
Dadon Z, Steinmetz Y, Levi N, Orlev A, Belman D, Butnaru A, Carasso S, Glikson M, Alpert EA, Gottlieb S. Artificial Intelligence-Powered Left Ventricular Ejection Fraction Analysis Using the LVivoEF Tool for COVID-19 Patients. J Clin Med 2023; 12:7571. [PMID: 38137638 PMCID: PMC10743829 DOI: 10.3390/jcm12247571] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2023] [Revised: 12/04/2023] [Accepted: 12/06/2023] [Indexed: 12/24/2023] Open
Abstract
We sought to prospectively investigate the accuracy of an artificial intelligence (AI)-based tool for left ventricular ejection fraction (LVEF) assessment using a hand-held ultrasound device (HUD) in COVID-19 patients and to examine whether reduced LVEF predicts the composite endpoint of in-hospital death, advanced ventilatory support, shock, myocardial injury, and acute decompensated heart failure. COVID-19 patients were evaluated with a real-time LVEF assessment using an HUD equipped with an AI-based tool vs. assessment by a blinded fellowship-trained echocardiographer. Among 42 patients, those with LVEF < 50% were older with more comorbidities and unfavorable exam characteristics. An excellent correlation was demonstrated between the AI and the echocardiographer LVEF assessment (0.774, p < 0.001). Substantial agreement was demonstrated between the two assessments (kappa = 0.797, p < 0.001). The sensitivity, specificity, PPV, and NPV of the HUD for this threshold were 72.7% 100%, 100%, and 91.2%, respectively. AI-based LVEF < 50% was associated with worse composite endpoints; unadjusted OR = 11.11 (95% CI 2.25-54.94), p = 0.003; adjusted OR = 6.40 (95% CI 1.07-38.09, p = 0.041). An AI-based algorithm incorporated into an HUD can be utilized reliably as a decision support tool for automatic real-time LVEF assessment among COVID-19 patients and may identify patients at risk for unfavorable outcomes. Future larger cohorts should verify the association with outcomes.
Collapse
Affiliation(s)
- Ziv Dadon
- Jesselson Integrated Heart Center, Shaare Zedek Medical Center, Jerusalem 9103102, Israel
- Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem 9112102, Israel
| | - Yoed Steinmetz
- Jesselson Integrated Heart Center, Shaare Zedek Medical Center, Jerusalem 9103102, Israel
- Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem 9112102, Israel
| | - Nir Levi
- Jesselson Integrated Heart Center, Shaare Zedek Medical Center, Jerusalem 9103102, Israel
- Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem 9112102, Israel
| | - Amir Orlev
- Jesselson Integrated Heart Center, Shaare Zedek Medical Center, Jerusalem 9103102, Israel
- Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem 9112102, Israel
| | - Daniel Belman
- Intensive Care Unit, Shaare Zedek Medical Center, Jerusalem 9103102, Israel
| | - Adi Butnaru
- Jesselson Integrated Heart Center, Shaare Zedek Medical Center, Jerusalem 9103102, Israel
| | - Shemy Carasso
- Jesselson Integrated Heart Center, Shaare Zedek Medical Center, Jerusalem 9103102, Israel
- The Azrieli Faculty of Medicine, Bar-Ilan University, Zefat 1311502, Israel
| | - Michael Glikson
- Jesselson Integrated Heart Center, Shaare Zedek Medical Center, Jerusalem 9103102, Israel
- Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem 9112102, Israel
| | - Evan Avraham Alpert
- Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem 9112102, Israel
- Department of Emergency Medicine, Shaare Zedek Medical Center, Jerusalem 9103102, Israel
| | - Shmuel Gottlieb
- Jesselson Integrated Heart Center, Shaare Zedek Medical Center, Jerusalem 9103102, Israel
- Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv 6997801, Israel
| |
Collapse
|
3
|
Dadon Z, Orlev A, Butnaru A, Rosenmann D, Glikson M, Gottlieb S, Alpert EA. Empowering Medical Students: Harnessing Artificial Intelligence for Precision Point-of-Care Echocardiography Assessment of Left Ventricular Ejection Fraction. Int J Clin Pract 2023; 2023:5225872. [PMID: 38078051 PMCID: PMC10699938 DOI: 10.1155/2023/5225872] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/12/2023] [Revised: 10/14/2023] [Accepted: 11/14/2023] [Indexed: 12/18/2023] Open
Abstract
Introduction Point-of-care ultrasound (POCUS) use is now universal among nonexperts. Artificial intelligence (AI) is currently employed by nonexperts in various imaging modalities to assist in diagnosis and decision making. Aim To evaluate the diagnostic accuracy of POCUS, operated by medical students with the assistance of an AI-based tool for assessing the left ventricular ejection fraction (LVEF) of patients admitted to a cardiology department. Methods Eight students underwent a 6-hour didactic and hands-on training session. Participants used a hand-held ultrasound device (HUD) equipped with an AI-based tool for the automatic evaluation of LVEF. The clips were assessed for LVEF by three methods: visually by the students, by students + the AI-based tool, and by the cardiologists. All LVEF measurements were compared to formal echocardiography completed within 24 hours and were evaluated for LVEF using the Simpson method and eyeballing assessment by expert echocardiographers. Results The study included 88 patients (aged 58.3 ± 16.3 years). The AI-based tool measurement was unsuccessful in 6 cases. Comparing LVEF reported by students' visual evaluation and students + AI vs. cardiologists revealed a correlation of 0.51 and 0.83, respectively. Comparing these three evaluation methods with the echocardiographers revealed a moderate/substantial agreement for the students + AI and cardiologists but only a fair agreement for the students' visual evaluation. Conclusion Medical students' utilization of an AI-based tool with a HUD for LVEF assessment achieved a level of accuracy similar to that of cardiologists. Furthermore, the use of AI by the students achieved moderate to substantial inter-rater reliability with expert echocardiographers' evaluation.
Collapse
Affiliation(s)
- Ziv Dadon
- Jesselson Integrated Heart Center, Shaare Zedek Medical Center, Jerusalem, Israel
- Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem, Israel
| | - Amir Orlev
- Jesselson Integrated Heart Center, Shaare Zedek Medical Center, Jerusalem, Israel
- Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem, Israel
| | - Adi Butnaru
- Jesselson Integrated Heart Center, Shaare Zedek Medical Center, Jerusalem, Israel
| | - David Rosenmann
- Jesselson Integrated Heart Center, Shaare Zedek Medical Center, Jerusalem, Israel
| | - Michael Glikson
- Jesselson Integrated Heart Center, Shaare Zedek Medical Center, Jerusalem, Israel
- Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem, Israel
| | - Shmuel Gottlieb
- Jesselson Integrated Heart Center, Shaare Zedek Medical Center, Jerusalem, Israel
- Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Evan Avraham Alpert
- Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem, Israel
- Department of Emergency Medicine, Shaare Zedek Medical Center, Jerusalem, Israel
| |
Collapse
|
4
|
Webb Z. The Use of Bedside Echocardiography to Diagnose Post-COVID Cardiomyopathy and Left Ventricular Thrombus in the Emergency Department. Cureus 2023; 15:e39699. [PMID: 37398785 PMCID: PMC10309017 DOI: 10.7759/cureus.39699] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/30/2023] [Indexed: 07/04/2023] Open
Abstract
This case report chronicles a 47-year-old male with no known past medical history, who presented to the emergency department with a chief complaint of progressive dyspnea and lower extremity edema. The patient was previously healthy until he contracted COVID-19 approximately six months prior to the date of presentation. He made a full recovery two weeks later. However, in the ensuing months, he progressively declined with worsening shortness of breath and lower extremity edema. On outpatient cardiology evaluation, he was found to have cardiomegaly on chest radiograph and sinus tachycardia on electrocardiogram. He was sent to the emergency department for further evaluation. In the emergency department, bedside echocardiography revealed dilated cardiomyopathy with left ventricular thrombus. Intravenous anticoagulation and diuresis were initiated, and the patient was subsequently admitted to the cardiac intensive care unit for further evaluation and management.
Collapse
Affiliation(s)
- Zachary Webb
- Emergency Medicine, Huntington Hospital, Huntington, USA
| |
Collapse
|