Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Videos

Prediction of left ventricular ejection fraction using simple quantitative clinical information.

K B Cease, J M Nicklas

    The American Journal of Medicine
    |September 1, 1986
    PubMed
    Summary

    This study explored whether simple clinical data could predict left ventricular ejection fraction (LVEF), a key measure of heart function. Using heart rate, blood pressure, and chest X-ray measurements, researchers developed a regression model to estimate LVEF. The model was tested on two groups of patients and showed strong correlations with actual LVEF values measured via radionuclide ventriculography. The model correctly identified reduced ejection fractions with high sensitivity and specificity. This approach could help doctors make quicker assessments and reduce the need for more invasive tests. The study highlights the potential of using routine clinical data to streamline heart function evaluation.

    Related Concept Videos

    You might also read

    Related Articles

    Articles linked to this work by shared authors, journal, and citation graph.

    Sort by
    Same author

    A phase I/II pharmacokinetic and pharmacogenomic study of calcitriol in combination with cisplatin and docetaxel in advanced non-small-cell lung cancer.

    Cancer chemotherapy and pharmacology·2013
    Same author

    Nuggets, pearls, and vignettes of master heart failure clinicians. Part 2-the physical examination.

    Congestive heart failure (Greenwich, Conn.)·2002
    Same author

    Guideline for the management of heart failure caused by systolic dysfunction: part II. Treatment.

    American family physician·2001
    Same author

    Guideline for the management of heart failure caused by systolic dysfunction: Part I. Guideline development, etiology and diagnosis.

    American family physician·2001
    Same author

    Prognostic importance of marital quality for survival of congestive heart failure.

    The American journal of cardiology·2001
    Same author

    Specialized heart failure centers--a success or an indicator of the failure of our health care delivery system.

    Clinical cardiology·2000

    Area of Science:

    • Cardiac imaging techniques in clinical diagnostics
    • Non-invasive cardiovascular assessment in internal medicine

    Background:

    Physicians rely on left ventricular ejection fraction (LVEF) to assess heart function and prognosis. Radionuclide ventriculography remains a gold standard for measuring LVEF. However, this method involves radiation exposure and is not always accessible. Prior research has shown that non-invasive tools like chest radiography and blood pressure monitoring are routinely used but not fully utilized for LVEF prediction. This gap motivated the search for a simpler alternative using clinical data already collected during patient evaluations. No prior work had resolved how well these basic metrics could estimate LVEF. The need for a cost-effective and accessible method is clear. This paper introduces a regression model using clinical variables to predict LVEF. The study aims to bridge this diagnostic gap with a practical solution.

    Purpose Of The Study:

    The study aimed to evaluate if clinical data routinely collected during patient exams could predict LVEF measured via radionuclide ventriculography. Researchers focused on variables like heart rate, pulse pressure, and chest radiography measurements. These parameters are easy to obtain during standard clinical assessments. The goal was to develop a regression model that could estimate LVEF without requiring specialized imaging. The researchers hypothesized that these variables could provide a reliable approximation of LVEF. They sought to validate this model using a separate dataset. The study aimed to improve diagnostic efficiency and reduce reliance on invasive procedures. This approach could streamline patient management and resource allocation in clinical settings.

    Keywords:
    left ventricular ejection fractionclinical data analysiscardiac function predictionnon-invasive heart assessment

    Frequently Asked Questions

    The model used heart rate, pulse pressure, thoracic width, and plain film heart volume to predict ejection fraction.

    The model achieved a correlation coefficient of 0.73 in the training dataset and 0.78 in the verification dataset.

    Thoracic width was a significant predictor in the model, likely reflecting anatomical and physiological variations affecting heart size.

    The model identified ejection fractions below 40 with 87% sensitivity and 83% specificity, indicating strong diagnostic potential.

    Related Experiment Videos

    Main Methods:

    The study used multiple regression analysis to develop a predictive model for LVEF. A training dataset of 64 patients was selected to cover the full range of ejection fraction values. Each patient underwent cardiac catheterization, chest radiography, and radionuclide ventriculography. Clinical variables included heart rate, pulse pressure, thoracic width, and heart volume. These parameters were extracted from routine clinical data. The model was optimized using these variables to predict LVEF. The regression formula was tested on a separate verification dataset of 41 patients. This approach ensured the model’s generalizability and reliability.

    Main Results:

    The regression model achieved a correlation coefficient of 0.73 in the training dataset. Heart volume, heart rate, pulse pressure, and thoracic width were significant predictors. The model was validated on a separate dataset of 41 patients, yielding a higher correlation of 0.78. The model correctly identified LVEF below 40 with 87% sensitivity and 83% specificity. These results suggest the model’s accuracy in detecting reduced ejection fractions. The model’s simplicity relies on basic clinical measurements. No invasive procedures or advanced imaging were required. This approach could reduce the need for radionuclide ventriculography in certain clinical scenarios.

    Conclusions:

    The authors propose that a regression model using clinical variables can predict LVEF with reasonable accuracy. The model’s performance in the verification dataset supports its potential clinical utility. Heart volume, heart rate, and pulse pressure were key contributors to the model’s accuracy. The researchers suggest this method could assist physicians in initial patient assessments. The model’s simplicity allows for quick calculations using routine clinical data. This approach may reduce reliance on more invasive diagnostic tools. The authors note that further validation is needed to confirm the model’s effectiveness in broader populations. This method could facilitate more efficient patient management and resource allocation.

    The authors propose the model may reduce reliance on radionuclide ventriculography but not fully replace it.

    The study suggests this model could assist physicians in initial patient assessments and optimize diagnostic resource use.