Background: Left ventricular hypertrophy is often associated with hypertension, which is not necessarily the cause of hypertrophy. Non-hypertension-related aetiologies often have a strong impact on patient management, and therefore require a thorough and careful workup. When considering all left ventricular hypertrophies, even the mild ones, the number of patients who need a workup increases drastically. This raises the need for a tool to evaluate the pretest probability of the origin of left ventricular hypertrophy.
Aim: To predict the hypertensive origin of left ventricular hypertrophy using machine learning on first-line clinical, laboratory and echocardiographic variables.
Methods: We used a retrospective single-centre population of 591 patients with left ventricular hypertrophy, starting at 12mm maximal left ventricular wall thickness. After splitting data in a training and testing set, we trained three different algorithms: decision tree; random forest; and support vector machine. Model performances were validated on the testing set.
Results: All models exhibited good areas under receiver operating characteristic curves: 0.82 (95% confidence interval: 0.77-0.88) for the decision tree; 0.90 (95% confidence interval 0.85-0.94) for the random forest; and 0.90 (95% confidence interval: 0.85-0.94) for the support vector machine. After threshold selection, the last model had the best balance between its specificity of 0.96 (95% confidence interval: 0.91-0.99) and its sensitivity of 0.31 (95% confidence interval: 0.17-0.44). All algorithms relied on similar most influential predictor variables. Online calculators were developed and made publicly available.
Conclusions: Machine learning models were able to determine the hypertensive origin of left ventricular hypertrophy with good performances. Implementation in clinical practice could reduce the number of aetiological workups needed in patients presenting with left ventricular hypertrophy.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.1016/j.acvd.2023.06.005 | DOI Listing |
Ultrasound J
January 2025
Health Sciences North Research Institute, Sudbury, ON, Canada.
The duration of mechanical systole-also termed the flow time (FT) or left ventricular ejection time (LVET)-is measured by Doppler ultrasound and increasingly used as a stroke volume (SV) surrogate to guide patient care. Nevertheless, confusion exists as to the determinants of FT and a critical evaluation of this measure is needed. Using Doppler ultrasound of the left ventricular outflow tract velocity time integral (LVOT VTI) as well as strain and strain rate echocardiography as grounding principles, this brief commentary offers a model for the independent influences of FT.
View Article and Find Full Text PDFInt J Legal Med
January 2025
Institute for Legal Medicine, Faculty of Medicine, Saarland University, Campus Homburg, Building 49.1, Kirrberger Straße 100, 66421, Homburg/Saar, Germany.
Aortic regurgitation is a common valve disease and can be caused by delineated findings such as fenestrations or hardly discernible alterations of the aortic root geometry. Therefore, aortic regurgitation can be a challenging diagnosis during an autopsy. Cardiac surgeons, however, are confronted with comparable problems during surgery and have developed a refined knowledge of the anatomy of the aortic root including its geometry.
View Article and Find Full Text PDFEur Heart J Cardiovasc Imaging
January 2025
Department of Cardiovascular Medicine, Gunma University Graduate School of Medicine, Maebashi, Gunma, Japan.
Aims: Left ventricular (LV) diastolic dysfunction and heart failure with preserved ejection fraction (HFpEF) are common cardiac complications of patients with systemic sclerosis (SSc). Exercise stress echocardiography is often used in symptomatic patients with SSc to detect abnormal increases in pulmonary pressures during exercise, but the pathophysiologic and prognostic significance of exercise stress echocardiography to assess the presence of HFpEF in these patients is unclear.
Methods And Results: Patients with SSc (n=140) underwent ergometry exercise stress echocardiography with simultaneous expired gas analysis.
Artif Organs
January 2025
Division of Cardiology, Department of Medicine, Columbia University College of Physicians and Surgeons and NewYork-Presbyterian Hospital, New York, New York, USA.
Background: GLP-1 RAs improve cardiometabolic outcomes in obese, diabetic, and heart failure patients. Data on the safety and efficacy of GLP-1 RA in advanced heart failure with durable LVAD is limited.
Objectives: To assess the safety and efficacy of GLP-1 RA in durable LVAD patients.
J Vet Intern Med
January 2025
Boehringer Ingelheim Pharma GmbH & Co., Ingelheim, Germany.
Background: Myxomatous mitral valve disease (MMVD) is frequently diagnosed in small breed dogs. Pimobendan oral solution has been developed to improve dosing accuracy in small and toy breed dogs.
Hypothesis/objectives: Demonstrate bioequivalence of pimobendan oral solution with pimobendan chewable tablets using a pharmacokinetic and a pharmacodynamic study in healthy purpose bred dogs.
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!