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Development and validation of machine learning algorithms to predict posthypertensive origin in left ventricular
Maxime Beneyto1, Ghada Ghyaza2, Eve Cariou1
1Cardiac Imaging Centre, Toulouse University Hospital, 31059 Toulouse, France.
Insights
Machine learning effectively predicts the hypertensive origin of left ventricular hypertrophy, reducing the need for extensive patient workups. This tool aids in differentiating causes of left ventricular hypertrophy.
Area of Science:
- Cardiology
- Medical Informatics
- Machine Learning
Background:
- Left ventricular hypertrophy (LVH) is frequently associated with hypertension, but non-hypertensive causes significantly impact patient management.
- A thorough workup is crucial for identifying the etiology of LVH, especially with increasing prevalence of mild cases.
- There is a need for tools to accurately assess the pretest probability of hypertensive LVH.
Purpose of the Study:
- To develop and validate machine learning models for predicting the hypertensive origin of LVH.
- Utilize first-line clinical, laboratory, and echocardiographic variables for prediction.
- Improve diagnostic accuracy and streamline patient management.
Main Methods:
- Retrospective analysis of 591 patients with LVH (maximal wall thickness ≥12mm).
- Data split into training and testing sets.
- Trained and validated decision tree, random forest, and support vector machine algorithms.
Main Results:
- All models demonstrated strong predictive performance (AUCs ranging from 0.82 to 0.90).
- Support vector machine achieved high specificity (0.96) and sensitivity (0.31) after threshold selection.
- Key predictor variables were consistent across algorithms; online calculators were developed.
Conclusions:
- Machine learning models accurately predict the hypertensive origin of LVH.
- Clinical implementation can decrease the number of etiological workups required for LVH patients.
- These tools support efficient and targeted patient evaluation.
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.
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