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Applying machine learning to detect early stages of cardiac remodelling and dysfunction
František Sabovčik1, Nicholas Cauwenberghs1, Dmitry Kouznetsov2
1Research Unit Hypertension and Cardiovascular Epidemiology, KU Leuven Department of Cardiovascular Sciences, University of Leuven, Campus Sint Rafaël, Kapucijnenvoer 33, Block h, Box 7001, B 3000 Leuven, Belgium.
Insights
Machine learning models accurately predict subclinical left ventricular diastolic dysfunction (LVDD) and hypertrophy (LVH) using routine clinical data. This aids in identifying at-risk individuals for further cardiac evaluation and preventive care.
Area of Science:
- Cardiology
- Biomedical Engineering
- Artificial Intelligence in Medicine
Background:
- Left ventricular diastolic dysfunction (LVDD) and hypertrophy (LVH) are key indicators of cardiovascular risk.
- Current screening methods for these subclinical abnormalities lack targeted strategies.
- Identifying individuals who would benefit most from cardiac phenotyping is crucial for early intervention.
Purpose of the Study:
- To evaluate the effectiveness of machine learning (ML) classifiers in detecting subclinical LVDD and LVH.
- To assess the utility of routinely collected clinical, biochemical, and electrocardiographic data for this purpose.
- To develop a screening tool for identifying individuals requiring further cardiac assessment.
Main Methods:
- 1407 general population participants were analyzed.
- Echocardiography defined LVDD (n=252) and LVH (n=272).
- Supervised ML algorithms (XGBoost, Random Forest) were trained on 67 clinical features using nested 10-fold cross-validation.
Main Results:
- XGBoost and Random Forest models achieved high accuracy in predicting LVDD (AUC 86.2-88.1%) and LVH (AUC 77.7-78.5%).
- Key predictors included age, BMI, blood pressure components, hypertension history, and ECG variables.
- The models demonstrated strong performance in classifying subclinical left ventricular abnormalities.
Conclusions:
- ML classifiers integrating routine data can accurately predict LVDD and LVH.
- These models offer a potential tool for pre-selecting individuals for echocardiography and preventive measures.
- This approach can enhance the efficiency of cardiovascular risk stratification.
Aims:
Both left ventricular (LV) diastolic dysfunction (LVDD) and hypertrophy (LVH) as assessed by echocardiography are independent prognostic markers of future cardiovascular events in the community. However, selective screening strategies to identify individuals at risk who would benefit most from cardiac phenotyping are lacking. We, therefore, assessed the utility of several machine learning (ML) classifiers built on routinely measured clinical, biochemical, and electrocardiographic features for detecting subclinical LV abnormalities.
Methods And Results:
We included 1407 participants (mean age, 51 years, 51% women) randomly recruited from the general population. We used echocardiographic parameters reflecting LV diastolic function and structure to define LV abnormalities (LVDD, n = 252; LVH, n = 272). Next, four supervised ML algorithms (XGBoost, AdaBoost, Random Forest (RF), Support Vector Machines, and Logistic regression) were used to build classifiers based on clinical data (67 features) to categorize LVDD and LVH. We applied a nested 10-fold cross-validation set-up. XGBoost and RF classifiers exhibited a high area under the receiver operating characteristic curve with values between 86.2% and 88.1% for predicting LVDD and between 77.7% and 78.5% for predicting LVH. Age, body mass index, different components of blood pressure, history of hypertension, antihypertensive treatment, and various electrocardiographic variables were the top selected features for predicting LVDD and LVH.
Conclusion:
XGBoost and RF classifiers combining routinely measured clinical, laboratory, and electrocardiographic data predicted LVDD and LVH with high accuracy. These ML classifiers might be useful to pre-select individuals in whom further echocardiographic examination, monitoring, and preventive measures are warranted.
