Why the study?
Hypertension often escapes diagnosis or requires confirmation by several office visits, making ECG-based opportunistic screening potentially important in initial evaluation.
Does a machine learning algorithm using ECG and clinical parameters accurately detect hypertension in individuals without cardiovascular disease?
Cross-Sectional (n=1,091)
Does a machine learning algorithm using ECG and clinical parameters accurately detect hypertension in individuals without cardiovascular disease?
A machine learning model using basic anthropometric and ECG-derived features can accurately detect undiagnosed hypertension in patients without known cardiovascular disease.
May aid ECG-ML hypertension screening in primary care; hypothesis-generating and requires prospective validation before adoption.
OBJECTIVES: Hypertension is a major risk factor for cardiovascular disease (CVD), which often escapes the diagnosis or should be confirmed by several office visits. The ECG is one of the most widely used diagnostic tools and could be of paramount importance in patients' initial evaluation. METHODS: We used machine learning techniques based on clinical parameters and features derived from the ECG, to detect hypertension in a population without CVD. We enrolled 1091 individuals who were classified as hypertensive or normotensive, and trained a Random Forest model, to detect the existence of hypertension. We then calculated the values for the Shapley additive explanations (SHAP), a sophisticated feature importance analysis, to interpret each feature's role in the Random Forest's results. RESULTS: Our Random Forest model was able to distinguish hypertensive from normotensive patients with accuracy 84.2%, specificity 78.0%, sensitivity 84.0% and area under the receiver-operating curve 0.89, using a decision threshold of 0.6. Age, BMI, BMI-adjusted Cornell criteria (BMI multiplied by RaVL+SV 3 ), R wave amplitude in aVL and BMI-modified Sokolow-Lyon voltage (BMI divided by SV 1 +RV 5 ), were the most important anthropometric and ECG-derived features in terms of the success of our model. CONCLUSION: Our machine learning algorithm is effective in the detection of hypertension in patients using ECG-derived and basic anthropometric criteria. Our findings open new horizon in the detection of many undiagnosed hypertensive individuals who have an increased CVD risk.
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Angelaki et al. (2022) conducted a cross-sectional in Hypertension (n=1,091). Machine learning (Random Forest) model using clinical and ECG features was evaluated on Detection of hypertension (Accuracy 84.2%, AUC 0.89).
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