Blood pressure control measures and cardiovascular outcomes: a prospective hypertensive cohort

We investigated whether blood pressure (BP) control measures, visit-to-visit BP variability, and time in therapeutic range (TTR) are associated with future cardiovascular outcomes in hypertensive patients. Among 1,408 hypertensive patients without cardiovascular disease, we prospectively evaluated the incident major cardiovascular events over 6 years. In newly diagnosed patients, antihypertensive drug treatment was initiated. We estimated two markers of on-treatment BP control, (1) visit-to-visit BPV as the coefficient of variation of office systolic BP (BP-CV), and (2) TTR calculated as the percentage of office systolic BP measurements within 120–140mmHg across visits. The hypertensive cohort (672 males, mean age 60 years, 31% newly diagnosed) had a mean systolic/diastolic BP of 142/87 mmHg. The mean number of visits was 4.9 ± 2.6, while the mean attained systolic/diastolic BP during follow-up was 137/79 mmHg using 2.7 ± 1.1 antihypertensive drugs. The BP-CV and TTR were 9.1 ± 4.1% and 45 ± 29%, respectively, and the incidence of the composite outcome was 8.3% (n = 117). After adjustment for relevant confounders and standardization to z-scores, BP-CV and TTR were associated with a 43% (95% CI, 27–62%) increase and a 33% (95% CI, 15–47%) reduction in the outcome. However, the joint evaluation of TTR and BP-CV in a common multivariable model indicated that a standardized change of TTR was associated with the outcome to a greater extent than BP-CV (mean hazard ratios of 30% vs. 24%, respectively). When combined with the higher BP standardized-CV quartile, the lower TTR quartile predicted the outcome by 2.3 times (95% CI, 1.1–5.4) compared to the inverse TTR and BP-CV quartile pattern. High BP-CV or low TTR was associated with future cardiovascular events in a cohort of treated hypertensive patients. As a determinant, the extent of TTR value appears greater than BP-CV when these measures are considered in the same multivariable model.</p

Bland-Altman plots between the average value of (Original and estimated LVEF) and their corresponding difference for hours (22:00–23:00, 08:00–09:00, 00:00–01:00) using the Tree, GPR, XGBOOST and SVM, respectively.

Bland-Altman plots between the average value of (Original and estimated LVEF) and their corresponding difference for hours (22:00–23:00, 08:00–09:00, 00:00–01:00) using the Tree, GPR, XGBOOST and SVM, respectively.</p

Average RMSE values through 24-Hours, using regression models with best hyperparameters.

Average RMSE values through 24-Hours, using regression models with best hyperparameters.</p

Original and estimated LVEF values per patient for hours (22:00–23:00, 08:00–09:00, 00:00–01:00) using the Tree, GPR, XGBOOST and SVM, respectively.

Original and estimated LVEF values per patient for hours (22:00–23:00, 08:00–09:00, 00:00–01:00) using the Tree, GPR, XGBOOST and SVM, respectively.</p

Feature importance over the Tree regression model (10:00–11:00pm).

This study used normalized importance scores for the ECG features (QTc, QRS, ST-T, TP, Entropy, and Instant Frequency) to estimate the LVEF levels in the three HF categories. Indicating that QTc is the most important feature for evaluating the LVEF levels.</p

ECG features for the three groups (HFpEF, HFmEF, and HFrEF).

ECG features for the three groups (HFpEF, HFmEF, and HFrEF).</p

Definitions of ECG features.

Heart Failure (HF) significantly impacts approximately 26 million people worldwide, causing disruptions in the normal functioning of their hearts. The estimation of left ventricular ejection fraction (LVEF) plays a crucial role in the diagnosis, risk stratification, treatment selection, and monitoring of heart failure. However, achieving a definitive assessment is challenging, necessitating the use of echocardiography. Electrocardiogram (ECG) is a relatively simple, quick to obtain, provides continuous monitoring of patient’s cardiac rhythm, and cost-effective procedure compared to echocardiography. In this study, we compare several regression models (support vector machine (SVM), extreme gradient boosting (XGBOOST), gaussian process regression (GPR) and decision tree) for the estimation of LVEF for three groups of HF patients at hourly intervals using 24-hour ECG recordings. Data from 303 HF patients with preserved, mid-range, or reduced LVEF were obtained from a multicentre cohort (American and Greek). ECG extracted features were used to train the different regression models in one-hour intervals. To enhance the best possible LVEF level estimations, hyperparameters tuning in nested loop approach was implemented (the outer loop divides the data into training and testing sets, while the inner loop further divides the training set into smaller sets for cross-validation). LVEF levels were best estimated using rational quadratic GPR and fine decision tree regression models with an average root mean square error (RMSE) of 3.83% and 3.42%, and correlation coefficients of 0.92 (p</div

Clinical characteristics of the heart failure patients based on their LVEF categories.

Clinical characteristics of the heart failure patients based on their LVEF categories.</p

An illustration of the overall procedure followed in this study.

An illustration of the overall procedure followed in this study.</p

RMSE per hour using the best hyperparameters combination for the four regression models.

Red circles show the hours of occurrence of the lowest RMSE values. Where QTc and QRS are found to be the most important features for evaluating the LVEF levels.</p