Aims Lowering low-density lipoprotein (LDL-C) and blood pressure (BP) levels to guideline recommended values reduces the risk of major adverse cardiac events in patients who underwent coronary... Show moreAims Lowering low-density lipoprotein (LDL-C) and blood pressure (BP) levels to guideline recommended values reduces the risk of major adverse cardiac events in patients who underwent coronary artery bypass grafting (CABG). To improve cardiovascular risk management, this study evaluated the effects of mobile health (mHealth) on BP and cholesterol levels in patients after standalone CABG.Methods and results This study is a post hoc analysis of an observational cohort study among 228 adult patients who underwent standalone CABG surgery at a tertiary care hospital in The Netherlands. A total of 117 patients received standard care, and 111 patients underwent an mHealth intervention. This consisted of frequent BP and weight monitoring with regimen adjustment in case of high BP. Primary outcome was difference in systolic BP and LDL-C between baseline and value after three months of follow-up. Mean age in the intervention group was 62.7 years, 98 (88.3%) patients were male. A total of 26 449 mHealth measurements were recorded. At three months, systolic BP decreased by 7.0 mmHg [standard deviation (SD): 15.1] in the intervention group vs. -0.3 mmHg (SD: 17.6; P < 0.00001) in controls; body weight decreased by 1.76 kg (SD: 3.23) in the intervention group vs. -0.31 kg (SD: 2.55; P = 0.002) in controls. Serum LDL-C was significantly lower in the intervention group vs. controls (median: 1.8 vs. 2.0 mmol/L; P = 0.0002).Conclusion This study showed an association between home monitoring after CABG and a reduction in systolic BP, body weight, and serum LDL-C. The causality of the association between the observed weight loss and decreased LDL-C in intervention group patients remains to be investigated. Show less
Aims Pulmonary arterial hypertension (PAH) is a rare but serious disease associated with high mortality if left untreated. This study aims to assess the prognostic cardiac magnetic resonance (CMR)... Show moreAims Pulmonary arterial hypertension (PAH) is a rare but serious disease associated with high mortality if left untreated. This study aims to assess the prognostic cardiac magnetic resonance (CMR) features in PAH using machine learning. Methods and results Seven hundred and twenty-three consecutive treatment-naive PAH patients were identified from the ASPIRE registry; 516 were included in the training, and 207 in the validation cohort. A multilinear principal component analysis (MPCA)-based machine learning approach was used to extract mortality and survival features throughout the cardiac cycle. The features were overlaid on the original imaging using thresholding and clustering of high- and low-risk of mortality prediction values. The 1-year mortality rate in the validation cohort was 10%. Univariable Cox regression analysis of the combined short-axis and four-chamber MPCA-based predictions was statistically significant (hazard ratios: 2.1, 95% CI: 1.3, 3.4, c-index = 0.70, P = 0.002). The MPCA features improved the 1-year mortality prediction of REVEAL from c-index = 0.71 to 0.76 (P ≤ 0.001). Abnormalities in the end-systolic interventricular septum and end-diastolic left ventricle indicated the highest risk of mortality.Conclusion: The MPCA-based machine learning is an explainable time-resolved approach that allows visualization of prognostic cardiac features throughout the cardiac cycle at the population level, making this approach transparent and clinically interpretable. In addition, the added prognostic value over the REVEAL risk score and CMR volumetric measurements allows for a more accurate prediction of 1-year mortality risk in PAH. Show less
