Effects of a stepwise, structured LDL-C lowering strategy in patients post-acute coronary syndrome.

OBJECTIVE: Low-density lipoprotein cholesterol (LDL-C) lowering constitutes a cornerstone of secondary prevention of atherosclerotic cardiovascular disease (ASCVD), yet a considerable number of patients do not achieve guideline-recommended LDL­C targets. The 2016 European guidelines recommended titration of LDL­C lowering medication in a set number of steps, starting with oral medication. We aimed to investigate the effects of this stepwise approach in post-acute coronary syndrome (ACS) patients. METHODS: In a multicentre, prospective, non-randomised trial, we evaluated a three-step strategy aiming to reduce LDL­C to ≤ 1.8 mmol/l in post-ACS patients with prior ASCVD and/or diabetes mellitus. Steps, undertaken every 4-6 weeks, included: 1) start high-intensity statin (HIST); 2) addition of ezetimibe; 3) addition of proprotein convertase subtilisin/kexin type 9 inhibitors (PCSK9i). The primary outcome was the proportion of patients achieving LDL-C ≤ 1.8 mmol/l after Steps 1 and 2 (using oral medications alone). Secondary outcomes examined the prevalence of meeting the target throughout all steps ( https://onderzoekmetmensen.nl/nl/trial/21157 ). RESULTS: Out of 999 patients, 84% (95% confidence intervals (CI): 81-86) achieved the LDL­C target using only statin and/or ezetimibe. In an intention-to-treat analysis, the percentages of patients meeting the LDL­C target after each step were 69% (95% CI: 67-72), 84% (95% CI: 81-86), and 87% (95% CI: 85-89), respectively. There were protocol deviations for 23, 38 and 23 patients at each respective step. CONCLUSION: Through stepwise intensification of lipid-lowering therapy, 84% of very high-risk post-ACS patients achieved an LDL­C target of ≤ 1.8 mmol/l with oral medications alone. Addition of PCSK9i further increased this rate to 87% (95% CI: 85-89).

Machine learning-based analysis of non-invasive measurements for predicting intracardiac pressures.

Aims: Early detection of congestion has demonstrated to improve outcomes in heart failure (HF) patients. However, there is limited access to invasively haemodynamic parameters to guide treatment. This study aims to develop a model to estimate the invasively measured pulmonary capillary wedge pressure (PCWP) using non-invasive measurements with both traditional statistics and machine learning (ML) techniques. Methods and results: The study involved patients undergoing right-sided heart catheterization at Erasmus MC, Rotterdam, from 2017 to 2022. Invasively measured PCWP served as outcomes. Model features included non-invasive measurements of arterial blood pressure, saturation, heart rate (variability), weight, and temperature. Various traditional and ML techniques were used, and performance was assessed using R2 and area under the curve (AUC) for regression and classification models, respectively. A total of 853 procedures were included, of which 31% had HF as primary diagnosis and 49% had a PCWP of 12 mmHg or higher. The mean age of the cohort was 59 ± 14 years, and 52% were male. The heart rate variability had the highest correlation with the PCWP with a correlation of 0.16. All the regression models resulted in low R2 values of up to 0.04, and the classification models resulted in AUC values of up to 0.59. Conclusion: In this study, non-invasive methods, both traditional and ML-based, showed limited correlation to PCWP. This highlights the weak correlation between traditional HF monitoring and haemodynamic parameters, also emphasizing the limitations of single non-invasive measurements. Future research should explore trend analysis and additional features to improve non-invasive haemodynamic monitoring, as there is a clear demand for further advancements in this field.