Real-world data on treatment patterns in at least high cardiovascular risk patients on dual and triple lipid lowering therapy in a Hellenic nationwide e-prescription database.

Background: Despite recent guidelines appropriate lipid-lowering treatment (LLT) remains suboptimal in everyday clinical practice. Aims: We aimed to describe clinical practice of use of LLT for at least high CV risk populations in a Hellenic real-world setting and assess how this relates to the European Society of Cardiology treatment guidelines. Methods: We analyzed data from a retrospective cohort study of the National Registry of patients with dyslipidemia between 1/7/2017 and 30/6/2019 who were at least of high CV risk and filled a dual or triple lipid-lowering treatment (dLLT, tLLT) prescription. The primary outcomes of interest of this analysis were to report on the patterns of LLT use in at least high CV risk patients. Results: A total of 994,255 (45.4% of Greeks on LLT) were of at least high CV risk and 120,490 (5.5%) were on dLLT or tLLT. The percentage of patients with reported statin intolerance ranged from 2 to 10%. While persistence was reported to be satisfactory (>85% for both dLLT or tLLT), adherence was low (ranging between 14 and 34% for dLLT). In 6-month intervals, the percentage of patients achieving a low-density lipoprotein cholesterol (LDL-C) target below 100 md/dL ranged from 20% to 23% for dLLT and 34%-37% for tLLT. Conclusions: The prevalence of at least high CV risk patients among patients receiving LLT in Greece is substantial. Despite the high persistence and probably due to the low adherence to treatment, LDL-C remains above targets in more than two thirds of patients.

Convolutional Neural Networks for Raw Signal Classification in CNC Turning Process Monitoring.

This study addresses the need for advanced machine learning-based process monitoring in smart manufacturing. A methodology is developed for near-real-time part quality prediction based on process-related data obtained from a CNC turning center. Instead of the manual feature extraction methods typically employed in signal processing, a novel one-dimensional convolutional architecture allows the trained model to autonomously extract pertinent features directly from the raw signals. Several signal channels are utilized, including vibrations, motor speeds, and motor torques. Three quality indicators-average roughness, peak-to-valley roughness, and diameter deviation-are monitored using a single model, resulting in a compact and efficient classifier. Training data are obtained via a small number of experiments designed to induce variability in the quality metrics by varying feed, cutting speed, and depth of cut. A sliding window technique augments the dataset and allows the model to seamlessly operate over the entire process. This is further facilitated by the model's ability to distinguish between cutting and non-cutting phases. The base model is evaluated via k-fold cross validation and achieves average F1 scores above 0.97 for all outputs. Consistent performance is exhibited by additional instances trained under various combinations of design parameters, validating the robustness of the proposed methodology.