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Introduction Achieving optimal low-density lipoprotein cholesterol (LDL-C) reduction in high-risk patients remains a challenge, even with combination lipid-lowering therapy. This study evaluated LDL-C target attainment (≤70 mg/dL per European Society of Cardiology/European Atherosclerosis Society (ESC/EAS) 2019 guidelines) in high-risk patients receiving statin plus ezetimibe therapy at a single tertiary care center in Peshawar, Pakistan. Methodology A retrospective analysis of a cross-sectional dataset was conducted at Hayatabad Medical Complex (HMC) over 12 months. A total of 123 high-risk patients, as defined by ESC/EAS 2019 criteria, who had been on statin plus ezetimibe therapy for a minimum of three months (mean duration: 4.2 ± 1.1 months), were included. LDL-C levels were recorded at a single post-treatment follow-up time point. Paired t-tests were used to assess LDL-C changes, and chi-square tests along with multivariable logistic regression were employed to identify predictors of target attainment. Results Mean LDL-C decreased significantly from 156.3 ± 32.5 mg/dL to 84.7 ± 24.1 mg/dL (mean reduction: 71.6 mg/dL; 95% CI: 66.8-76.4; p < 0.001). Overall, 53 of 123 patients (43.1%; 95% CI: 34.6-51.8%) achieved the LDL-C target. Target attainment was lower among diabetic patients (24 of 71; 33.8%) and smokers (13 of 39; 33.3%). In multivariable analysis, diabetes (aOR: 0.52; 95% CI: 0.28-0.97) and smoking (aOR: 0.55; 95% CI: 0.27-0.98) were independently associated with lower target attainment. High-intensity statin use was positively associated with achieving LDL-C goals (33 of 68; 48.5%; aOR: 1.89; 95% CI: 1.02-3.49). Conclusion This single-center study highlights that despite combination therapy, more than half of high-risk patients failed to achieve LDL-C targets. The findings underscore the need for more individualized strategies, improved adherence, and possibly adjunctive therapies. Although the data are from a single center in Peshawar, the trends reflect common challenges in real-world lipid management across similar low- to middle-income healthcare settings.
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http://dx.doi.org/10.7759/cureus.87963 | DOI Listing |
PLoS One
September 2025
School of Electrical and Information Engineering, Hunan Institute of Technology, Hengyang, Hunan, China.
Knowledge tracing can reveal students' level of knowledge in relation to their learning performance. Recently, plenty of machine learning algorithms have been proposed to exploit to implement knowledge tracing and have achieved promising outcomes. However, most of the previous approaches were unable to cope with long sequence time-series prediction, which is more valuable than short sequence prediction that is extensively utilized in current knowledge-tracing studies.
View Article and Find Full Text PDFPLoS One
September 2025
The George Institute for Global Health, Imperial College London, London, United Kingdom.
Background: Tobacco use remains a major public health challenge in sub-Saharan Africa, with significant gendered dimensions. Place of residence is an important determinant, as rural and urban contexts shape exposure, access, and consumption patterns. This study investigates rural-urban disparities in tobacco use among women in sub-Saharan Africa, with a focus on quantifying the relative contributions of socioeconomic factors.
View Article and Find Full Text PDFMedicine (Baltimore)
September 2025
Department of Respiratory and Critical Care Medicine, Hejiang People's Hospital, Luzhou City, Sichuan Province, China.
Chronic obstructive pulmonary disease (COPD) requires effective management that often depends on the knowledge level of primary family caregivers. This study assessed caregiver knowledge using a validated scale and identified key factors associated with higher COPD knowledge. A cross-sectional survey was conducted from April 2020 to January 2024 at a tertiary hospital in Southwest China.
View Article and Find Full Text PDFPLoS One
September 2025
Center for Studies of Education and Psychology of Ethnic Minorities in Southwest China, Southwest University, Chongqing, China.
Background: Educational hypogamy, where women marry men with lower educational attainment, reflects evolving gender roles and societal norms. In China, the rapid expansion of education, coupled with persistent traditional values, provides a unique context to study this phenomenon.
Methods: Using data from the 2013, 2015, 2017, 2018, and 2021 waves of the China General Social Survey (CGSS), this study applies logistic regression models and Random Forest machine learning techniques to analyze the impact of education on women's selection of hypogamy.
Health Inf Sci Syst
December 2025
School of Information Science and Automation, Northeastern University, Shenyang, 110819 China.
Accurate prediction of drug-target interactions (DTIs) is crucial for improving the efficiency and success rate of drug development. Despite recent advancements, existing methods often fail to leverage interaction features at multiple granular levels, resulting in suboptimal data utilization and limited predictive performance. To address these challenges, we propose CF-DTI, a coarse-to-fine drug-target interaction model that integrates both coarse-grained and fine-grained features to enhance predictive accuracy.
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