An intensive, structured, mobile devices-based healthcare intervention to optimize the lipid-lowering therapy improves lipid control after an acute coronary syndrome

FRONTIERS IN CARDIOVASCULAR MEDICINE(2022)

引用 0|浏览5
暂无评分
摘要
AimsDespite the evidence, lipid-lowering treatment (LLT) in secondary prevention remains insufficient, and a low percentage of patients achieve the recommended LDL cholesterol (LDLc) levels by the guidelines. We aimed to evaluate the efficacy of an intensive, mobile devices-based healthcare lipid-lowering intervention after hospital discharge in patients hospitalized for acute coronary syndrome (ACS). Methods and resultsAmbiespective register in which a mobile devices-based healthcare intervention including periodic follow-up, serial lipid level controls, and optimization of lipid-lowering therapy, if appropriate, was assessed in terms of serum lipid-level control at 12 weeks after discharge. A total of 497 patients, of which 462 (93%) correctly adhered to the optimization protocol, were included in the analysis. At the end of the optimization period, 327 (70.7%) patients had LDLc levels <= 70 mg/dL. 40% of patients in the LDLc <= 70 mg/dL group were upgraded to very-high intensity lipid-lowering ability therapy vs. 60.7% in the LDLc > 70 mg/dL group, p < 0.001. Overall, 38.5% of patients had at least a change in their LLT. Side effects were relatively infrequent (10.7%). At 1-year follow-up, LDLc levels were measured by the primary care physician in 342 (68.8%) of the whole cohort of 497 patients. In this group, 71.1% of patients had LDLc levels <= 70 mg/dL. ConclusionAn intensive, structured, mobile devices-based healthcare intervention after an ACS is associated with more than 70% of patients reaching the LDLc levels recommended by the clinical guidelines. In patients with LDLc measured at 1-year follow-up, 71.1% had LDLc levels <= 70 mg/dL.
更多
查看译文
关键词
ischemic heart disease,secondary prevention,cardiovascular risk factors,lipid-lowering therapy,mobile devices-based healthcare
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要