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Low-Density Lipoprotein Cholesterol Target Attainment in Patients With Established Cardiovascular Disease: Analysis of Routine Care Data

机译:患有型心血管疾病患者的低密度脂蛋白胆固醇靶率达到:常规护理数据分析

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Background Direct feedback on quality of care is one of the key features of a learning health care system (LHS), enabling health care professionals to improve upon the routine clinical care of their patients during practice. Objective This study aimed to evaluate the potential of routine care data extracted from electronic health records (EHRs) in order to obtain reliable information on low-density lipoprotein cholesterol (LDL-c) management in cardiovascular disease (CVD) patients referred to a tertiary care center. Methods We extracted all LDL-c measurements from the EHRs of patients with a history of CVD referred to the University Medical Center Utrecht. We assessed LDL-c target attainment at the time of referral and per year. In patients with multiple measurements, we analyzed LDL-c trajectories, truncated at 6 follow-up measurements. Lastly, we performed a logistic regression analysis to investigate factors associated with improvement of LDL-c at the next measurement. Results Between February 2003 and December 2017, 250,749 LDL-c measurements were taken from 95,795 patients, of whom 23,932 had a history of CVD. At the time of referral, 51% of patients had not reached their LDL-c target. A large proportion of patients (55%) had no follow-up LDL-c measurements. Most of the patients with repeated measurements showed no change in LDL-c levels over time: the transition probability to remain in the same category was up to 0.84. Sequence clustering analysis showed more women (odds ratio 1.18, 95% CI 1.07-1.10) in the cluster with both most measurements off target and the most LDL-c measurements furthest from the target. Timing of drug prescription was difficult to determine from our data, limiting the interpretation of results regarding medication management. Conclusions Routine care data can be used to provide feedback on quality of care, such as LDL-c target attainment. These routine care data show high off-target prevalence and little change in LDL-c over time. Registrations of diagnosis; follow-up trajectory, including primary and secondary care; and medication use need to be improved in order to enhance usability of the EHR system for adequate feedback.
机译:背景技术对护理质量的直接反馈是学习保健系统(LHS)的关键特征之一,使医疗保健专业人员能够在实践期间改善其患者的常规临床护理。目的本研究旨在评估从电子健康记录(EHRS)中提取的常规护理数据的潜力,以获得对心血管疾病(CVD)患者的低密度脂蛋白胆固醇(LDL-C)管理的可靠信息(CVD)患者提到的第三级护理中央。方法我们从CVD历史的患者的EHR提取了所有LDL-C测量,提到了大学医疗中心UTRECHT。我们评估了在推荐时和每年的LDL-C目标达到。在多次测量的患者中,我们分析了LDL-C轨迹,在6个随访测量时截断。最后,我们进行了逻辑回归分析,以调查与下一次测量的LDL-C相关的因素。结果2003年2月至2017年12月,250,749名LDL-C测量率为95,795名患者,其中23,932名具有CVD的历史。在推荐时,51%的患者尚未达到其LDL-C目标。大部分患者(55%)没有后续LDL-C测量。大多数重复测量的患者显示出LDL-C水平的变化随时间:保持在相同类别的过渡概率高达0.84。序列聚类分析显示群集中的更多女性(差异比率1.18,95%CI 1.07-1.10),其均有最多的测量结果和最远的LDL-C测量从目标最远。从我们的数据中难以确定药物处方的时序,限制了对药物管理管理结果的解释。结论常规护理数据可用于提供有关护理质量的反馈,例如LDL-C目标达到。这些常规护理数据显示出高偏移的患病率,随着时间的推移,LDL-C的变化很小。诊断注册;随访轨迹,包括初级和二级护理;需要改进和药物用途,以提高EHR系统的可用性以获得足够的反馈。

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