Parhofer Klaus G, Anastassopoulou Anastassia, Calver Henry, Becker Christian, Rathore Anirudh S, Dave Raj, Zamfir Cosmin
Ludwig Maximilians University, Medical Clinic IV, Großhadern, 81377 Munich, Germany.
Daiichi Sankyo Europe GmbH, Zielstattstraße 48, 81379 Munich, Germany.
J Clin Med. 2023 Jan 16;12(2):705. doi: 10.3390/jcm12020705.
Statin intolerance (SI) (partial and absolute) could lead to suboptimal lipid management. The lack of a widely accepted definition of SI results into poor understanding of patient profiles and characteristics. This study aims to estimate SI and better understand patient characteristics, as reflected in clinical practice in Germany using supervised machine learning (ML) techniques. This retrospective cohort study utilized patient records from an outpatient setting in Germany in the IQVIA™ Disease Analyzer. Patients with a high cardiovascular risk, atherosclerotic cardiovascular disease, or hypercholesterolemia, and those on lipid-lowering therapies between 2017 and 2020 were included, and categorized as having “absolute” or “partial” SI. ML techniques were applied to calibrate prevalence estimates, derived from different rules and levels of confidence (high and low). The study included 292,603 patients, 6.4% and 2.8% had with high confidence absolute and partial SI, respectively. After deploying ML, SI prevalence increased approximately by 27% and 57% (p < 0.00001) in absolute and partial SI, respectively, eliciting a maximum estimate of 12.5% SI with high confidence. The use of advanced analytics to provide a complementary perspective to current prevalence estimates may inform the identification, optimal treatment, and pragmatic, patient-centered management of SI in Germany.
他汀类药物不耐受（SI）（部分和绝对不耐受）可能导致脂质管理不理想。由于缺乏对SI的广泛接受的定义，导致对患者概况和特征的了解不足。本研究旨在估计SI，并更好地了解患者特征，这在德国的临床实践中通过监督机器学习（ML）技术得以体现。这项回顾性队列研究利用了德国门诊环境中IQVIA™疾病分析仪的患者记录。纳入了具有高心血管风险、动脉粥样硬化性心血管疾病或高胆固醇血症的患者，以及2017年至2020年间接受降脂治疗的患者，并将其分类为具有“绝对”或“部分”SI。应用ML技术校准从不同规则和置信水平（高和低）得出的患病率估计值。该研究纳入了292,603名患者，分别有6.4%和2.8%的患者被高度置信为绝对和部分SI。在应用ML后，绝对和部分SI的SI患病率分别增加了约27%和57%（p < 0.00001），得出高度置信下SI的最高估计值为12.5%。使用先进的分析方法为当前的患病率估计提供补充视角，可能有助于德国SI的识别、优化治疗以及务实的、以患者为中心的管理。
