门诊护理中 SGLT-2 抑制剂依从性影响因素的预测模型:处方索赔数据分析的启示。

IF 2 Q3 PHARMACOLOGY & PHARMACY
Pharmacy Pub Date : 2024-04-22 DOI:10.3390/pharmacy12020072
Nadia Khartabil, C. Morello, Etienne Macedo
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引用次数: 0

摘要

钠-葡萄糖共转运体 2 抑制剂(SGLT2i)是一种新型口服降糖药,对 2 型糖尿病 (T2D)、心力衰竭 (HF) 和慢性肾脏病 (CKD) 患者的心血管和代谢均有益处。在非卧床情况下,预测 SGLT-2i 服用依从性的真实世界数据非常有限。本研究旨在利用 EPIC 数据集中的电子处方索赔数据建立一个预测模型,从而预测 T2D 和/或 HF 和/或 CKD 患者是否坚持服用 SGLT-2i。这是一项回顾性研究,研究对象是 2020 年 1 月 1 日至 2021 年 4 月 30 日期间在加州大学圣地亚哥分校医疗中心门诊药房处方 SGLT-2i 的 174 名成年患者。用覆盖天数比例(PDC)来衡量依从性。使用 R 软件包确定回归和非线性回归预测模型来预测依从性。模型中包括年龄、性别、种族/民族、血红蛋白 A1c 和保险计划。此外,还使用韦尔奇 t 检验法对基于血红蛋白 A1c (HbA1c) 和肾小球滤过率 (GFR) 的糖尿病控制情况进行了评估,P 值为 0.05。测量依从性的最佳预测模型是简单决策树。它的曲线下面积(AUC)最高,为 74%,准确率为 82%。该模型考虑了 21 个主要节点预测变量,包括糖化血红蛋白、年龄、性别和保险计划支付金额。依从率与 HbA1c 成反比,与计划支付金额成正比。至于次要结果,从基线到治疗后 90 天的 HbA1c 值,不依从组一直较高:PDC ≥ 0.80 和 PDC < 0.80 的 HbA1c 值分别为 7.4% 和 9.6%,P < 0.001。基线 eGFR 为 55.18 mL/min/1.73m2 ,90 天时为 54.23 mL/min/m2。研究结束时(最少治疗 90 天),两组的平均 eGFR 有统计学差异:PDC ≥ 0.80 和 PDC < 0.80 组分别为 53.1 vs. 59.6 mL/min/1.73 m2,P < 0.001。依从性预测模型将帮助临床医生根据不依从性风险评分来定制治疗方案。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Predictive Modeling of Factors Influencing Adherence to SGLT-2 Inhibitors in Ambulatory Care: Insights from Prescription Claims Data Analysis.
Sodium-glucose cotransporter 2 inhibitors (SGLT2i) are novel oral anti-hyperglycemic drugs that demonstrate cardiovascular and metabolic benefits for patients with type 2 diabetes (T2D), heart failure (HF), and chronic kidney disease (CKD). There is limited knowledge of real-world data to predict adherence to SGLT-2i in an ambulatory setting. The study aims to predict SGLT-2i adherence in patients with T2D and/or HF and/or CKD by building a prediction model using electronic prescription claims data presented within EPIC datasets. This is a retrospective study of 174 adult patients prescribed SGLT-2i at UC San Diego Health ambulatory pharmacies between 1 January 2020 to 30 April 2021. Adherence was measured by the proportion of days covered (PDC). R packages were used to identify regression and non-linear regression predictive models to predict adherence. Age, gender, race/ethnicity, hemoglobin A1c, and insurance plan were included in the model. Diabetes control based on hemoglobin A1c (HbA1c) and the glomerular filtration rate (GFR) was also evaluated using Welch t-test with a p-value of 0.05. The best predictive model for measuring adherence was the simple decision tree. It had the highest area under the curve (AUC) of 74% and accuracy of 82%. The model accounted for 21 variables with the main node predictors, including glycated hemoglobin, age, gender, and insurance plan payment amount. The adherence rate was inversely proportional to HbA1c and directly proportional to the plan payment amount. As for secondary outcomes, HbA1c values from baseline till 90 days post-treatment duration were consistently higher in the non-compliant group: 7.4% vs. 9.6%, p < 0.001 for the PDC ≥ 0.80 and PDC < 0.80, respectively. Baseline eGFR was 55.18 mL/min/1.73m2 vs. 54.23 mL/min/m2 at 90 days. The mean eGFR at the end of the study (minimum of 90 days of treatment) was statistically different between the groups: 53.1 vs. 59.6 mL/min/1.73 m2, p < 0.001 for the PDC ≥ 0.80 and PDC < 0.80, respectively. Adherence predictive models will help clinicians to tailor regimens based on non-adherence risk scores.
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来源期刊
Pharmacy
Pharmacy PHARMACOLOGY & PHARMACY-
自引率
9.10%
发文量
141
审稿时长
11 weeks
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