用于检测服用血管紧张素 II 受体阻滞剂患者药物诱发肝损伤的时间序列深度学习的开发与验证:多中心分布式研究网络方法。

IF 2.3 Q3 MEDICAL INFORMATICS
Healthcare Informatics Research Pub Date : 2023-07-01 Epub Date: 2023-07-31 DOI:10.4258/hir.2023.29.3.246
Suncheol Heo, Jae Yong Yu, Eun Ae Kang, Hyunah Shin, Kyeongmin Ryu, Chungsoo Kim, Yebin Chegal, Hyojung Jung, Suehyun Lee, Rae Woong Park, Kwangsoo Kim, Yul Hwangbo, Jae-Hyun Lee, Yu Rang Park
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引用次数: 0

摘要

研究目的本研究旨在开发并验证一种基于多中心、多模型、时间序列的深度学习模型,用于预测服用血管紧张素受体阻滞剂(ARB)患者的药物性肝损伤(DILI)。该研究采用了国家级多中心方法,利用了韩国六家医院的电子健康记录(EHR):利用韩国六家医院的电子病历进行了一项回顾性队列分析,共有 10,852 名患者的数据被转换为通用数据模型。研究评估了服用 ARBs 患者的 DILI 发生率,并与对照组进行了比较。使用可解释的时间序列模型分析了重要变量的时间模式:结果:服用 ARBs 的患者中 DILI 的总发生率为 1.09%。每种特定 ARB 药物和机构的发病率各不相同,其中缬沙坦的发病率最高(1.24%),奥美沙坦的发病率最低(0.83%)。根据接收者操作特征曲线下的平均面积,DILI 预测模型显示出不同的性能,其中替米沙坦(0.93)、洛沙坦(0.92)和厄贝沙坦(0.90)显示出较高的分类性能。从模型中得出的综合注意分数凸显了血细胞比容、白蛋白、凝血酶原时间和淋巴细胞等变量在预测DILI中的重要性:实施基于多中心的时间序列分类模型为临床医生提供了与 ARB 使用者 DILI 相关的时间模式方面的宝贵证据。这些信息有助于就适当的药物使用和治疗策略做出明智的决定。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Development and Verification of Time-Series Deep Learning for Drug-Induced Liver Injury Detection in Patients Taking Angiotensin II Receptor Blockers: A Multicenter Distributed Research Network Approach.

Development and Verification of Time-Series Deep Learning for Drug-Induced Liver Injury Detection in Patients Taking Angiotensin II Receptor Blockers: A Multicenter Distributed Research Network Approach.

Development and Verification of Time-Series Deep Learning for Drug-Induced Liver Injury Detection in Patients Taking Angiotensin II Receptor Blockers: A Multicenter Distributed Research Network Approach.

Development and Verification of Time-Series Deep Learning for Drug-Induced Liver Injury Detection in Patients Taking Angiotensin II Receptor Blockers: A Multicenter Distributed Research Network Approach.

Objectives: The objective of this study was to develop and validate a multicenter-based, multi-model, time-series deep learning model for predicting drug-induced liver injury (DILI) in patients taking angiotensin receptor blockers (ARBs). The study leveraged a national-level multicenter approach, utilizing electronic health records (EHRs) from six hospitals in Korea.

Methods: A retrospective cohort analysis was conducted using EHRs from six hospitals in Korea, comprising a total of 10,852 patients whose data were converted to the Common Data Model. The study assessed the incidence rate of DILI among patients taking ARBs and compared it to a control group. Temporal patterns of important variables were analyzed using an interpretable timeseries model.

Results: The overall incidence rate of DILI among patients taking ARBs was found to be 1.09%. The incidence rates varied for each specific ARB drug and institution, with valsartan having the highest rate (1.24%) and olmesartan having the lowest rate (0.83%). The DILI prediction models showed varying performance, measured by the average area under the receiver operating characteristic curve, with telmisartan (0.93), losartan (0.92), and irbesartan (0.90) exhibiting higher classification performance. The aggregated attention scores from the models highlighted the importance of variables such as hematocrit, albumin, prothrombin time, and lymphocytes in predicting DILI.

Conclusions: Implementing a multicenter-based timeseries classification model provided evidence that could be valuable to clinicians regarding temporal patterns associated with DILI in ARB users. This information supports informed decisions regarding appropriate drug use and treatment strategies.

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来源期刊
Healthcare Informatics Research
Healthcare Informatics Research MEDICAL INFORMATICS-
CiteScore
4.90
自引率
6.90%
发文量
44
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