Medical Affairs and Pharmacovigilance, Bayer AG, Berlin, Germany.
Research and Development, Bayer AG, Wuppertal, Germany.
Int J Popul Data Sci. 2023 Oct 2;8(1):2144. doi: 10.23889/ijpds.v8i1.2144. eCollection 2023.
In randomised controlled trials (RCTs), bleeding outcomes are often assessed using definitions provided by the International Society on Thrombosis and Haemostasis (ISTH). Information relating to bleeding events in real-world evidence (RWE) sources are not identified using these definitions. To assist with accurate comparisons between clinical trials and real-world studies, algorithms are required for the identification of ISTH-defined bleeding events in RWE sources.
To present a novel algorithm to identify ISTH-defined major and clinically-relevant non-major (CRNM) bleeding events in a US Electronic Health Record (EHR) database.
The ISTH definition for major bleeding was divided into three subclauses: fatal bleeds, critical organ bleeds and symptomatic bleeds associated with haemoglobin reductions. Data elements from EHRs required to identify patients fulfilling these subclauses (algorithm components) were defined according to International Classification of Diseases, 9th and 10th Revisions, Clinical Modification disease codes that describe key bleeding events. Other data providing context to bleeding severity included in the algorithm were: 'interaction type' (diagnosis in the inpatient or outpatient setting), 'position' (primary/discharge or secondary diagnosis), haemoglobin values from laboratory tests, blood transfusion codes and mortality data.
In the final algorithm, the components were combined to align with the subclauses of ISTH definitions for major and CRNM bleeds. A matrix was proposed to guide identification of ISTH bleeding events in the EHR database. The matrix categorises bleeding events by combining data from algorithm components, including: diagnosis codes, 'interaction type', 'position', decreases in haemoglobin concentrations ( 2 g/dL over 48 hours) and mortality.
The novel algorithm proposed here identifies ISTH major and CRNM bleeding events that are commonly investigated in RCTs in a real-world EHR data source. This algorithm could facilitate comparison between the frequency of bleeding outcomes recorded in clinical trials and RWE. Validation of algorithm performance is in progress.
在随机对照试验（RCT）中，出血结局通常使用国际血栓与止血学会（ISTH）提供的定义进行评估。在真实世界证据（RWE）来源中，并未使用这些定义来识别与出血事件相关的信息。为了帮助准确比较临床试验和真实世界研究，需要在 RWE 来源中为 ISTH 定义的出血事件识别制定算法。
提出一种新算法，用于识别美国电子健康记录（EHR）数据库中 ISTH 定义的主要和临床相关非主要（CRNM）出血事件。
ISTH 主要出血定义分为三个子条款：致命性出血、重要器官出血和与血红蛋白降低相关的有症状出血。根据描述关键出血事件的国际疾病分类第 9 版和第 10 版修订版临床修正疾病代码，定义 EHR 中识别符合这些子条款的患者所需的数据元素（算法组件）。其他提供出血严重程度背景信息的数据包括：“交互类型”（住院或门诊诊断）、“位置”（主要/出院或次要诊断）、来自实验室检查的血红蛋白值、输血代码和死亡率数据。
在最终算法中，组件组合在一起以与 ISTH 主要和 CRNM 出血定义的子条款保持一致。提出了一个矩阵来指导 EHR 数据库中 ISTH 出血事件的识别。该矩阵通过组合算法组件中的数据来对出血事件进行分类，包括：诊断代码、“交互类型”、“位置”、血红蛋白浓度下降（48 小时内下降 2g/dL）和死亡率。
这里提出的新算法可识别 RCT 中常见的 ISTH 主要和 CRNM 出血事件，在真实世界的 EHR 数据源中。该算法可以促进临床试验和 RWE 中记录的出血结局频率之间的比较。算法性能的验证正在进行中。
