Supplementary materials: Distinguishing cardiac catheter ablation energy modalities by applying natural language processing to electronic health records

These are peer-reviewed supplementary tables for the article 'Distinguishing cardiac catheter ablation energy modalities by applying natural language processing to electronic health records' published in the Journal of Comparative Effectiveness Research.Table 1: ICD10 codes for atrial fibrillationTable 2: ICD10 codes for atrial flutterTable 3: Inpatient and outpatient atrial fibrillation ablation procedure codesTable 4: Illustrative screenshot of validation and natural language processing (NLP) table linksTable 5: 60-day de novo sensitivity analysisAim: Catheter ablation is used to treat symptomatic atrial fibrillation (AF) and is performed using either cryoballoon (CB) or radiofrequency (RF) ablation. There is limited real world data of CB and RF in the US as healthcare codes are agnostic of energy modality. An alternative method is to analyze patients’ electronic health records (EHRs) using Optum’s EHR database. Objective: To determine the feasibility of using patients’ EHRs with natural language processing (NLP) to distinguish CB versus RF ablation procedures. Data Source: Optum R ? de-identified EHR dataset, Optum R ? Cardiac Ablation NLP Table. Methods: This was a retrospective analysis of existing de-identified EHR data. Medical codes were used to create an ablation validation table. Frequency analysis was used to assess ablation procedures and their associated note terms. Two cohorts were created (1) index procedures, (2) multiple procedures. Possible note term combinations included (1) cryoablation (2) radiofrequency (3) ablation, or (4) both. Results: Of the 40,810 validated cardiac ablations, 3777 (9%) index ablation procedures had available and matching NLP note terms. Of these, 22% (n = 844) were classified as ablation, 27% (n = 1016) as cryoablation, 49% (n = 1855) as radiofrequency ablation, and 1.6% (n = 62) as both. In the multiple procedures analysis, 5691 (14%) procedures had matching note terms. 24% (n = 1362) were classified as ablation, 27% as cryoablation, 47% as radiofrequency ablation, and 2%as both. Conclusion: NLP has potential to evaluate the frequency of cardiac ablation by type, however, for this to be a reliable real-world data source, mandatory data entry by providers and standardized electronic health reporting must occur.

本数据集为发表于《比较有效性研究杂志》上的文章《通过应用自然语言处理区分心脏导管消融的能量模式》的同行评审补充表格。表1：心房颤动（AF）的ICD10编码；表2：心房扑动（AF）的ICD10编码；表3：住院和门诊心房颤动消融手术代码；表4：验证和自然语言处理（NLP）表格链接的示例截图；表5：60天新发敏感性分析。研究目的：导管消融被用于治疗症状性心房颤动（AF），其操作方式为冷球囊消融（CB）或射频消融（RF）。由于美国医疗代码对能量模式不敏感，CB和RF在现实世界中的数据有限。一种替代方法是利用Optum的电子健康记录（EHR）数据库分析患者的EHR。研究目标：确定使用患者的EHR结合自然语言处理（NLP）区分CB与RF消融手术的可行性。数据来源：Optum R 的匿名EHR数据集，Optum R 的心脏消融NLP表格。研究方法：这是一项对现有匿名EHR数据的回顾性分析。使用医疗代码创建了一个消融验证表格。通过频率分析评估消融手术及其相关的笔记术语。创建了两个队列：（1）索引手术，（2）多次手术。可能的笔记术语组合包括（1）冷冻消融，（2）射频，（3）消融，或（4）两者。研究结果：在40,810个已验证的心脏消融中，3777（9%）个索引消融手术有可用的匹配NLP笔记术语。其中，22%（n = 844）被分类为消融，27%（n = 1016）为冷冻消融，49%（n = 1855）为射频消融，1.6%（n = 62）为两者。在多次手术分析中，5691（14%）个手术有匹配的笔记术语。24%（n = 1362）被分类为消融，27%为冷冻消融，47%为射频消融，2%为两者。研究结论：自然语言处理有潜力评估不同类型心脏消融的频率，然而，为了使其成为可靠的现实世界数据来源，必须实施提供者的强制数据录入和标准化电子健康报告。