IBM, Round Rock, TX, United States.
Biomedical Cybernetics Laboratory, Brigham and Women's Hospital, Boston, MA, United States.
J Med Internet Res. 2022 Oct 21;24(10):e35860. doi: 10.2196/35860.
COVID-19 has been observed to be associated with venous and arterial thrombosis. The inflammatory disease prolongs hospitalization, and preexisting comorbidities can intensity the thrombotic burden in patients with COVID-19. However, venous thromboembolism, arterial thrombosis, and other vascular complications may go unnoticed in critical care settings. Early risk stratification is paramount in the COVID-19 patient population for proactive monitoring of thrombotic complications.
The aim of this exploratory research was to characterize thrombotic complication risk factors associated with COVID-19 using information from electronic health record (EHR) and insurance claims databases. The goal is to develop an approach for analysis using real-world data evidence that can be generalized to characterize thrombotic complications and additional conditions in other clinical settings as well, such as pneumonia or acute respiratory distress syndrome in COVID-19 patients or in the intensive care unit.
We extracted deidentified patient data from the insurance claims database IBM MarketScan, and formulated hypotheses on thrombotic complications in patients with COVID-19 with respect to patient demographic and clinical factors using logistic regression. The hypotheses were then verified with analysis of deidentified patient data from the Research Patient Data Registry (RPDR) Mass General Brigham (MGB) patient EHR database. Data were analyzed according to odds ratios, 95% CIs, and P values.
The analysis identified significant predictors (P<.001) for thrombotic complications in 184,831 COVID-19 patients out of the millions of records from IBM MarketScan and the MGB RPDR. With respect to age groups, patients 60 years and older had higher odds (4.866 in MarketScan and 6.357 in RPDR) to have thrombotic complications than those under 60 years old. In terms of gender, men were more likely (odds ratio of 1.245 in MarketScan and 1.693 in RPDR) to have thrombotic complications than women. Among the preexisting comorbidities, patients with heart disease, cerebrovascular diseases, hypertension, and personal history of thrombosis all had significantly higher odds of developing a thrombotic complication. Cancer and obesity were also associated with odds>1. The results from RPDR validated the IBM MarketScan findings, as they were largely consistent and afford mutual enrichment.
The analysis approach adopted in this study can work across heterogeneous databases from diverse organizations and thus facilitates collaboration. Searching through millions of patient records, the analysis helped to identify factors influencing a phenotype. Use of thrombotic complications in COVID-19 patients represents only a case study; however, the same design can be used across other disease areas by extracting corresponding disease-specific patient data from available databases.
COVID-19 与静脉和动脉血栓形成有关。这种炎症性疾病会延长住院时间，而预先存在的合并症会增加 COVID-19 患者的血栓负担。然而，在重症监护环境中，静脉血栓栓塞、动脉血栓形成和其他血管并发症可能会被忽视。早期风险分层对于 COVID-19 患者群体至关重要，可主动监测血栓并发症。
本探索性研究的目的是使用电子健康记录 (EHR) 和保险索赔数据库中的信息，描述与 COVID-19 相关的血栓并发症危险因素。目标是开发一种使用真实世界数据证据进行分析的方法，该方法可以推广到其他临床环境中，例如 COVID-19 患者或重症监护病房中的肺炎或急性呼吸窘迫综合征中的血栓并发症和其他疾病。
我们从保险索赔数据库 IBM MarketScan 中提取了去识别患者数据，并使用逻辑回归针对 COVID-19 患者的血栓并发症提出了关于患者人口统计学和临床因素的假设。然后，使用 Mass General Brigham (MGB) 患者 EHR 数据库的 Research Patient Data Registry (RPDR) 去识别患者数据对假设进行了验证。根据优势比、95%置信区间和 P 值对数据进行了分析。
在来自 IBM MarketScan 和 MGB RPDR 的数百万份记录中，对 184831 例 COVID-19 患者进行了分析，确定了血栓并发症的显著预测因素 (P<.001)。就年龄组而言，60 岁及以上的患者发生血栓并发症的可能性高于 60 岁以下的患者（在 MarketScan 中为 4.866，在 RPDR 中为 6.357）。就性别而言，男性发生血栓并发症的可能性高于女性（在 MarketScan 中的优势比为 1.245，在 RPDR 中的优势比为 1.693）。在预先存在的合并症中，患有心脏病、脑血管疾病、高血压和个人血栓病史的患者发生血栓并发症的可能性均显著增加。癌症和肥胖症也与优势比>1 相关。RPDR 的结果验证了 IBM MarketScan 的发现，因为它们基本一致且相互补充。
本研究采用的分析方法可以跨来自不同组织的异构数据库进行协作。通过搜索数百万份患者记录，该分析有助于确定影响表型的因素。在 COVID-19 患者中使用血栓并发症只是一个案例研究；然而，通过从可用数据库中提取相应的疾病特定患者数据，可以在其他疾病领域使用相同的设计。
