JMIR Publications

ABSTRACT

Background:

COVID-19 has been observed to be associated with venous and arterial thrombosis. The inflammatory disease prolongs hospitalization, and preexisting comorbidities can intensify thrombotic burden in COVID-19 patients. 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.

Objective:

This exploratory research seeks to characterize thrombotic complications associated with COVID-19 using information from Electronic Health Record (EHR) databases 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.

Methods:

We extracted deidentified patient data from the insurance claims database, IBM MarketScan, and formulated hypotheses on thrombotic complications in COVID-19 patients with respect to patient demographic and clinical factors using logistic regression. The analysis then verified the hypotheses with deidentified patient data from the Mass General Brigham (MGB) patient EHR database, the Research Patient Data Registry (RPDR). A combination of odds ratios, 95% confidence intervals (CI), and P-values were obtained via the statistical analysis.

Results:

The analysis identified significant predictors (with P-value <.0001) for thrombotic complications in COVID-19 patients based on millions of records from IBM MarketScan and MGB RPDR. With respect to age groups, patients 60 years old and older had higher odds to have thrombotic complications than those under 60 years old. In terms of gender, males were more likely to have thrombotic complications than females. Among the pre-existing comorbidities, heart disease, cerebrovascular diseases, hypertension, and personal history of thrombosis all had significantly higher odds of developing a thrombotic complication. Cancer and obesity had odds greater than 1 as well. The results from RPDR validated the IBM MarketScan findings. They are largely consistent and afford mutual enrichment.

Conclusions:

The analysis approach can work across heterogeneous databases from diverse organizations and thus facilitates collaboration. Searching through millions of patient records, the analysis helped identify factors influencing a phenotype. Use of thrombotic complications in COVID-19 patients is just a case study, and the same design can be used across other disease areas.