Developing an Algorithm to Better Identify Pregnant Women with Inherited Bleeding Disorders within Electronic Health Records

Background: When using electronic health records (EHRs) to conduct population-based studies on inherited bleeding disorders (IBDs), multiple studies demonstrated that using diagnoses codes alone result in a high number of false positive identifications (J Blood Med. 2017 Aug 30;8:131-139). To improve data integrity, in-depth chart reviews are often required which typically can only be done from a local institutional level. This limits the potential to use national research infrastructure such as the National Patient-Centered Clinical Research Network (PCORnet) which draws its data from millions of EHRs across healthcare institutions in the United States.

Objective: The objective of this study was to test the diagnostic accuracy of an algorithm that utilized common data definitions and multiple data elements in EHR to identify pregnant women with IBDs.

Methods: We used data for pregnant women with IBDs who delivered at our institution from January 1, 2016 to December 31, 2020 to test the accuracy of the algorithm. Women with IBD included those with the following diagnoses: hemophilia and hemophilia carriers, von Willebrand disease and rarer bleeding disorders including factor II, V, VII, X, XI and XIII deficiencies. We did not include the following diagnoses: Platelet function disorder, fibrinogen disorder or hereditary hemorrhagic telangiectasia. We used EHR data elements from EPIC Clarity data model that conformed to those from the PCORnet Common Data Model (CDM). The PCORnet CDM can help ensure consistent data definitions and formats across multiple sites to facilitate large-scale patient–centered research.

The original version of our algorithm included the following: (i) ICD-9/10 codes for an inherited bleeding disorder (Table 1) based on discharge diagnosis of any patient encounter visit types including inpatient, outpatient and emergency visits (criterion 1); (ii) Medications used for management of IBD (criterion 2); and (iii) Coagulation factor test and results (criterion 3). A validated diagnosis of IBD required fulfilling either criteria (1) and (2) or criteria (1) and (3).

To develop our algorithm in an iterative fashion, we ran sequential queries (queries 1.0-1.1) with revisions to our initial criteria. The results of our retrospective queries were confirmed to have an IBD (true positive) using our local registry and by manual chart review. The local registry is a clinical database that is housed with EPIC and includes accurate demographic, phenotypic, and laboratory data of all persons with a confirmed diagnosis of IBD seen at our federally-funded adult hemophilia treatment center. Sensitivity and the positive predictive value of the algorithm to identify pregnant women with IBDs were calculated.

Results: Using our original algorithm (query 1.0), there were 301 pregnant women who fulfilled criterion 1 and had at least one live birth or fetal death (>20 weeks gestation) at our institution during the study period. Of these, only 25 fulfilled criteria 2 and/or criteria 3. For the 276 cases that did not fulfill criteria 2 or 3, we verified the diagnosis through our local registry or through a manual review of the charts.

We found that the following ICD-diagnosis codes for IBD – Z14.8; 286.9/D68.8/D68.9; 286.3/D68.2 – resulted in contamination and could not be used to diagnose carriers of IBD or the rarer bleeding disorders. These diagnoses codes were removed and we added a fourth criteria in our revised algorithm (Table 2). For cases that did not fulfill criteria 2 or 3, a case was considered as having a diagnosis of IBD if they had at least two identical ICD diagnosis code for an IBD in at least 2 separate patient encounter visit types (criterion 4). The revised algorithm (query 1.1) identified 35 pregnant women with IBD, of which 32 were confirmed to be true positive. The 3 women incorrectly identified (false positive) by our revised algorithm had seen a hematologist for work-up for a possible IBD which was eventually ruled out. From the original cohort of 301 pregnant women who had an ICD diagnosis code of an IBD, one patient who was a carrier for hemophilia A was missed (false negative) by our revised algorithm. The sensitivity of the revised algorithm was 97.0% and the PPV was 91.4%

Conclusion: This study demonstrates the feasibility of an algorithm to accurately identify pregnant women with specific types of IBD within an EHR.