Identifying Hemolytic Disease of the Fetus and Newborn within a Large Integrated Health Care System

Abstract

Objective This study aims to identify hemolytic disease of the fetus and newborn (HDFN) pregnancies using electronic health records (EHRs) from a large integrated health care system. Study Design A retrospective cohort study was performed among pregnant patients receiving obstetrical care at Kaiser Permanente Southern California health care system between January 1, 2008, and June 30, 2022. Using structured (diagnostic/procedural codes, medication, and laboratory records) and unstructured (clinical notes analyzed via natural language processing) data abstracted from EHRs, we extracted HDFN-specific “indicators” (maternal positive antibody test and abnormal antibody titer, maternal/infant HDFN diagnosis and blood transfusion, hydrops fetalis, infant intravenous immunoglobulin [IVIG] treatment, jaundice/phototherapy, and first administrated Rho[D] Immune Globulin) to identify potential HDFN pregnancies. Chart reviews and adjudication were then performed on select combinations of indicators for case ascertainment. HDFN due to ABO alloimmunization alone was excluded. The HDFN frequency and proportion of each combination were fully analyzed. Results Among the 464,711 eligible pregnancies, a total of 136 pregnancies were confirmed as HDFN pregnancies. The percentage of the HDFN-specific indicators ranged from 0.02% (infant IVIG treatment) to 34.53% (infant jaundice/phototherapy) among the eligible pregnancies, and 32.35% (infant IVIG treatment) to 100% (maternal positive antibody test) among the 136 confirmed HDFN pregnancies. Four combination groups of four indicators, four combination groups of five indicators, and the unique combination of six indicators showed 100% of HDFN pregnancies, while 80.88% of confirmed HDFN pregnancies had the indicator combination of maternal positive antibody test, maternal/infant HDFN diagnosis, and infant jaundice/phototherapy. Conclusion We successfully identified HDFN pregnancies by leveraging a combination of medical indicators extracted from structured and unstructured data that may be used in future pharmacoepidemiologic studies. Traditional indicators (positive antibody test results, high titers, and clinical diagnosis codes) alone did not accurately identify HDFN pregnancies, highlighting an unmet need for improved practices in HDFN coding.