Electronic health records (EHRs) represent powerful tools to study rare diseases. We developed and validated EHR algorithms to identify SLE births across centers.
We developed algorithms in a training set using an EHR with over 3 million subjects and validated algorithms at two other centers. Subjects at all 3 centers were selected using ≥ 1 SLE ICD-9 or SLE ICD-10-CM codes and ≥ 1 ICD-9 or ICD-10-CM delivery code. A subject was a case if diagnosed with SLE by a rheumatologist and had a birth documented. We tested algorithms using SLE ICD-9 or ICD-10-CM codes, antimalarial use, a positive antinuclear antibody ≥ 1:160, and ever checked dsDNA or complements using both rule-based and machine learning methods. Positive predictive values (PPVs) and sensitivities were calculated. We assessed the impact of case definition, coding provider, and subject race on algorithm performance.
Algorithms performed similarly across all three centers. Increasing the number of SLE codes, adding clinical data, and having a rheumatologist use the SLE code all increased the likelihood of identifying true SLE patients. All the algorithms had higher PPVs in African American vs. Caucasian SLE births. Using machine learning methods, total number of SLE codes and a SLE code from a rheumatologist were the most important variables in the model for SLE case status.
We developed and validated algorithms that use multiple types of data to identify SLE births in the EHR. Algorithms performed better in African American mothers than Caucasian mothers.