Identification of patients with hemoglobin SS/Sβ thalassemia disease and pain crises within electronic health records.

Electronic health records (EHRs) are a source of big data that provide opportunities for conducting population-based studies and creating learning health systems, especially for rare conditions such as sickle cell disease (SCD). The objective of our study is to validate algorithms for accurate identification of patients with hemoglobin (Hb) SS/Sβ thalassemia and acute care encounters for pain among SCD patients within EHR warehouse. We used data for children receiving care at Children's Hospital of Wisconsin from 2013 to 2016 to test the accuracy of the 2 algorithms. The algorithm for genotype identification used composite information (blood test results, transcranial Doppler) along with diagnoses codes. Acute pain encounters were identified using diagnoses codes and further refined by using prescription of IV pain medications. Sensitivities and specificities were calculated for the algorithms. Predictive values for the algorithm to identify SCD genotype were calculated. For all assessments, the local SCD registry and patients' charts were considered gold standards. These included 360 children with SCD, of whom 51% were females. Our algorithm to identify patients with HbSS/Sβ thalassemia demonstrated sensitivity of 89.9% (confidence interval [CI], 85.1%-93.7%) and specificity of 97.1% (CI, 92.7%-99.2%). This algorithm had a positive and negative predictive value of 97.9% (CI, 94.8%-99.9%) and 88.7% (CI, 82.6%-93.3%), respectively. Acute pain crises encounters were identified with a sensitivity and specificity of 95.1% (CI, 86.3%-99.0%) and 96.1% (CI, 88.3%-99.6%). This study demonstrates the feasibility to accurately identify patients with specific types of SCD and pain crises within an EHR.