Automated phenotyping of congenital heart disease for dynamic patient aggregation and outcome reporting

Objectives Accurate characterization of patients with congenital heart disease is fundamental to research, outcomes reporting, quality improvement, and clinical decision-making. Here we present an approach to computing the anatomy of patients with congenital heart disease based on the whole of their diagnostic and surgical codes. Materials and Methods All diagnostic and procedure codes for patients cared for between 1981 and 2020 at Boston Children’s Hospital were extracted from a database containing diagnostic codes from echocardiograms, and procedural codes from surgical and catheterization procedures. The pipeline sequentially (1) mapped each of the 7500 native codes to algorithm codes; (2) computed the parent anatomy for each study using a pre-defined hierarchy; (3) computed the parent anatomy for the patient, based on highest ranking parent anatomy; and (4) computed the subcategories and mandatory co-variate findings for each patient. Thereafter, diagnostic accuracy of 500 unseen patients was adjudicated against clinical documentation by clinical experts. Results A total of 514 541 echocardiograms on 161 735 patients were available for this study. Phenotypes of congenital cardiac diseases were assigned in 84 285 patients (52%), and the remainder were computed to have normal anatomy. Clinicians agreed with algorithm assignments in 96.4% (482 of 500 patients), with disagreements most often representing definitional differences. An interactive dashboard enabled by the output of this algorithm is presented. Conclusions The computation of detailed congenital heart defect phenotypes from raw diagnostic and procedure codes is possible with a high degree of accuracy and efficiency. This framework may enable tools to support interactive outcomes reporting and clinical decision support.