Electronic Heath Record Phenotypes to Classify Sickle Cell Anemia Versus Other Subtypes of Sickle Cell Disease
Authors: Strouse John J , Kayle Mariam , Oyedeji Charity I , Rothman Jennifer A. , Bihlmeyer Nathan , Ashley-Koch Allison E. , Garrett Melanie E. , Telen Marilyn J. 
Published: December 2024

Introduction: Sickle cell disease (SCD) comprises a group of severe hemoglobinopathies caused by mutations in the β-globin gene (HBB) and includes sickle cell anemia (homozygous SCD, HbSS) and compound heterozygous conditions, such as hemoglobin SC disease (HbSC) and sickle-βnull and sickle-βplus thalassemia (HbSβ0, HbSβ+). Approximately 100,000 people in the U.S. have SCD (60% of whom are adults), and worldwide over 300,000 infants with SCD are born each year. We have limited population-based data about complications and outcomes in people with SCD in the United States and need automated methods to determine subtypes of SCD in real world data from electronic health records to support learning healthcare systems.

Methods: We used data from 674 participants in the Outcome Modifying Genes in SCD Study (OMG-SCD) with confirmed genotype to evaluate the feasibility of computable phenotyping using clinical features to classify the participants by genotype. We used logistic regression to examine the discriminatory ability of individual variables to classify participants into two groups, those with sickle cell anemia (HbSS/HbSb0) and those with other genotypes (HbSC, HbSb+). We received approval to use real-world longitudinal electronic health record data from American Society of Hematology Research Consortium (ASH-RC) to develop a multivariable logistic regression model to classify participants into the same two groups. We will use model regularization techniques (e.g. LASSO, elastic net) to select only the most important predictors for the final model. We will include clinical features (complications of SCD, disease modifying therapies, and laboratory results) and calculate the area under the receiver operator characteristic curve (AUC) for each model and by age group (&lt; 18 years, 18 to 39.99 years, and &gt; 40 years).

Results: We included 559 (83%) adults with HbSS, 24 (3.6%) with HbSβ0, 67 (9.9%) with HbSC, and 24 (3.4%) with HbSβ+. Several readily available laboratory values, including hemoglobin concentration [area under the receiver operator characteristic curve (AUC)=0.908], mean corpuscular volume (AUC=0.801), and reticulocyte percentage (AUC=0.912) performed well to classify participants into these two groups in OMG-SCD. Other variables with statistically significant associations had more modest discriminatory ability [white blood cell count (AUC=0.729), platelets (AUC=0.719) and treatment with hydroxyurea (AUC=0.614). The ASH RC data from 2008 to 2023 includes ~8000 children and adults with longitudinal data, an investigator-confirmed diagnosis of SCD, and no hematopoietic stem cell transplantation. Analyzes using these data are ongoing.

Conclusions: Real-real world data from electronic health records may be used to classify subtypes of SCD. We identified an effective strategy with excellent AUCs using a limited set of widely available clinical information including clinical features and laboratory values in a research dataset and will apply this approach to a large dataset of electronic health records. Challenges include developing automated approaches to identify steady-state laboratory results and limited access to certain tests results to quantify variant hemoglobins (hemoglobin electrophoresis, high performance liquid chromatography). We plan to validate these results using additional electronic heath records data submitted to the ASH RC with an investigator diagnosis of SCD from 2022-2023.