Faculty Collaboration Database

Identification of patients with hemoglobin SS/Sβ⁰ thalassemia disease and pain crises within electronic health records. Blood Adv 2018 Jun 12;2(11):1172-1179

Date

05/25/2018

Pubmed ID

29792312

Pubmed Central ID

PMC5998922

DOI

10.1182/bloodadvances.2018017541

Scopus ID

2-s2.0-85060321728 (requires institutional sign-in at Scopus site)   13 Citations

Abstract

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/Sβ⁰ 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/Sβ⁰ 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.

Author List

Singh A, Mora J, Panepinto JA

Author

Ashima Singh PhD Assistant Professor in the Pediatrics department at Medical College of Wisconsin

MESH terms used to index this publication - Major topics in bold

Adolescent
Algorithms
Child
Child, Preschool
Databases, Factual
Electronic Health Records
Female
Genotype
Hemoglobin, Sickle
Humans
Male
Pain
Retrospective Studies
Thalassemia