Related Experiment Video
Updated: Aug 12, 2025
Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack
Published on: May 15, 2020
Developing electronic health record algorithms that accurately identify patients with juvenile idiopathic arthritis
Hannah M Peterson1, Kelsi L Vela1, April Barnado2
1Lipscomb University College of Pharmacy and Health Sciences, Nashville, TN, United States.
Insights
Researchers developed algorithms to identify juvenile idiopathic arthritis (JIA) patients in electronic health records (EHRs). The best algorithm achieved 97% accuracy, enabling better JIA cohort identification for research.
Area of Science:
- Rheumatology
- Medical Informatics
- Data Science
Background:
- Juvenile idiopathic arthritis (JIA) is a complex autoimmune condition requiring accurate patient identification for research.
- Electronic Health Records (EHRs) contain valuable data but identifying specific patient cohorts like JIA can be challenging.
- Developing robust algorithms is crucial for leveraging EHR data in JIA research.
Purpose of the Study:
- To develop and validate algorithms for accurately identifying patients with juvenile idiopathic arthritis (JIA) within electronic health records (EHRs).
- To optimize the use of International Classification of Diseases (ICD) codes and clinical keywords for JIA case ascertainment.
- To provide researchers with reliable tools for building JIA cohorts from EHR data.
Main Methods:
- Algorithms were created using combinations of JIA-specific ICD-9 and ICD-10-CM codes, relevant keywords (e.g., "enthesitis", "uveitis"), and exclusion criteria for other autoimmune diseases.
- A training set of 200 patients was used to evaluate algorithm performance based on positive predictive value (PPV), sensitivity, and F-measure.
- The top-performing algorithm was validated in a separate patient cohort.
Main Results:
- 103 distinct algorithms were developed and tested.
- The highest performing algorithm achieved a PPV of 97% and an F-measure of 87% in the training set, identifying 1,131 JIA cases.
- Validation in a separate cohort yielded a PPV of 92% and an F-measure of 75% for the best algorithm.
- Three algorithms demonstrated a 97% PPV, offering flexibility for different research requirements.
Conclusions:
- Successfully developed and validated JIA-specific EHR algorithms using ICD codes.
- These algorithms provide a reliable method for accurately identifying JIA patient cohorts from EHR data.
- The availability of multiple high-performing algorithms allows researchers to select the best fit for their specific study needs.
Background:
The objective of this study was to develop an algorithm that accurately identifies juvenile idiopathic arthritis (JIA) patients in the electronic health record (EHR).
Methods:
Algorithms were developed in a de-identified EHR by searching for a priori JIA ICD-9 (International Classification of Diseases, Ninth Revision) and ICD-10-CM (International Classification of Diseases, Tenth Revision, Clinical Modification) codes and JIA-related keywords. Exclusion criteria were selected to remove other autoimmune diseases. A training set of 200 patients was randomly selected from patients containing ≥1 occurrence of a JIA ICD-9 or ICD-10-CM code. Case status was determined by a rheumatology clinic note documenting a JIA diagnosis before age 20. For each algorithm, positive predictive value (PPV), sensitivity, and F-measure were determined using the training set.
Results:
We developed 103 algorithms using combinations of ICD codes, keywords, and exclusion criteria. The algorithm requiring 4 or more counts of JIA ICD-9 or ICD-10-CM codes, keywords "enthesitis" and "uveitis", and exclusion of ICD-9 or ICD-10-CM codes for systemic lupus erythematosus, dermatomyositis, polymyositis, and dermatopolymyositis had the highest PPV of 97% in the training set with an F-measure of 87%. There were 1,131 JIA cases returned by this algorithm. We validated the highest performing algorithm in a separate cohort from the training set with a PPV of 92% and an F-measure of 75%.
Conclusion:
We developed and validated JIA EHR algorithms with ICD-9 and ICD-10-CM codes to accurately identify a JIA cohort. Three algorithms achieved PPVs of 97%, each with different algorithm criteria, allowing for users to select an algorithm to best fit their research needs.
Related Concept Videos
01:11Genome-wide Association Studies-GWAS
GWAS does not require the identification of the target gene involved in...
01:17Classification of Illness
An illness is a response to a disease in which the person's level of functioning is changed compared with a previous level. The general classification of illness includes acute and chronic.
Acute illness is severe...