Related Experiment Video
Updated: May 1, 2026
Symptom Assessment of Patients with Allergic Rhinitis Using an Allergen Exposure Chamber
Published on: March 3, 2023
Accuracy of phenotyping chronic rhinosinusitis in the electronic health record
Joy Hsu1, Jennifer A Pacheco, Whitney W Stevens
1Division of Allergy-Immunology, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA.
Background:
Chronic rhinosinusitis (CRS) is prevalent, morbid, and poorly understood. Extraction of electronic health record (EHR) data of patients with CRS may facilitate research on CRS. However, the accuracy of using structured billing codes for EHR-driven phenotyping of CRS is unknown. We sought to accurately identify CRS cases and controls using EHR data and to determine the accuracy of structured billing codes for identifying patients with CRS.
Methods:
We developed and validated distinct algorithms to identify patients with CRS and controls using International Classification of Diseases, Ninth Revision (ICD-9) and Current Procedural Terminology codes. We used blinded clinician chart review as the reference standard to evaluate algorithm and billing code accuracy.
Results:
Our initial control algorithm achieved a control positive predictive value (PPV) of 100% (i.e., negative predictive value of 100% for CRS). Our initial algorithm for CRS cases relied exclusively on billing codes and had a low case PPV (54%). Notably, ICD-9 code 471.x was associated with a case PPV of 85%, whereas the case PPV of ICD-9 code 473.x was only 34%. After multiple algorithm iterations, we increased the case PPV of our final algorithm to 91% by adding several requirements, e.g., that ICD-9 codes occur with 1 or more evaluations by a CRS specialist to enhance availability of objective clinical data for accurately phenotyping CRS.
Conclusion:
These algorithms are an important first step to identify patients with CRS, and may facilitate EHR-based research on CRS pathogenesis, morbidity, and management. Exclusive use of coded data for phenotyping CRS has limited accuracy, especially because CRS symptomatology overlaps with that of other illnesses. Incorporating natural language processing (e.g., to evaluate results of nasal endoscopy or sinus computed tomography) into future work may increase algorithm accuracy and identify patients whose disease status may not be ascertained by only using billing codes.
More Related Videos
06:22Machine Learning-Based Cough Tone Classification: Diagnostic Exploration of Chronic Obstructive Pulmonary Disease and Respiratory Tract Infections
Published on: September 19, 2025
03:40Acupoint Catgut Embedding Therapy in Traditional Chinese Medicine for Managing Allergic Rhinitis
Published on: December 20, 2024
Related Concept Videos
01:28Physical Assessment of the Respiratory Tract I: Health History
Subjective Data
Subjective data provides vital information about the patient's health history and symptoms. This data is typically collected through interviews in which patients describe their experiences, symptoms, and concerns.
Health history and...
01:30Methods of Documentation VII: EMR
01:23Chronic Pharyngitis
Etiology
It often arises from persistent viral or bacterial infections affecting sinuses and tonsils.
Additional contributing factors include inadequate dental hygiene, mouth breathing, recurring tonsillitis, allergic rhinitis, laryngopharyngeal reflux, and exposure to smoke, chemicals, and other environmental pollutants. Allergic reactions to pollen, mold, and pet dander, chronic cough, excessive voice usage,...
01:27Chronic Obstructive Pulmonary Disease-IV: Assessement and Diagnostic Studies
Medical History
01:24Chronic Obstructive Pulmonary Disease III: Chronic Bronchitis Features