ArticleAmerican journal of rhinology & allergy
Accuracy of phenotyping chronic rhinosinusitis in the electronic health record.
Article in American journal of rhinology & allergy. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
22 citing papers in PubMed.
- Temporal windowing of recurrent sinusitis improves EHR-based immunodeficiency classification.Journal of human immunity · 2026Article
- Female-Specific Risk of TAS2R Variants in Chronic Rhinosinusitis: A Hospital-Based Cohort Study From the Taiwan Precision Medicine Initiative.International forum of allergy & rhinology · 2026Article
- Large-scale automated phenotyping of cardiac arrest and withdrawal of life-sustaining therapy using electronic health record data.Resuscitation · 2026Article
- TriNetX and Real-World Evidence: A Critical Review of Its Strengths, Limitations, and Bias Considerations in Clinical Research.ASIDE internal medicine · 2025Article
- Risk Prediction of Chronic Rhinosinusitis with or without Nasal Polyps in Taiwanese Population Using Polygenic Risk Score for Nasal Polyps.Biomedicines · 2023Article
- From real-world electronic health record data to real-world results using artificial intelligence.Annals of the rheumatic diseases · 2023Review
- Evaluation of the portability of computable phenotypes with natural language processing in the eMERGE network.Scientific reports · 2023Article
- White blood cells and chronic rhinosinusitis: a Mendelian randomization study.Allergy, asthma, and clinical immunology : official journal of the Canadian Society of Allergy and Clinical Immunology · 2022Article
- Virtual Care and the Inverse Care Law: Implications for Policy, Practice, Research, Public and Patients.International journal of environmental research and public health · 2022Article
- Developing Clinical Prediction Models Using Primary Care Electronic Health Record Data: The Impact of Data Preparation Choices on Model Performance.Frontiers in epidemiology · 2022Article
- Electronic Medical Record-Based Case Phenotyping for the Charlson Conditions: Scoping Review.JMIR medical informatics · 2021Article
- Pathogenic and Uncertain Genetic Variants Have Clinical Cardiac Correlates in Diverse Biobank Participants.Journal of the American Heart Association · 2020Article
- A pathway to value-based care of chronic rhinosinusitis using a claims database.Laryngoscope investigative otolaryngology · 2019Article
- Using Calibration to Reduce Measurement Error in Prevalence Estimates Based on Electronic Health Records.Preventing chronic disease · 2018Article
- Feasibility of Large-Scale Identification of Sessile Serrated Polyp Patients Using Electronic Records: A Utah Study.Digestive diseases and sciences · 2017Article
- Economics of Chronic Rhinosinusitis.Current allergy and asthma reports · 2017Review
- The role of infection and antibiotics in chronic rhinosinusitis.Laryngoscope investigative otolaryngology · 2017Review
- Academic institution pilot study shows far fewer diagnoses of sinusitis than reported nationally.Laryngoscope investigative otolaryngology · 2016Article
- A diagnostic dilemma: chronic sinusitis diagnosed by non-otolaryngologists.International forum of allergy & rhinology · 2016Article
- Using Electronic Health Records for Population Health Research: A Review of Methods and Applications.Annual review of public health · 2016Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
backgroundChronic 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.
methodsWe 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.
resultsOur 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.
conclusionThese 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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.