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Updated: Jun 11, 2026

Imaging Features of Systemic Sclerosis-Associated Interstitial Lung Disease

Imaging Features of Systemic Sclerosis-Associated Interstitial Lung Disease

Published on: June 16, 2020

Development and Validation of Algorithms for Systemic Sclerosis Identification in Electronic Health Record Data.

Gulsen Ozen¹, Michael O'Rorke^2,3, Paul Romitti²

¹Division of Immunology and Rheumatology, Carver College of Medicine, University of Iowa, Iowa City.

ACR Open Rheumatology

|June 9, 2026

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Developing accurate algorithms for identifying systemic sclerosis (SSc) cases in electronic health records (EHR) is crucial. An algorithm using two outpatient SSc ICD codes, at least 30 days apart and excluding mimics, proved highly effective for SSc case identification.

Area of Science:

Medical Informatics
Rheumatology
Epidemiology

Background:

Systemic sclerosis (SSc) identification in electronic health records (EHR) is challenging.
Accurate case ascertainment is vital for clinical and epidemiological research.
Existing methods for SSc identification in EHR data require validation.

Purpose of the Study:

To develop and validate International Classification of Diseases (ICD) code-based algorithms for identifying SSc cases in EHR data.
To evaluate the performance of these algorithms using a gold standard.
To identify the most effective algorithm for reliable SSc case detection.

Main Methods:

Utilized a large multicenter EHR dataset (TriNetX Research Network) with ICD-9/ICD-10 codes for SSc.
Performed detailed medical record review on a sample of patients to confirm SSc diagnosis (gold standard).

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Last Updated: Jun 11, 2026

Imaging Features of Systemic Sclerosis-Associated Interstitial Lung Disease

Imaging Features of Systemic Sclerosis-Associated Interstitial Lung Disease

Published on: June 16, 2020

Compared seven prespecified algorithms against the gold standard using sensitivity, specificity, PPV, NPV, and ROC analyses.

Main Results:

The best-performing algorithm required at least two outpatient SSc ICD codes ≥30 days apart, excluding mimics within 24 months (sensitivity 96%, specificity 71%, PPV 90%, NPV 87%).
Algorithms based on inpatient ICD codes showed high specificity and PPV but low sensitivity and NPV.
Incorporating inpatient codes into outpatient algorithms did not enhance performance.

Conclusions:

An algorithm using two outpatient SSc ICD codes ≥30 days apart, excluding mimics, reliably identifies SSc cases.
This implementable approach surpasses previously published complex algorithms.
The validated algorithm supports robust clinical and epidemiologic research using large EHR datasets.