Clinical trial NCT06982482: Artificial Intelligence Based Models for Primary Sjögren's Syndrome Diagnosis
Official title: Artificial Intelligence Based Models for Primary Sjögren's Syndrome Diagnosis Using Laboratory Data: A Chinese Multicenter Retrospective Study
Study type: OBSERVATIONAL
Overall status: COMPLETED
Conditions: Primary Sjögren&#39;s Syndrome (pSS)
Lead sponsor: The Affiliated Nanjing Drum Tower Hospital of Nanjing University Medical School
Sponsor class: OTHER
First posted: 2025-05-21
Enrollment: 27432
Sex: ALL
Minimum age (years): 18
Maximum age (years): 65
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: yes
Observational model: CASE_CONTROL
Time perspective: RETROSPECTIVE
Sampling method: PROBABILITY_SAMPLE
Study population: This study includes patients with primary Sjögren's syndrome (pSS; diagnosed via 2016 ACR/EULAR or 2002 ACEG criteria: oral/ocular dryness, anti-SSA/Ro+, or positive biopsy) and controls (non-pSS autoimmune diseases or non-autoimmune sicca symptoms). Eligible participants were newly diagnosed at participating hospitals with complete lab data. Exclusions: pregnancy/breastfeeding, other autoimmune diseases, malignancies, severe infections, or missing lab records. Retrospective data spans multicenter clinical records, ensuring real-world validity.

Brief summary: The goal of this observational study is to develop and validate artificial intelligence (AI)-driven models for improving the diagnosis of Primary Sjögren's Syndrome (PSS) using routine laboratory test data. The main question it aims to answer is:

Can AI-based algorithms accurately diagnose Primary Sjögren's Syndrome by analyzing laboratory test results, and do they outperform traditional diagnostic criteria in Chinese populations?

Researchers will retrospectively analyze anonymized clinical records and laboratory data (e.g., autoantibody levels, inflammatory markers) from patients with suspected or confirmed PSS across multiple medical centers in China. No new interventions will be administered, as the study utilizes existing historical data to train and validate the AI models. The performance of AI algorithms will be compared with current diagnostic standards (e.g., ACR/EULAR criteria) in terms of sensitivity, specificity, and clinical utility.


Eligibility criteria: Inclusion Criteria:

* Patients with clinician-diagnosed primary Sjögren's syndrome (pSS) meeting the 2016 ACR/EULAR or 2002 ACEG classification criteria (objective oral/ocular dryness, positive anti-SSA/Ro antibodies, or focal lymphocytic sialadenitis on biopsy).
* Control groups: Individuals with non-pSS autoimmune diseases (e.g., rheumatoid arthritis, systemic lupus erythematosus) or non-autoimmune conditions (e.g., dry eye/sicca symptoms without systemic autoimmunity).

Exclusion Criteria:

* Pregnancy, breastfeeding, with a clear diagnosis of other autoimmune diseases, severe infection and malignant tumors.
* Not newly diagnosed in any of the hospitals.
* Without any available laboratory tests.

Primary outcomes:
measure: Diagnostic Accuracy of AI Models for Primary Sjögren's Syndrome (pSS)
timeFrame: Data Collection Period: January 1, 2013, to January 31, 2023 (retrospective analysis of historical records). Model Development and Validation: Completed within 12 months of data aggregation.
Secondary outcomes:
Other outcomes:
Locations:
city: Nanjing
country: China
facility: Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University
state: Jiangsu
Study officials:
affiliation: Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University
name: Xinran Yuan, MD
role: STUDY_CHAIR
References: