Prediction of Sjögren's disease diagnosis using matched electronic dental-health record data

Jason Mao; Grace Gomez Felix Gomez; Mei Wang; Huiping Xu; Thankam P Thyvalikakath

doi:10.1186/s12911-024-02448-9

Prediction of Sjögren's disease diagnosis using matched electronic dental-health record data

BMC Med Inform Decis Mak. 2024 Feb 9;24(1):43. doi: 10.1186/s12911-024-02448-9.

Authors

Jason Mao^{1

2

3}, Grace Gomez Felix Gomez^{2

3}, Mei Wang², Huiping Xu⁴, Thankam P Thyvalikakath^{5

6}

Affiliations

¹ Department of Biostatistics and Health Data Science, Indiana University Richard M. Fairbanks School of Public Health, 410 W. 10th Street, Indianapolis, IN, 46202, USA.
² Department of Dental Public Health and Dental Informatics, Indiana University School of Dentistry, 1121 W. Michigan Street, Indianapolis, IN, 46202, USA.
³ Center for Biomedical Informatics, Regenstrief Institute, 1101 West 10th Street, Indianapolis, IN, 46202, USA.
⁴ Department of Biostatistics and Health Data Science, Indiana University School of Medicine, 410 W. 10th Street, Indianapolis, IN, 46202, USA.
⁵ Department of Dental Public Health and Dental Informatics, Indiana University School of Dentistry, 1121 W. Michigan Street, Indianapolis, IN, 46202, USA. tpt@iu.edu.
⁶ Center for Biomedical Informatics, Regenstrief Institute, 1101 West 10th Street, Indianapolis, IN, 46202, USA. tpt@iu.edu.

Abstract

Background: Sjögren's disease (SD) is an autoimmune disease that is difficult to diagnose early due to its wide spectrum of clinical symptoms and overlap with other autoimmune diseases. SD potentially presents through early oral manifestations prior to showing symptoms of clinically significant dry eyes or dry mouth. We examined the feasibility of utilizing a linked electronic dental record (EDR) and electronic health record (EHR) dataset to identify factors that could be used to improve early diagnosis prediction of SD in a matched case-control study population.

Methods: EHR data, including demographics, medical diagnoses, medication history, serological test history, and clinical notes, were retrieved from the Indiana Network for Patient Care database and dental procedure data were retrieved from the Indiana University School of Dentistry EDR. We examined EHR and EDR history in the three years prior to SD diagnosis for SD cases and the corresponding period in matched non-SD controls. Two conditional logistic regression (CLR) models were built using Least Absolute Shrinkage and Selection Operator regression. One used only EHR data and the other used both EHR and EDR data. The ability of these models to predict SD diagnosis was assessed using a concordance index designed for CLR.

Results: We identified a sample population of 129 cases and 371 controls with linked EDR-EHR data. EHR factors associated with an increased risk of SD diagnosis were the usage of lubricating throat drugs with an odds ratio (OR) of 14.97 (2.70-83.06), dry mouth (OR = 6.19, 2.14-17.89), pain in joints (OR = 2.54, 1.34-4.76), tear film insufficiency (OR = 27.04, 5.37-136.), and rheumatoid factor testing (OR = 6.97, 1.94-25.12). The addition of EDR data slightly improved model concordance compared to the EHR only model (0.834 versus 0.811). Surgical dental procedures (OR = 2.33, 1.14-4.78) were found to be associated with an increased risk of SD diagnosis while dental diagnostic procedures (OR = 0.45, 0.20-1.01) were associated with decreased risk.

Conclusion: Utilizing EDR data alongside EHR data has the potential to improve prediction models for SD. This could improve the early diagnosis of SD, which is beneficial to slowing or preventing complications of SD.

Keywords: Electronic dental records; Electronic health records; Prediction; Sjögren’s disease.

MeSH terms

Case-Control Studies
Electronic Health Records*
Electronics
Humans
Indiana / epidemiology
Xerostomia*

Abstract

MeSH terms

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