Ассоциация увеита с педиатрическими аутоиммунными заболеваниями на основе исследования TriNetX.

Background

Uveitis refers to inflammation within the uveal structure of the eye 1 . Non-infectious uveitis can occur as a consequence of autoimmune diseases. The shared inflammatory mechanism is a potential explanation. However, our current study focuses on the occurrence of uveitis among the pediatric population. Pediatric patients comprise only up to 13.8% of those seen among uveitis clinics 2 . The fewer uveitis cases among children than adults contribute to the scarcity of large-scale studies on pediatric autoimmune diseases and uveitis. This lack of data may lead to delayed uveitis diagnosis for vulnerable pediatric patients.

To address these knowledge deficits, we performed a population-based retrospective study using the TriNetX electronic health registry in order to investigate the association between uveitis and autoimmune disease among pediatric patients.

Materials and methods

Data source

This was a retrospective population-based matched cohort study that used the TriNetX platform. TriNetX is a federal cloud-based electronic health record that collaborates with healthcare centers worldwide. It contains de-identified records for more than 100 million patients. Data within the database include base demographics, diagnoses (coded with the International Classification of Diseases, 9th Revision, Clinical Modification (ICD-9-CM) and ICD-10-CM), procedures, and medications (based on the Anatomical Therapeutical Chemical Classification (ATC)). The TriNetX platform complies with the Health Insurance Portability and Accountability Act (HIPAA). Approval from the China Medical University Hospital Institutional Review Board was not required for our study due to the HIPAA compliance of the database and the exclusive use of de-identified data as part of our retrospective analysis. Lastly, we would like to report that the findings from this study were reported in accordance with the Strengthening of the Reporting of Observational Studies in Epidemiology (STROBE) reporting guideline for observational studies.

Study population and related factors

Patients were recruited based on ICD-10 diagnostic codes into two cohorts: one with autoimmune disease (the autoimmune cohort) and the other without autoimmune disease (the non-autoimmune cohort).

The autoimmune diseases were further categorized into those with and those without rheumatic manifestations (see supplementary Table 1). Autoimmune disorders of the rheumatic subtype recruited for this study included juvenile idiopathic arthritis (JIA); ankylosing spondylitis; psoriatic arthritis, and other systemic autoimmune rheumatic diseases.

Autoimmune diseases without rheumatic manifestations included: Type 1 diabetes; Crohn’s; ulcerative colitis; celiac disease; and psoriasis.

To ensure diagnostic accuracy, patients with autoimmune diseases were recruited if the diagnosis was maintained for at least two outpatient encounters or more than one inpatient encounter. The index date for the autoimmune cohort was defined as the date of the first autoimmune diagnosis. Patients with a previous diagnosis of autoimmune disease were excluded.

The non-autoimmune cohort consisted of patients who had never been diagnosed with autoimmune disease within the same study period. Their index date was randomly generated. The follow-up for this cohort ended on the new onset of uveitis.

Exclusion criteria encompassed patients aged above 18 years, those with missing basic demographic information, a history of uveitis diagnosed before the study period, and those with underlying disease of viral hepatitis, human immunodeficiency virus infection, and tuberculosis (see supplementary Table 1).

Patients with autoimmune disease and those without were matched by a 1:1 ratio using propensity score analysis. Controls were randomly selected from participants with no history of the excluded conditions. Only participants with at least one recorded interaction with the ophthalmology services before enrollment were included. Furthermore, matching based on interactions with the ophthalmologic outpatient department (OPD) was utilized. The propensity score was estimated using a logistic regression model that considered potential covariates, including age (+/- 5 years), year of index, and gender.

Comorbidities of interest included: current smoker status (ICD-9-CM code V15.82, 305.1, 794.2); diabetes (ICD-9-CM code 250); lipid metabolism disorders (ICD-9-CM code 272); and hypertension (ICD-9-CM code 401–405, A26).

Outcome measures

The primary outcome of interest was the new onset of uveitis (see supplementary Table 1). This included ophthalmia nodosa, iridocyclitis, chorioretinal inflammation, unspecified choroidal degeneration, age-related choroidal atrophy, diffuse secondary atrophy of choroid, retinal vasculitis, angioid streaks of the macula, unspecified purulent endophthalmitis, pan-uveitis, and sympathetic uveitis.

Stratification analysis

Additional subgroup analysis was conducted in order to explore the impact of various factors on uveitis risk among patients with autoimmune diseases. These factors included baseline characteristics, disease subtypes, follow-up duration, and use of immunosuppressant medications.

Regarding baseline characteristics, participants were stratified based on age, sex, and race. Subgroup analysis based on follow-up duration examined 1-year, 2-year, and 5-year intervals from baseline.

For the analysis of immunosuppressant medications, prescription data based on the Anatomical Therapeutic Chemical (ATC) codes from the three months preceding the index date was examined (see Supplementary Table 2). Immunosuppressant use is defined as the administration of these medications within three months following the new diagnosis of an autoimmune disease.

Statistical analysis

The TriNetX platform’s built-in function was used to perform propensity score matching, which generated groups with balanced baseline characteristics. Greedy nearest-neighbor matching was used to establish a 1:1 ratio between the two groups. Factors considered for this matching included age at index, ethnicity, gender, healthcare utilization settings, and specific comorbidities of interest (e.g., diabetes mellitus, dyslipidemia, hypertension, and smoker status). To assess the balance of these baseline characteristics among the propensity score-matched populations, standardized mean differences (SMD) were used. An SMD value of less than 0.1 was defined as well-matched. Cox proportional hazard models were also applied, and hazard ratios (HRs) and their corresponding 95% confidence intervals (95% CI) were reported as part of these models. Additionally, the Kaplan-Meier method was employed, and log-rank tests were conducted. A two-sided p-value of less than 0.05 was defined as the threshold for statistical significance.

Results

Baseline characteristics

Supplementary Fig. 1 depicts our cohort construction. Between January 1st, 2004 and December 31st, 2022, 175,328 patients with autoimmune diseases (mean age 10.7 +/- 5.1 years; 55.7% female; 66% White) and 175,328 patients without autoimmune diseases (mean age 10.8+/- 5.1 years; 55.7% female; 66% White) were recruited (see Table 1 ). The two groups were well matched in terms of distribution by demographics, comorbidities, and settings of medical utilization (SMD < 0.1).

Risk of uveitis among patients with autoimmune diseases based on duration of follow-up

We assessed the uveitis risk among the autoimmune cohort compared to the non-autoimmune cohort. This assessment covered the period from the index date up to the follow-up durations of 1-year, 2-year, and 5-year (see Table 2 ; Fig. 1 ).

If the patient’s count is 1–10, the results indicate a count of 10.

Patients with autoimmune disease were found to have an overall increased risk of uveitis across our 18-year study period (Hazard ratio HR: 4.42 [95% CI: 3.98–4.9]). With regards to the specific uveitis diseases involved, increased risk was identified for the following: iridocyclitis (HR 5.12 [95% CI: 4.55–5.75]), chorioretinal inflammation (HR: 4.41 [95% CI: 2.98–6.54], retinal vasculitis (HR: 9.76 [95% CI: 3.26–29.27]), unspecified purulent endophthalmitis (HR 2.45 [95% CI: 1.73–3.47]), panuveitis (HR 2.98 [95% CI: 1.51–5.9]) and sympathetic uveitis (HR 12.53 [95% CI 4.43–35.44]).

Within the 1-year follow-up period after the index date, an overall increased risk for uveitis (HR 8.83 [95% CI: 6.83–11.4]) was found among the autoimmune cohort. Specifically, increased risks were observed for the following: iridocyclitis (HR 11.61 [95% CI 8.54–15.79]), chorioretinal inflammation (HR 5.46 [95% CI 2.29–13.03]), and unspecified purulent endophthalmitis (HR 3.54 [95% CI 1.53–8.19]).

During the 2-year follow-up period after the index date, an overall increased risk for uveitis (HR 7.65 [95% CI 6.25–9.35]) was found among the autoimmune cohort. Specifically, increased risks were observed for iridocyclitis (HR 10.01 [95% CI 7.87–12.72]), chorioretinal inflammation (HR 5.36 [95% CI 2.73–10.53]), unspecified purulent endophthalmitis (HR 3.19 [95% CI 1.66–6.1]), and sympathetic uveitis (HR 5.53 [95% CI 1.23–24.95]).

Within the 5-year follow-up period after the index date, an overall increased risk for uveitis (HR 5.78 [95% CI 5.02–6.65]) was found among the autoimmune cohort. Specifically, increased risks were observed for the following: iridocyclitis (HR 7.16 [95% CI 6.08–8.42]), chorioretinal inflammation (HR 3.87 [95% CI 2.36–6.36]), retinal vasculitis (HR 8.99 [95% CI 2.08–38.91]), unspecified purulent endophthalmitis (HR 2.5 [95% CI 1.62–3.88]), panuveitis (HR 2.32 [95% CI 1.05–5.12]), and sympathetic uveitis (HR 8.22 [95% CI 2.9-23.48]).

We also analyzed the risk of uveitis occurrence within a specific time point after the index diagnosis of autoimmune diseases. Increased risk of uveitis was observed at the various timepoints of: 7 days (HR 4.66 [95% CI 4.19–5.2]), 14 days (HR 4.63 [95% CI: 4.15–5.16]), 1 month (HR 4.58 [95% CI 4.11–5.11]), 2 months (HR 4.42 [95% CI 3.95–4.94]), 3 months (HR 4.31 [95% CI: 3.85–4.82]), 6 months (HR 4.08 [95% CI 3.64–4.58]), 9 months (HR 3.93 [95% CI 3.49–4.43]), and 12 months (HR 3.82 [95% CI: 3.38–4.31]) after the index date (see Supplementary Table 3).

The associated Kaplan-Meir curves are depicted in Fig. 1 . When stratified by follow-up duration, a significant association between new-onset uveitis and autoimmune diseases was observed across all follow-up periods, irrespective of duration.

Risk stratification based on autoimmune disease status or use of immunosuppressant medications

In comparison to the reference group (patients without autoimmune diseases), increased uveitis risk was seen among those with autoimmune disease and who had used immunosuppressant medications (HR 11.78 [95% 9.38–14.78]). Similarly, a comparative increased uveitis risk was seen among those with autoimmune diseases who had received glucocorticoids (HR 9.11 [95% CI 7.02–11.82]) and disease-modifying antirheumatic drugs (DMARDs) (HR 17.71 [95% CI 12.37–25.30] (see Table 3 ).

In comparison to the autoimmune cohort who had never used immunosuppressant medications as the reference group, patients from the autoimmune cohort who had used immunosuppressant medications were found to have an increased risk for uveitis (HR 7.83 [95% CI: 6.27–9.77]).

Subgroup analysis

The risk of uveitis was further examined through subgroup analysis based on age, sex, ethnicity, and subtypes of rheumatic diseases (see Supplementary Table 4, Supplementary Table 5, Supplementary Fig. 2, and Supplementary Fig. 3).

In terms of age groups, an increased risk of uveitis was seen among patients from the autoimmune cohort that were aged 0–6 years old (HR 11.47 [95% CI: 8.89–14.79]), 7–12 years old (HR 4.33 [95% CI: 3.57–5.24]), and 13–18 years old (HR 3.11 [95% CI: 2.65–3.65]).

In terms of gender, an increased risk of uveitis was seen among patients from the autoimmune cohort that were female (HR: 5.71 [95% CI: 4.97–6.57]) as well as male (HR: 3.7 [95% CI: 3.11–4.39]).

In terms of ethnicity, an increased risk of uveitis was seen among patients from the autoimmune cohort that were white (HR: 4.38 [95% CI: 3.87]), black (HR: 2.96 [95% CI: 2.22–3.95]), and Asian (HR: 6.18 [95% CI: 2.35–16.25]) ethnic backgrounds.

In terms of subtypes of rheumatic diseases, increased risk of uveitis were seen among those who were diagnosed with inflammatory arthritis (HR: 25.62 [95% CI: 19.16-34]), systemic autoimmune rheumatic diseases (HR: 3.84 [95% CI: 2.38–6.2]), systemic vasculitis (HR: 4.68 [95% CI: 2.73–8.03]), inflammatory bowel disease (HR: 7.9 [95% CI: 5.9-10.57]), psoriasis (HR: 1.96 [95% CI: 1.3–2.97]), multiple sclerosis (HR: 6.2 [95% CI: 2.5-15.42]), and immune thrombocytopenic purpura (HR: 2.13 [95% CI: 1.2–3.77]).

Discussion

Novel findings

Our results showed an increased risk for uveitis among patients diagnosed with autoimmune disease compared to those without. This risk for uveitis has been consistently demonstrated across all follow-up periods up to 18 years from the index date. The uveitis diseases at increased risk include iridocyclitis, chorioretinal inflammation, unspecified purulent endophthalmitis, panuveitis, retinal vasculitis and sympathetic uveitis. Furthermore, compared to those without autoimmune disease who have not received any immunosuppressant medications, patients who used immunosuppressant medications exhibited an increased risk for uveitis.

Subgroup analysis based on the subtypes of autoimmune disease demonstrated a significantly increased uveitis risk associated with inflammatory arthritis, systemic autoimmune rheumatic diseases, systemic vasculitis, inflammatory bowel disease, psoriasis, multiple sclerosis, and immune thrombocytopenic purpura.

Clinical implications

Our findings have clinical relevance as we found increased uveitis risk among autoimmune patients treated with immunosuppressant medications compared to those who did not use such medications. Our study may be one of the first to report such findings. This is important when considering the fact that autoimmune medications are not presently mentioned among the existing guidelines for various autoimmune conditions. For example, the American College of Rheumatology (ACR) guidelines for JIA recommended regular ophthalmologic examinations at 3-month intervals for high-risk patients. For JIA patients deemed low-risk, such ophthalmologic examinations were recommended to be done less frequently. Such risk-stratification protocol only considered the subtype of JIA, age at diagnosis, JIA duration, and antinuclear antibody (ANA) status 3 . Therefore, our findings emphasize the importance of regular ophthalmologic screening among pediatric patients with autoimmune diseases, even among those with history of immunosuppressant medications.

Pathogenesis between autoimmune disease and uveitis

Regarding pathogenesis, the exact mechanisms behind the link between autoimmune disease and uveitis remain unknown. However, autoimmune dysfunction is thought to play a role. The prevailing theory suggests autoimmune conditions may incite immune responses against retinal structures through a cross-reactive process (see Supplementary Fig. 4).

Comparison to literature

Regarding comparison of our results with other studies, we summarized ten large-scale studies that have explored uveitis association among autoimmune patients 4 - 13 (see Supplementary Table 6 ). In a population-based study conducted by Tseng et al., 832 patients of all age groups with uveitis were recruited to retrospectively investigate the associations with autoimmune diseases. Their result showed that 123 (14.8%) of their uveitis patients had rheumatic diseases. The most prevalent autoimmune diseases associated with uveitis found in their study included ankylosing spondylitis (39.8% of all their cases), Behcet disease (18.7%), sarcoidosis (9.8%), psoriasis (7.3%), JIA (7.3%), juvenile ankylosing spondylitis (4.1%), rheumatoid arthritis (2.4%), SLE (2.4%), IBD (0.8%). Additionally, Tseng et al. found that anterior uveitis was the most common subtype, affecting 84 patients (68.3%). This was followed by panuveitis (28 patients, 22.8%), posterior uveitis (6 patients, 4.9%), and intermediate uveitis (3 patients, 2.4%). These findings mostly align with existing literature (see Supplementary Table 6) and our own. Furthermore, our study stands out by focusing on the pediatric population. This differentiates us from most studies (including Tseng et al.), because these studies often focus on the general population rather than specifically on children. This underscores the clinical importance of our research, as we contribute valuable insights from a relatively underexplored age group.

We recognize the significant clinical value of examining this issue from a non-ophthalmologist perspective. In Supplementary Table 5, we found that inflammatory arthritis, systemic autoimmune rheumatic diseases, systemic vasculitis, psoriasis, multiple sclerosis, immune thrombocytopenic purpura, and inflammatory bowel disease have a notable impact on pediatric patients with uveitis. In the past, it was generally understood that autoimmune diseases were associated with uveitis. However, our study has clearly defined which specific autoimmune diseases are indeed linked. This provides non-ophthalmologists with a clearer understanding and allows them to detect warning signs earlier and refer pediatric patients with these conditions for specialist evaluation in a timely manner. Additionally, we observed that rheumatic diseases within the autoimmune spectrum are associated with a higher risk of uveitis. This elevated risk may be attributed to the nature of rheumatic diseases, which are often characterized by systemic inflammation and immune dysregulation, leading to a higher propensity for ocular involvement. In contrast, autoimmune conditions without systemic inflammation, such as autoimmune hepatitis or type 1 diabetes mellitus, tend to have a lower risk of uveitis.

Interestingly, there seems to be a preference for the female gender among autoimmune conditions (see Supplementary Table 6). Our study further extrapolated upon this by demonstrating that both genders have an increased risk for uveitis after the diagnosis of autoimmune diseases. Laboratory studies have shown sex hormones from both genders can influence the responses of T cells and B cells 14 . Consequently, it is theoretically possible that gender may affect the development of ocular inflammatory disorders. However, the absence of similar subgroup analysis on the influence of gender on uveitis risk in other studies makes such an assessment challenging.

Another important point from our study was the heightened risk of uveitis among autoimmune patients with a history of immunosuppressant medication usage compared to those without similar medication usage. To the best of our knowledge, our research may be among the first to report such associations. This finding contrasts with other studies. For example, Bison et al. conducted a study involving 184 JIA patients in order to assess their uveitis risk. Within their analysis, Bison et al. also evaluated how immunosuppressant use would affect uveitis incidence. Their results showed no statistically significant difference in uveitis incidence between autoimmune patients who had received immunosuppressant medications (including DMARDS) and those who didn’t use immunosuppressant 15 . We hypothesize that selection bias may have played a role. Pediatric patients with autoimmune diseases who require immunosuppressant medications may be more likely to develop uveitis, possibly due to the clinical severity of their condition. Therefore, it is possible our study may have recruited a selection of autoimmune patients who were more predisposed to developing uveitis. Furthermore, autoimmune patients who received immunosuppressants would more likely be closely monitored by clinicians. This may result in an overestimation of uveitis among such a cohort despite this potential bias, our findings based on a population-based cohort study underscore the importance of clinicians to be vigilant for uveitis in pediatric patients with autoimmune diseases, even if there is a history of immunosuppressant medication use.

Strengths and limitations

In terms of strengths, our study represents one of the most extensive investigations that used real-world data to explore the association between uveitis and autoimmune diseases in pediatric patients. Notably, we employed propensity score matching as well as a longitudinal design. As part of our subgroup analysis, we also investigated the uveitis risk at specific time points. This ranged from the first 7 days after and up to 12 months after the index date. The significance of such an analysis lies in its capacity to eliminate any residual confounding factors present at the index date, which predisposes autoimmune patients to develop uveitis immediately after the index date. Such a relationship between uveitis and baseline confounders would unlikely persist over an extended period. Therefore, including such an analysis as part of our study design provides additional evidence of our findings’ reliability.

However, some limitations warrant discussion. Firstly, since our study relied on the retrospective analysis of diagnostic codes, we could not access data on specific aspects that may have affected our results. For instance, information regarding the frequency and dosage of the immunosuppressant medications used by our recruited autoimmune population was unavailable.

Besides, one of the limitations of this study is the inability to determine the rate of loss to follow-up within our cohort. The TriNetX database does not provide explicit information on patients who may have been lost to follow-up after their last recorded clinical encounter. This lack of follow-up data may affect the accuracy of our outcome.

Furthermore, as discussed, we cannot ascertain whether the observed risk associated with immunosuppressive medications is due to the severity of the underlying disease or represents a direct effect of the medications themselves. Consequently, we recommend that clinicians closely monitor the ocular health of pediatric patients receiving immunosuppressants.

Another limitation was that our study only assessed the risk of uveitis based on the number of reported uveitis events. Our study lacks additional information on the severity of uveitis, such as best-corrected visual acuity, the presence of anterior chamber inflammation, intraocular pressure, and vitreous cavity inflammation which has been included in some studies on similar topics 16 – 18 . These additional data would be relevant for clinicians caring for at-risk patients and should be explored in the future.

Conclusion

In conclusion, our study showed that the pediatric population with autoimmune diseases was at significantly increased risk for uveitis development. Our findings have clinical relevance for clinicians caring for at-risk children with autoimmune diseases.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary Material 1

Author contributions

AYH, HTK, CJL, NYH, CCW, YLL, JCCW, YHW, CTL, HSC, YYT wrote and edited the main text of the paper. AYH, HTK, CJL, NYH, CCW, YLL, JCCW, YHW, CTL, HSC, YYT participated in the design of the study and drafting of the paper. AYH, HTK, CJL, NYH, CCW, YLL, JCCW, YHW, CTL, HSC, YYT designed, discussed, edited and guided the overall process of the paper. AYH, HTK, CJL, NYH, CCW, YLL, JCCW, YHW, CTL, HSC, YYT contributed to the drafting of the paper. AYH, HTK, CJL, NYH, CCW, YLL, JCCW, YHW, CTL, HSC, YYT contributed to the design of the study. AYH, HTK, CJL, NYH, CCW, YLL, JCCW, YHW, CTL, HSC, YYT provided valuable insight and contributed to the drafting of the paper. All authors (AYH, HTK, CJL, NYH, CCW, YLL, JCCW, YHW, CTL, HSC, YYT) contributed equally to the manuscript revisions All authors (AYH, HTK, CJL, NYH, CCW, YLL, JCCW, YHW, CTL, HSC, YYT) approved the final version of the manuscript and agree to be held accountable for the content therein.

Funding

Not applicable.

Data availability

The TriNetX platform is a fully deidentified multinational, cloud-based database that adheres to all the relevant adheres to all standards laid out by Section §164,514(b) (1) of the Health Insurance Portability and Accountability Act (HIPPA) as well as ISO 27001:2013. Due to privacy restrictions, data from the TriNetX database is not publicly available. However, the population-level aggregate and deidentified data supporting the findings of this study are openly accessible upon reasonable request to the TriNetX administrators through their website (https://trinetx.com/) or by contacting the TriNetX administrators directly (Privacy@TriNetX.com). Alternatively, the corresponding author may also be contacted (doctoraga@gmail.com).

Declarations

Competing interests

The authors declare no competing interests.

Ethics approval

The study adhered to the tenets of the Declaration of Helsinki. China Medical University Hospital Institutional Review Board approval and ethics approval was not required as the TriNetX database has been accredited by an expert to be HIPAA-compliant (as defined in Section § 164,514(b)(1) of HIPAA), certified to the ISO 27001:2013 standard and only de-identified data was retrospectively used.

Footnotes

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Alan Y. Hsu and Hou-Ting Kuo contributed equally as first authors.

Contributor Information

Chun-Ju Lin, Email: doctoraga@gmail.com.

Ning-Yi Hsia, Email: deepwhite1111@gmail.com.

James Cheng-Chung Wei, Email: jccwei@gmail.com.

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Associated Data

Supplementary Materials

Supplementary Material 1

Data Availability Statement

The TriNetX platform is a fully deidentified multinational, cloud-based database that adheres to all the relevant adheres to all standards laid out by Section §164,514(b) (1) of the Health Insurance Portability and Accountability Act (HIPPA) as well as ISO 27001:2013. Due to privacy restrictions, data from the TriNetX database is not publicly available. However, the population-level aggregate and deidentified data supporting the findings of this study are openly accessible upon reasonable request to the TriNetX administrators through their website (https://trinetx.com/) or by contacting the TriNetX administrators directly (Privacy@TriNetX.com). Alternatively, the corresponding author may also be contacted (doctoraga@gmail.com).