Development of Clinical Decision Models for the Prediction of Systemic Lupus Erythematosus and Sjogren's Syndrome Overlap
←
→
Page content transcription
If your browser does not render page correctly, please read the page content below
Journal of
Clinical Medicine
Article
Development of Clinical Decision Models for the Prediction of
Systemic Lupus Erythematosus and Sjogren’s Syndrome Overlap
Yan Han, Ziyi Jin, Ling Ma, Dandan Wang, Yun Zhu, Shanshan Chen, Bingzhu Hua, Hong Wang
and Xuebing Feng *
Department of Rheumatology and Immunology, The Affiliated Drum Tower Hospital of Nanjing University
Medical School, Nanjing 210008, China
* Correspondence: fengxuebing@hotmail.com
Abstract: Objective: To explore the clinical features of patients with systemic lupus erythematosus
and Sjögren’s syndrome overlap (SLE-SS) compared to concurrent SLE or primary SS (pSS) patients,
we utilized a predictive machine learning-based tool to study SLE-SS. Methods: This study included
SLE, pSS, and SLE-SS patients hospitalized at Nanjing Drum Hospital from December 2018 to
December 2020. To compare SLE versus SLE-SS patients, the patients were randomly assigned to
discovery cohorts or validation cohorts by a computer program at a ratio of 7:3. To compare SS
versus SLE-SS patients, computer programs were used to randomly assign patients to the discovery
cohort or the validation cohort at a ratio of 7:3. In the discovery cohort, the best predictive features
were determined using a least absolute shrinkage and selection operator (LASSO) logistic regression
model among the candidate clinical and laboratory parameters. Based on these factors, the SLE-SS
prediction tools were constructed and visualized as a nomogram. The results were validated in
a validation cohort, and AUC, calibration plots, and decision curve analysis were used to assess
the discrimination, calibration, and clinical utility of the predictive models. Results: This study of
SLE versus SLE-SS included 290 patients, divided into a discovery cohort (n = 203) and a validation
cohort (n = 87). The five best characteristics were selected by LASSO logistic regression in the
discovery cohort of SLE versus SLE-SS and were used to construct the predictive tool, including
Citation: Han, Y.; Jin, Z.; Ma, L.; dry mouth, dry eye, anti-Ro52 positive, anti-SSB positive, and RF positive. This study of SS versus
Wang, D.; Zhu, Y.; Chen, S.; Hua, B.; SLE-SS included 266 patients, divided into a discovery cohort (n = 187) and a validation cohort
Wang, H.; Feng, X. Development of
(n = 79). In the discovery cohort of SS versus SLE-SS, by using LASSO logistic regression, the eleven
Clinical Decision Models for the
best features were selected to build the predictive tool, which included age at diagnosis (years),
Prediction of Systemic Lupus
fever, dry mouth, photosensitivity, skin lesions, arthritis, proteinuria, hematuria, hypoalbuminemia,
Erythematosus and Sjogren’s
anti-dsDNA positive, and anti-Sm positive. The prediction model showed good discrimination,
Syndrome Overlap. J. Clin. Med. 2023,
12, 535. https://doi.org/10.3390/
good calibration, and fair clinical usefulness in the discovery cohort. The results were validated in
jcm12020535 a validation cohort of patients. Conclusion: The models are simple and accessible predictors, with
good discrimination and calibration, and can be used as a routine tool to screen for SLE-SS.
Academic Editor: Chang-Hee Suh
Received: 13 December 2022 Keywords: systemic lupus erythematosus; Sjögren’s syndrome; overlap; prediction model
Revised: 31 December 2022
Accepted: 4 January 2023
Published: 9 January 2023
1. Introduction
Systemic lupus erythematosus (SLE) is a typical autoimmune connective tissue disease
that can clinically accumulate in multiple organs throughout the body and is more prevalent
Copyright: © 2023 by the authors.
in women of reproductive age [1,2]. Sjögren’s syndrome (SS) is a chronic inflammatory
Licensee MDPI, Basel, Switzerland.
autoimmune disease mainly involving the exocrine glands. SS is clinically manifested
This article is an open access article
distributed under the terms and
as dryness of the mouth and eyes, but extra glandular organs may also be involved,
conditions of the Creative Commons
either independently or in combination with other connective tissue diseases, including
Attribution (CC BY) license (https:// rheumatoid arthritis and systemic lupus erythematosus [3–5]. These two diseases often
creativecommons.org/licenses/by/ coexist in the same individual, which may affect the proper diagnosis and prognosis for
4.0/). these patients. The association of SLE and SS was first described in 1959, and SLE with SS
J. Clin. Med. 2023, 12, 535. https://doi.org/10.3390/jcm12020535 https://www.mdpi.com/journal/jcmJ. Clin. Med. 2023, 12, 535 2 of 10
is considered a distinct subtype of SLE [6]. In recent years, the number of patients with SLE
and SS overlap has gradually increased, and studies have shown that 8.3–19.0% of SLE
patients also have SS [7]. Another study in China even showed that 24 out of 55 SLE cases
(43.6%) had secondary SS [8].
In addition to finding that the prognosis of patients with SLE-SS overlap is different
from that of patients with a single disease, previous studies have also revealed that these
patients have unique clinical characteristics [9–11]. Unfortunately, the conclusions in
different reports are varied, and more than 10 types of clinical manifestations are involved,
which is difficult to use for routine clinical implementation. Therefore, it is necessary to
develop a practical and reliable screening technique to help identify patients with SLE-SS
overlap early to improve their long-term outcome.
In this study, we retrospectively analyzed clinical data related to patients with SLE, SS,
and SLE-SS. The risk factors affecting patients with SLE or SS in SLE-SS were extracted,
and a decision model for predicting patients with SLE or SS versus SLE-SS was constructed.
This model may provide a simple and effective tool for identifying patients with SLE-SS.
2. Materials and Methods
2.1. Patients and Controls
Data were retrospectively collected from 477 patients with SLE (n = 211), pSS (n= 187),
and SLE-SS (n = 79) between December 2018 and December 2020 from the Department
of Rheumatology and Immunology of Nanjing Drum Tower Hospital. The diagnosis of
SLE was given according to the 1997 American College of Rheumatology (ACR) revised
criteria for the classification of systemic lupus erythematosus [12], and the diagnosis of
pSS was based on the 2002 American-European Consensus group criteria [13]. SLE-SS
is defined as meeting the diagnostic criteria for both SLE and secondary SS, excluding
patients with lymphoma, nodal disease, hepatitis virus infection, AIDS, radiation therapy,
and anti-acetylcholine drugs.
2.2. Data Collection
Data on demographic characteristics and clinical and laboratory findings were col-
lected retrospectively. The clinical features assessed included fever, alopecia, asthenia, dry
mouth, dry eye, photosensitivity, skin lesions, oral ulcer, Raynaud phenomenon, cavities,
epistaxis, arthritis, interstitial lung disease, pulmonary arterial hypertension, and vasculitis.
Laboratory findings included proteinuria, hematuria, anemia, leukocytopenia, thrombo-
cytopenia, hypoalbuminemia, increased blood urea nitrogen, increased serum creatinine,
increased erythrocyte sedimentation rate (ESR), increased C-reactive protein, hypocomple-
mentemia, ANA, anti-dsDNA antibody, anti-SSA antibody, anti-Ro52 antibody, anti-SSB
antibody, anti-Sm antibody, anti-RNP antibody, anti-cardiolipin antibody, rheumatoid fac-
tor (RF)-positive, and IgG elevation. Normal values for the laboratory tests were as follows:
hemoglobin ≥ 110 g/L (female) or 120 g/L (male); platelets 100–300 × 109 /L; leukocytes
4–10 × 109 /L; serum albumin ≥ 35 g/L; blood urea nitrogen (BUN) ≤ 7.5 mmol/L; serum
creatinine ≤ 133 µmol/L; erythrocyte sedimentation rate (ESR) ≤ 20 (female) or ≤15 mm/h
(male); CRP ≤ 8 mg/L; complement C3 ≥ 0.8 g/L or C4 ≥ 0.2 g/L; ANA-negative
(J. Clin. Med. 2023, 12, 535 3 of 10
For SLE versus SLE-SS, a total of 290 patients were randomly assigned to the discovery
cohort (n = 203) or the validation cohort (n = 87) using a computer program at a ratio of
7:3. Similarly, for SS versus SLE-SS, a total of 266 patients were randomly assigned to the
discovery cohort (n = 187) or the validation cohort (n = 79) using a computer program at
a ratio of 7:3. For the training group, the sample size followed the 10-fold criterion, that
is, each prediction variable needs at least 10 observations to produce a reasonable and
stable estimate [14,15]. In this study, 5 predictive variables were selected as the final model,
requiring at least 50 observations and a sample size of at least 200 for the training group
based on a 25% incidence. PASS 15 software (NCSS, LLC, Kaysville, Utah) was used to
calculate the sample size of the verification group [16]. The minimum number of positive
events was 10, the minimum number of negative events was 50, and the minimum sample
size of the verification group was 60. A sufficient sample size is included in this study.
We developed prediction tools using the discovery cohorts. A least absolute shrinkage
and selection operator (LASSO) logistic regression model and 10-fold cross-validation
were applied to select the best predictive features among the clinical characteristics and
laboratory parameters in the discovery cohort that were statistically different between SLE
and SLE-SS or SS and SLE-SS. The performance of the predictive tool was evaluated for
discrimination, calibration, and clinical usefulness. To assess the discrimination of the
predictive model, the discriminatory ability was measured using the area under the receiver
operating characteristic (AUC) curve. Calibration curves were then plotted to evaluate the
calibration effect of the SLE-SS prediction models. Decision curve analysis was used by
quantifying the net benefits to determine the clinical utility of the SLE-SS predictive models.
All data analyses used R software (version 4.0.3). In R software, Packages (“rms”) and
(“rmda”) were operated. All tests were two-sided, and a p-value < 0.05 was considered
statistically significant.
3. Results
3.1. Patient Characteristics
For SLE versus SLE-SS, a total of 290 patients were included in this study, and a random
sample at a ratio of 7:3 was divided into discovery (n = 203) and validation groups (n = 87).
Regarding SS versus SLE-SS, a total of 266 patients were included in this study and were
randomized in a 7:3 ratio to the discovery (n = 187) and validation groups (n = 79). A flow
chart is shown in Figure 1. Table S1 summarizes the clinical characteristics and laboratory
parameters of the discovery and validation groups. The characteristics of the two groups
of patients were comparable. We also summarize the clinical characteristics and laboratory
parameters of SLE, SS, and SLE-SS in the discovery and validation cohorts in Tables S2 and S3.
3.2. Development of the Prediction Tool for SLE or SLE-SS
The 11 candidate clinical and laboratory indicators that were statistically different
between SLE-SS and SLE were reduced to the five most useful predictive markers by using
LASSO logistic regression (Figure 2A,B). These characteristics included dry mouth, dry eye,
anti-Ro52 positive, anti-SSB positive, and RF positive. The results of the logistic regression
analysis for SLE versus SLE-SS are given in Table 1. We constructed prediction tools based
on these factors for SLE in SLE-SS and visualized them as nomograms (Figure 3A).J. Clin. Med. 2023, 12, x FOR PEER REVIEW 4 of 10
J. Clin. Med. 2023, 12, 535 4 of 10
Figure 1. Flowchart for constructing the SLE or SS in the SLE-SS prediction tool.
Table 1. Risk factors for patients with SLE or SS to develop SLE-SS.
Prediction Model
Intercept
Figure 1. Flowchart for and Variable
constructing the SLE or SS in the SLE-SS prediction tool.
β Odds Ratio (95% CI) p-Value
Dry mouth 1.83 6.24(2.00–19.80) 0.00
3.2. Development of the Prediction
Dry eye
Tool for SLE or SLE-SS 3.42(0.95–12.89)
1.23 0.06
11 candidateAnti-Ro52
Thedevelop
SLE clinicalpositive
and laboratory 0.96
indicators2.60(1.13–6.18) 0.03 different
that were statistically
SLE-SS Anti-SSB positive 1.02 2.77(1.12–6.91) 0.03
between SLE-SS and SLE were reduced to the five most useful predictive markers by using
RF positive 1.84 6.29(2.37–17.32) 0.00
LASSO logistic regression (Figure
Age at diagnosis 2A,B). These
(years) −0.02characteristics included dry0.28
0.98(0.94–1.02) mouth, dry
eye, anti-Ro52 positive, anti-SSB
Fever
positive, and RF positive.
2.24
The results of the
9.40(2.17–48.81)
logistic re-
0.00
gression analysis for SLEDryversus
mouth SLE-SS are given
−3.02 in Table 1. We constructed
0.049(0.01–0.31) 0.00prediction
Photosensitivity 3.22 24.94(2.72–363.01)
tools based on these factors for SLE in SLE-SS and visualized them as nomograms 0.01 (Figure
Skin lesions 3.64 38.04(6.25–341.38) 0.00
3A).
SS develop Arthritis 0.75 2.12(0.53–8.82) 0.29
SLE-SS Proteinuria 1.07 2.91(0.68–13.06) 0.15
Hematuria 2.54 12.0(2.62–78.66) 0.00
Hypoalbuminemia 2.89 17.95(4.07–106.35) 0.00
Anti-dsDNA positive 2.46 11.75(2.82–61.23) 0.00
Anti-Sm positive 0.02 0.98(0.12–8.81) 0.99
β is the regression coefficient. OR: Odds ratio; CI: Confidence interval.J. Clin. Med. 2023, 12, x FOR PEER REVIEW 5 of 10
J. Clin. Med. 2023, 12, 535 5 of 10
Figure
Figure2.2.Clinical
Clinicaland
and laboratory parameterselection
laboratory parameter selection using
using the the LASSO
LASSO binary
binary logistic
logistic regression
regression
model.
model.(A,B)
(A,B)Results
Resultsfor
forthe
the11
11variables includedin
variables included inthe
theLASSO
LASSOregression
regression associated
associated withwith
SLESLE in
SLE-SS and the
in SLE-SS andcorresponding coefficients
the corresponding forfor
coefficients thethe
corresponding
correspondingvariables
variablesatatdifferent
different values.
values. (C,D)
Results
(C,D) for the for
Results 17 variables included
the 17 variables in theinLASSO
included the LASSOregression associated
regression with
associated withSS SSand
and their
their corre-
sponding coefficients for the corresponding variables at different values of coefficients.
corresponding coefficients for the corresponding variables at different values of coefficients. LASSO, LASSO,
minimum absolute shrinkage, and selection operators.
minimum absolute shrinkage, and selection operators.
Table 1.Similarly,
Risk factors for patients
as shown with SLE
in Figure orLASSO
2C,D, SS to develop
logisticSLE-SS.
regression was used to reduce
the 17 candidate laboratory indicators that were statistically different between SLE-SS and
SS to the 11 most useful predictive markers Prediction
including age at diagnosis Model
(years), fever, dry
Intercept and Variable
β
mouth, photosensitivity, skin lesions, arthritis, proteinuria,Odds Ratiohypoalbuminemia,
hematuria, (95% CI) p-Value
anti-dsDNA positive, Dryand anti-Sm
mouth positive. The results
1.83of the logistic regression analysis 0.00
6.24(2.00–19.80) of
SS and SLE-SS are shown in
Dry eye Table 1. Based on these factors,
1.23 we constructed
3.42(0.95–12.89) a prediction
0.06
tool for SS in SLE-SS and visualized it as a nomogram (Figure 3B).
Anti-Ro52 positive 0.96 2.60(1.13–6.18) 0.03
SLE develop SLE-SS
Anti-SSB positive 1.02 2.77(1.12–6.91) 0.03
RF positive 1.84 6.29(2.37–17.32) 0.00
Age at diagnosis (years) −0.02 0.98(0.94–1.02) 0.28
Fever 2.24 9.40(2.17–48.81) 0.00
Dry mouth −3.02 0.049(0.01–0.31) 0.00
SS develop SLE-SS
Photosensitivity 3.22 24.94(2.72–363.01) 0.01
Skin lesions 3.64 38.04(6.25–341.38) 0.00Proteinuria 1.07 2.91(0.68–13.06) 0.15
Hematuria 2.54 12.0(2.62–78.66) 0.00
Hypoalbuminemia 2.89 17.95(4.07–106.35) 0.00
Anti-dsDNA positive 2.46 11.75(2.82–61.23) 0.00
J. Clin. Med. 2023, 12, 535 Anti-Sm positive 0.02 0.98(0.12–8.81) 0.996 of 10
β is the regression coefficient. OR: Odds ratio; CI: Confidence interval.
Nomogram prediction
Figure3.3.Nomogram
Figure prediction of
of SLE-SS.
SLE-SS. (A)
(A)The
Theprediction
predictionmodel
modelofofSLE
SLEinin
SLE-SS was
SLE-SS visualized
was visual-
as a nomogram. (B) The prediction model of SS in SLE-SS was visualized as a nomogram.
ized as a nomogram. (B) The prediction model of SS in SLE-SS was visualized as a nomogram.
3.3. Model Performance Assessment
Similarly, as shown in Figure 2C,D, LASSO logistic regression was used to reduce
the 17The AUC oflaboratory
candidate SLE versus SLE-SS and
indicators thatSS versus
were SLE-SS was
statistically 0.880 between
different (95% CI: SLE-SS
0.826–0.934)
and
and 0.970 (95% CI: 0.946–0.995), respectively, indicating that the predictive
SS to the 11 most useful predictive markers including age at diagnosis (years), fever, tool had good
dry
discrimination
mouth, in the discovery
photosensitivity, cohort
skin lesions, (Figureproteinuria,
arthritis, 4A,D). In this study, the
hematuria, calibration curves
hypoalbuminemia,
for both SLEpositive,
anti-dsDNA versus SLE-SS and SS positive.
and anti-Sm versus SLE-SS also showed
The results good nomogram
of the logistic regressionagreement
analysis
in the discovery cohorts (Figure 4C,F). The DCA of the SLE versus
of SS and SLE-SS are shown in Table 1. Based on these factors, we constructed SLE-SS prediction tool
a prediction
is presented in Figure 5A. The decision curve showed that
tool for SS in SLE-SS and visualized it as a nomogram (Figure 3B). if the threshold probability of
a patient and a doctor was >5 andment in the discovery cohorts (Figure 4C,F). The DCA of the SLE versus SLE-SS prediction
tool is presented in Figure 5A. The decision curve showed that if the threshold probability
of a patient and a doctor was >5 andJ. Clin. Med. 2023, 12, 535 8 of 10
4. Discussion
Previous studies have explored potential risk factors for SLE or SS in SLE-SS, including
demographics, clinical characteristics, and laboratory parameters [10,11,17,18]. However,
no studies have investigated models that incorporate multiple laboratory findings in
clinical decision making. In this study, the SLE-SS overlapping disease prediction models
were established using the minimum absolute contraction and selection operator (LASSO)
logistic regression model between the candidate clinical features and laboratory parameters
and were visualized as nomograms to help predict the risk of SLE or SS in SLE-SS.
The clinical features of SS versus SLE-SS, such as dry mouth, dry eye, and a unique
autoantibody profile associated with pSS, were identified. In Yang’s study, we found that
the frequency of dry mouth and dry eye was significantly higher in SLE-SS than in the
SLE group [18]. Most previous studies have shown that anti-SSA/Ro and anti-SSB/La
antibodies are significantly more positive in SLE-SS than in the SLE group [19]. Regarding
RF positivity, Santos and Manoussakis et al. showed that RF positivity was significantly
higher in both SLE-SS groups than in the SLE group [20]. Our results were largely consistent
with previous studies. We conclude that dry mouth, dry eye, anti-Ro52 positive, anti-SSB
positivity, and RF positivity are risk factors for SLE progressing to SLE-SS. Therefore, we
should be aware of the development of combined SS when young SLE patients present
with dry mouth, dry eye, or positive anti-Ro52, anti-SSB, or RF.
Meanwhile, patients with SLE-SS also have some clinical characteristics and autoan-
tibody profiles specific to SLE. Szanto and Manoussakis et al. showed a higher rate of
anti-dsDNA antibody positivity in SLE-SS than in the SS group and a significantly higher
frequency of hypoalbuminemia in SLE-SS than in the SS group [10,11]. Patients with SLE-SS
are also more likely to have renal involvement, such as proteinuria and hematuria. The
study by Szanto and Yang et al. suggests that SLE-SS patients are more likely to develop
renal damage than patients with SS [20,21]. In our study, we constructed a predictive
model with risk factors such as proteinuria, hematuria, hypoalbuminemia, and positive
anti-dsDNA antibodies, suggesting that when SS patients present with these symptoms,
they are more likely to have combined SLE.
There are several limitations of this study. First, our collected data are retrospective,
and various biases are inevitable due to the nature of the study itself, even though all three
groups of patients were subjected to the same conditions and criteria to avoid selection bias
and confounding factors. Second, the sample size was relatively small, and we welcome
external validation in a wider population.
5. Conclusions
Based on the patient’s clinical symptoms, the development of SS should be monitored
when SLE patients are positive for anti-RO52, anti-SSB, and RF. However, the presence
of SS patients with albuminuria, hematuria, hypoproteinemia, positive anti-dsDNA, and
positive anti-SM may indicate the presence of SLE. On the basis of clinical symptoms and
serum indicators, this study established a risk prediction model for SLE or SS transitioning
into SLE-SS with high accuracy, which may help with early diagnosis and the selection of
appropriate treatment.
Supplementary Materials: The following supporting information can be downloaded at: https:
//www.mdpi.com/article/10.3390/jcm12020535/s1, Table S1: Patient demographic and clinical
characteristics in the discovery and validation cohorts; Table S2: Clinical characteristics of SLE and
SLE-SS in the discovery and validation cohorts; Table S3: Clinical characteristics of SS and SLE-SS in
the discovery and validation cohorts.
Author Contributions: Y.H. and L.M. collected the data. Y.H. analyzed the results and drafted the
manuscript. X.F. designed and supervised the project and edited the manuscript. Z.J., D.W., Y.Z.,
B.H., H.W. and S.C. helped with data collection and analysis. All authors have read and agreed to the
published version of the manuscript.J. Clin. Med. 2023, 12, 535 9 of 10
Funding: This work was supported by the National Key Research and Development Program of
China (2019YFE0111700) and the National Natural Science Foundation of China (No.81971517).
Institutional Review Board Statement: The study was approved by the Ethics Committee of Drum
Tower Hospital (No. 2020-093).
Informed Consent Statement: The consent is waived because it is a retrospective chart review study.
Data Availability Statement: Data are available on request from the corresponding author.
Conflicts of Interest: The authors declare no conflict of interest.
References
1. Kiriakidou, M.; Ching, C.L. Systemic Lupus Erythematosus. Ann. Intern. Med. 2020, 172, 81–96. [CrossRef] [PubMed]
2. Ramaswamy, M.; Tummala, R.; Streicher, K.; da Costa, A.N.; Brohawn, P.Z. The Pathogenesis, Molecular Mechanisms, and
Therapeutic Potential of the Interferon Pathway in Systemic Lupus Erythematosus and Other Autoimmune Diseases. Int. J. Mol.
Sci. 2021, 22, 11286. [CrossRef] [PubMed]
3. Stefanski, A.-L.; Tomiak, C.; Pleyer, U.; Dietrich, T.; Burmester, G.R.; Dörner, T. The Diagnosis and Treatment of Sjögren’s
Syndrome. Dtsch. Arztebl. Int. 2017, 114, 354–361. [CrossRef] [PubMed]
4. Fox, R.I. Sjögren’s syndrome. Lancet 2005, 366, 321–331. [CrossRef]
5. Psianou, K.; Panagoulias, I.; Papanastasiou, A.D.; de Lastic, A.-L.; Rodi, M.; Spantidea, P.I.; Degn, S.E.; Georgiou, P.; Mouzaki, A.
Clinical and immunological parameters of Sjögren’s syndrome. Autoimmun. Rev. 2018, 17, 1053–1064. [CrossRef]
6. Heaton, J.M. Sjögren’s syndrome and systemic lupus erythematosus. Br. Med. J. 1959, 1, 466–469. [CrossRef]
7. Gilboe, I.-M.; Kvien, T.K.; Uhlig, T.; Husby, G. Sicca symptoms and secondary Sjögren’s syndrome in systemic lupus erythe-
matosus: Comparison with rheumatoid arthritis and correlation with disease variables. Ann. Rheum. Dis. 2001, 60, 1103–1109.
[CrossRef]
8. Lei, Y.; Weihong, Y.; Gangbo, L.; Jinqi, Z. Salivary gland TcmO_4 in patients with systemic lupus erythematosus combined with
Sjögren’s syndrome imaging study. J. Kunming Med. Coll. 2007, 28, 59–61.
9. Hernández-Molina, G.; Zamora-Legoff, T.; Romero-Díaz, J.; Nuñez-Alvarez, C.A.; Cárdenas-Velázquez, F.; Hernández-Hernández,
C.; Calderillo, M.L.; Marroquín, M.; Recillas-Gispert, C.; Ávila-Casado, C.; et al. Predicting Sjögren’s syndrome in patients with
recent-onset SLE. Rheumatology 2013, 52, 1438–1442. [CrossRef]
10. Santos, C.S.; Morales, C.M.; Castro, C.; Alvarez, E.D. Polyautoimmunity in systemic lupus erythematosus: Secondary Sjogren
syndrome. Z. Rheumatol. 2021. [CrossRef]
11. Manoussakis, M.N.; Georgopoulou, C.; Zintzaras, E.; Spyropoulou, M.; Stavropoulou, A.; Skopouli, F.N.; Moutsopoulos, H.M.
Sjögren’s syndrome associated with systemic lupus erythematosus: Clinical and laboratory profiles and comparison with primary
Sjögren’s syndrome. Arthritis Rheum. 2004, 50, 882–891. [CrossRef] [PubMed]
12. Hochberg, M.C. Updating the American College of Rheumatology revised criteria for the classification of systemic lupus
erythematosus. Arthritis Rheum. 1997, 40, 1725. [CrossRef]
13. Vitali, C.; Bombardieri, S.; Jonsson, R.; Moutsopoulos, H.M.; Alexander, E.L.; Carsons, S.E.; Daniels, T.E.; Fox, P.C.; Fox, R.I.;
Kassan, S.S.; et al. Classification criteria for Sjögren’s syndrome: A revised version of the European criteria proposed by the
American-European Consensus Group. Ann. Rheum. Dis. 2002, 61, 554–558. [CrossRef]
14. Van Smeden, M.; Moons, K.G.; De Groot, J.A.; Collins, G.S.; Altman, D.G.; Eijkemans, M.J.; Reitsma, J.B. Sample size for binary
logistic prediction models: Beyond events per variable criteria. Stat. Methods Med. Res. 2019, 28, 2455–2474. [CrossRef] [PubMed]
15. Hillberg, N.S.; Hogenboom, J.; Hommes, J.; Van Kuijk, S.; Keuter, X.; van der Hulst, R. Risk of major postoperative com-plications
in breast reconstructive surgery with and without an acellular dermal matrix: A development of a prognostic pre-diction model.
JPRAS Open 2022, 33, 92–105. [CrossRef] [PubMed]
16. Feng, D.; Cortese, G.; Baumgartner, R. A comparison of confidence/credible interval methods for the area under the ROC curve
for continuous diagnostic tests with small sample size. Stat. Methods Med. Res. 2017, 26, 2603–2621. [CrossRef]
17. Ruacho, G.; Kvarnström, M.; Zickert, A.; Oke, V.; Rönnelid, J.; Eketjäll, S.; Elvin, K.; Gunnarsson, I.; Svenungsson, E. Sjögren
Syndrome in Systemic Lupus Erythematosus: A Subset Characterized by a Systemic Inflammatory State. J. Rheumatol. 2020, 47,
865–875. [CrossRef]
18. Yao, Q.; Altman, R.D.; Wang, X. Systemic lupus erythematosus with Sjögren syndrome compared to systemic lupus erythematosus
alone: A meta-analysis. J. Clin. Rheumatol. 2012, 18, 28–32. [CrossRef]
19. Baer, A.N.; Maynard, J.W.; Shaikh, F.; Magder, L.S.; Petri, M. Secondary Sjogren’s syndrome in systemic lupus erythematosus
defines a distinct disease subset. J. Rheumatol. 2010, 37, 1143–1149. [CrossRef]J. Clin. Med. 2023, 12, 535 10 of 10
20. Szanto, A.; Szodoray, P.; Kiss, E.; Kapitany, A.; Szegedi, G.; Zeher, M. Clinical, serologic, and genetic profiles of patients with
associated Sjögren’s syndrome and systemic lupus erythematosus. Hum. Immunol. 2006, 67, 924–930. [CrossRef]
21. Yang, Y.; Li, Z.; Wang, L.; Zhang, F. The clinical and laboratory characteristics of Sjögren’s syndrome that progresses to systemic
lupus erythematosus: A retrospective case-control study. Int. J. Rheum. Dis. 2013, 16, 173–177. [CrossRef] [PubMed]
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual
author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to
people or property resulting from any ideas, methods, instructions or products referred to in the content.You can also read
