Prediction of Spontaneous Abortion in Women with Systemic Lupus Erythematosus (SLE) Based on Machine Learning Model: Insights from the Egyptian College of Rheumatology (ECR)–SLE Cohort

Nevin Hammam¹, Walaa N. Ismail², Iman I EL-Gazzar³, Osman Hammam⁴, Noha M Khalil⁵, Eman F Mohamed⁶, Nermeen Noshy⁷, Dina F El-Essawi⁸, Rawhya R El Shereef⁹, Faten Ismail⁹, Marwa ElKhalifa¹⁰, Hanan M Fathi¹¹, Soha Senara¹¹, Samar Tharwat¹², Samah I Nasef¹³, Amany R El-Najjar¹⁴, Ahmed M Abdalla¹⁵, Ali Bakhiet¹⁶, Ahmed Elsaman¹⁷ and Tamer A Gheita³, ¹Rheumatology Department, Faculty of Medicine, Assiut University, Assiut, Egypt, Boston, MA, ²Faculty of Computers and Information, Minia University, Minia;, Minia, Egypt, ³Rheumatology Department, Faculty of Medicine, Cairo University, Cairo, Egypt, ⁴Department of Rheumatology and Rehabilitation, Faculty of Medicine, New Valley University, New Valley, Assiut, Egypt, ⁵Internal Medicine Department, Rheumatology Unit, Faculty of Medicine, Cairo University, Cairo, Egypt, ⁶Internal Medicine Department, Rheumatology Unit, Faculty of Medicine (Girls), Al-Azhar University, Cairo, Egypt, ⁷Internal Medicine Department, Rheumatology Unit, Faculty of Medicine, Ain-Shams University, Cairo, Egypt, ⁸Internal Medicine Department, Rheumatology Unit, Egyptian Atomic Energy Authority (EAEA), Cairo, Egypt, ⁹Rheumatology Department, Faculty of Medicine, Minia University, Minia, Egypt, ¹⁰Alexandria faculty of Medicine, Alexandria, Egypt, ¹¹Rheumatology Department, Faculty of Medicine, Fayoum University,, Fayoum, Egypt, ¹²Rheumatology Unit, Internal Medicine, Mansoura University, Dakahlia, ¹³Rheumatology Department, Faculty of Medicine, Suez-Canal University, Ismailia, Egypt, ¹⁴Rheumatology Department, Faculty of Medicine, Zagazig University, Sharkia, Egypt, ¹⁵Rheumatology Department, Faculty of Medicine, Aswan University, Aswan, Egypt, ¹⁶Higher institute of Computer Science and Information Systems, Culture & Science City, Giza, Egypt, ¹⁷Rheumatology Department, Faculty of Medicine, Sohag University, Sohag, Egypt

Meeting: ACR Convergence 2024

Keywords: Systemic lupus erythematosus (SLE)

Session Information

Date: Saturday, November 16, 2024

Title: SLE – Diagnosis, Manifestations, & Outcomes Poster I

Session Type: Poster Session A

Session Time: 10:30AM-12:30PM

Background/Purpose: Systemic lupus erythematosus (SLE) predominantly affects women of childbearing period. SLE increases the risk of adverse pregnancy outcome including spontaneous abortion (SA), which causes emotional stress to women and seriously affects family life. Predicting SA is essential for pregnant women with SLE to minimize its risk.

Several studies have shown that some clinical and laboratory indicators could predict the risk of SA in SLE women. However, traditional statistical methods only found a modest correlation between some factors and SLE-SA and are frequently associated with poor predictive performance upon validation. Thus, this study aimed to develop a predictive model applying Extreme Gradient Boosting (XGBoost) optimization to identify the risk factors for SA among women with SLE.

Methods: Data were derived from the Egyptian College of Rheumatology (ECR)-SLE cohort, a national multicentre study that was created by specialized rheumatologists across the country¹. The study included 3,296 adult SLE women fulfilling the SLICC classification criteria and those having SA data available were selected. SA was defined as pregnancy loss up to 20 weeks gestation.

Forty-seven variables including patient demographics, clinical manifestations, disease activity, and damage using SLE disease activity index (SLEDAI) and SLICC damage index (SLICC-DI), comorbidities, medications, and laboratory data for each patient were used as inputs for building and testing XGBoost. The model was evaluated using area under the receiver operating characteristic curve (AUCROC), and was compared to the logistic regression (LR) model results. Then the importance and direction of each variable contributing to the risk of SA were evaluated using Shapley additive explanation (SHAP). Stata statistical software version 15 (Stata-Corp) and the Python language (version 3.7.12) were utilized for data analysis.

Results: A total of 3,296 SLE women [mean ±SD age; 32.5 ±10.1 years; and median disease duration 48 months]. The mean SLEDAI score was 11.3±9.5. About 13.9% of the included patients had at least one abortion (Table 1). By applying optimized XGBoost, we obtained a model characterized by an AUC-ROC value of 0.98, while the LR has an AUC-ROC value of 0.73 (Figure 1).

Figure 2 shows the influence of variables on SA in the prediction model. Positive antiphospholipid antibodies, low complement 3, longer disease duration, hypertension, mucocutaneous ulcers, anticoagulants, and steroid use were among the important factors associated with SA in SLE patients.

Conclusion: Using information obtained in the clinical settings, the machine learning model identified patients at higher risk of spontaneous abortion in women with SLE better than the traditional statistical method. Further longitudinal studies are necessary to evaluate the clinical utility of the proposed prediction model.

Table 1: Characteristics of SLE patients.

Figure 1: The ROC graph of the Harris Hawks (HH) Optimization XGBoost (HH-XGBoost) and Logistic regression for predicting SA in women with systemic lupus erythematosus.

Figure 2: Overall SHAP values for the variables in Shapely plots to display both the feature importance and feature contribution to the model prediction. Overall SHAP values for the variables in Shapely plots to display both the feature importance and feature contribution to the model prediction. Shapley plots show the SHAP values in the order of the important variables that contribute to spontaneous abortion. The x-axis represents the marginal contribution of a feature to the change in the predicted probability of development of spontaneous abortion. Colors indicate the value of the variable: red represents higher numerical values of the variable and blue represent lower numerical values. As all categorical variables were converted into binary indicators, zero (i.e., absence) is indicated with blue dots and one (i.e., presence) is represented by red dots

Disclosures: N. Hammam: None; W. N. Ismail: None; I. I EL-Gazzar: None; O. Hammam: None; N. M Khalil: None; E. F Mohamed: None; N. Noshy: None; D. F El-Essawi: None; R. R El Shereef: None; F. Ismail: None; M. ElKhalifa: None; H. M Fathi: None; S. Senara: None; S. Tharwat: None; S. I Nasef: None; A. R El-Najjar: None; A. M Abdalla: None; A. Bakhiet: None; A. Elsaman: None; T. A Gheita: None.

To cite this abstract in AMA style:

Hammam N, N. Ismail W, I EL-Gazzar I, Hammam O, M Khalil N, F Mohamed E, Noshy N, F El-Essawi D, R El Shereef R, Ismail F, ElKhalifa M, M Fathi H, Senara S, Tharwat S, I Nasef S, R El-Najjar A, M Abdalla A, Bakhiet A, Elsaman A, A Gheita T. Prediction of Spontaneous Abortion in Women with Systemic Lupus Erythematosus (SLE) Based on Machine Learning Model: Insights from the Egyptian College of Rheumatology (ECR)–SLE Cohort [abstract]. Arthritis Rheumatol. 2024; 76 (suppl 9). https://acrabstracts.org/abstract/prediction-of-spontaneous-abortion-in-women-with-systemic-lupus-erythematosus-sle-based-on-machine-learning-model-insights-from-the-egyptian-college-of-rheumatology-ecr-sle-cohort/. Accessed .

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