Comment on: Prevalence of systemic lupus erythematosus in Spain: higher than previously reported in other countries? Reply

Verdu, R; Pego-Reigosa, José María; Seoane‐Mato, Daniel; Valle, Mercedes Morcillo; Sánchez, Deseada Palma; Martínez, María José Moreno; González, Marta Mayor; Buruaga, Joana Atxotegi Sáenz de; Onaindia, Irati Urionagüena; Cáceres, B. A. Blanco; Silva-Fernández, Lucía; Sivera, Francisca; Blanco, Francisco J.; Sánchez-Piedra, Carlos; Dı́az-González, Federico; Bustabad, Sagrario

doi:10.1093/rheumatology/keaa762

Cited by 6 publications

(7 citation statements)

References 3 publications

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“…In the EPISER study (n=4,916), the prevalence of RA and SLE were both <1%. 23,24 In the present study of 108 patients with silicosis, autoimmune disease was recorded in 16 patients (15%), of whom 7 (7% of the study population) had RA and 4 (4% of the study population) had SLE. Although the number of patients is not substantial, the percentages illustrate the differences in the prevalence of autoimmune diseases between patients with silicosis and the general population.…”

Section: Discussionmentioning

confidence: 54%

Autoimmune findings in patients with silicosis in Spain

González Fernández,

Ros Lucas,

Molina Molina

et al. 2024

DIC

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Background Occupational exposure to silica is related to autoimmune diseases and features of autoimmunity, mainly autoantibodies. The study objectives were to estimate the prevalence of silicosis with associated autoimmune findings or diagnosed autoimmune diseases in Spain, and to assess the clinical and functional characteristics of affected patients. Methods This is a multicentre prospective study in patients diagnosed with silicosis. Autoantibodies analysed were antinuclear antibodies, isotypes IgA, IgM and IgG, rheumatoid factor, anticyclic citrullinated peptide, anti-Scl70, anti-Ro, and anti-LA. Pulmonary function tests were performed. Results Autoimmunity was assessed in 105 patients. Autoimmune findings were recorded in 29 (27%) patients, including antinuclear antibodies ( n =21), anti-Ro ( n =7), rheumatoid factor ( n =5) and anti-Scl70 ( n =3). Autoimmune disease was diagnosed in 16 (15%) patients, mainly rheumatoid arthritis ( n =7) and systemic lupus erythematosus ( n =4). Patients with silicosis and autoimmune findings had a lower mean time of exposure to silica and showed a trend toward lower values in pulmonary function tests. Conclusions Autoimmune findings and diagnosis of autoimmune diseases were frequent in patients with silicosis in Spain.

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Section: Discussionmentioning

confidence: 54%

Autoimmune findings in patients with silicosis in Spain

González Fernández,

Ros Lucas,

Molina Molina

et al. 2024

DIC

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“…Commonly known as lupus, it varies in prevalence depending on geographic location, ethnicity, and research study design. In the United States, an estimated 241 cases per 100,000 adults have been reported, while in Spain, the updated figure is 210 cases per 100,000 inhabitants [1]. The Lupus Foundation of America estimates that approximately 161,000 to 322,000 individuals in the U.S. are affected by SLE, translating to a prevalence of approximately 0.05% to 0.1% of the population.…”

Section: Introductionmentioning

confidence: 99%

Systemic Lupus Erythematosus: How Machine Learning Can Help Distinguish between Infections and Flares

Usategui,

Arroyo,

Torres

et al. 2024

Bioengineering

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Systemic Lupus Erythematosus (SLE) is a multifaceted autoimmune ailment that impacts multiple bodily systems and manifests with varied clinical manifestations. Early detection is considered the most effective way to save patients’ lives, but detecting severe SLE activity in its early stages is proving to be a formidable challenge. Consequently, this work advocates the use of Machine Learning (ML) algorithms for the diagnosis of SLE flares in the context of infections. In the pursuit of this research, the Random Forest (RF) method has been employed due to its performance attributes. With RF, our objective is to uncover patterns within the patient data. Multiple ML techniques have been scrutinized within this investigation. The proposed system exhibited around a 7.49% enhancement in accuracy when compared to k-Nearest Neighbors (KNN) algorithm. In contrast, the Support Vector Machine (SVM), Binary Linear Discriminant Analysis (BLDA), Decision Trees (DT) and Linear Regression (LR) methods demonstrated inferior performance, with respective values around 81%, 78%, 84% and 69%. It is noteworthy that the proposed method displayed a superior area under the curve (AUC) and balanced accuracy (both around 94%) in comparison to other ML approaches. These outcomes underscore the feasibility of crafting an automated diagnostic support method for SLE patients grounded in ML systems.

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“…In the United States, it has been estimated at 241 cases/100,000 adults, and in Spain, it has been updated in 210 cases per 100,000 inhabitants. 1 SLE impacts more frequently on young fertile females and represents an increase in mortality, although improved SLE treatments have increased survival. Factors such as aging, the increasing use of selective biological therapies, but it is being a chronic disease, other comorbidities, malignancy, infections, malnutrition, etc.…”

Section: Introductionmentioning

confidence: 99%

Machine learning, a new tool for the detection of immunodeficiency patterns in systemic lupus erythematosus

Usategui,

Barbado,

Torres

et al. 2023

Journal of Investigative Medicine

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Systemic lupus erythematosus (SLE) is a complex autoimmune disease that affects several organs and causes variable clinical symptoms. Early diagnosis is currently the most effective way to save the lives of patients with SLE. But it is very difficult to detect in the early stages of the disease. Because of this, this study proposes a machine learning system to help diagnose patients with SLE. To carry out the research, the extreme gradient boosting method has been implemented due to its performance characteristics, as it allows high performance, scalability, accuracy, and low computational load. From this method we try to recognize patterns in the data obtained from patients, which allow the classification of SLE patients with high accuracy and differentiate these patients from controls. Several machine learning methods have been analyzed in this study. The proposed method achieves a higher prediction value of patients who may suffer from SLE than the rest of the compared systems. The proposed algorithm achieved an improvement in accuracy of 4.49% over k-Nearest Neighbors. As for the Support Vector Machine and Gaussian Naive Bayes (GNB) methods, they achieved a lower performance than the proposed one, reaching values of 83% and 81%, respectively. It should be noted that the proposed system showed a higher area under the curve (90%) and a balanced accuracy (90%) than the other machine learning methods. This study shows the usefulness of ML techniques for identifying and predicting SLE patients. These results demonstrate the possibility of developing automatic diagnostic support systems for SLE patients based on machine learning techniques.

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Comment on: Prevalence of systemic lupus erythematosus in Spain: higher than previously reported in other countries? Reply

Cited by 6 publications

References 3 publications

Autoimmune findings in patients with silicosis in Spain

Autoimmune findings in patients with silicosis in Spain

Systemic Lupus Erythematosus: How Machine Learning Can Help Distinguish between Infections and Flares

Machine learning, a new tool for the detection of immunodeficiency patterns in systemic lupus erythematosus

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