Iñigo Rua-Figueroa, M Jesus García de Yébenes, Julia Martinez-Barrio, Maria Galindo Izquierdo, Jaime Calvo Alén, Antonio Fernandez-Nebro, Raúl Menor-Almagro, Loreto Carmona, Beatriz Tejera Segura, Eva Tomero, Mercedes Freire-González, Clara Sangüesa, Loreto Horcada, Ricardo Blanco, Esther Uriarte Itzazelaia, Javier Narváez, José Carlos Rosas Gómez de Salazar, Silvia Gómez-Sabater, Claudia Moriano Morales, Jose L Andreu, Vicente Torrente Segarra, Elena Aurrecoechea, Ana Perez, Javier Nóvoa Medina, Eva Salgado, Nuria Lozano-Rivas, Carlos Montilla, Esther Ruiz-Lucea, Marta Arevalo, Carlota Iñiguez, María Jesús García-Villanueva, Lorena Exposito, Mónica Ibáñez-Barceló, Gema Bonilla, Irene Carrión-Barberà, Celia Erausquin, Jorge Juan Fragio Gil, Angela Pecondón, Francisco J Toyos, Tatiana Cobo, Alejandro Muñoz-Jiménez, Jose Oller, Joan M Nolla, J M Pego-Reigosa
{"title":"SLESIS-R:基于 RELESSER 前瞻性队列的系统性红斑狼疮患者严重感染预测评分改进版","authors":"Iñigo Rua-Figueroa, M Jesus García de Yébenes, Julia Martinez-Barrio, Maria Galindo Izquierdo, Jaime Calvo Alén, Antonio Fernandez-Nebro, Raúl Menor-Almagro, Loreto Carmona, Beatriz Tejera Segura, Eva Tomero, Mercedes Freire-González, Clara Sangüesa, Loreto Horcada, Ricardo Blanco, Esther Uriarte Itzazelaia, Javier Narváez, José Carlos Rosas Gómez de Salazar, Silvia Gómez-Sabater, Claudia Moriano Morales, Jose L Andreu, Vicente Torrente Segarra, Elena Aurrecoechea, Ana Perez, Javier Nóvoa Medina, Eva Salgado, Nuria Lozano-Rivas, Carlos Montilla, Esther Ruiz-Lucea, Marta Arevalo, Carlota Iñiguez, María Jesús García-Villanueva, Lorena Exposito, Mónica Ibáñez-Barceló, Gema Bonilla, Irene Carrión-Barberà, Celia Erausquin, Jorge Juan Fragio Gil, Angela Pecondón, Francisco J Toyos, Tatiana Cobo, Alejandro Muñoz-Jiménez, Jose Oller, Joan M Nolla, J M Pego-Reigosa","doi":"10.1136/lupus-2023-001096","DOIUrl":null,"url":null,"abstract":"Objective To develop an improved score for prediction of severe infection in patients with systemic lupus erythematosus (SLE), namely, the SLE Severe Infection Score-Revised (SLESIS-R) and to validate it in a large multicentre lupus cohort. Methods We used data from the prospective phase of RELESSER (RELESSER-PROS), the SLE register of the Spanish Society of Rheumatology. A multivariable logistic model was constructed taking into account the variables already forming the SLESIS score, plus all other potential predictors identified in a literature review. Performance was analysed using the C-statistic and the area under the receiver operating characteristic curve (AUROC). Internal validation was carried out using a 100-sample bootstrapping procedure. ORs were transformed into score items, and the AUROC was used to determine performance. Results A total of 1459 patients who had completed 1 year of follow-up were included in the development cohort (mean age, 49±13 years; 90% women). Twenty-five (1.7%) had experienced ≥1 severe infection. According to the adjusted multivariate model, severe infection could be predicted from four variables: age (years) ≥60, previous SLE-related hospitalisation, previous serious infection and glucocorticoid dose. A score was built from the best model, taking values from 0 to 17. The AUROC was 0.861 (0.777–0.946). The cut-off chosen was ≥6, which exhibited an accuracy of 85.9% and a positive likelihood ratio of 5.48. Conclusions SLESIS-R is an accurate and feasible instrument for predicting infections in patients with SLE. SLESIS-R could help to make informed decisions on the use of immunosuppressants and the implementation of preventive measures. Data are available upon reasonable request.","PeriodicalId":18126,"journal":{"name":"Lupus Science & Medicine","volume":"87 1","pages":""},"PeriodicalIF":3.7000,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"SLESIS-R: an improved score for prediction of serious infection in patients with systemic lupus erythematosus based on the RELESSER prospective cohort\",\"authors\":\"Iñigo Rua-Figueroa, M Jesus García de Yébenes, Julia Martinez-Barrio, Maria Galindo Izquierdo, Jaime Calvo Alén, Antonio Fernandez-Nebro, Raúl Menor-Almagro, Loreto Carmona, Beatriz Tejera Segura, Eva Tomero, Mercedes Freire-González, Clara Sangüesa, Loreto Horcada, Ricardo Blanco, Esther Uriarte Itzazelaia, Javier Narváez, José Carlos Rosas Gómez de Salazar, Silvia Gómez-Sabater, Claudia Moriano Morales, Jose L Andreu, Vicente Torrente Segarra, Elena Aurrecoechea, Ana Perez, Javier Nóvoa Medina, Eva Salgado, Nuria Lozano-Rivas, Carlos Montilla, Esther Ruiz-Lucea, Marta Arevalo, Carlota Iñiguez, María Jesús García-Villanueva, Lorena Exposito, Mónica Ibáñez-Barceló, Gema Bonilla, Irene Carrión-Barberà, Celia Erausquin, Jorge Juan Fragio Gil, Angela Pecondón, Francisco J Toyos, Tatiana Cobo, Alejandro Muñoz-Jiménez, Jose Oller, Joan M Nolla, J M Pego-Reigosa\",\"doi\":\"10.1136/lupus-2023-001096\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Objective To develop an improved score for prediction of severe infection in patients with systemic lupus erythematosus (SLE), namely, the SLE Severe Infection Score-Revised (SLESIS-R) and to validate it in a large multicentre lupus cohort. Methods We used data from the prospective phase of RELESSER (RELESSER-PROS), the SLE register of the Spanish Society of Rheumatology. A multivariable logistic model was constructed taking into account the variables already forming the SLESIS score, plus all other potential predictors identified in a literature review. Performance was analysed using the C-statistic and the area under the receiver operating characteristic curve (AUROC). Internal validation was carried out using a 100-sample bootstrapping procedure. ORs were transformed into score items, and the AUROC was used to determine performance. Results A total of 1459 patients who had completed 1 year of follow-up were included in the development cohort (mean age, 49±13 years; 90% women). Twenty-five (1.7%) had experienced ≥1 severe infection. According to the adjusted multivariate model, severe infection could be predicted from four variables: age (years) ≥60, previous SLE-related hospitalisation, previous serious infection and glucocorticoid dose. A score was built from the best model, taking values from 0 to 17. The AUROC was 0.861 (0.777–0.946). The cut-off chosen was ≥6, which exhibited an accuracy of 85.9% and a positive likelihood ratio of 5.48. Conclusions SLESIS-R is an accurate and feasible instrument for predicting infections in patients with SLE. SLESIS-R could help to make informed decisions on the use of immunosuppressants and the implementation of preventive measures. Data are available upon reasonable request.\",\"PeriodicalId\":18126,\"journal\":{\"name\":\"Lupus Science & Medicine\",\"volume\":\"87 1\",\"pages\":\"\"},\"PeriodicalIF\":3.7000,\"publicationDate\":\"2024-04-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Lupus Science & Medicine\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1136/lupus-2023-001096\",\"RegionNum\":2,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"RHEUMATOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Lupus Science & Medicine","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1136/lupus-2023-001096","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"RHEUMATOLOGY","Score":null,"Total":0}
SLESIS-R: an improved score for prediction of serious infection in patients with systemic lupus erythematosus based on the RELESSER prospective cohort
Objective To develop an improved score for prediction of severe infection in patients with systemic lupus erythematosus (SLE), namely, the SLE Severe Infection Score-Revised (SLESIS-R) and to validate it in a large multicentre lupus cohort. Methods We used data from the prospective phase of RELESSER (RELESSER-PROS), the SLE register of the Spanish Society of Rheumatology. A multivariable logistic model was constructed taking into account the variables already forming the SLESIS score, plus all other potential predictors identified in a literature review. Performance was analysed using the C-statistic and the area under the receiver operating characteristic curve (AUROC). Internal validation was carried out using a 100-sample bootstrapping procedure. ORs were transformed into score items, and the AUROC was used to determine performance. Results A total of 1459 patients who had completed 1 year of follow-up were included in the development cohort (mean age, 49±13 years; 90% women). Twenty-five (1.7%) had experienced ≥1 severe infection. According to the adjusted multivariate model, severe infection could be predicted from four variables: age (years) ≥60, previous SLE-related hospitalisation, previous serious infection and glucocorticoid dose. A score was built from the best model, taking values from 0 to 17. The AUROC was 0.861 (0.777–0.946). The cut-off chosen was ≥6, which exhibited an accuracy of 85.9% and a positive likelihood ratio of 5.48. Conclusions SLESIS-R is an accurate and feasible instrument for predicting infections in patients with SLE. SLESIS-R could help to make informed decisions on the use of immunosuppressants and the implementation of preventive measures. Data are available upon reasonable request.
期刊介绍:
Lupus Science & Medicine is a global, peer reviewed, open access online journal that provides a central point for publication of basic, clinical, translational, and epidemiological studies of all aspects of lupus and related diseases. It is the first lupus-specific open access journal in the world and was developed in response to the need for a barrier-free forum for publication of groundbreaking studies in lupus. The journal publishes research on lupus from fields including, but not limited to: rheumatology, dermatology, nephrology, immunology, pediatrics, cardiology, hepatology, pulmonology, obstetrics and gynecology, and psychiatry.