Mauricio Restrepo-Escobar1, Paula A Granda-Carvajal2, Daniel C Aguirre3, Johanna Hernández-Zapata4, Gloria M Vásquez1, Fabián Jaimes1. 1. Department of Internal Medicine, Universidad de Antioquia, Medellín, Colombia. 2. Department of Internal Medicine and Subspecialties, Hospital Pablo Tobón Uribe, Medellín, Colombia. 3. Medical Research Institute, Universidad de Antioquia, Medellín, Colombia. 4. Department of Pediatrics, Universidad de Antioquia, Medellín, Colombia.
Abstract
INTRODUCTION: Having reliable predictive models of prognosis/the risk of infection in systemic lupus erythematosus (SLE) patients would allow this problem to be addressed on an individual basis to study and implement possible preventive or therapeutic interventions. OBJECTIVE: To identify and analyze all predictive models of prognosis/the risk of infection in patients with SLE that exist in medical literature. METHODS: A structured search in PubMed, Embase, and LILACS databases was carried out until May 9, 2020. In addition, a search for abstracts in the American Congress of Rheumatology (ACR) and European League Against Rheumatism (EULAR) annual meetings' archives published over the past eight years was also conducted. Studies on developing, validating or updating predictive prognostic models carried out in patients with SLE, in which the outcome to be predicted is some type of infection, that were generated in any clinical context and with any time horizon were included. There were no restrictions on language, date, or status of the publication. To carry out the systematic review, the CHARMS (Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies) guideline recommendations were followed. The PROBAST tool (A Tool to Assess the Risk of Bias and Applicability of Prediction Model Studies) was used to assess the risk of bias and the applicability of each model. RESULTS: We identified four models of infection prognosis in patients with SLE. Mostly, there were very few events per candidate predictor. In addition, to construct the models, an initial selection was made based on univariate analyses with no contraction of the estimated coefficients being carried out. This suggests that the proposed models have a high probability of overfitting and being optimistic. CONCLUSIONS: To date, very few prognostic models have been published on the infection of SLE patients. These models are very heterogeneous and are rated as having a high risk of bias and methodological weaknesses. Despite the widespread recognition of the frequency and severity of infections in SLE patients, there is no reliable predictive prognostic model that facilitates the study and implementation of personalized preventive or therapeutic measures.Protocol registration number: PROSPERO CRD42020171638.
INTRODUCTION: Having reliable predictive models of prognosis/the risk of infection in systemic lupus erythematosus (SLE) patients would allow this problem to be addressed on an individual basis to study and implement possible preventive or therapeutic interventions. OBJECTIVE: To identify and analyze all predictive models of prognosis/the risk of infection in patients with SLE that exist in medical literature. METHODS: A structured search in PubMed, Embase, and LILACS databases was carried out until May 9, 2020. In addition, a search for abstracts in the American Congress of Rheumatology (ACR) and European League Against Rheumatism (EULAR) annual meetings' archives published over the past eight years was also conducted. Studies on developing, validating or updating predictive prognostic models carried out in patients with SLE, in which the outcome to be predicted is some type of infection, that were generated in any clinical context and with any time horizon were included. There were no restrictions on language, date, or status of the publication. To carry out the systematic review, the CHARMS (Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies) guideline recommendations were followed. The PROBAST tool (A Tool to Assess the Risk of Bias and Applicability of Prediction Model Studies) was used to assess the risk of bias and the applicability of each model. RESULTS: We identified four models of infection prognosis in patients with SLE. Mostly, there were very few events per candidate predictor. In addition, to construct the models, an initial selection was made based on univariate analyses with no contraction of the estimated coefficients being carried out. This suggests that the proposed models have a high probability of overfitting and being optimistic. CONCLUSIONS: To date, very few prognostic models have been published on the infection of SLEpatients. These models are very heterogeneous and are rated as having a high risk of bias and methodological weaknesses. Despite the widespread recognition of the frequency and severity of infections in SLEpatients, there is no reliable predictive prognostic model that facilitates the study and implementation of personalized preventive or therapeutic measures.Protocol registration number: PROSPERO CRD42020171638.