Development and validation of a new algorithm for attribution of neuropsychiatric events in systemic lupus erythematosus

医学 归属 系统性红斑狼疮 红斑狼疮 算法 内科学 免疫学 疾病 计算机科学 社会心理学 心理学 抗体
作者
Alessandra Bortoluzzi,Carlo Alberto Scirè,Stefano Bombardieri,Luisa Caniatti,Fabrizio Conti,Salvatore De Vita,Andrea Doria,Gianfranco Ferraccioli,Elisa Gremese,Elisa Mansutti,Alessandro Mathieu,Marta Mosca,Melissa Padovan,Matteo Piga,Anǵela Tincani,Maria Rosaria Tola,Paola Tomietto,Guido Valesini,Margherita Zen,Marcello Govoni
出处
期刊:Rheumatology [Oxford University Press]
卷期号:54 (5): 891-898 被引量:144
标识
DOI:10.1093/rheumatology/keu384
摘要

The aim of this study was to develop and validate an algorithm to assist the attribution of neuropsychiatric (NP) events to underlying disease in SLE patients.Phase 1 identified and categorized candidate items to be included in the algorithm for the attribution of an NP event to SLE and their relative weights through a literature-informed consensus-driven process. Using a retrospective training cohort of SLE, phase 2 validated items selected in phase 1 and refined weights through a data-driven process, fitting items as independent variables and expert evaluation (clinical judgement) as reference standard in logistic models. Phase 3 consisted of a validation process using an external multicentre retrospective SLE cohort.Phase 1 identified four different items: timing of the NP event, type of event, confounding factors and favouring factors. The training and validating cohorts included 228 and 221 patients, respectively. Each patient experienced at least one NP event characterized using the ACR case definition. In these samples, items selected in phase 1 showed good performance in discriminating patients with NPSLE: the area under the receiver operating characteristic curve using dichotomous outcomes was 0.87 in the training set and 0.82 in the validating set. Relevant cut-offs of the validated score identify events with a positive predictive value of 100% (95% CI 93.2, 100) and 86.3% (95% CI 76.2, 93.2) in the training and validating cohorts, respectively.A new algorithm based on a probability score was developed and validated to determine the relationship between NP events and SLE.
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