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Development and validation of a new algorithm for attribution of neuropsychiatric events in systemic lupus
Alessandra Bortoluzzi1, Carlo Alberto Scirè2, Stefano Bombardieri2
1Department of Medical Science, Section of Hematology and Rheumatology, University of Ferrara and Azienda Ospedaliero Universitaria Sant'Anna di Cona, Ferrara, Epidemiology Unit, Italian Society of Rheumatology, Milan, Rheumatology Unit, Department of Clinical and Experimental Medicine, University of Pisa, Pisa, Department of Neuroscience, S. Anna Hospital, Cona, Ferrara, Department of Internal Medicine and Medical Specialties, Sapienza University of Rome, Rome, Rheumatology Clinic, Azienda Ospedaliero Universitaria 'S. Maria della Misericordia' and DSMB, Department of Medical and Biological Sciences, University of Udine, Udine, Department of Clinical and Experimental Medicine, Division of Rheumatology, University of Padova, Padova, Division of Rheumatology and Internal Medicine, Institute of Rheumatology and Affine Sciences, CIC, Catholic University of the Sacred Heart, Rome, Rheumatology Unit, Department of Medical Sciences, University of Cagliari and AOU University Clinic, Cagliari, Rheumatology and Clinical Immunology Unit, Spedali Civili and University of Brescia, Brescia and Internal Medicine, AOU 'Ospedali Riuniti' of Trieste, Trieste, Italy brtlsn1@unife.it.
Objective:
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.
Methods:
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.
Results:
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.
Conclusion:
A new algorithm based on a probability score was developed and validated to determine the relationship between NP events and SLE.
