B-101 Assessing the Performance of Cerebrospinal Fluid Kappa Immunoglobulin Free Light Chain as a Diagnostic Marker for Multiple Sclerosis Using Automated Chart Review
Authors: Miller J , Vanderboom P , Willrich M 
Published: October 2024

Abstract

Background
Multiple sclerosis (MS) is an immune-mediated central nervous system (CNS) demyelinating disease which is diagnosed based on CNS lesions' spatial and temporal dissemination. The 2017 revised McDonald Criteria include cerebrospinal fluid (CSF) specific oligoclonal bands (OCBs) to indicate dissemination in time, but their assessment is labor-intensive and requires a paired serum sample. Kappa immunoglobulin free light chain in CSF (KCSF) has emerged as a more sensitive but less specific MS biomarker, measured from a single CSF sample. Our institution's MS testing panel uses KCSF initially to rule out MS, with reflex to OCB for positive confirmation if KCSF is borderline positive (≥0.06 mg/dL). However, manual correlation of laboratory and clinical data hinders the direct comparison of the diagnostic performance between these markers. Herein we report a retrospective assessment of KCSF and OCB diagnostic performance using automated chart review.

Methods
Between January 1st, 2021 and December 31st, 2022, there were 1,237 patients with simultaneously measured OCB and KCSF. CSF-specific OCBs were measured by separating CSF and serum proteins using an agarose isoelectric focusing gel with a pH gradient from 3-10 (Sebia). After transfer to nitrocellulose paper, antisera were applied to visualize IgG specific bands and quantify CSF-specific OCBs. KCSF was quantitated using nephelometry by applying free light chain antisera (Binding Site) to CSF samples using a Siemens BNII analyzer. An automated chart review process utilizing keyword matching was implemented to identify patients with MS. An initial filter phase required presence of the term “multiple sclerosis”, while a second required the presence of any of the following terms: “primary progressive,” “secondary progressive,” “relapsing remitting,” “progressive relapsing,” or “tumefactive” to identify patients with diagnosed MS. The automated method’s performance was then compared to a manual chart review of 200 randomly selected patients. Subsequently, the automated process was used to determine the MS diagnosis status of all 1,237 patients.

Results
Of the 200 manually reviewed patients, 44 MS diagnoses were found. The automated process correctly identified 41 of these with a resulting sensitivity and specificity of 93.18% and 95.51%, respectively. Applied to the entire cohort, the automated review identified 275 MS diagnoses. For the entire cohort, the area under the curve (AUC) for the receiver operating characteristic (ROC) curves for OCB and KCSF were 0.824 (95% confidence interval: 0.794, 0.854) and 0.808 (0.777, 0.839) respectively. Our institution’s diagnostic cutoff of ≥2 for OCB yielded a sensitivity and specificity of 74.2% (68.3%, 79.1%) and 84.9% (73.8%, 89.5%), respectively. The best combination for sensitivity and specificity for KCSF with the Youden index yielded a cutoff ≥0.0654 mg/dL (sensitivity of 79.6% [73.1%, 84.0%] and specificity of 72.4% [62.8%, 76.8%]), which is similar to previous publications. This supports the use of automated chart review and suggests the KCSF cutoff has been stable over time.

Conclusions
KCSF exhibits comparable MS diagnostic performance to OCB yet only requires one CSF sample and can be measured rapidly. When validated, mining unstructured data allows for test performance characterization in large patient cohorts without the need for manual chart review.