Senel M, Tumani H, Lauda F, Presslauer S, Mojib-Yezdani R, Otto M, Brettschneider J. Cerebrospinal fluid immunoglobulin kappa light chain in clinically isolated syndrome and multiple sclerosis. PLoS One. 2014 Apr ;9(4):e88680.
BACKGROUND:Oligoclonal bands (OCB) are the most widely used CSF test to support the diagnosis of MS and to predict conversion of clinically isolated syndrome (CIS) to multiple sclerosis (MS). Since OCB tests are based on non-quantitative and difficult to standardise techniques, measurement of immunoglobulin kappa free light chains (KFLC) may represent an easier to use quantitative test.
METHODS: KFLC were measured in CSF and serum of 211 patients using ELISA. These include patients without any inflammatory central nervous system reaction (NIND, n = 77), MS (n = 20), viral CNS infections (V-CNS-I, n = 10), neuroborreliosis (NB, n = 17) and other bacterial CNS infections (B-CNS-I, n = 10). Furthermore a cohort of 77 patients with CIS, including 39 patients that remained CIS over follow-up of two years (CIS-CIS) and 38 patients that developed MS over the same follow-up time (CIS-MS).
RESULTS: CSF-serum ratio of KFLC (Q KFLC) was elevated in all patients with MS, 86.8% of patients with CIS-MS and 61.5% of patients with CIS-CIS. It was significantly elevated in CIS with presence of OCB (p<0.001). Q KFLC significantly correlated with other CSF variables such as CSF leukocyte count (p<0.001, R = 0.46), CSF CXCL13 levels (p<0.001, R = 0.64) and also intrathecal IgG synthesis (p<0.001, R = 0.74) as determined by nephelometry and quotient diagram. OCB were detected in 66.7% of CIS-CIS and in 92.1% of CIS-MS.
CONCLUSIONS: Although the measurement of CSF KFLC is a rapid and quantitative easy to standardize tool, it is almost equal but not superior to OCB with regard to diagnostic sensitivity and specificity in patients with early MS.
Sensitivity and specificity are statistical measures of the performance of a test. Sensitivity (also called the true positive rate), measures the proportion of actual positives which are correctly identified as such (e.g. the percentage of sick people who are correctly identified as having the condition). Specificity (sometimes called the true negative rate) measures the proportion of negatives which are correctly identified as such (e.g. the percentage of healthy people who are correctly identified as not having the condition). These two measures are closely related to the concepts of type I and type II errors. A perfect predictor would be described as 100% sensitive (i.e. predicting all people from the sick group as sick) and 100% specific (i.e. not predicting anyone from the healthy group as sick).
For any test, there is usually a trade-off between the measures. For example: in an airport security setting in which one is testing for potential threats to safety, scanners may be set to trigger on low-risk items like belt buckles and keys (low specificity), in order to reduce the risk of missing objects that do pose a threat to the aircraft and those aboard (high sensitivity).
| Condition (as determined by "Gold standard") | ||||
| Condition positive | Condition negative | |||
| Test outcome | Test outcome positive | True positive | False positive (Type I error) | Precision = Σ True positive
Σ Test outcome positive
|
| Test outcome negative | False negative (Type II error) | True negative | Negative predictive value = Σ True negative
Σ Test outcome negative
| |
| Sensitivity = Σ True positive
Σ Condition positive
| Specificity = Σ True negative
Σ Condition negative
| Accuracy | ||