ABSTRACT
Background
Cerebrospinal fluid (CSF) analysis is key to diagnosing multiple sclerosis (MS). In particular, the presence of CSF‐specific oligoclonal bands (OCB) is examined alongside differential diagnostic aspects. This study aims to investigate the diagnostic value of OCB in MS compared to other neurological disorders.
Methods
Patients who presented to the Department of Neurology of the Medical University of Vienna between January 1, 2004 and December 31, 2020, due to neurological complaints and underwent lumbar puncture (LP) for diagnostic purposes were included in this retrospective analysis. Exclusion criteria were incomplete laboratory data and/or a missing final diagnosis.
Results
Of 5744 patients who underwent LP, 5225 were available for analysis. Of these, 745 (14.3%) were diagnosed with MS, whereby CSF‐specific OCB showed a sensitivity of 92.8% and a specificity of 90.4%. While the positive predictive value (PPV) was merely 61.5%, the negative predictive value (NPV) yielded 98.7%. In comparison, OCB displayed low sensitivity rates for most other neurological disease groups. Of note, neuroborreliosis (n = 66) had a relatively high OCB positive rate of 70%, followed by CNS human immunodeficiency virus infections (n = 32) with 62.5%.
Conclusions
CSF‐specific OCB have a high sensitivity and specificity and a very high NPV for diagnosing MS. However, the PPV was lower in this cohort. These findings must be carefully considered when diagnosing MS and interpreting OCB, especially in the context of differential diagnostic workup.
Keywords: multiple sclerosis, negative predictive value, oligoclonal bands, positive predictive value, sensitivity, specificity
1. Introduction
Multiple sclerosis (MS) is a chronic inflammatory, autoimmune‐mediated disease of the central nervous system (CNS) characterized by demyelination and diffuse neurodegeneration [1].
The three key elements for diagnosing MS today consist of the patient's medical history and clinical presentation, followed by magnetic resonance imaging (MRI) and cerebrospinal fluid (CSF) diagnostics, besides other paraclinical tools such as optic coherence tomography (OCT) [2, 3, 4]. Since the 2017 revision of the McDonald criteria, the importance of CSF was reemphasized, as CSF‐specific oligoclonal bands (OCB) were introduced as a surrogate marker of dissemination in time (DIT) [5]. CSF investigation is also crucial in the differential‐diagnostic workup, especially with regard to other inflammatory or infectious diseases. Taking into account the frequent over‐ and misinterpretation of MRI findings, especially in patients with migraine or vascular disease, CSF diagnostics—and particularly OCB—could prevent numerous misdiagnoses and, consequently also mistreatment [6, 7]. According to a study by Solomon and colleagues, the most frequent diagnoses initially labeled MS were migraine, fibromyalgia, nonspecific neurological symptoms, and psychiatric conditions. Notably, 70% received a disease‐modifying therapy (DMT [13% Natalizumab]), and 31% suffered unnecessary morbidity due to misdiagnosis [7]. At the time of initial misdiagnosis, only 67% of patients underwent a CSF investigation; among those, only 12% exhibited positive OCB [7]. Importantly, the specificity of OCB for MS, while generally high, can exhibit considerable variability, particularly in differentiating MS from other inflammatory etiologies [8]. CSF‐specific OCB have been described in a wide range of neurological diseases such as CNS lymphomas, paraneoplastic neurological diseases, neurosyphilis, neurotuberculosis as well as autoimmune encephalitis (AIE), Guillain–Barré syndrome or prion diseases [8]. Thus, it is imperative to account for the heterogeneity of previous studies and the pivotal role of the reference cohort when evaluating the diagnostic value of CSF‐specific OCB [6, 8].
This retrospective study aims to investigate the diagnostic value of CSF‐specific OCB in MS compared to other neurological diseases in a reference population of over 5000 patients from a single tertiary center in Austria who underwent CSF examination due to neurological complaints.
2. Methods
2.1. Patients and Definitions
For this retrospective cohort study, we used the hospital data information system of the Department of Neurology, Medical University of Vienna, which serves as both a primary and reference center mainly for Vienna and its geographical catchment area [9]. Data were collected from all patients who underwent a diagnostic lumbar puncture (LP) at the Department of Neurology of the Medical University of Vienna due to neurological complaints between January 1, 2004 and December 31, 2020. Exclusion criteria were incomplete laboratory data and/or a missing final diagnosis. In addition, the validity of the final diagnosis was rechecked using the data available in the medical records.
The diagnosis of MS was made retrospectively for all patients according to the 2017 revised McDonald criteria [5]. For the diagnosis of a clinically isolated syndrome (CIS), dissemination in space (DIS), yet not DIT, was required as a criterion in addition to a typical clinical manifestation [10]. Thus, in this study, all CIS patients are, per definition, OCB negative. Following this, patients with a clinical presentation typical of a demyelinating event but without DIS and DIT were classified as demyelinating events of unknown significance (DEUS). The Okuda criteria were applied to diagnose radiologically isolated syndrome (RIS) [11]. All other patients were diagnosed according to best clinical practice.
2.2. Determination of Oligoclonal Bands (OCB)
Oligoclonal bands were detected using isoelectric focusing followed by IgG‐specific immunofixation. The bands in CSF were compared to bands in serum that had been adjusted for IgG concentration. According to clinic standards, a sample was considered OCB positive if two or more bands were present in the CSF, whether isolated (Type II) or additional (Type III) [12].
2.3. Ethics
The ethics committee of the Medical University Vienna approved the study (ethical approval number: EK 1986/2021). As datasets were exported pseudonymously from local databases, including data obtained in routine practice, the ethics committee waived the need for written informed consent from study participants.
2.4. Statistics
All statistical analyses and graphical representations were performed in R Version 4.3.1. The following statistical parameters of diagnostic accuracy were calculated: Sensitivity, Specificity, Positive Predictive Value (PPV), and Negative Predictive Value (NPV). The significance level was set at a two‐sided p‐value < 0.05.
3. Results
Of 5744 patients, 5225 were available for analysis, of which 1123 (21.5%) showed positive CSF‐specific OCB (see also Figure 1).
FIGURE 1.
Inclusion flow chart. LP, lumbar puncture; OCB, oligoclonal bands.
As a next step, patients were assigned to disease groups according to the final (suspected) diagnosis or, if no diagnosis could be established, the indication for LP (see Figure 2). Table 1 gives an overview of the resulting disease groups and the most frequent entities of each disease group. Of note, the group “exclusion diagnosis” covers cases where LP was performed to exclude a disease based on clinical suspicion or paraclinical findings, that is, exclusion of subarachnoid hemorrhage for thunderclap headache, and no other final diagnosis was established.
FIGURE 2.
Overview of disease groups according to indication for lumbar puncture (LP) as well as the respective proportion of OCB positive/negative patients and the sensitivity of OCB regarding the respective disease group or entity. CIS, clinically isolated syndrome; CNS, central nervous system; lhs, left‐hand side; PNS, peripheral nervous system; PPMS, primary‐progressive multiple sclerosis; rhs, right‐hand side; RIS, radiologically isolated syndrome; RRMS, relapsing–remitting multiple sclerosis; SPMS, secondary‐progressive multiple sclerosis.
TABLE 1.
Disease groups (n) as well as the respective most frequent entities (n) of each group.
| Disease group (n) | Most frequent entities within the respective group (n) | ||
|---|---|---|---|
| Autoimmune‐CNS (n = 1254) | MS (n = 745) | AIE (n = 136) | Isolated NNO (n = 116) |
| Exclusion diagnosis (n = 851) | Suspected SAH (n = 138) | Suspected neuroborreliosis (n = 80) | WML not further specified (n = 78) |
| PNS (n = 576) | PNP (n = 204) | Isolated cranial nerve affection (n = 154) | Polyradiculitis (n = 145) |
| Infectious‐CNS (n = 446) | Herpesviridae (n = 67) | Neuroborreliosis (n = 66) | Neurosyphilis (n = 61) |
| Neurodegenerative (n = 445) | NPH (n = 157) | Dementia (n = 139) | HSP (n = 36) |
| Vascular (n = 352) | Ischemic stroke (n = 220) | SAH (n = 50) | Vascular leucencephalopathy (n = 21) |
| Headache (n = 348) | IIH (n = 297) | Migraine (n = 29) | SIH (n = 17) |
| Neoplasia (n = 209) | Primary CNS tumor (n = 71) | Meningeosis carcinomatosa (n = 61) | Paraneoplastic syndrome (n = 19) |
| Neuromuscular (n = 172) | MND (n = 115) | Myopathy (n = 31) | MG (n = 23) |
| Epilepsy (n = 162) | |||
| Psychiatric (n = 152) | Dissociative disorder (n = 137) | Delirium (n = 10) | Psychosis (n = 5) |
| Nutritious‐toxic/metabolic (n = 81) | Toxic encephalopathy (n = 15) | Leukodystrophy (n = 9) | Fabry disease (n = 5) |
| Others (n = 76) | Hydrocephalus (n = 13) | TGA (n = 13) | Syringomyelia (n = 7) |
| Autoimmune‐systemic (n = 63) | Collagenoses/vasculitides (n = 58) | PMR (n = 5) | |
| Disturbance of consciousness (n = 20) | Coma (n = 15) | Syncope (n = 5) | |
| Infectious‐PNS (n = 18) | Zoster (n = 14) | Spondylodiscitis (n = 4) | |
Abbreviations: AIE, autoimmune encephalitis; CNS, central nervous system; HSP, hereditary spastic paraplegia; IIH, idiopathic intracranial hypertension; MG, myasthenia gravis; MND, motor neuron disease; MS, multiple sclerosis; NNO, neuritis nervi optici; NPH, normal pressure hydrocephalus; PMR, polymyalgia rheumatica; PNP, polyneuropathy; PNS, peripheral nervous system; SAH, subarachnoid hemorrhage; SIH, spontaneous intracranial hypotension; TGA, transient global amnesia; WML, white matter lesions.
The rate of positive CSF‐specific OCB was 68.1% (n = 854/1254) in the autoimmune‐CNS group overall, and 92.8% in MS (n = 691/745). In the group of relapsing–remitting MS (RRMS) patients (90.3% of the MS collective, n = 673/745), even 93.3% (n = 628/673) were OCB positive. All other disease groups showed lower rates of OCB positivity, as shown in Table 2. Of note, in the “infectious‐CNS” group, the sensitivity of OCB was 31.2% (n = 139/446). Within this group, in particular, neuroborreliosis and human immunodeficiency virus (HIV) infections of the CNS (neuroHIV) showed an OCB positivity rate of 70% (n = 46/66) and 62.5% (n = 20/32), respectively. Furthermore, in the neoplasia group, the rate of OCB positivity, particularly in meningeal carcinomatosis and paraneoplastic syndromes, reached 21.3% (n = 13/61) and 36.8% (n = 7/19), respectively.
TABLE 2.
Disease groups according to LP indication with the respective number of patients with positive/negative OCB as well as the respective sensitivity, specificity, PPV, NPV.
| Disease group | Patients | Sensitivity | Specificity | PPV | NPV |
|---|---|---|---|---|---|
| (by OCB) | (95% confidence interval) | ||||
| 1. Autoimmune‐CNS |
1254 (854+, 400−) |
68.1% (65.4%, 70.7%) |
93.2% (92.4%, 94%) |
76.0% (73.4%, 78.5%) |
90.2% (89.3%, 91.1%) |
| MS |
745 (691+, 54−) |
92.8% (90.6%, 94.5%) |
90.4% (89.5%, 91.2%) |
61.5% (58.6%, 64.4%) |
98.7% (98.3%, 99%) |
| RRMS |
673 (628+, 45−) |
93.3% (91.2%, 95.1%) |
89.1% (88.2%, 90.0%) |
55.9% (53.0%, 58.9%) |
98.9% (98.5%, 99.2%) |
| PPMS |
58 (50+, 8−) |
86.2% (74.6%, 93.9%) |
79.2% (78.1%, 80.3%) |
4.5% (3.3%, 5.8%) |
99.8% (99.6%, 99.9%) |
| SPMS |
14 (13+, 1−) |
92.9% (66.1%, 99.8%) |
78.7% (77.6%, 79.8%) |
1.2% (0.6%, 2.0%) |
100% (99.9%, 100%) |
| RIS |
27 (24+, 3−) |
88.9% (70.8%, 97.6%) |
78.9% (77.7%, 80%) |
2.1% (1.4%, 3.2%) |
99.9% (99.8%, 100%) |
| 2. Exclusion diagnosis |
851 (3+, 848−) |
0.4% (0.1%, 1%) |
74.4% (73.1%, 75.7%) |
0.3% (0.1%, 0.8%) |
79.3% (78.1%, 80.6%) |
| 3. PNS |
576 (16+, 560−) |
2.8% (1.6%, 4.5%) |
76.2% (74.9%, 77.4%) |
1.4% (0.8%, 2.3%) |
86.3% (85.3%, 87.4%) |
| 4. Infectious—CNS |
446 (139+, 307−) |
31.2% (26.9%, 35.7%) |
79.4% (78.2%, 80.5%) |
12.4% (10.5%, 14.4%) |
92.5% (91.7%, 93.3%) |
| 5. Neurodegenerative |
445 (10+, 435−) |
2.2% (1.1%, 4.1%) |
76.7% (75.5%, 77.9%) |
0.9% (0.4%, 1.6%) |
89.4% (88.4%, 90.3%) |
| 6. Vascular |
352 (15+, 337−) |
4.3% (2.4%, 6.9%) |
77.3% (76.1%, 78.4%) |
1.3% (0.7%, 2.2%) |
91.8% (90.9%, 92.6%) |
| 7. Headache |
348 (14+, 334−) |
4% (2.2%, 6.7%) |
77.3% (76.1%, 78.4%) |
1.2% (0.7%, 2.1%) |
91.9% (91.0%, 92.7%) |
| 8. Neoplasia |
209 (34+, 175−) |
16.3% (11.5%, 22%) |
78.3% (77.1%, 79.4%) |
3.0% (2.1%, 4.2%) |
95.7% (95.1%, 96.3%) |
| 9. Neuromuscular |
172 (3+, 169−) |
1.7% (0.4%, 5%) |
77.8% (76.7%, 79%) |
0.3% (0.1%, 0.8%) |
95.9% (95.2%, 96.5%) |
| 10. Epilepsy |
162 (11+, 151−) |
6.8% (3.4%, 11.8%) |
78% (76.9%, 79.2%) |
1.0% (0.5%, 1.7%) |
96.3% (95.7%, 96.9%) |
| 11. Psychiatric |
152 (3+, 149−) |
2% (0.4%, 5.7%) |
77.9% (76.8%, 79.1%) |
0.3% (0.1%, 0.8%) |
96.4% (95.7%, 96.9%) |
| 12. Nutritious‐toxic/metabolic |
81 (4+, 77−) |
4.9% (1.4%, 12.2%) |
78.2% (77.1%, 79.4%) |
0.4% (0.1%, 0.9%) |
98.1% (97.7%, 98.5%) |
| 13. Others |
76 (3+, 73−) |
3.9% (0.8%, 11.1%) |
78.2% (77.1%, 79.4%) |
0.3% (0.1%, 0.8%) |
98.2% (97.8%, 98.6%) |
| 14. Autoimmune—systemic |
63 (7+, 56−) |
11.1% (4.6%, 21.6%) |
78.4% (77.2%, 79.5%) |
0.6% (0.3%, 1.3%) |
98.6% (98.2%, 99.0%) |
| 15. Disturbance of consciousness |
20 (0+, 20−) |
0% (0%, 16.8%) |
78.4% (77.3%, 79.5%) |
0.0% (0.0%, 0.3%) |
99.5% (99.2%, 99.7%) |
| 16. Infectious—PNS |
18 (7+, 11−) |
38.9% (17.3%, 64.3%) |
78.6% (77.4%, 79.7%) |
0.6% (0.3%, 1.3%) |
99.7% (99.5%, 99.9%) |
Abbreviations: CIS, clinically isolated syndrome; CNS, central nervous system; MS, multiple sclerosis; NPV, negative predictive value; OCB, oligoclonal bands; PNS, peripheral nervous system; PPMS, primary‐progressive MS; PPV, positive predictive value; RIS, radiologically isolated syndrome; RRMS, relapsing–remitting MS; SPMS, secondary‐progressive MS.
For further analysis, we also calculated parameters of diagnostic accuracy of OCB for all disease groups with the whole remaining cohort as reference population (see Table 2). Notably, in the headache disease group, the PPV for migraine (n = 29) was 0.4%.
Moreover, we visualized the individual diseases included in the autoimmune‐CNS group with the respective number of OCB‐positive/negative patients (see Figure 3).
FIGURE 3.
Overview of the individual disease entities in the autoimmune‐CNS group with their respective number of OCB positive and negative patients as well as the respective sensitivity with the related 95% confidence interval (red area). ADEM, acute disseminated encephalomyelitis; CIS, clinically isolated syndrome; CNS vasculitis including primary and systemic variants; CNS, central nervous system; DEUS, demyelinating event of unknown significance; lhs, left hand side; rhs, right hand side; MOGAD, myelin‐oligodendrocyte‐glycoprotein antibody‐associated disease; MS, multiple sclerosis; NMOSD, neuromyelitis optica spectrum disorder; NNO, neuritis nervi optici; OCB, oligoclonal bands; PPMS, primary‐progressive multiple sclerosis; RIS, radiologically isolated syndrome; RRMS, relapsing–remitting multiple sclerosis; SPMS; secondary‐progressive multiple sclerosis.
Subsequently, we analyzed the diagnostic accuracy of OCB for MS and other entities of the autoimmune CNS group again, but using the group of autoimmune‐CNS disorders as the reference cohort instead of the whole cohort (see Table 3). Here, the specificity of OCB positivity for MS decreased from 90.4% across the whole cohort to 68% within the “autoimmune‐CNS” cohort. Moreover, the NPV dropped from 98.7% to 86.5%, whereas the PPV increased from 61.5% to 80.9%.
TABLE 3.
Disease entities in the autoimmune‐CNS group with the according number of patients with positive/negative OCB as well as the respective sensitivity, specificity, PPV, NPV.
| Autoimmune‐CNS group | Patients | Sensitivity | Specificity | PPV | NPV |
|---|---|---|---|---|---|
| (by OCB) | (95% confidence interval) | ||||
| MS |
745 (691+, 54−) |
92.8% (90.6%, 94.5%) |
68.0% (63.7%, 72.0%) |
80.9% (78.1%, 83.5%) |
86.5% (82.8%, 89.7%) |
| RRMS |
673 (628+, 45−) |
93.3% (91.2%, 95.1%) |
61.1% (57.0%, 65.1%) |
73.5% (70.4%, 76.5%) |
88.8% (85.2%, 91.7%) |
| PPMS |
58 (50+, 8−) |
86.2% (74.6%, 93.9%) |
32.8% (30.1%, 35.5%) |
5.9% (4.4%, 7.6%) |
98.0% (96.1%, 99.1%) |
| SPMS |
14 (13+, 1−) |
92.9% (66.1%, 99.8%) |
32.2% (29.6%, 34.9%) |
1.5% (0.8%, 2.6%) |
99.8% (98.6%, 100.0%) |
| RIS |
27 (24+, 3−) |
88.9% (70.8%, 97.6%) |
32.4% (29.7%, 35.1%) |
2.8% (1.8%, 4.2%) |
99.3% (97.8%, 99.8%) |
| AIE |
136 (34+, 102−) |
25.0% (18.0%, 33.1%) |
26.7% (24.1%, 29.4%) |
4.0% (2.8%, 5.5%) |
74.5% (69.9%, 78.7%) |
|
NNO (isolated) |
116 (28+, 88−) |
24.1% (16.7%, 33.0%) |
27.4% (24.8%, 30.1%) |
3.3% (2.2%, 4.7%) |
78.0% (73.6%, 82.0%) |
|
Myelitis (isolated) |
70 (36+, 34−) |
51.4% (39.2%, 63.6%) |
30.9% (28.3%, 33.6%) |
4.2% (3.0%, 5.8%) |
91.5% (88.3%, 94.0%) |
| CNS‐vasculitis | 36 | 16.7% | 30.4% | 0.7% | 92.5% |
| primary and systemic | (6+, 30−) | (6.4%, 32.8%) | (27.8%, 33.0%) | (0.3%, 1.5%) | (89.5%, 94.9%) |
| DEUS |
31 (23+, 8−) |
74.2% (55.4%, 88.1%) |
32.1% (29.4%, 34.7%) |
2.7% (1.7%, 4.0%) |
98.0% (96.1%, 99.1%) |
| NMOSD |
18 (8+, 10−) |
44.4% (21.5%, 69.2%) |
31.6% (29.0%, 34.2%) |
0.9% (0.4%, 1.8%) |
97.5% (95.5%, 98.8%) |
| ADEM |
12 (2+, 10−) |
16.7% (2.1%, 48.4%) |
31.4% (28.8%, 34.1%) |
0.2% (0.0%, 0.8%) |
97.5% (95.5%, 98.8%) |
| CNS‐sarcoidosis |
9 (1+, 8) |
11.1% (0.3%, 48.2%) |
31.5% (28.9%, 34.1%) |
0.1% (0.0%, 0.7%) |
98.0% (96.1%, 99.1%) |
| Uveitis/Papillitis |
6 (1+, 5−) |
16.7% (0.4%, 64.1%) |
31.7% (29.1%, 34.3%) |
0.1% (0.0%, 0.7%) |
98.8% (97.1%, 99.6%) |
| MOGAD |
4 (0+, 4−) |
0.0% (0.0%, 60.2%) |
31.7% (29.1%, 34.3%) |
n/a |
99.0% (97.5%, 99.7%) |
Abbreviations: ADEM, acute disseminated encephalomyelitis; AIE, autoimmune encephalitis; CIS, clinically isolated syndrome; CNS, central nervous system; DEUS, demyelinating event of unknown significance; MOGAD, myelin‐oligodendrocyte‐glycoprotein antibody‐associated disease; MS, multiple sclerosis; NMOSD, neuromyelitis optica spectrum disease; NNO, neuritis nervi optici; NPV, negative predictive value; PPMS, primary‐progressive multiple sclerosis; PPV, positive predictive value; RIS, radiologically isolated syndrome; RRMS, relapsing–remitting multiple sclerosis; SPMS, secondary‐progressive multiple sclerosis.
4. Discussion
Here, we aimed to determine the diagnostic value of CSF‐specific OCB in MS compared to other neurological diseases in a reference population of over 5000 patients undergoing LP for CSF examination due to neurological complaints.
CSF‐specific OCB showed a high sensitivity (92.8%), specificity (90.4%), and a very high NPV (98.7%) for the diagnosis of MS. Of note, the PPV was substantially lower (61.5%). This finding strongly reinforces the widely acknowledged—yet often overlooked—principle that while MS is highly unlikely in the absence of OCB, a positive OCB result by itself does not establish an MS diagnosis.
Moreover, when analyzing the diagnostic accuracy of OCB only within the group of autoimmune‐CNS disorders, the specificity of CSF‐specific OCB for MS dropped from 90.4% to 68%. This underpins the critical importance of the reference cohort when analyzing and interpreting diagnostic accuracy [6, 8, 13].
In line with previous studies, the sensitivity and specificity of CSF‐specific OCB in most other disease groups were very low (< 10%). Nevertheless, it is essential to note that the presence of CSF‐specific OCB does not exclude other etiologies, not even those that are non‐inflammatory [14, 15, 16]. Hence, the clinical picture and additional paraclinical features must be considered when interpreting OCB.
In clinical practice, the added value of OCB mainly comprises the following aspects:
First, confirming OCB positivity can establish an MS diagnosis in the presence of a typical demyelinating event and MRI findings consistent with DIS, allowing treatment to begin without delay. Second, the exclusion of diagnoses in cases that often lead to misdiagnosis of MS, namely migraine, vascular and psychiatric disorders, constitutes a crucial point. In migraine, MRI might lead to a misdiagnosis of MS since white matter lesions can be found in up to 40% of patients [17]. Although the primary distinction should be based on the patient's medical history, clinical findings, and a meticulous interpretation of MRI findings, CSF analysis with OCB can serve as a valuable adjunct in cases of diagnostic uncertainty (PPV for migraine 0.4%). Conversely, differentiating MS from psychiatric disorders may not always be straightforward, particularly if white matter lesions are present [18, 19]. Therefore, in these cases, CSF analysis including OCB can provide more clarity (PPV for the psychiatric disease group was 0.3%).
On the other hand, diseases other than MS can be associated with a relatively high OCB‐positive rate, as demonstrated in this work. A remarkably high rate of CNS‐specific OCB was found in neuroborreliosis (70%) and neuroHIV (62.5%). Nevertheless, in these cases, a distinction based on the clinical presentation and routine CSF parameters, eventually aided by other paraclinical findings, should allow a clear differentiation and, thus, render the risk of misinterpretation quite low. In addition, in the neoplasia group meningeal carcinomatosis and paraneoplastic syndromes showed a higher rate of OCB positivity (21.3% and 36.8%, respectively), though absolute numbers were relatively low. These results align with the existing evidence and may also be explained from a pathophysiological perspective, as any process triggering a B‐cell response may, in principle, lead to intrathecal IgG synthesis [6, 8, 20, 21, 22]. Depending on whether the trigger persists—as in MS—or subsides due to natural course or effective treatment, the detection of OCB should either persist or disappear again over time [23, 24, 25]. However, data on repeated LP were too sparsely available to allow reliable analyses regarding OCB persistence.
4.1. Limitations
A number of limitations is acknowledged. First, the retrospective design induces a variety of potential biases, though the detailed and standardized patient characterization partly mitigated this. Second, the case numbers of the different disease groups and entities vary significantly, which may affect the generalizability of the findings. Thus, replication of our data in an independent cohort is needed for confirmation. Of note, according to the current definition of CIS as per McDonald 2017, all CIS patients were OCB negative [10]. In addition, RIS patients showed a relatively high OCB positive rate, which might be explained by selection bias for LP indication in patients with suspicion of RIS, where a higher perceived risk of MS, that is, a higher MRI lesion load, will likely increase the likelihood of performing CSF [11]. Finally, the study cohort almost exclusively consisted of patients of Caucasian origin, limiting generalizability to other ethnicities.
5. Conclusion
In this cohort of over 5000 patients with neurological complaints, CSF‐specific OCB showed very high sensitivity and specificity as well as a very high NPV for the diagnosis of MS. Nevertheless, PPV was clearly lower, and the specificity dropped significantly when analyzed within the subset of CNS autoimmune diseases, which must be kept in mind when interpreting OCB, particularly in the differential diagnostic work‐up of MS.
Accordingly, CSF analyses including OCB play a key role in avoiding misdiagnosis of MS, especially in the context of migraine, vascular or psychiatric disorders. On the other hand, other disorders associated with OCB‐positivity, such as neuroborreliosis or neuroHIV, are clearly distinguishable based on the clinical picture and paraclinical features.
Author Contributions
Tobias Monschein and Fritz Leutmezer: conceptualization. Tobias Monschein, Benjamin Scheicher, Markus Ponleitner, Nik Krajnc, Fabian Föttinger, Tobias Zrzavy, Paulus Rommer, Barbara Kornek, Gabriel Bsteh, Thomas Perkmann, Thomas Berger, and Fritz Leutmezer: methodology. Tobias Monschein, Benjamin Scheicher, Markus Ponleitner, Nik Krajnc, Fabian Föttinger, Tobias Zrzavy, Paulus Rommer, Barbara Kornek, Gabriel Bsteh, Thomas Perkmann, Thomas Berger, and Fritz Leutmezer: data curation. Tobias Monschein: formal analysis. Thomas Berger and Fritz Leutmezer: supervision. Tobias Monschein: visualization. Tobias Monschein: writing – original draft. Benjamin Scheicher, Markus Ponleitner, Nik Krajnc, Fabian Föttinger, Tobias Zrzavy, Paulus Rommer, Barbara Kornek, Gabriel Bsteh, Thomas Perkmann, Thomas Berger, and Fritz Leutmezer: writing – review and editing.
Funding
The authors have not declared a specific grant for this research from any funding agency in the public, commercial, or not‐for‐profit sectors.
Conflicts of Interest
Tobias Monschein has participated in meetings sponsored by, received speaker honoraria or travel funding from Biogen, BMS/Celgene, Merck, Novartis, Roche, Sanofi‐Genzyme, and Teva. Benjamin Scheicher has declared no conflicts of interest related to this work. Markus Ponleitner has received speaker or consulting honoraria from Amicus, Sanofi‐Aventis, and Novartis and participated in meetings sponsored by and received travel funding from Amicus, Merck, Novartis, and Sanofi‐Genzyme. Nik Krajnc has participated in meetings sponsored by, received speaker honoraria or travel funding from Alexion, BMS/Celgene, Janssen‐Cilag, Merck, Neuraxpharm, Novartis, Roche, and Sanofi‐Genzyme and held a grant for a Multiple Sclerosis Clinical Training Fellowship Programme from the European Committee for Treatment and Research in Multiple Sclerosis (ECTRIMS). Fabian Föttinger has participated in meetings sponsored by, received speaker honoraria or travel funding from Novartis. Tobias Zrzavy has participated in meetings sponsored by or received travel funding from Biogen, Merck, Novartis, Roche, Sanofi‐Genzyme, and Teva. Paulus Rommer has received honoraria for consultancy/speaking from AbbVie, Allmiral, Alexion, Biogen, Merck, Novartis, Roche, Sandoz, Sanofi Genzyme and has received research grants from Amicus, Biogen, Merck, Roche. Barbara Kornek: has received honoraria for speaking and for consulting from Biogen, BMS/Celgene, GSK, Johnson&Johnson, Merck, Novartis, Roche, Teva and Sanofi‐Genzyme outside of the submitted work. No conflicts of interest with respect to the present study. Gabriel Bsteh has participated in meetings sponsored by, received speaker honoraria or travel funding from Biogen, BMS, Janssen, Lilly, Medwhizz, Merck, Neuraxpharm, Novartis, Roche, Sanofi‐Genzyme, and Teva, and received honoraria for consulting Adivo Associates, Biogen, BMS, Janssen, Merck, Novartis, Roche, Sanofi‐Genzyme, and Teva. He has received unrestricted research grants from BMS and Novartis. He serves on the Executive Committee of the European Committee for Treatment and Research in Multiple Sclerosis (ECTRIMS) and the Board of Directors of the International Multiple Sclerosis Visual System Consortium (IMSVISUAL). Thomas Perkmann has declared no conflicts of interest related to this work. Thomas Berger has participated in meetings sponsored by and received honoraria (lectures, advisory boards, consultations) from pharmaceutical companies marketing treatments for MS: Allergan, Bayer, Biogen, Bionorica, BMS/Celgene, Genesis, GSK, GW/Jazz Pharma, Horizon, Janssen‐Cilag, MedDay, Merck, Novartis, Octapharma, Roche, Sandoz, Sanofi‐Genzyme, Teva, and UCB. His institution has received financial support in the past 12 months by unrestricted research grants (Biogen, Bayer, BMS/Celgene, Merck, Novartis, Roche, Sanofi‐Genzyme, Teva) and for participation in clinical trials in multiple sclerosis sponsored by Alexion, Bayer, Biogen, Merck, Novartis, Octapharma, Roche, Sanofi‐Genzyme, Teva. Fritz Leutmezer: has participated in meetings sponsored by, received speaker honoraria or travel funding from Actelion, Almirall, Biogen, BMS/Celgene, Johnson&Johnson, MedDay, Merck, Novartis, Roche, Sanofi‐Genzyme and Teva, and received honoraria for consulting Biogen, BMS/Celgene, Merck, Novartis, Roche, Sanofi‐Genzyme and Teva.
Acknowledgements
Open Access funding provided by Medizinische Universitat Wien/KEMÖ.
Monschein T., Scheicher B., Ponleitner M., et al., “The Value of Oligoclonal Bands in Multiple Sclerosis Compared to Other Neurological Disorders: A Retrospective Data Analysis From an Austrian Tertiary Center,” European Journal of Neurology 32, no. 12 (2025): e70471, 10.1111/ene.70471.
Data Availability Statement
Anonymized data supporting the findings of this study are available from the corresponding author upon reasonable request by a qualified researcher and upon approval by the data‐clearing committees of the Medical University of Vienna.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
Anonymized data supporting the findings of this study are available from the corresponding author upon reasonable request by a qualified researcher and upon approval by the data‐clearing committees of the Medical University of Vienna.