We read with interest the article by Kalincik et al. [1] comparing fingolimod, dimethyl fumarate and teriflunomide in a cohort of relapsing-remitting multiple sclerosis (MS) patients. The authors investigated several endpoints and performed various sensitivity analyses, and we commend them for reporting technical details in the online supplementary material. We, however, have some concerns about the design, analysis and reporting of the study.
1. In the primary analyses, three separate propensity score models were developed to construct a matched cohort for each of the three pairwise comparisons. Supplementary Table 6 clearly indicates the existence of zero or low frequencies in some variables (e.g., most active previous therapy and magnetic resonance imaging [MRI] T2 lesions). Yet, those variables were used as covariates in the propensity score models, unsurprisingly resulting in extremely high point estimates and standard errors (SE; as reported in Supplementary Table 7). For example, teriflunomide was not the most active therapy for any patient in the dimethyl fumarate cohort (n=0 from Supplementary Table 6), but that category was nevertheless included in the propensity score model, leading to an unrealistic point estimate of 18.65 with SE of 434.5 (Supplementary Table 7). Even higher SEs (greater than 1000) are observed in the other propensity score models. Propensity scores estimated from these poorly constructed models were then used to cr...
We read with interest the article by Kalincik et al. [1] comparing fingolimod, dimethyl fumarate and teriflunomide in a cohort of relapsing-remitting multiple sclerosis (MS) patients. The authors investigated several endpoints and performed various sensitivity analyses, and we commend them for reporting technical details in the online supplementary material. We, however, have some concerns about the design, analysis and reporting of the study.
1. In the primary analyses, three separate propensity score models were developed to construct a matched cohort for each of the three pairwise comparisons. Supplementary Table 6 clearly indicates the existence of zero or low frequencies in some variables (e.g., most active previous therapy and magnetic resonance imaging [MRI] T2 lesions). Yet, those variables were used as covariates in the propensity score models, unsurprisingly resulting in extremely high point estimates and standard errors (SE; as reported in Supplementary Table 7). For example, teriflunomide was not the most active therapy for any patient in the dimethyl fumarate cohort (n=0 from Supplementary Table 6), but that category was nevertheless included in the propensity score model, leading to an unrealistic point estimate of 18.65 with SE of 434.5 (Supplementary Table 7). Even higher SEs (greater than 1000) are observed in the other propensity score models. Propensity scores estimated from these poorly constructed models were then used to create three matched cohorts, which are the basis for the primary analyses in this work. Readers need to be skeptical about any inference (estimated SE of the treatment effect, and consequently the confidence intervals/p-values) made from these cohorts, because of the instability in the propensity score models. Further, while teriflunomide was not the ‘most active previous therapy’ for any patient in the original (unmatched) dimethyl fumarate cohort (n=0 and 0% from Supplementary Table 6), Table 1 reported n=14 (2%) patients with this therapy after matching. Naturally the matched cohort should not produce more patients than originally present in the unmatched cohort for any category.
2. A threshold lower than 10% or 20% in absolute value for standardized mean differences (i.e. Cohen’s d) is normally considered to assess imbalances in baseline covariates. However, in this study, a standardized mean difference was reported to be equal to 26% for relapse activity prior to the baseline for the comparison of fingolimod vs. teriflunomide matched sample (Table 1). Neither the standardized or raw difference in proportions was reported for any of the categorical variables in Table 1, even though some of the percentages in matched cohorts were substantially different (e.g., relapse rate). Large residual differences observed in the distribution of the covariates (likely due to poorly built propensity score models) will contribute to bias in the resulting estimates. Furthermore, matching by country, a crucial variable which would allow minimizing outcome assessment bias [2] was not reported in Table 1, but as Supplementary Table 4 which clearly shows that matching by country is far from being obtained. Even more important, since the matching process was conducted in a variable ratio manner for the primary analyses, standardized differences in Table 1 should be replaced with weighted standardized means or proportion differences to obtain a correct check of residual baseline imbalances after matching [3].
3. In the primary analysis, missing baseline MRI values were imputed to generate 17 imputed datasets (MRI information was available for only 20-27% of the population as reported in Supplementary Table 6). In a propensity score analysis, multiple imputation (instead of single imputation) would substantially complicate the analysis due to the pooling of estimates from the 17 imputed datasets (using Rubin’s rules). Both Supplementary Tables 7 and 11 included one set of estimates from each stage of the analysis (propensity score analysis and primary analysis of the matched cohorts respectively), making it unclear how the results were pooled. If the results were not pooled and a single imputed dataset was used for the analysis (as suggested by Supplementary Tables 7 and 11), then such a process would fail to account for the uncertainty in the missing values, leading to SEs and p-values that are smaller than expected.
4. We applaud the authors for conducting a series of sensitivity analyses to evaluate the robustness of their findings. However, readers would have more confidence in the findings if the supplementary materials included more details of how those sensitivity analyses were done. For example, when 1:1 matching was done, it is not clear whether and how the authors have accounted for the matched-pairs designs. In particular, despite having almost identical sample sizes in some matched cohorts (e.g., comparing analyses of ‘no MRI data included’ vs. ‘matching on 2-year relapse rate’ for fingolimod vs. dimethyl fumarate in Supplementary Table 11), high variability in p-values in most cases deserve further explanation.
5. As for the PS adjusted treatment effect analyses, this work claims that individual ARRs were calculated and used in the assessment of primary endpoint analysis. This approach is controversial [4]. Furthermore, the use of individual ARRs is contradicted in the statistical analysis section in which the authors state that a weighted negative binomial accounting for matching has been used. It is unclear whether individual ARRs were fed into a negative binomial and it is important to note that, if they were, results may be biased. The authors do not make clear whether standard errors and p-values properly accounted both for matching and weighting in all assessed endpoints (they include a cluster term in the negative binomial model which only accounts for matching). Table 1 presented below reports a back-calculation of the standard deviation (SD) for ARRs, which should correspond to a stable population parameter, in particular the column ‘SD right’ which is less prone to rounding effects present the original ARRs confidence interval values. This standard deviation is benchmarked against a recent work on a new drug for MS [5]. For the studies OPERA I and II consistent and stable SD values are obtained (around 1), while highly inconsistent and underestimated SDs are obtained for the MSBASE study, especially when the weighting scheme should have been attributing within the matched group the weight of 1 to the treatment arm represented by one single patient. Our Table 1 shows that the reported estimated standard errors are incorrect (i.e. generally smaller) due to the use of a wrong weighting scheme or lack of accounting for weighting properly and, consequently, p-values significance has been inflated dramatically.
6. The large number of patients included made statistically significant a very small and perhaps not clinically meaningful difference, increasing the risk of overinterpretation of the results. From a clinical standpoint an ARR difference between 0.20 and 0.26, that is an ARR ratio of 0.80 or, more intuitively, 1 relapse over 5 years vs. 1 relapse over 4 years is close to negligible overall. This represents an effect size that no future trial would likely be powered or interested to detect, especially as it comes from an ARR threshold (0.20) quite prone to the presence of noise in detecting a relapse.
To recapitulate, we wish to highlight the need for caution while interpreting the findings of this paper. Real world evidence (RWE) is an important and necessary component of research to assess the effectiveness and safety of various therapies outside the context of randomized clinical trials. However, because RWE is prone to various sources of bias, rigorous and careful analysis, interpretation and reporting are needed to ensure that results are reliable, reproducible and useful to inform clinical decision making.
REFERENCES
[1] Kalincik T et al. Comparison of fingolimod, dimethyl fumarate and teriflunomide for multiple sclerosis. Journal of Neurology, Neurosurgery & Psychiatry. 2019: jnnp-2018-319831. doi:10.1136/jnnp-2018-31983.
[2] Bovis F et al. Expanded disability status scale progression assessment heterogeneity in multiple sclerosis according to geographical areas. Ann Neurol. 2018 Oct;84(4):621-625.
[3] Austin, Peter C. Assessing balance in measured baseline covariates when using many‐to‐one matching on the propensity‐score. Pharmacoepidemiology and drug safety 17.12 (2008): 1218-1225.
[4] Suissa S et al. Statistical Treatment of Exacerbations in Therapeutic Trials of Chronic Obstructive Pulmonary Disease. American Journal of Respiratory and Critical Care Medicine. 2006;173(8):842-846.
[5] Hauser SL, Bar-Or A, Comi G, Giovannoni G, Hartung HP, Hemmer B, Lublin F, Montalban X, Rammohan KW, Selmaj K, Traboulsee A, Wolinsky JS, Arnold DL, Klingelschmitt G, Masterman D, Fontoura P, Belachew S, Chin P, Mairon N, Garren H, Kappos L; OPERA I and OPERA II Clinical Investigators. Ocrelizumab versus Interferon Beta-1a in Relapsing Multiple Sclerosis. N Engl J Med. 2017 Jan 19;376(3):221-234.
Table 1 – Back-calculation of ARR standard deviation to benchmark flaws in the reported standard errors and p-values
Study Drug n ARR Lower ARR Upper ARR SD left SD right
OPERA I [5] Ocrelizumab 410 0.16 0.12 0.2 1.29 1.00
Interferon 411 0.29 0.24 0.36 0.85 0.97
OPERA II [5] Ocrelizumab 417 0.16 0.12 0.2 1.30 1.01
Interferon 418 0.29 0.23 0.36 1.05 0.98
ARR: Annualized relapse rate; SD: standard deviation; DMF: dimethyl fumarate
Correspondence to:
Robert W. Platt, Department of Epidemiology, Biostatistics, and Occupational Health, McGill University, 1020 Pine Ave W, Montreal, Quebec H3A 1A2, Canada.
Email: robert.platt@mcgill.ca
We thank Dr Platt and colleagues for their critical review of our work, especially of the methodology that we have used in this study. It is understandable that comparative studies of treatment effectiveness trigger constructive discussions among industry and academics. We also vehemently agree that rigorous methodology and cautious interpretation of results is mandatory, especially for analyses of observational data.1 2 Therefore, in this letter, we will provide additional clarifications in response to the concerns raised.
We appreciate that the categories that are underrepresented in multivariable logistic regression models may lead to inflation of estimates of the corresponding coefficients and their variance. Such inflation would, however, result in an overly conservative matching rather than the opposite. Due to the use of a caliper, patients with an extreme propensity score can not be matched to patients within the bulk of the distribution of the propensity scores. Such patients were excluded from the matched cohorts.
The issue of residual imbalance is important in any non-randomised comparative study. We acknowledge that the standardised mean difference in annualised relapse rates (ARR) between teriflunomide and fingolimod exceeded the nominal threshold of 20%. It is therefore reassuring that the sensitivity analyses, in which the residual imbalance fell below the accepted threshold of 20% (patients with prior on-treatment relapses, Cohen’s d 14%, and...
We thank Dr Platt and colleagues for their critical review of our work, especially of the methodology that we have used in this study. It is understandable that comparative studies of treatment effectiveness trigger constructive discussions among industry and academics. We also vehemently agree that rigorous methodology and cautious interpretation of results is mandatory, especially for analyses of observational data.1 2 Therefore, in this letter, we will provide additional clarifications in response to the concerns raised.
We appreciate that the categories that are underrepresented in multivariable logistic regression models may lead to inflation of estimates of the corresponding coefficients and their variance. Such inflation would, however, result in an overly conservative matching rather than the opposite. Due to the use of a caliper, patients with an extreme propensity score can not be matched to patients within the bulk of the distribution of the propensity scores. Such patients were excluded from the matched cohorts.
The issue of residual imbalance is important in any non-randomised comparative study. We acknowledge that the standardised mean difference in annualised relapse rates (ARR) between teriflunomide and fingolimod exceeded the nominal threshold of 20%. It is therefore reassuring that the sensitivity analyses, in which the residual imbalance fell below the accepted threshold of 20% (patients with prior on-treatment relapses, Cohen’s d 14%, and analysis with no MRI data included, Cohen’s d 16%) confirmed the results of the primary analysis. We have chosen not to explicitly report absolute differences in proportions reported in Table 1; this information is redundant as the absolute differences can easily be calculated from the proportions shown in the table.
Dr Platt and colleagues make an important point that the comparison of baseline patient characteristics should account for the weights used due to matching in a one-to-multiple variable ratio. We have recalculated the differences for the primary analysis and have observed that our original standardised mean differences overestimated the true weighted standardised mean differences present (see the Table below). Reassuringly, the compared groups are in reality more closely aligned than what the Table 1 in our article would suggest.
TABLE: Recalculated weighted standardised mean differences (Cohen’s d) for continuous baseline characteristics. (DMF, dimethyl fumarate)
DMF vs. teriflunomide fingolimod vs. DMF fingolimod vs. teriflunomide
age 0.006 0.01 0.02
disease duration 0.006 0.003 0.01
disability (EDSS) 0.04 0.002 0.026
relapses 12 months pre-baseline 0.05 0.015 0.03
Combining the results of multiple imputation is a standard procedure, inherent in multiple imputation methodology.3 We have calculated the mode in order to combine the 17 datasets into one. The resulting variable represents the combined result of the 17 imputed data sets and therefore reflects the values with the greatest support within the imputed data space.
Similar to our previous studies, we have chosen to match the studied patients on country.4 This is a conservative decision that aims to mitigate systematic differences in patient follow-up (modelled as the surrogate ‘country’ variable). However, there are methods that are considerably more effective in accounting for inter-centre heterogeneity. We have taken precautions to minimise the heterogeneity in the studied cohort directly – through adjusting for the length and the frequency of recorded follow-up as well as its consistency across the participating centres (the requirement of Neurostatus certification at each centre, adjustment for visit frequency, pairwise censoring, and the quality control process). In fact, differences in treating conventions access to therapies among centres and regions increase the chance that patients will be matched with comparable counterparts who were offered a different therapy for reasons unrelated to their disease severity.
We have calculated ARR in individual patients in order to derive point and interval estimates of the distributions of ARR. The estimates (mean and variance) were weighted for variable one-to-multiple matching and duration of pairwise-censored follow-up. The presented mean ARRs and their 95% confidence intervals are based on this method.
Individual estimation of ARR in a cohort where 75% of patients have a recorded, pairwise-censored follow up greater than 1 year (and the minimum required follow-up of 6 months) is subject to only a negligible risk of inflation due to short follow-up. Notionally, estimation of ARR of a population based on individual observed ARRs from a sample follows standard inferential reasoning within frequentist framework. It enables direct estimation of ARR in studied sample. As in our previous studies, we have used a negative binomial model to compare the incidence of relapse events throughout the pairwise-censored follow-up between the matched patients.4 5 Here, we have used the overall number of relapses from either treatment group and cumulative follow-up, as appropriate. As stated in the Methods, all analyses used weights to account for variable matching ratio and either ‘cluster’ or ‘frailty’ terms to account for the paired data structure. We agree that it would have been more accurate to use the term ‘incidence of relapses’ rather than ‘ARR’ in the description of the negative binomial model in the Methods section. Most importantly, all three methods that we have employed to evaluate relapse outcomes (the two methods described above and the survival model) showed consistent results in the primary analysis.
Appropriately, the authors have closely examined our estimates of variance for ARRs. Unfortunately, the table presented in their communication is difficult to understand. It is unclear what method for the back-calculation of standard deviations from the reported 95% confidence intervals was applied, given that it resulted in two divergent values for both our study and the given examples of randomised clinical trials – reported as ‘SD left’ and ‘SD right’. As described above, all of our analyses used weights to adjust for variable matching ratio, and these were also used to calculate interval estimates for the weighted mean ARRs. It is reasonable to observe variability in the error recorded in each sample, especially in observational studies, which naturally encapsulate more broadly defined cohorts in exchange for greater ecological validity. Furthermore, one would expect the variability in the error to be contingent on the specific selection of study samples, as a result of matching to other treatment groups. We agree that the implications of variable standard deviations across studies are of research interest, but it is not clear that their bench marking to a particular randomised control trial is justified.
We thank the authors for pointing out the inconsistency in the reported exposure to therapies between the groups before and after matching. Sixteen patients treated with dimethyl fumarate were previously exposed to teriflunomide as their highest-efficacy treatment, and the corresponding entry in Supplementary Table 6 should be 16 (2%). We apologise for the typographical error.
A well-powered analysis is not a weakness of a study. We are aware that a statistically significant difference and clinically meaningful difference are complementary but different concepts. Studies carried out within the frequentist framework are reliant on testing of significance of null hypotheses and are destined to provide binary answers – an arbitrary outcome that reflects its origin in randomised controlled trials. Therefore, a result that rejects a null hypothesis provides its reader with more certainty than a result that fails to do so, even when the difference may be of minimal clinical significance. The role of the clinical readership is to interpret clinical meaningfulness of the results. We refrained from attempting to develop cut-offs for what represents clinically meaningful difference and chose to leave this decision to the readers. More robust and intuitive answers to this problem lie in Bayesian methods. We are strong proponents of such methods, as the interpretation of their results is more intuitive for a clinical reader.
The title of their discussion point suggests that Dr Platt and colleagues consider reports that require further clarification of some of its concepts to be methodologically flawed. In the study under discussion, we have utilised the methodology previously used in several studies of comparative effectiveness in the MSBase data set, in particular the comparison of treatment escalation to fingolimod or natalizumab.4 We have systematically addressed the relevant sources of bias, in particular indication bias.1 We take great comfort in the fact that our previous comparative analyses have been highly convergent with the results of pivotal randomised controlled trials.5 6 We have now provided clarification of additional points in response to our colleagues’ review of our article. We value their constructive criticism and believe that our thorough response further strengthens the credibility of the reported results.
References
1. Kalincik T, Butzkueven H. Observational data: Understanding the real MS world. Mult Scler 2016;22(13):1642-48. doi: 10.1177/1352458516653667 [published Online First: 2016/06/09]
2. Trojano M, Tintore M, Montalban X, et al. Treatment decisions in multiple sclerosis - insights from real-world observational studies. Nat Rev Neurol 2017;13(2):105-18. doi: 10.1038/nrneurol.2016.188 [published Online First: 2017/01/14]
3. Heraud-Bousquet V, Larsen C, Carpenter J, et al. Practical considerations for sensitivity analysis after multiple imputation applied to epidemiological studies with incomplete data. BMC medical research methodology 2012;12:73. doi: 10.1186/1471-2288-12-73 [published Online First: 2012/06/12]
4. Kalincik T, Horakova D, Spelman T, et al. Switch to natalizumab vs fingolimod in active relapsing-remitting multiple sclerosis. Ann Neurol 2015;77:425-35. [published Online First: 2014/12/27]
5. Kalincik T, Brown JWL, Robertson N, et al. Comparison of alemtuzumab with natalizumab, fingolimod, and interferon beta for multiple sclerosis: a longitudinal study. Lancet Neurol 2017;16(4):271-81.
6. He A, Spelman T, Jokubaitis V, et al. Comparison of switch to fingolimod or interferon beta/glatiramer acetate in active multiple sclerosis. JAMA Neurol 2015;72(4):405-13. doi: 10.1001/jamaneurol.2014.4147 [published Online First: 2015/02/11]
Dear Editor,
We have read with great interest the work by Scarpazza et al that provided a longitudinal MRI evaluation of natalizumab-related Progressive Multifocal Leukoencephalopathy (NTZ-PML) lesions in Multiple Sclerosis (MS) patients (1).
Their central finding was the high percentage (78.1%) of patients, who eventually developed NTZ-PML, in whom highly suggestive lesions were already retrospectively detectable on pre-diagnostic MRI exams. Furthermore, the pre-diagnostic phase proved to be relatively long (150.8±74.9 days), with an estimated percentage increase of the lesions’ volume of 62.8% per month (1).
Given the widely recognized crucial role of a timely NTZ-PML identification in reducing mortality and residual disability (1), these results present the neurological and neuroradiological communities with an important clinical challenge, prompting a major effort to ensure an early diagnosis of this condition.
Although redefining the timing of MRI surveillance, with up to one brain MRI exam every 3-4 months for high-risk patients, appears as a justified strategy, we think that improving the accuracy of early identification of NTZ-PML is also mandatory.
In our opinion, such achievement should be pursued using two complementary approaches: (i) a specific training addressed to neuroradiologists working in the field of MS, who should be aware of the relevance of even very small asymptomatic PML lesions and how to differentiate them from new M...
Dear Editor,
We have read with great interest the work by Scarpazza et al that provided a longitudinal MRI evaluation of natalizumab-related Progressive Multifocal Leukoencephalopathy (NTZ-PML) lesions in Multiple Sclerosis (MS) patients (1).
Their central finding was the high percentage (78.1%) of patients, who eventually developed NTZ-PML, in whom highly suggestive lesions were already retrospectively detectable on pre-diagnostic MRI exams. Furthermore, the pre-diagnostic phase proved to be relatively long (150.8±74.9 days), with an estimated percentage increase of the lesions’ volume of 62.8% per month (1).
Given the widely recognized crucial role of a timely NTZ-PML identification in reducing mortality and residual disability (1), these results present the neurological and neuroradiological communities with an important clinical challenge, prompting a major effort to ensure an early diagnosis of this condition.
Although redefining the timing of MRI surveillance, with up to one brain MRI exam every 3-4 months for high-risk patients, appears as a justified strategy, we think that improving the accuracy of early identification of NTZ-PML is also mandatory.
In our opinion, such achievement should be pursued using two complementary approaches: (i) a specific training addressed to neuroradiologists working in the field of MS, who should be aware of the relevance of even very small asymptomatic PML lesions and how to differentiate them from new MS lesions on conventional brain MRI scans (2), and (ii) the identification of new MRI markers that could help in an early neuroradiological identification of PML.
It is noteworthy to mention that recent works described the presence of significant magnetic susceptibility changes, revealing a paramagnetic dipole, at the level of the subcortical U-fibers adjacent to PML lesions (3, 4). This paramagnetic effect, which has been speculatively attributed to iron accumulation inside macrophages and microglial cells following myelin degeneration, has been reported as an early and constant finding in this condition (3, 4).
Given the known absence of significant paramagnetic effects within new MS plaques (5), this sign, which still needs validation studies on larger patient cohorts, could potentially improve the accuracy of an early neuroradiological differential diagnosis between these two conditions, eventually leading to the implementation of susceptibility-weighted sequences in routine brain MRI exams of high-risk patients.
References
1. Scarpazza C, Signori A, Prosperini L, et al. Early diagnosis of progressive multifocal leucoencephalopathy: longitudinal lesion evolution. Journal of neurology, neurosurgery, and psychiatry 2018.
2. Wijburg MT, Witte BI, Vennegoor A, et al. MRI criteria differentiating asymptomatic PML from new MS lesions during natalizumab pharmacovigilance. Journal of neurology, neurosurgery, and psychiatry 2016;87(10):1138-45.
3. Hodel J, Outteryck O, Verclytte S, et al. Brain Magnetic Susceptibility Changes in Patients with Natalizumab-Associated Progressive Multifocal Leukoencephalopathy. AJNR American journal of neuroradiology 2015;36(12):2296-302.
4. Pontillo G, Cocozza S, Lanzillo R, et al. Brain Susceptibility Changes in a Patient with Natalizumab-Related Progressive Multifocal Leukoencephalopathy: A Longitudinal Quantitative Susceptibility Mapping and Relaxometry Study. Frontiers in neurology 2017;8:294.
5. Zhang Y, Gauthier SA, Gupta A, et al. Quantitative Susceptibility Mapping and R2* Measured Changes during White Matter Lesion Development in Multiple Sclerosis: Myelin Breakdown, Myelin Debris Degradation and Removal, and Iron Accumulation. AJNR American journal of neuroradiology 2016;37(9):1629-35.
Corresponding Author:
Giuseppe Pontillo, MD
Department of Advanced Biomedical Sciences
University “Federico II”, Via Pansini, 5, 80131 - Naples - Italy
E-mail: giuseppe.pon@gmail.com
To the Editor,
We read with interest the work from Licher et al. [1] in which the authors tried to quantify the burden of common neurological diseases (i.e. dementia, stroke and parkinsonism) in 12 102 individuals (6 982 women and 5 120 men) aged ≥ 45 years and free from these diseases at baseline. All these individuals were recruited between 1990 and 2016 into the prospective population-based Rotterdam Study. At the end of their analyzes, the authors concluded that one in two women and one in three men will develop dementia, stroke or parkinsonism during their lifetime, and that the risk for women to develop both stroke and dementia during their life is almost twice that of men [1].
By reading the article from Licher et al. [1], we were extremely surprised by the fact that the authors did not consider the impact of the difference in life expectancies between men and women on their results and conclusions. This is particularly well underlined by the fact that the authors did not clearly precise the age structures of the two populations they studied [1]. In our view, this information is critical as, although the reasons for this difference are still debated and may probably be multi-factorial [2], it is well known that women live longer than men. This trend is confirmed by the 2018 World Health Statistics report [3] that estimates that in 2016, the life expectancies of men and women at birth were respectively 69.8 and 74.2 years at the international level. The...
To the Editor,
We read with interest the work from Licher et al. [1] in which the authors tried to quantify the burden of common neurological diseases (i.e. dementia, stroke and parkinsonism) in 12 102 individuals (6 982 women and 5 120 men) aged ≥ 45 years and free from these diseases at baseline. All these individuals were recruited between 1990 and 2016 into the prospective population-based Rotterdam Study. At the end of their analyzes, the authors concluded that one in two women and one in three men will develop dementia, stroke or parkinsonism during their lifetime, and that the risk for women to develop both stroke and dementia during their life is almost twice that of men [1].
By reading the article from Licher et al. [1], we were extremely surprised by the fact that the authors did not consider the impact of the difference in life expectancies between men and women on their results and conclusions. This is particularly well underlined by the fact that the authors did not clearly precise the age structures of the two populations they studied [1]. In our view, this information is critical as, although the reasons for this difference are still debated and may probably be multi-factorial [2], it is well known that women live longer than men. This trend is confirmed by the 2018 World Health Statistics report [3] that estimates that in 2016, the life expectancies of men and women at birth were respectively 69.8 and 74.2 years at the international level. Therefore, knowing that stroke, dementia and parkinsonism are more prevalent in the elderly than in younger people [4], it is clear for us that what the authors attributed to sex is in fact the direct consequence of ageing, an aspect that was not studied by the authors. By not highlighting this factor, they led readers to misinterpretations, including general newspapers journalists that assume that this difference is a gender gap that should be filled.
To conclude, by not considering the impact of the difference in life expectancies between men and women on their study, the authors introduced an important bias in their study, leading them to draw erroneous conclusions. Age is the real culprit, not gender!
References
1 Licher S, Darweesh SKL, Wolters FJ, et al. Lifetime risk of common neurological diseases in the elderly population. J Neurol Neurosurg Psychiatry 2018;:jnnp-2018-318650.
2 Marais GAB, Gaillard J-M, Vieira C, et al. Sex gap in aging and longevity: can sex chromosomes play a role? Biol Sex Differ 2018;9:33.
3 World Health Organization. World Health Statistics 2018- Monitoring health for the Sustainable Development Goals. Available from http://apps.who.int/iris/bitstream/handle/10665/272596/9789241565585-eng.... Accessed on 2018/10/22.
4 GBD 2015 Neurological Disorders Collaborator Group. Global, regional, and national
burden of neurological disorders during 1990-2015: a systematic analysis for the
Global Burden of Disease Study 2015. Lancet Neurol 2017;16:877–97.
We thank Abat et al. for re-emphasizing an important interpretation of our work, namely that sex-differences in life-expectancy likely influenced the presented lifetime risks [1]. Indeed, in our paper we repeatedly discussed in several sections (for instance in the methods) that differences in life-expectancy between men and women could differentially affect their lifetime risk. It was for this reason that we consequently decided to analyze the data in a sex-specific manner while taking the competing risk of death into account in order to prevent potential overestimation.
Abat et al. unfortunately also allege that we attributed the observed sex-differences in disease risk to sex-specific effects on a biological level. The authors have seemingly missed our discussion at length arguing that observed differences in lifetime risk may be primarily attributed to the effects of differences in life-expectancy between men and women: “Apart from a longer life-expectancy in general, these findings may be explained by smaller differences in life-expectancy between men and women in the Netherlands (1.8 years), compared with the USA (4.8 years). With longer life-expectancy, individuals in this study simply had more time to develop these diseases in a timeframe with high age-specific incidence rates.”
It seems thus that ours and Abat and co-authors’ interpretation of our findings is pretty much congruent, i.e. age, irrespective of sex, should be consid...
We thank Abat et al. for re-emphasizing an important interpretation of our work, namely that sex-differences in life-expectancy likely influenced the presented lifetime risks [1]. Indeed, in our paper we repeatedly discussed in several sections (for instance in the methods) that differences in life-expectancy between men and women could differentially affect their lifetime risk. It was for this reason that we consequently decided to analyze the data in a sex-specific manner while taking the competing risk of death into account in order to prevent potential overestimation.
Abat et al. unfortunately also allege that we attributed the observed sex-differences in disease risk to sex-specific effects on a biological level. The authors have seemingly missed our discussion at length arguing that observed differences in lifetime risk may be primarily attributed to the effects of differences in life-expectancy between men and women: “Apart from a longer life-expectancy in general, these findings may be explained by smaller differences in life-expectancy between men and women in the Netherlands (1.8 years), compared with the USA (4.8 years). With longer life-expectancy, individuals in this study simply had more time to develop these diseases in a timeframe with high age-specific incidence rates.”
It seems thus that ours and Abat and co-authors’ interpretation of our findings is pretty much congruent, i.e. age, irrespective of sex, should be considered as the main driver for the observed differences in lifetime risks of these diseases. Yet, in order not to rule out other interpretations prematurely, we also indicate that consideration should be given to potential sex-specific risk factors. For example, we observed a lower educational level for women compared to men which may have led to a lower resilience for dementia in women.
Reference
1 Licher S, Darweesh SKL, Wolters FJ, et al. Lifetime risk of common neurological diseases in the elderly population. J Neurol Neurosurg Psychiatry 2018;:jnnp-2018-318650.
Komatsu et al. presented an interesting clinicopathological case of anti-myelin oligodendrocyte glycoprotein (MOG) demyelinating disease of the CNS. (1) Their patient had a rather unusual subacute encephalopathic presentation with extensive supratentorial fluid-attenuation inversion recovery white matter hyperintensities. The authors focused mainly on the conspicuous MRI punctuate and curvilinear enhancement pattern within the hemispheric lesions.
It is well established that intraparenchymal punctuate and curvilinear gadolinium enhancement may arise in the context of Moyamoya syndrome, various endotheliopathies and most commonly, in disorders causing small vessels blood-brain barrier disruption. (2)These entities are associated histologically with perivascular cellular infiltrates and include inflammatory autoimmune diseases (i.e. primary or secondary angiitis of the CNS, neurosarcoidosis, histiocytosis and demyelinating diseases of the CNS), pre-lymphoma states (i.e. sentinel lesions of primary CNS lymphoma), non-Hodgkin lymphoma (i.e. intravascular lymphoma) and CLIPPERS syndrome. (2) Notably, among demyelinating disorders, multiple sclerosis and aquaporin-4 antibody (AQP4-Ab) neuromyelitis optica spectrum disorders (NMOSD) manifest this specific neuroimaging pattern in rare cases. (2,3)
We agree with Komatsu et al. that their case is the first report of the perivascular enhancement in anti-MOG antibody disease. Indeed, gadolinium enhancement was observed in...
Komatsu et al. presented an interesting clinicopathological case of anti-myelin oligodendrocyte glycoprotein (MOG) demyelinating disease of the CNS. (1) Their patient had a rather unusual subacute encephalopathic presentation with extensive supratentorial fluid-attenuation inversion recovery white matter hyperintensities. The authors focused mainly on the conspicuous MRI punctuate and curvilinear enhancement pattern within the hemispheric lesions.
It is well established that intraparenchymal punctuate and curvilinear gadolinium enhancement may arise in the context of Moyamoya syndrome, various endotheliopathies and most commonly, in disorders causing small vessels blood-brain barrier disruption. (2)These entities are associated histologically with perivascular cellular infiltrates and include inflammatory autoimmune diseases (i.e. primary or secondary angiitis of the CNS, neurosarcoidosis, histiocytosis and demyelinating diseases of the CNS), pre-lymphoma states (i.e. sentinel lesions of primary CNS lymphoma), non-Hodgkin lymphoma (i.e. intravascular lymphoma) and CLIPPERS syndrome. (2) Notably, among demyelinating disorders, multiple sclerosis and aquaporin-4 antibody (AQP4-Ab) neuromyelitis optica spectrum disorders (NMOSD) manifest this specific neuroimaging pattern in rare cases. (2,3)
We agree with Komatsu et al. that their case is the first report of the perivascular enhancement in anti-MOG antibody disease. Indeed, gadolinium enhancement was observed in 6 out of 108 MRIs among adult patients with CNS MOG autoimmunity in a large French natiowide cohort. (4) Leptomeningeal enhancement, thought to indicate cortical encephalitis, was detected in only 3 scans. Most importantly, none of the patients in this study or other related publications, demonstrated punctuate or fine, linear-shaped enhancing lesions.
Intriguingly, the enhancement pattern in the case of Komatsu et al. shares many similarities with the imaging abnormalities seen in a recently described disorder, namely autoimmune glial fibrillary acidic protein (GFAP) astrocytopathy. This is a novel form of a steroid-responsive meningoencephalomyelitis associated with IgG binding to GFAP. A characteristic pattern on neuroimaging of brain linear, perivascular enhancement, oriented radially to the ventricles was identified in the seminal paper by Fang et al. (5) This striking abnormality was also observed in a substantial proportion of patients with the disorder in subsequent cohorts. (6)
Therefore, it would be useful to know the CSF status of GFAP-Ab of the forementioned patient. Besides, about 40% of individuals with GFAP antibody-positive meningoencephalomyelitis had coexisting neural autoantibodies. (5, 6)
Many questions regarding GFAP autoimmunity and, to a lesser extent, MOG-Ab–associated diseases remain to be answered in future studies. Overall, it seems that in the right clinical context, when perivascular enhancement on MRI is observed, one should be alert in making a differential diagnosis of GFAP astrocytopathy rather than anti-MOG antibody demyelinating disease.
1. Komatsu T, Matsushima S, Kaneko K, et al. Perivascular enhancement in anti-MOG antibody demyelinating disease of the CNS. J Neurol Neurosurg Psychiatry 2019;90:111-112.
2. Taieb, G., Duran-Peña, A., de Chamfleur, N.M. et al. Neuroradiology 2016; 58: 221-235 https://doi.org/10.1007/s00234-015-1629-y
3. Pekcevik Y, Izbudak I. Perivascular enhancement in a patient with neuromyelitis optica spectrum disease during an optic neuritis attack. J Neuroimaging 2015; 25:686–687
4. Cobo-Calvo I, Ruiz A, Maillart E, et al. Clinical spectrum and prognostic value of CNS MOG autoimmunity in adults. The MOGADOR study. Neurology 2018; 90 (21) e1858-e1869; DOI: 10.1212/WNL.0000000000005560
5. Fang B, McKeon A, Hinson SR, et al. Autoimmune Glial Fibrillary Acidic Protein Astrocytopathy: A Novel Meningoencephalomyelitis. JAMA Neurol. 2016;73(11):1297–1307. doi:10.1001/jamaneurol.2016.2549
6. Zarkali A, Cousins O, Athauda D, et al. Glial fibrillary acidic protein antibody-positive meningoencephalomyelitis. Practical Neurology 2018;18:315-319
Re: A unifying theory for cognitive abnormalities in functional neurological disorders, fibromyalgia and chronic fatigue syndrome
Viraj Bharambe Specialist registrar in neurology
John C Williamson Specialist registrar in neurology
Andrew J Larner Consultant Neurologist
Cognitive Function Clinic
Walton Centre for Neurology and Neurosurgery
Lower Lane
Fazakerley
Liverpool
L9 7LJ
UK
e-mail: a.larner@thewaltoncentre.nhs.uk
Teodoro et al. present evidence for shared cognitive symptoms in fibromyalgia, chronic fatigue syndrome, and functional neurological disorders, and hypothesize that functional cognitive disorders (FCD) may share similar symptoms.1 We present data which speak to this issue.
We have previously reported preliminary data examining performance on the mini-Addenbrooke’s Cognitive Examination (MACE) by patients diagnosed with fibromyalgia2 as part of a larger study of MACE.3 Here, we update these data for fibromyalgia patients (n = 17; F:M = 17:0; age range 33-56 years, median 49) and compare them to MACE performance by patients diagnosed with FCD (n = 43; F:M = 18:25; age range 28-82 years, median 58).4
There was no statistical difference (p > 0.1) in the proportions of patients scoring below the two cut-off scores (≤21/30, ≤25/30) defined in the index MACE report.5 Looking at MACE subscores (Attention, Registration,...
Re: A unifying theory for cognitive abnormalities in functional neurological disorders, fibromyalgia and chronic fatigue syndrome
Viraj Bharambe Specialist registrar in neurology
John C Williamson Specialist registrar in neurology
Andrew J Larner Consultant Neurologist
Cognitive Function Clinic
Walton Centre for Neurology and Neurosurgery
Lower Lane
Fazakerley
Liverpool
L9 7LJ
UK
e-mail: a.larner@thewaltoncentre.nhs.uk
Teodoro et al. present evidence for shared cognitive symptoms in fibromyalgia, chronic fatigue syndrome, and functional neurological disorders, and hypothesize that functional cognitive disorders (FCD) may share similar symptoms.1 We present data which speak to this issue.
We have previously reported preliminary data examining performance on the mini-Addenbrooke’s Cognitive Examination (MACE) by patients diagnosed with fibromyalgia2 as part of a larger study of MACE.3 Here, we update these data for fibromyalgia patients (n = 17; F:M = 17:0; age range 33-56 years, median 49) and compare them to MACE performance by patients diagnosed with FCD (n = 43; F:M = 18:25; age range 28-82 years, median 58).4
There was no statistical difference (p > 0.1) in the proportions of patients scoring below the two cut-off scores (≤21/30, ≤25/30) defined in the index MACE report.5 Looking at MACE subscores (Attention, Registration, Verbal fluency, Clock Drawing, Memory recall), the proportions scoring at ceiling were not statistically different (p > 0.1) in the fibromyalgia and FCD groups. Ranking performance in cognitive domains from worst to best showed the same pattern in both patient groups, namely Verbal fluency, Memory recall, Registration, Attention, and Clock drawing.
Although numbers are small and the cognitive testing relatively simple, and the group demographics differ (both gender and age p < 0.05), nevertheless we suggest this evidence supports the hypothesis of Teodoro et al. of shared cognitive symptoms in fibromyalgia and FCD.
References
1. Teodoro T, Edwards MJ, Isaacs JD. A unifying theory for cognitive abnormalities in functional neurological disorders, fibromyalgia and chronic fatigue syndrome: systematic review. J Neurol Neurosurg Psychiatry 2018; 89: 1308-19. doi: 10.1136/jnnp-2017-317823.
2. Williamson J, Larner AJ. Cognitive dysfunction in patients with fibromyalgia. Br J Hosp Med 2016; 77: 116. doi: 10.12968/hmed.2016.77.2.116.
3. Williamson J, Larner AJ. MACE for the diagnosis of dementia and MCI: 3-year pragmatic diagnostic test accuracy study. Dement Geriatr Cogn Disord 2018; 45: 300-7. doi: 10.1159/000484438.
4. Bharambe V, Larner AJ. Functional cognitive disorders: demographic and clinical features contribute to a positive diagnosis. Neurodegener Dis Manag 2018; 8: 377-83. doi: 10.2217/nmt-2018-0025.
5. Hsieh S, McGrory S, Leslie F, et al. The Mini-Addenbrooke’s Cognitive Examination: a new assessment tool for dementia. Dement Geriatr Cogn Disord 2015; 39: 1-11. doi: 10.1159/000366040.
Conflicts of interest
The authors declare no conflict of interest
The interplay among statins, serum cholesterol, and spontaneous intracerebral hemorrhage (ICH) with and without prior history of ischemic stroke is controversial.
Studies over the last decade, like the GERFHS study,[1] have concluded that increasing serum cholesterol levels may decrease the risk of ICH. This finding was confirmed in one of the largest observational studies[2] which estimated an adjusted hazard ratio (HR) of 0.94 (0.92-0.96) with every 10 mg increase in baseline serum total cholesterol level. Similar interaction was observed with increasing LDL cholesterol quartiles (LDL > 168 mg/dL; HR 0.53 [0.45-0.63]).[2]
However, the evidence on the effect of statins in ICH is less clear. Studies ranging from the SPARCL trial[3] which showed an increased risk of recurrent ICH with high dose statins to the recent meta-analysis by Ziff et al.,[4] which described no significant increase of the risk of ICH with statins, are few examples. Similar non-significant trends were seen in the risk of ICH after prior ischemic stroke and prior ICH.[3] Prior retrospective studies also described a neutral effect of statins on recurrent ICH. Interestingly, analysis from the largest administrative database in Israel[2] showed a surprising result; statin use might be associated with decreased ICH risk. Furthermore, an indirect, albeit unique measurement of dose-response using average atorvastatin equivalent daily dose (AAEDD) churned out interesting figures – a HR of 0....
The interplay among statins, serum cholesterol, and spontaneous intracerebral hemorrhage (ICH) with and without prior history of ischemic stroke is controversial.
Studies over the last decade, like the GERFHS study,[1] have concluded that increasing serum cholesterol levels may decrease the risk of ICH. This finding was confirmed in one of the largest observational studies[2] which estimated an adjusted hazard ratio (HR) of 0.94 (0.92-0.96) with every 10 mg increase in baseline serum total cholesterol level. Similar interaction was observed with increasing LDL cholesterol quartiles (LDL > 168 mg/dL; HR 0.53 [0.45-0.63]).[2]
However, the evidence on the effect of statins in ICH is less clear. Studies ranging from the SPARCL trial[3] which showed an increased risk of recurrent ICH with high dose statins to the recent meta-analysis by Ziff et al.,[4] which described no significant increase of the risk of ICH with statins, are few examples. Similar non-significant trends were seen in the risk of ICH after prior ischemic stroke and prior ICH.[3] Prior retrospective studies also described a neutral effect of statins on recurrent ICH. Interestingly, analysis from the largest administrative database in Israel[2] showed a surprising result; statin use might be associated with decreased ICH risk. Furthermore, an indirect, albeit unique measurement of dose-response using average atorvastatin equivalent daily dose (AAEDD) churned out interesting figures – a HR of 0.86 (0.79-0.94) for every 10mg/d increase of AAEDD. This dose-dependent effect of statins in reducing the risk of ICH is yet to be thoroughly investigated. Choice of statin might also be of significance, as it has been suggested that lipophilic statins (like atorvastatin and simvastatin) are associated with a much higher risk of recurrent ICH as compared to rosuvastatin and pravastatin (hydrophilic compounds). One would assume that the statin-mediated decrease in serum cholesterol would contribute to decreased vascular integrity and stability, and an elevated risk of bleeding.
How small vessel hemorrhagic risk factors like cerebral microbleeds (CMBs), a ‘surrogate marker’ for ICH contributes to the risk of ICH in the setting of hypercholesterolemia and statin use is unknown. Current evidence points towards statin use and increased risk of CMBs. While reports of patients on statins having twice the burden CMBs as compared to those not on statins seem conclusive, recent evidence suggests a clinical equipoise of CMBs, pointing to a novel biological mechanism.[5] These complex interactions stress the importance of exploring new pathological pathways and effective treatments for ICH.
The confounding elements of using administrative databases, as large as they may be, and non-uniform retrospective study groups, fail to answer these questions:
1. Do statins independently increase the true risk of ICH (spontaneous and recurrent)?
2. Do statins play a similar role after adjusting to the prevalence of small vessel disease?
3. Is there an ideal statin and an ideal serum cholesterol range?
4. Is there an optimal time window to start statins for prevention of ischemic stroke keeping in mind the risk of hemorrhagic transformation?
Only future randomized controlled trials can answer, and help elucidate the complex relationship among statins, cholesterol, and ICH.
References
1. Martini SR, Flaherty ML, Brown WM, et al. Risk factors for intracerebral hemorrhage differ according to hemorrhage location. Neurology 2012;79:2275-2282.
2. Saliba W, Rennert HS, Barnett-Griness O, et al. Association of statin use with spontaneous intracerebral hemorrhage. A cohort study. Neurology 2018;91:e400-e409.
3. Goldstein LB, Amarenco P, Szarek M, Callahan A, Hennerici M, Sillesen H, Zivin JA, Welch KMA. Hemorrhagic stroke in the Stroke Prevention by Aggressive Reduction in Cholesterol Levels study. Neurology 2008;70:2364-2370.
4. Ziff OJ, Banerjee G, Ambler G, et al. Statins and the risk of intracerebral haemorrhage inpatients withstroke:systematic review and meta-analysis. J Neurol Neurosurg Psychiatry 2018. Published Online First: 27 August 2018. doi: 10.1136/jnnp-2018-318483.
5. Martí-Fàbregas J, Medrano-Martorell S, Merino E, et al. Statins do not increase Markers of Cerebral Angiopathies in patients with Cardioembolic Stroke. Sci Rep 2018;8:1492.
We read with great interest the recent report of Banerjee and colleagues of three cases of minimally symptomatic cerebral amyloid angiopathy-related inflammation (CAA-ri) cases, drawing attention to the possibility of a wider spectrum of clinical manifestations in patients with CAA-ri than previously described1.
Cerebral amyloid angiopathy-related inflammation (CAA-ri) is a rare form of meningoencephalitis, presenting acutely with cognitive decline, seizures, headache and /or encephalopathy in most patients. The prognosis is poor even in patients under immunosuppressive treatment, with a mortality rate of 30% and only a minority of patients making a full recovery2-3. However, in the three cases reported by Banerjee and colleagues, patients presented with mild symptoms despite the magnitude of the MRI findings and made a full clinical and radiologic recovery in 2 of the cases with immunosuppressant treatment and in the remaining without any treatment.
We have recently evaluated a similar patient, that presented with mild transient symptoms in whom the diagnosis was made following the finding of characteristic CAA-ri changes in brain MRI.
He was a 62 year-old-man with a past history of hypertension, who was referred to the Emergency Department (ER) due to moderate frontal headache followed by left hemisensory numbness with Jacksonian march lasting a few minutes. He was asymptomatic on arrival at the ER and his neurological examination was normal. He perfor...
We read with great interest the recent report of Banerjee and colleagues of three cases of minimally symptomatic cerebral amyloid angiopathy-related inflammation (CAA-ri) cases, drawing attention to the possibility of a wider spectrum of clinical manifestations in patients with CAA-ri than previously described1.
Cerebral amyloid angiopathy-related inflammation (CAA-ri) is a rare form of meningoencephalitis, presenting acutely with cognitive decline, seizures, headache and /or encephalopathy in most patients. The prognosis is poor even in patients under immunosuppressive treatment, with a mortality rate of 30% and only a minority of patients making a full recovery2-3. However, in the three cases reported by Banerjee and colleagues, patients presented with mild symptoms despite the magnitude of the MRI findings and made a full clinical and radiologic recovery in 2 of the cases with immunosuppressant treatment and in the remaining without any treatment.
We have recently evaluated a similar patient, that presented with mild transient symptoms in whom the diagnosis was made following the finding of characteristic CAA-ri changes in brain MRI.
He was a 62 year-old-man with a past history of hypertension, who was referred to the Emergency Department (ER) due to moderate frontal headache followed by left hemisensory numbness with Jacksonian march lasting a few minutes. He was asymptomatic on arrival at the ER and his neurological examination was normal. He performed a cranial CT scan that revealed a right subcortical temporoparietal hypodensity which led to the request of a brain MRI that showed a right pseudotumoral temporoparietal white matter lesion, hyperintense on T2-weighted sequences without gadolinium enhancement, and multiple corticosubcortical microbleeds in T2* co-localized with the T2-lesion. CSF evaluation disclosed a mild elevation of proteins (53mg/dl) with a normal white cell count and no oligoclonal bands. Alzheimer disease (AD) biomarkers in CSF were consistent with a DA-related pathophysiology Aβ1-42: 494 pg/mL (normal range (NR) >542), total-tau: 252 pg/mL (NR <212), phospho-tau: 126 pg/mL (NR < 32). We further evaluated the presence of antibodies against Aβ which were positive (48.17 ng/mL). Neuropsychological evaluation and EEG were normal. At this point, a diagnosis of probable CAA-ri was made, according to the clinicoradiological criteria of CAA-ri4. The Apo-E genotype was ɛ4/ɛ4 further supporting the CAA-ri diagnosis. Given the benign course of the disease, a conservative approach was adopted, and steroid therapy and biopsy were deferred. The patient has been followed for 2 years and remains asymptomatic. A 3-month and 1-year follow up MRI showed complete reversion of the temporoparietal T2-hyperintense lesion and stabilization of the number of microbleeds.
Given the findings reported by Banerjee and colleagues and our own, we agree that CAA-ri might be underdiagnosed, its clinical spectrum might be wider than previously thought, and the entity might be underdiagnosed in patients with mild clinical symptoms. We also believe that the increasing availability of brain MRI will probably keep expanding the clinical spectrum of manifestations associated with CAA-ri.
Dr. Banerjee and colleagues suggested that milder phenotypes of CAA-ri might relate to a genotype of ApoE other than ɛ4/ɛ4, which is known to associate with a higher risk of developing the classic form of CAA-ri. However, in our patient, the genotype of Apo E was ɛ4/ɛ4, and previous reports could not find a relation between the ApoE genotype and the clinical course of CAA-ri5. Still, information regarding the relationship between ApoE genotype and the CAA-ri clinical course is scarce, and further studies will be needed to evaluate this hypothesis.
Finally, in our patient, we identify antibodies against Aβ in the CSF. Although their precise role remains to be established we can speculate a possible implication in the overlap described by Banerjee and colleagues, between the entity of minimal symptomatic amyloid angiopathy and amyloid-related imaging abnormalities (ARIA) found in patients with AD under anti-amyloid immunotherapy
To conclude our case reinforces the existence of a variant of minimal symptomatic CAA-ri described by Dr. Banerjee and colleagues. A high index of suspicion is key to identifying these patients. In the cases described so far, patients’ demographic characteristics and radiologic findings resembled those with a classic form of CAA-ri (the majority were men, with a mean age of 63 years). Further studies are needed to characterize this different phenotype, including CSF findings, ApoE genotype as well as the role of antibodies against Aβ in the CSF.
Acknowledgments
The authors thank Dr. Fabrizio Piazza for the evaluation of antibodies against Aβ in the CSF.
References
1. Banerjee G, Alvares D, Bowen J, et al. J Neurol Neurosurg Psychiatry. Epub ahead of print: [March 13, 2018]. doi:10.1136/jnnp-2017-317347
2. Corovic A, Kelly S, Markus HS. Int J Stroke. 2018: 13(3): 257-267. doi.org/10.1177/1747493017741569
3. M, Caetano A, Pinto M, et al. Stroke-Like Episodes Heralding a Reversible Encephalopathy: Microbleeds as the Key to the Diagnosis of Cerebral Amyloid Angiopathy–Related Inflammation—A Case Report and Literature Review J Stroke Cerebrovasc Dis. 2015 24(9), e245-e250. 10.1016/j.jstrokecerebrovasdis.2015.04.042
4. Auriel E, Charidimou A, Gurol E, et al. Validation of Clinicoradiological Criteria for the Diagnosis of Cerebral Amyloid Angiopathy-Related Inflammation. JAMA Neurol. 2016; 73 (2):197-292. doi: 10.1001/jamaneurol.2015.4078
5. Kinnecom MS, Lev MH, Wendell L, et al. Neurology. 2007:68 (17): 1411-6. doi: 10.1212/01.wnl.0000260066.98681.2e
Imagine you are an epilepsy health professional seeing a patient with clinical symptoms of depression. What should you do? If you have read Noble et al.’s [1] recent JNNP review, entitled ‘Cognitive-behavioural therapy does not meaningfully reduce depression in most people with epilepsy…’ you may have become sceptical about the potential of CBT, or psychotherapy in general, to alleviate depression in people with epilepsy (PWE). This recent systematic review pooled data from five small randomised controlled trials (RCTs), with some elements of CBT for PWE, and performed an analysis of reliable change. ‘Pooled risk difference indicated likelihood of reliable improvement in depression symptoms was significantly higher for those randomised to CBT’, but the authors focused on the finding that ‘only’ 30% of patients receiving interventions, compared to 10% of controls, could be considered ‘reliably improved’. Emphasising the fact that over 2/3 of patients did not meet this criterion for improvement, the authors suggest CBT is ‘ineffective’, has ‘limited benefit’ and could even lead to lower ‘self–esteem’ and ‘helplessness’. Notably, the latter conclusions were based on hypothetical reactions to treatment, rather than empirically supported outcomes.
Therefore, the purpose of this letter, written by the Psychology Task Force of the International League Against...
Imagine you are an epilepsy health professional seeing a patient with clinical symptoms of depression. What should you do? If you have read Noble et al.’s [1] recent JNNP review, entitled ‘Cognitive-behavioural therapy does not meaningfully reduce depression in most people with epilepsy…’ you may have become sceptical about the potential of CBT, or psychotherapy in general, to alleviate depression in people with epilepsy (PWE). This recent systematic review pooled data from five small randomised controlled trials (RCTs), with some elements of CBT for PWE, and performed an analysis of reliable change. ‘Pooled risk difference indicated likelihood of reliable improvement in depression symptoms was significantly higher for those randomised to CBT’, but the authors focused on the finding that ‘only’ 30% of patients receiving interventions, compared to 10% of controls, could be considered ‘reliably improved’. Emphasising the fact that over 2/3 of patients did not meet this criterion for improvement, the authors suggest CBT is ‘ineffective’, has ‘limited benefit’ and could even lead to lower ‘self–esteem’ and ‘helplessness’. Notably, the latter conclusions were based on hypothetical reactions to treatment, rather than empirically supported outcomes.
Therefore, the purpose of this letter, written by the Psychology Task Force of the International League Against Epilepsy (ILAE), is to argue that caution is needed when considering the authors’ conclusions and potential implications for the psychological care of PWE. Moreover, we offer alternative interpretations of the findings and raise the pertinent issue of how psychotherapy for PWE may continue to improve. Importantly, we hope to encourage health professionals to continue to refer patients for psychotherapy, which is an effective intervention for a substantial subgroup of PWE.
Consistent with Reuber’s [2] editorial commentary, Cognitive-behavioural therapy does meaningfully reduce depression in people with epilepsy, we would like to highlight the heterogeneity of the studies pooled and how this impacts the findings. First, the interventions were very diverse, and most would not be considered standardised CBT protocols for depression. Interestingly, one trial that utilised a standardised CBT protocol, resulted in 50% reliable change reductions in depressive symptoms, equivalent to CBT in the general population [3]. Second, over 10% of patients in the analyses had depressive symptoms within the non-clinical ranges. Further, unlike previous analysis of reliable change in depression [3], this review failed to control for baseline levels of depression severity. Third, Noble et al. collapsed data from four different self-report depression measures, only one designed for PWE. Depression in PWE can have distinct symptomatology, given the presence of seizures (peri-ictal depression) and anti-seizure medication effects, both of which can limit the validity of generic depression measures [4].
Noble et al. [1] describe their conclusion that 30% reliable improvements in depressive symptoms across trials is ‘ineffective’ as a ‘value judgement’, illustrating subjectivity, which Reuber’s [2] editorial commentary argued could be interpreted completely in reverse. That is, one could conclude from the data that the treatments are ‘effective’ for PWE. There is little consensus about what we expect a ‘reliable improvement’ to be in psychological distress for PWE, or for patients with a disabling neurological disorder in general. A stated goal of epilepsy treatment is “no seizures, no side-effects.” However, many PWE continue to have seizures, and all biological treatments have potential side-effects. We argue that if just under 1 in 3 PWE reliably improve with CBT, which has no known side-effects, this is better than a possible alternative of unmanaged depression. Arguments regarding quantifying ‘reliable improvement’ aside, we do agree with Noble et al.’s [1] conclusions that there is ‘substantial room for improvement’ in the treatment of depression for PWE.
One important limitation of previous trials is the relatively short duration of psychotherapy offered to PWE, a factor that Noble et al. [1] acknowledge. Across the five RCTs, there was only an average of 7 hours (8 sessions) of psychotherapy, the adherence of which is unclear [5]. Thus, it is very likely that participants did not receive a sufficient dosage of CBT, especially given a minimum of 12 sessions is indicated for depression in the general population [3]. In addition, many PWE experience cognitive difficulties, including memory impairment, which may require more intensive and tailored CBT [5]. These limitations need to be addressed and psychotherapy should be tailored to the unique needs of PWE. One advantage of CBT is that many of the behavioural skills; such as problem solving, sleep hygiene and controlled relaxation can also be tailored to assist with the self-management of epilepsy (e.g. avoidance of seizure triggers), which Noble et al. did not consider.
Another critical area for improvement is the treatment of comorbid anxiety symptoms within psychotherapy for depression. Anxiety and depression are highly comorbid, and in clinical practice it is difficult to evaluate them separately [4]. As such, transdiagnostic treatments, which treat depression and anxiety in one protocol, are increasingly being adopted and proven to be effective in the general population. We disagree with Noble et al.’s [1] comments regarding the ‘disappointing’ evidence for the treatment of anxiety, as only one small trial is cited assessing the impact of CBT for depression (not anxiety), on a secondary anxiety measure. A conclusion of insufficient evidence would have been more accurate, given the state of the anxiety literature.
Depression in PWE remains underdiagnosed and treated, perhaps partially due to uncertainty about effective treatments [4]. At worse this results in poorer quality of life and higher suicide rates in PWE. Thus, the development of more effective psychotherapies, including alternatives to CBT, is warranted. However, the ILAE Psychology Task Force believes that it is inaccurate to label CBT as ‘ineffective’ based on the findings of Noble et al.’s [1] review. Instead, we encourage health professionals to interpret the Noble et al. [1] conclusions with caution, given the concerns raised with respect to depression outcome measures, dosage and quality of the psychotherapies, and interpretation of results. Further, even with a conservative estimate of 30% responders to the psychotherapies, we posit that CBT shows promise for treating depression in PWE and should remain a strong treatment consideration for the referring clinician.
This document was written by experts selected by the International League Against Epilepsy (ILAE) and was approved for publication by the ILAE. Opinions expressed by the authors, however, do not necessarily represent the policy or position of the ILAE.
References:
1. Noble AJ, Reilly J, Temple J, et al. Cognitive-behavioural therapy does not meaningfully reduce depression in most people with epilepsy: a systematic review of clinically reliable improvement. Journal of Neurology, Neurosurgery & Psychiatry 2018 doi: doi: 10.1136/jnnp-2018-317997
2. Reuber M. Cognitive-behavioural therapy does meaningfully reduce depression in people with epilepsy. Journal of Neurology, Neurosurgery and Psychiatry 2018 doi: 10.1136/jnnp-2018-318743
3. Ogles BM, Lambert MJ, Sawyer JD. Clinical Significance of the National Institute of Mental Health Treatment of Depression Collaborative Research Program Data. Journal of Consulting and Clinical Psychology 1995;63(2):321-26. doi: 10.1037/0022-006X.63.2.321
4. Kwon O-Y, Park S-P. Depression and Anxiety in People with Epilepsy. Journal of Clinical Neurology (Seoul, Korea) 2014;10(3):175-88. doi: 10.3988/jcn.2014.10.3.175
5. Modi AC, Wagner J, Smith AW, et al. Implementation of psychological clinical trials in epilepsy: Review and guide. Epilepsy and Behavior 2017;74:104-13. doi: 10.1016/j.yebeh.2017.06.016
Dear Editor,
We read with interest the article by Kalincik et al. [1] comparing fingolimod, dimethyl fumarate and teriflunomide in a cohort of relapsing-remitting multiple sclerosis (MS) patients. The authors investigated several endpoints and performed various sensitivity analyses, and we commend them for reporting technical details in the online supplementary material. We, however, have some concerns about the design, analysis and reporting of the study.
1. In the primary analyses, three separate propensity score models were developed to construct a matched cohort for each of the three pairwise comparisons. Supplementary Table 6 clearly indicates the existence of zero or low frequencies in some variables (e.g., most active previous therapy and magnetic resonance imaging [MRI] T2 lesions). Yet, those variables were used as covariates in the propensity score models, unsurprisingly resulting in extremely high point estimates and standard errors (SE; as reported in Supplementary Table 7). For example, teriflunomide was not the most active therapy for any patient in the dimethyl fumarate cohort (n=0 from Supplementary Table 6), but that category was nevertheless included in the propensity score model, leading to an unrealistic point estimate of 18.65 with SE of 434.5 (Supplementary Table 7). Even higher SEs (greater than 1000) are observed in the other propensity score models. Propensity scores estimated from these poorly constructed models were then used to cr...
Show MoreWe thank Dr Platt and colleagues for their critical review of our work, especially of the methodology that we have used in this study. It is understandable that comparative studies of treatment effectiveness trigger constructive discussions among industry and academics. We also vehemently agree that rigorous methodology and cautious interpretation of results is mandatory, especially for analyses of observational data.1 2 Therefore, in this letter, we will provide additional clarifications in response to the concerns raised.
We appreciate that the categories that are underrepresented in multivariable logistic regression models may lead to inflation of estimates of the corresponding coefficients and their variance. Such inflation would, however, result in an overly conservative matching rather than the opposite. Due to the use of a caliper, patients with an extreme propensity score can not be matched to patients within the bulk of the distribution of the propensity scores. Such patients were excluded from the matched cohorts.
The issue of residual imbalance is important in any non-randomised comparative study. We acknowledge that the standardised mean difference in annualised relapse rates (ARR) between teriflunomide and fingolimod exceeded the nominal threshold of 20%. It is therefore reassuring that the sensitivity analyses, in which the residual imbalance fell below the accepted threshold of 20% (patients with prior on-treatment relapses, Cohen’s d 14%, and...
Show MoreDear Editor,
Show MoreWe have read with great interest the work by Scarpazza et al that provided a longitudinal MRI evaluation of natalizumab-related Progressive Multifocal Leukoencephalopathy (NTZ-PML) lesions in Multiple Sclerosis (MS) patients (1).
Their central finding was the high percentage (78.1%) of patients, who eventually developed NTZ-PML, in whom highly suggestive lesions were already retrospectively detectable on pre-diagnostic MRI exams. Furthermore, the pre-diagnostic phase proved to be relatively long (150.8±74.9 days), with an estimated percentage increase of the lesions’ volume of 62.8% per month (1).
Given the widely recognized crucial role of a timely NTZ-PML identification in reducing mortality and residual disability (1), these results present the neurological and neuroradiological communities with an important clinical challenge, prompting a major effort to ensure an early diagnosis of this condition.
Although redefining the timing of MRI surveillance, with up to one brain MRI exam every 3-4 months for high-risk patients, appears as a justified strategy, we think that improving the accuracy of early identification of NTZ-PML is also mandatory.
In our opinion, such achievement should be pursued using two complementary approaches: (i) a specific training addressed to neuroradiologists working in the field of MS, who should be aware of the relevance of even very small asymptomatic PML lesions and how to differentiate them from new M...
To the Editor,
Show MoreWe read with interest the work from Licher et al. [1] in which the authors tried to quantify the burden of common neurological diseases (i.e. dementia, stroke and parkinsonism) in 12 102 individuals (6 982 women and 5 120 men) aged ≥ 45 years and free from these diseases at baseline. All these individuals were recruited between 1990 and 2016 into the prospective population-based Rotterdam Study. At the end of their analyzes, the authors concluded that one in two women and one in three men will develop dementia, stroke or parkinsonism during their lifetime, and that the risk for women to develop both stroke and dementia during their life is almost twice that of men [1].
By reading the article from Licher et al. [1], we were extremely surprised by the fact that the authors did not consider the impact of the difference in life expectancies between men and women on their results and conclusions. This is particularly well underlined by the fact that the authors did not clearly precise the age structures of the two populations they studied [1]. In our view, this information is critical as, although the reasons for this difference are still debated and may probably be multi-factorial [2], it is well known that women live longer than men. This trend is confirmed by the 2018 World Health Statistics report [3] that estimates that in 2016, the life expectancies of men and women at birth were respectively 69.8 and 74.2 years at the international level. The...
Dear Editor,
We thank Abat et al. for re-emphasizing an important interpretation of our work, namely that sex-differences in life-expectancy likely influenced the presented lifetime risks [1]. Indeed, in our paper we repeatedly discussed in several sections (for instance in the methods) that differences in life-expectancy between men and women could differentially affect their lifetime risk. It was for this reason that we consequently decided to analyze the data in a sex-specific manner while taking the competing risk of death into account in order to prevent potential overestimation.
Abat et al. unfortunately also allege that we attributed the observed sex-differences in disease risk to sex-specific effects on a biological level. The authors have seemingly missed our discussion at length arguing that observed differences in lifetime risk may be primarily attributed to the effects of differences in life-expectancy between men and women: “Apart from a longer life-expectancy in general, these findings may be explained by smaller differences in life-expectancy between men and women in the Netherlands (1.8 years), compared with the USA (4.8 years). With longer life-expectancy, individuals in this study simply had more time to develop these diseases in a timeframe with high age-specific incidence rates.”
It seems thus that ours and Abat and co-authors’ interpretation of our findings is pretty much congruent, i.e. age, irrespective of sex, should be consid...
Show MoreKomatsu et al. presented an interesting clinicopathological case of anti-myelin oligodendrocyte glycoprotein (MOG) demyelinating disease of the CNS. (1) Their patient had a rather unusual subacute encephalopathic presentation with extensive supratentorial fluid-attenuation inversion recovery white matter hyperintensities. The authors focused mainly on the conspicuous MRI punctuate and curvilinear enhancement pattern within the hemispheric lesions.
Show MoreIt is well established that intraparenchymal punctuate and curvilinear gadolinium enhancement may arise in the context of Moyamoya syndrome, various endotheliopathies and most commonly, in disorders causing small vessels blood-brain barrier disruption. (2)These entities are associated histologically with perivascular cellular infiltrates and include inflammatory autoimmune diseases (i.e. primary or secondary angiitis of the CNS, neurosarcoidosis, histiocytosis and demyelinating diseases of the CNS), pre-lymphoma states (i.e. sentinel lesions of primary CNS lymphoma), non-Hodgkin lymphoma (i.e. intravascular lymphoma) and CLIPPERS syndrome. (2) Notably, among demyelinating disorders, multiple sclerosis and aquaporin-4 antibody (AQP4-Ab) neuromyelitis optica spectrum disorders (NMOSD) manifest this specific neuroimaging pattern in rare cases. (2,3)
We agree with Komatsu et al. that their case is the first report of the perivascular enhancement in anti-MOG antibody disease. Indeed, gadolinium enhancement was observed in...
Re: A unifying theory for cognitive abnormalities in functional neurological disorders, fibromyalgia and chronic fatigue syndrome
Viraj Bharambe Specialist registrar in neurology
John C Williamson Specialist registrar in neurology
Andrew J Larner Consultant Neurologist
Cognitive Function Clinic
Walton Centre for Neurology and Neurosurgery
Lower Lane
Fazakerley
Liverpool
L9 7LJ
UK
e-mail: a.larner@thewaltoncentre.nhs.uk
Teodoro et al. present evidence for shared cognitive symptoms in fibromyalgia, chronic fatigue syndrome, and functional neurological disorders, and hypothesize that functional cognitive disorders (FCD) may share similar symptoms.1 We present data which speak to this issue.
We have previously reported preliminary data examining performance on the mini-Addenbrooke’s Cognitive Examination (MACE) by patients diagnosed with fibromyalgia2 as part of a larger study of MACE.3 Here, we update these data for fibromyalgia patients (n = 17; F:M = 17:0; age range 33-56 years, median 49) and compare them to MACE performance by patients diagnosed with FCD (n = 43; F:M = 18:25; age range 28-82 years, median 58).4
There was no statistical difference (p > 0.1) in the proportions of patients scoring below the two cut-off scores (≤21/30, ≤25/30) defined in the index MACE report.5 Looking at MACE subscores (Attention, Registration,...
Show MoreThe interplay among statins, serum cholesterol, and spontaneous intracerebral hemorrhage (ICH) with and without prior history of ischemic stroke is controversial.
Studies over the last decade, like the GERFHS study,[1] have concluded that increasing serum cholesterol levels may decrease the risk of ICH. This finding was confirmed in one of the largest observational studies[2] which estimated an adjusted hazard ratio (HR) of 0.94 (0.92-0.96) with every 10 mg increase in baseline serum total cholesterol level. Similar interaction was observed with increasing LDL cholesterol quartiles (LDL > 168 mg/dL; HR 0.53 [0.45-0.63]).[2]
However, the evidence on the effect of statins in ICH is less clear. Studies ranging from the SPARCL trial[3] which showed an increased risk of recurrent ICH with high dose statins to the recent meta-analysis by Ziff et al.,[4] which described no significant increase of the risk of ICH with statins, are few examples. Similar non-significant trends were seen in the risk of ICH after prior ischemic stroke and prior ICH.[3] Prior retrospective studies also described a neutral effect of statins on recurrent ICH. Interestingly, analysis from the largest administrative database in Israel[2] showed a surprising result; statin use might be associated with decreased ICH risk. Furthermore, an indirect, albeit unique measurement of dose-response using average atorvastatin equivalent daily dose (AAEDD) churned out interesting figures – a HR of 0....
Show MoreWe read with great interest the recent report of Banerjee and colleagues of three cases of minimally symptomatic cerebral amyloid angiopathy-related inflammation (CAA-ri) cases, drawing attention to the possibility of a wider spectrum of clinical manifestations in patients with CAA-ri than previously described1.
Show MoreCerebral amyloid angiopathy-related inflammation (CAA-ri) is a rare form of meningoencephalitis, presenting acutely with cognitive decline, seizures, headache and /or encephalopathy in most patients. The prognosis is poor even in patients under immunosuppressive treatment, with a mortality rate of 30% and only a minority of patients making a full recovery2-3. However, in the three cases reported by Banerjee and colleagues, patients presented with mild symptoms despite the magnitude of the MRI findings and made a full clinical and radiologic recovery in 2 of the cases with immunosuppressant treatment and in the remaining without any treatment.
We have recently evaluated a similar patient, that presented with mild transient symptoms in whom the diagnosis was made following the finding of characteristic CAA-ri changes in brain MRI.
He was a 62 year-old-man with a past history of hypertension, who was referred to the Emergency Department (ER) due to moderate frontal headache followed by left hemisensory numbness with Jacksonian march lasting a few minutes. He was asymptomatic on arrival at the ER and his neurological examination was normal. He perfor...
Imagine you are an epilepsy health professional seeing a patient with clinical symptoms of depression. What should you do? If you have read Noble et al.’s [1] recent JNNP review, entitled ‘Cognitive-behavioural therapy does not meaningfully reduce depression in most people with epilepsy…’ you may have become sceptical about the potential of CBT, or psychotherapy in general, to alleviate depression in people with epilepsy (PWE). This recent systematic review pooled data from five small randomised controlled trials (RCTs), with some elements of CBT for PWE, and performed an analysis of reliable change. ‘Pooled risk difference indicated likelihood of reliable improvement in depression symptoms was significantly higher for those randomised to CBT’, but the authors focused on the finding that ‘only’ 30% of patients receiving interventions, compared to 10% of controls, could be considered ‘reliably improved’. Emphasising the fact that over 2/3 of patients did not meet this criterion for improvement, the authors suggest CBT is ‘ineffective’, has ‘limited benefit’ and could even lead to lower ‘self–esteem’ and ‘helplessness’. Notably, the latter conclusions were based on hypothetical reactions to treatment, rather than empirically supported outcomes.
Therefore, the purpose of this letter, written by the Psychology Task Force of the International League Against...
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