MD Luciano Rinaldi

MD Francesca Rinaldi

Paola Perini

MD Massimiliano Calabrese

MD Dario Seppi

PhD Paola Grossi

MD Irene Mattisi

MD Luisa Barzon

MD Carlo Mengoli

Mariacolomba Sanzari

MD Mariella Sanzari

MD Giorgio Palu'

MD Paolo Gallo

No evidence of JC-virus reactivation in natalizumab-treated Multiple Sclerosis patients

à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

INTRODUCTION

Natalizumab is the most recent drug approved for the treatment of relapsing-remitting multiple sclerosis (RRMS). [START_REF] Lutterotti | Getting specific: monoclonal antibodies in multiple sclerosis[END_REF] Natalizumab, a humanized monoclonal antibody, blocks the interaction between the integrin α 4 chain expressed by lymphocytes and the adhesion molecule VCAM-1 expressed by endothelial cells, thus preventing migration of lymphocytes into the central nervous system (CNS). [START_REF] Yednock | Prevention of experimental autoimmune encephalomyelitis by antibodies against alpha 4 beta 1 integrin[END_REF] Controlled trials showed the efficacy of natalizumab in reducing relapse-rate, disability progression, and lesion accumulation on magnetic resonance imaging (MRI) in RRMS patients. 3 4 However, natalizumab therapy has unexpectedly been associated with a potentially fatal disease, progressive multifocal leucoencephalopathy (PML). Before the introduction of monoclonal antibodies, PML was mainly associated with HIV infection, chemotherapy, or immunosuppressive therapy, and was thought to be caused by the reactivation in immunocompromised patients of the polyomavirus JC (JCV), which is normally latent in the majority of healthy individuals. [5][START_REF] Hou | Progressive multifocal leukoencephalopathy: JC virus induced demyelination in the immune compromised host[END_REF][START_REF] Kleinschmidt-Demasters | Progressive multifocal leukoencephalopathy complicating treatment with natalizumab and interferon beta-1a for multiple sclerosis[END_REF][START_REF] Langer-Gould | Progressive multifocal leukoencephalopathy in a patient treated with natalizumab[END_REF] For this reason, in Europe natalizumab is currently approved as monotherapy only for RRMS patients with high disease activity despite treatment with other immunomodulating drugs, as well as for patients with a rapidly deteriorating disease. However, given the risk of PML (30 cases are currently reported), natalizumab is subject to specific international, European and national pharmacovigilance programs, calling clinicians for restricted criteria in patient selection, and an appropriate monitoring schedule. [START_REF] Kappos | Natalizumab treatment for multiple sclerosis: recommendations for patient selection and monitoring[END_REF][START_REF] Krumbholz | Delayed allergic reaction to natalizumab associated with early formation of neutralizing antibodies[END_REF][START_REF] Leussink | Type III systemic allergic reaction to natalizumab[END_REF] Yet, we still do not know how to anticipate which patients will be at greatest risk of developing PML, and no reliable markers have been identified, based on which a specific monitoring program can be set up to recognize PML early. A recent pilot study describing a subclinical reactivation of the JCV in natalizumab-treated RRMS patients, showed the appearance of JCV DNA in urine, followed by the evidence of JCV DNA in circulating immune cells [START_REF] Chen | Asymptomatic reactivation of JC virus in patients treated with natalizumab[END_REF] , thus suggesting that the detection of JCV in urine may predict a higher risk for PML. Some attempts have also been done trying to explain the relationship between immunological changes, the clinical effects and the risk for PML in natalizumab treated RRMS patients. As expected by the expression of α4 integrin on leukocytes, in the registration studies it was observed that natalizumab-treated patients had a reversible increase in leukocyte count, except neutrophils. 3 4 13 Beside such effects, a number of other immunological alterations, not linked to the inhibition of lymphocyte migration into the CNS, have been described in natalizumab-treated patients. Among these, the mobilization of CD34 + hematopoietic precursors 14 15 and B cell precursors [START_REF] Krumbholz | Natalizumab disproportionately increases circulating pre-B and B cells in multiple sclerosis[END_REF] , in which latent JCV can initiate viral moltiplication.

The purpose of this study was to evaluate whether JC virus reactivation occurred in the urine, plasma, or both in 42 RRMS patients, treated with natalizumab over an 18-month period, and followed by a thorough immunological, clinical and MRI monitoring program.

MATERIALS AND METHODS

Patients

Fourty-two consecutive patients (24 women and 18 men; mean age 34.4±8.9 years, range 20-59; mean disease duration 10.3±7.1 years (range 1-29); with a diagnosis of RRMS [START_REF] Polman | Diagnostic criteria for multiple sclerosis: 2005 revisions to the "McDonald Criteria[END_REF] and regularly followed at the Multiple Sclerosis Centre of Veneto Region (Padova), were treated with natalizumab for at least one year, from July 2007 to October 2009. In the same period, a subgroup of 19 patients reached 18 months of treatment. All patients fulfilled the Italian Agency of Drug (AIFA) criteria for natalizumab treatment (Tysabri), i.e. not responsive to interferon (IFN) beta/glatiramer acetate (GA) (criterion A), or presenting an aggressive disease course at onset (criterion B). Thirty-eight patients (90.5%) fulfilled criterion A, having at least one relapse in the previous year while on therapy with immunomodulatory or immunosuppressive drugs (20 on IFN β-1a, 2 on IFN β-1b, 2 on GA, 9 on cyclophosphamide, 5 on mitoxantrone). Four patients (9%) fulfilled criterion B. The first administration of natalizumab was preceded by a "washout" period of at least one month for immunomodulatory, and six months for immunosuppressive drugs [START_REF] Gold | Expert opinion: guidelines for the use of natalizumab in multiple sclerosis patients previously treated with immunomodulating therapies[END_REF] . Patient mean Expanded Disability Status Scale (EDSS) score at T0 was 3.3±1.2 (range 1-5.5), while the relapse rate during the twelve months before starting natalizumab was 2.1±0.8 (Table 1). The study was approved by the local Ethic Committee.

Clinical follow-up

At baseline all patients underwent a complete physical and neurological examination.

Neurological disability was assessed by the EDSS [START_REF] Kurtzke | Rating neurologic impairment in multiple sclerosis: an expanded disability status scale (EDSS)[END_REF] . Based on the accurate surveillance program indicated by AIFA, all patients underwent a clinical follow up consisting of trimestrial EDSS score assessment and record of side effects and adverse events. Patients filled in a questionnaire for the most common immediate and delayed hypersensitivity reactions to natalizumab (i.e. itch, cutaneous rash, fever, arthralgia, headache, fatigue). The side effects frequency was reported according to the World Health Organization recommendations (very common, > 10%; common, >1% and < 10%; uncommon, > 0.1% and < 1%; rare, > 0.01% and < 0.1%; very rare, < 0.01%). [START_REF] Royer | International reporting of adverse drug reactions. Final report of CIOMS ADR Working Group[END_REF]

Detection of Viral DNA by qualitative PCR assay

JCV presence was assessed in urine and plasma by qualitative real-time polymerase-chain reaction (PCR) at baseline, after 1 month, and after 6, 12, 13 and 18 months of natalizumab therapy.

Nucleic acid was extracted from 250 μl of each specimen (urine and plasma) using the BioRobot 9604 workstation (Qiagen) and the QIAamp Virus BioRobot Kit (Qiagen) according to the manufacture's protocol. The DNA was eluted in 100 μl. Each real-time PCR mixture consisted of [START_REF] Andreoletti | Semiquantitative detection of JCV-DNA in peripheral blood leukocytes from HIV-1-infected patients with or without progressive multifocal leukoencephalopathy[END_REF] μl Taqman universal PCR master mix (Applied Biosystems, Foster City, CA, USA) 600 nM TaQ primers, 250 nM JC-P Taqman probe (Table 2), 17 μl water PCR grade and 5 μl of extracted sample. Thermal conditions were 95° C for 10 minutes followed by 45 cycles at 95° C for 15 seconds, 60° C for 1 minute. Reactions were amplified in a 7900HT Sequence Detection System (Applied Biosystems). In case of positive assay, a quantitative analysis was performed with a detection limit of 42 DNA copies/ml.

Laboratory and viral follow-up

Laboratory tests included complete blood count and differential, aspartate and alanine aminotransferase, γGT, total bilirubin, Herpes Simplex virus (HSV) 1 and 2, Cytomegalovirus (CMV) and Toxoplasma Gondii antibodies. Complete blood count and liver function were carried out monthly, while HSV 1 and 2, CMV and Toxoplasma Gondii antibodies every 12 months.

Immunological follow-up

For the prospective flow cytometry analysis of blood immune cell subsets, whole blood direct staining was performed using a standard procedure. [START_REF] Rinaldi | Longitudinal analysis of immune cell phenotypes in early stage multiple sclerosis: distinctive patterns characterize MRI-active patients[END_REF]

Neuropsychological follow-up

Cognitive functions were tested using the Rao's Brief Repeatable Battery (BRB) of Neuropsychological Test, version A; 22 quality of life, fatigue and mood were evaluated by Multiple Sclerosis Quality Of Life-54 scale (MSQoL-54), Fatigue Impact Scale (FIS), Fatigue Severity Scale (FSS) and Beck Depression Inventory (BDI) scale. MSQoL-54, FIS, FSS and BDI scale were readministered one year after therapy start, whereas cognitive evaluation after two years.

MRI follow-up

Cerebral MRI was performed on all patients at baseline and after 12 months of natalizumab therapy, using the protocol for image acquisition currently used in our Centre. [START_REF] Calabrese | Detection of cortical inflammatory lesions by double inversion recovery magnetic resonance imaging in patients with multiple sclerosis[END_REF]

Statistical analysis

All data were prospectively collected, using pre-treatment values as referral. Statistical analysis was performed using GraphPad Instat Software and SPSS. The prevalence of JC virus detected in urine and plasma at each time point (baseline, month 1, 6, 12, 13 and 18) was compared with the use of a two-sided Fisher's exact test. The absolute count and percentage of the immune cell subsets were compared to the baseline values at each month, using a repeated measures one-way ANOVA, with post test (Dunnet) as correction for multiple comparisons. A general linear model for repeated measures was used to compare immune cell subset changes during the follow-up between natalizumab treated and untreated MS patients (control group). Wilcoxon matched-pairs signedranks test was used to compare the clinical variables at baseline and after one year of natalizumab treatment. Differences were considered significant if p<0.05.

RESULTS

Clinical outcomes

In October 2009, 42 out of 70 RRMS patients (60%) currently on natalizumab therapy at our Centre, reached at least one-year follow-up (19 of which got to 18 months). In these patients the mean EDSS score remained stable (3.2±1.3, range 1-6.0) after twelve months of natalizumab, being 3.3±1.2, (range 1-5.5) at baseline. Individual EDSS score did not change in 15 patients (35.7%), decreased in 15 (35.7%), and increased (not more than 0.5 points) in the remaining 12 (28.6%).

Thirty patients (71.4%) were free from disability progression after twelve months of treatment.

Thirty-nine patients (92.8%) remained relapse-free from baseline to month 12, with a remarkable reduction in the mean relapse rate compared to the previous year (from 2.1±0.8 to 0.09±0.3; p<0.0001) (Table 1). Nineteen patients showed a similar clinical trend (data not shown). We observed only three bouts of mild intensity, occurred some days after the first natalizumab infusion in two patients and after eight infusions in one case: all relapses were successfully treated with high-dose methylprednisolone and all patients could continue natalizumab. Collectively, 27 patients (64%) were disease free after 1 year of natalizumab therapy. [START_REF] Havrdova | Effect of natalizumab on clinical and radiological disease activity in multiple sclerosis: a retrospective analysis of the Natalizumab Safety and Efficacy in Relapsing-Remitting Multiple Sclerosis (AFFIRM) study[END_REF] Common side effects and laboratory abnormalities in our patient cohort are reported in Table 3.

Notably, one patient presented a first generalized epileptic fit, after the 10 th natalizumab infusion: once ruled out PML by brain MRI and CSF search for JCV DNA, this patient started anti-epileptic drugs, and continued natalizumab.

JCV oucomes

Detection of Viral DNA in Urine

In our cohort of 42 RRMS patients, we observed that all patients who were positive to JCV in urine at baseline, remained persistently positive throughout the follow-up (i.e., at all time points tested, month 1, 6, 12, 13, 18). The prevalence of JCV in urine was 26% at baseline (11/42) and did not change significantly from baseline to month 12 (13/42, 31%, two-sided Fisher's exact test, p=0.49), and to month 18 (3/19 patients who have reached this time point at present, two-sided Fisher's exact test, p=0.52; Table 4).

Detection of Viral DNA in Plasma

In our RRMS cohort, no patient was JCV positive in plasma at baseline. Three patients, however, got plasma JCV positive between baseline and month 18. Two of them were only transiently JCV positive in plasma (one at month 6, the other at month 13), and both patients got negative within one month after the last natalizumab infusion, without getting plasma JCV positive any more till month 18. The third patient got JCV positive in plasma at month 12, and remained persistently positive in consecutive plasma samples, prompting precautionary therapy discontinuation. This patient did not develop any sign or symptom suggestive of PML up to now.

All the three patients who converted to JCV in plasma were JCV positive in urine since the baseline (Table 4), and fulfilled the criterion A for natalizumab treatment: the two patients who got transiently plasma JCV positive were previously treated with interferon β and immunosuppressors, the third one only with immunomodulatory drugs.

PML

None of our natalizumab-treated patients followed for 12 (42 patients) and 18 months (19 patients), showed either a worsening of the clinical picture suggestive of PML, or MRI findings consistent with this diagnosis, including the patient with a first epileptic seizure, whose CSF resulted JCV DNA negative.

Other microbiological outcomes

The analysis of antibodies (IgM and IgG) against HSV1-2, CMV and Toxoplasma Gondii provided no evidence for an altered immune response toward these agents.

Immunological oucomes

We observed that all the changes in circulating immune cells occurred since the first administration of natalizumab and persisted through the year. Such changes were natalizumab related as shown by the comparison with a cohort of untreated MS patients. [START_REF] Rinaldi | Longitudinal analysis of immune cell phenotypes in early stage multiple sclerosis: distinctive patterns characterize MRI-active patients[END_REF] Except for the B cell percentage, the mean values of all the other circulating immune cells analysed remained within normal ranges (Table 5).

Leucocyte subsets

The leukocyte count significantly rose since the first natalizumab administration (p=0.0003), while it remained stable in the control group, as well as the absolute count and the percentage of all the leukocyte subsets. On the contrary, in natalizumab treated patients we observed a prompt and marked elevation of the lymphocyte (Δ=+100%, p<0.0001) and eosinophil count (Δ=+180%, p=0.0051), accompanied by a rise of their percentage (Δ=+40% and +100%, p<0.0001 and p=0.0149, respectively), and accounting for the increased leukocyte count observed. The monocyte count showed only a trend of increase, while the neutrophil and basophil count did not significantly change throughout the treatment.

B lymphocytes dramatically increase during natalizumab therapy

Among circulating lymphocytes, the most dramatic increase was observed in B cells, which promptly tripled after the first natalizumab dose (Δ=+250%, p<0.0001), with a corresponding increased percentage (Δ=+110%, p=0.0001, Fig. 1). This highly significant rise of B cells was persistent and natalizumab related, as demonstrated by the comparison with the control group (p<0.0001 by general linear model analysis, Fig. 2). Though less conspicuous, NK cell count increased as well (Δ=+110%, p=0.0008), while their percentage remained unchanged. T lymphocyte count showed the less remarkable increase (Δ=+70%, p<0.0001) during natalizumab treatment, thus accounting for the T cell percentage decrease observed (Δ=-13%, p=0.0002).

Overall, these data show that natalizumab does not simply cause a lymphocytosis, but it determines a clear redistribution in the lymphocyte compartment, differentially affecting T, B and NK cells, in favour of B cells.

CD4 + and CD8 + T cells

Interestingly, while the count of both CD4 + and CD8 + T cells increased (Δ=+80% and +90%, p<0.0001 and p=0.0073, respectively), their percentage remained stable, as well as CD4/CD8 ratio, showing that these T cell subsets are proportionally affected by natalizumab. NK-like (CD3 + CD16 + CD56 + ) T cells did not show any significant change during Tysabri treatment.

Quality of life and fatigue

Thirty-nine patients filled in the MSQoL-54: 28 (72%) reported an improvement in their quality of life (≥5 points of score increase), while only 6 patients (15%) had a worsening of their quality of life. The mean MSQoL-54 score at 12 months was significantly higher, compared to baseline, in both the physical health composite score (70.6±14.5 vs 57.2±19.0, p<0.001) and the mental health composite score (72.1±16.2 vs 58.4±19.6, p<0.001). Moreover, we observed a significant fatigue decrease in 17 out of 35 patients (49%) who filled in the FIS questionnaire, despite the marked state of exhaustion frequently reported the day after natalizumab infusion. Only 9 (26%) patients reported a worsening of fatigue (≥10 point increase in FIS score). Finally, we did not observe a significant change in patient mood after one year of natalizumab therapy.

MRI outcomes

Brain MRI scans were available for all our patients at month 12. The T2 lesion volume remained stable in 40 patients (95.2%); one patient developed a new supratentorial T2 lesion, while another patient developed two new supratentorial lesions, all typical for MS. Only one patient (2.4%) had a new Gd+ lesion at follow-up.

DISCUSSION

Natalizumab represents a major breakthrough in the treatment of MS, a chronic inflammatory disease of the CNS, which can cause a relevant disability in young adult patients. Indeed, the data from our cohort of 42 MS patients strongly confirm the efficacy of natalizumab in controlling the inflammatory component of MS: 64% of our patients resulted disease-free after 1 year of natalizumab treatment, with no disability progression, a marked reduction in the relapse rate, and no evidence of burden increase on brain MRI. Moreover, the majority of our patients (72%) reported a significant improvement in their quality of life, as well as a decrease in fatigue. Besides its efficacy, we observed that natalizumab was generally well tolerated, compared to other current therapeutic regimes, and no major side effects were registered during the 18-month follow-up. However, natalizumab therapy has been associated with PML, a potentially fatal disease which was never described in MS patients before the introduction of monoclonal antibodies as immunomodulatory treatment. PML is a rare, opportunistic, demyelinating viral infection of the CNS caused by JCV, and normally affecting immunocompromised patients (HIV, chemotherapy, immunosuppressive therapy). At January the 25 th 2010, 30 cases of PML in natalizumab-treated MS patients have been confirmed by FDA and EMEA, 22 as monotherapy, with a calculated risk of about 1:1000. Such a risk raises some concerns, due to the high mortality rate of this disease, even though two recent reports have described an effective therapeutic protocol for the treatment of PML. Moreover, the mechanism by which natalizumab can cause PML is still not known, and there are no reliable predictive markers of disease. Indeed, conflicting opinions have been expressed on the predictive value of JCV viraemia for the risk of PML 12 25-28 , and no consensus exists yet in recommending the use of serial testing for blood and urine JCV DNA. In this scenario, it is of paramount importance to assess the protocols of pharmacovigilance proposed for the prediction and early recognition of PML. Thus, we decided to include urine and plasma JCV DNA determination in our monitoring schedule of natalizumab-treated patients, to evaluate whether and when a subclinical reactivation of JCV occurs during treatment, and whether this may precede the development of PML.

In our cohort of 42 RRMS patients, the prevalence of JCV in urine at baseline was 26%, similar to that previously reported in normal subjects, as well as MS and Crohn's patients before natalizumab treatment. 12 28-31 During the therapy follow-up no significant increase in this prevalence was observed at 12 and 18 months. Moreover, while no patient had JCV in plasma at baseline, the occurrence of two transient and one persistent JCV viraemia, did not significantly change JCV prevalence. Indeed, neither any patient developed PML, nor did we find any evidence of subclinical JCV reactivation in our patients after 18 months of natalizumab therapy.

Our findings are in contrast with a recent pilot study, in which a subclinical JCV reactivation was described in a consistent proportion of 19 natalizumab-treated MS patients. [START_REF] Chen | Asymptomatic reactivation of JC virus in patients treated with natalizumab[END_REF] After 12 months of treatment, the Authors observed a significant increase (300%) in the prevalence of JCV in urine, followed, after 18 months, by an increase of JCV in PBMC. None of the patients in that study developed PML. Whether a subclinical JCV reactivation may herald the development of PML could not be determined. However, in a recently described case of PML, the absence of JCV in plasma would question such an occurrence, and even the absence of JCV in CSF can not exclude an ongoing PML, due to the modest sensitivity (60-80%) of the test. [START_REF] Lindå | Progressive multifocal leukoencephalopathy after natalizumab monotherapy[END_REF] Generally, in the MS patients who developed PML and whose data are available, JCV was undetectable both in plasma and PBMC, despite its positivity in CSF. 7 8 Moreover, in a very recent report on 200 natalizumabtreated patients, JCV DNA was detected only in 1,5% of the patients, either in plasma or CSF.

Notably, all patients converted to undetectable viral DNA after cessation of therapy, and no patient had clinical evidence of PML. [START_REF] Sadiq | JCV detection in multiple sclerosis patients treated with natalizumab[END_REF] Another crucial aspect in the monitoring of JCV is the potential host reservoir of this widespread virus. [START_REF] Ransohoff | Natalizumab for multiple sclerosis[END_REF] Apart from the kidney, JCV can be detected in peripheral blood lymphocytes, particularly B cells, as well as in B-cell precursors from the bone marrow. [START_REF] Azzi | Human polyomaviruses DNA detection in peripheral blood leukocytes from immunocompetent and immunocompromised individuals[END_REF][START_REF] Monaco | JC virus infection of hematopoietic progenitor cells, primary B lymphocytes, and tonsillar stromal cells: implications for viral latency[END_REF][START_REF] Tornatore | Detection of JC virus DNA in peripheral lymphocytes from patients with and without progressive multifocal leukoencephalopathy[END_REF] Interestingly, a release of B cells and B-cell progenitors has been shown in patients receiving natalizumab 10 15 , and our findings of a prompt and marked, natalizumab-dependent increase in the circulating B cells of all patients are strongly consistent with these recent studies. However, despite this dramatic phenomenon, likely due to the effect of natalizumab on B cell precursors expressing VLA-4 in bone marrow [START_REF] Major | Reemergence of PML in natalizumab-treated patients--new cases, same concerns[END_REF] , we had no evidence that this event resulted in JC viraemia in our patients.

In conclusion, despite the limits of this study (low number of patients followed up to 18 months or further, and no JCV DNA analysis in PBMCs), our data confirm the overall efficacy and good safety profile of natalizumab therapy, and show that transient shedding of live JCV may occur not only in urine, but also in plasma, with no apparent pathogenic relevance. While our and others' findings do not support urine JCV determination as a valuable tool for natalizumab-treated patients follow-up, the persistent positivity of JCV DNA in plasma or PBMCs as predictor of PML deserves further evaluation. Indeed, waiting for a consensus and new guidelines in this regard, we and others propose to precautionary discontinue therapy in case of persistent JCV plasma positivity. Extending the investigation on virological and immunological effects of natalizumab will be essential for a better management of this drug in MS patients.

  Stained cells were analysed on a two-laser flow cytometer (FACSCalibur, BD Immunocytometry Systems, San Jose, CA), using Cell Quest and DIVA softwares (BD Biosciences) for data acquisition and analysis, respectively. Reported percentage values are referred to the parental lymphocyte population. The absolute count of each lymphocyte subsets was calculated based on the percentage and the differential WBC. The following mAbs were used: CD45 FITC (pan-leukocyte antigen), CD3 PerCP (T lymphocytes), CD4 PE-Cy5 (CD4 + T cells), CD8 PE (CD8 + T cells), CD16 FITC (Natural Killer, NK Lymphocytes), CD16 PE (NK Lymphocytes), CD19 PE (B lymphocytes), CD56 PE (NK Lymphocytes) (all purchased from BD Biosciences).

Figure 1 :

 1 Figure 1: Diagram representing the longitudinal distribution of B cell count and percentage in the natalizumab treated and a reference group of untreated MS patients followed for one year: in treated patients B cells markedly increase since the first natalizumab administration, while in untreated patients B cells clearly remain stable over time, thus demonstrating that the B cell rise in treated patients is driven by natalizumab. Dots represent the mean values of each time point, plus SD.

TAQJ1 5 'TAQJ2 5 'TAQJ3 5 '

 555 AGT GTC CAT TTG AGG CAG CAA 3'T antigen GGC ACA TGG CAA TGC TGT T 3' T antigen ACA GTG TCC ATT TGA GGC AGT AAA 3'

Figure 1 B

 1 Figure 1

Table 1 .

 1 Demographic and clinical data of patient population (n=42).

	EDSS, Expanded Disability Status Scale;
	DMT, disease modifying therapy
	a At study entry
	****p<0.0001

Table 2 .

 2 Primers and probe used in the JC real-time PCR assay.

Table 3 .

 3 Frequency of side effects.

	3.3±1.2
	3 (7%)
	16 (38%)
	6 (14%)
	10 (24%)
	7 (17%)

Table 4 .

 4 MS patients who had detectable JC viral loads during 18 months of treatment with natalizumab. The patients who got JCV positive in plasma at month 6 and 13, reversed to negativity at month 7 and 14, respectively. The only patient who resulted consecutively JCV positive in plasma at month 12 and 13, discontinued natalizumab.

	Very common			Common
	(> 10%)	n	%	(1%-10%)	n	%
	Exhaustion	20	47.6	Tachicardia	4	9.5
	Urinary/upper respiratory infections	14	33.3	HSV labialis	4	9.5
	Headache	15	35.7	Mild urticaria	3	7.1
	Fever	8	19	Relapse		3	7.1
	Itch	8	19	Oral Afte	2	4.7
	Arthralgia	6	14.2	Nausea		2	4.7
	Face cutaneous rash	6	14.2	Sweating	2	4.7
				Alopecia		2	4.7
				Chills		1	3.3
				Herpes Zoster	1	3.3
				Pneumonia	1	3.3
				Epileptic seizure	1	3.3
				Serum Liver Enzyme Elevation	0	0
	Time of testing		Urine		Plasma
		no. positive/no. tested	no. positive/no. tested
	Baseline		11/42 (26%)	0/42 (0%)
	At month 1		12/42 (29%)	0/42 (0%)
	At month 6		12/42 (29%)	1/42 (2%)
	At month 12		13/42 (31%)	1/42 (2%)
	At month 13		13/42 (31%)	2/42 (5%)
	At month 18		3/19 (16%)	0/19 (0%)


PhD Jeremy C Hobart
email: jeremy.hobart@pms.ac.uk

PhD Stefan J Cano

MD Alan J Thompson

Effect sizes can be misleading: Is it time to change the way we measure change?

Keywords: rating scales, responsiveness, psychometrics, Rasch analysis, measurement

Objectives: Previous comparisons of the ability to detect change of the Barthel Index (BI) and Functional Independence Measure motor scale (FIMm) have implied these two scales are equally responsive when examined using traditional effect size statistics. Clinically, this is counterintuitive, as the FIMm has greater potential to detect change than the BI, and raises concerns about the validity of effect size statistics as indicators of rating scale responsiveness. To examine these concerns in this study we applied a sophisticated psychometric analysis, Rasch measurement to BI and FIMm data.

Methods: BI and FIMm data were examined from 976 people at a single neurorehabilitation unit. Rasch analysis was used to compare the responsiveness of the BI and FIMm at the group comparison level (effect sizes, relative efficiency, relative precision) and for each individual person in the sample by computing the significance of their change.

Results: Group-level analyses from both interval measurements and ordinal scores implied the BI and FIMm had equivalent responsiveness (BI and FIMm effect size ranges -0.82 to -1.12 and -0.77 to -1.05, respectively). However, individual person-level analyses indicated that the FIMm detected significant improvement in almost twice as many people as the BI (50%, n=496 versus 31%, n = 298), and recorded less people as unchanged on discharge (FIMm= 4% n=38; BI=12% n=115). This difference was found to be statistically significant 273.81; p<0.000).

Conclusions:

Findings demonstrate that effect size calculations are limited and potentially misleading indicators of rating scale responsiveness at the group comparison-level. Rasch analysis at the individual person-level showed the FIMm's superior responsiveness, supporting clinical expectation, and its added value as a method for examining and comparing rating scale responsiveness.

INTRODUCTION

Rating scales must be able to detect clinically important change if they are to be used as outcome measures in clinical trials. [START_REF]Patient reported outcome measures: use in medical product development to support labeling claims, draft guidance[END_REF][START_REF] Hobart | Improving the evaluation of therapeutic intervention in MS: the role of new psychometric methods[END_REF][START_REF] Hobart | Rating scales as outcome measures for clinical trials in neurology: problems, solutions, and recommendations[END_REF] The relative responsiveness of competing rating scales is a critical factor in the selection of scales for studies. [START_REF] Cano | CDIP-58 can measure the impact of botulinum toxin treatment in cervical dystonia[END_REF][START_REF] Hobart | How responsive is the Multiple Sclerosis Impact Scale (MSIS-29)? A comparison with other self-report scales[END_REF] This study examines the responsiveness of two widely used activity limitation rating scales, the Barthel Index (BI) [START_REF] Mahoney | Functional evaluation: the Barthel index[END_REF] and Functional Independence Measure motor scale (FIMm), [START_REF] Granger | Advances in functional assessment for medical rehabilitation[END_REF] in 1400 people who have undergone neurorehabilitation. Previously, [START_REF] O'connor | Exploring rating scale responsiveness: Does the total score reflect the sum of its parts?[END_REF] we demonstrated problems with the BI (substantial item and scale ceiling / floor effects), which cautioned against its appropriateness in evaluating neurorehabilitation. This led us to hypothesize that the BI would be more responsive if its items had more response categories. We tested this hypothesis by comparing the BI with the FIMm, a scale that uses the same items but has more item response categories. [19] Results showed that the FIMm had greater potential to detect change (smaller item and total score floor and ceiling effects than the BI), and detected change in more people undergoing rehabilitation. Despite this evidence of better potential to detect change, the FIMm and BI had almost identical effect size calculations implying the same ability to detect change at the group comparison level. This finding is counter intuitive clinically, and questions the validity of effect size statistics as indicators of rating scale responsiveness.

To explore this issue, we examined the relative responsiveness of the BI and FIMm in the same dataset using a more sophisticated psychometric method, Rasch measurement, [START_REF] Rasch | Probabilistic models for some intelligence and attainment tests[END_REF][START_REF] Andrich | Rasch models for measurement[END_REF][START_REF] Wright | Solving measurement problems with the Rasch model[END_REF]. This method advances the analysis of rating scale responsiveness in three specific ways. First, Rasch analysis enables interval-level (linear) measurements of activity limitation to be estimated from ordinal-level BI and FIMm total scores. This is valuable because fixed changes in ordinal total scores (eg 10 points) imply variable changes in interval-level measurements across the scale range. [START_REF] Hobart | Improving the evaluation of therapeutic intervention in MS: the role of new psychometric methods[END_REF][START_REF] Hobart | Rating scales as outcome measures for clinical trials in neurology: problems, solutions, and recommendations[END_REF][START_REF] Wright | Observations are always ordinal: measurements, however must be interval[END_REF]. Thus, analysing total scores may hide responsiveness differences between scales.

Second, Rasch analysis enables a legitimate examination of changes in activity limitation at the individual person-level, in addition to comparisons at the group-level. In contrast, traditional psychometric analyses are not recommended for individual person decision making. [START_REF] Hobart | Rating scales as outcome measures for clinical trials in neurology: problems, solutions, and recommendations[END_REF][START_REF] Mchorney | Individual-patient monitoring in clinical practice: are available health status surveys adequate?[END_REF] The third benefit is that Rasch analysis enables scales measuring the same construct, as the BI and FIMm purport, to be equated on a common metric. [START_REF] Hobart | Improving the evaluation of therapeutic intervention in MS: the role of new psychometric methods[END_REF] This enables people's measurements on the BI and FIMm to be compared on an identical "ruler" of activity limitation. [START_REF] Wright | Best test design: Rasch measurement[END_REF]

METHODS

Sample

Data were available for 1495 people who underwent neurorehabilitation at a single UK unit. In our analyses we included cases with complete admission and discharge data, and excluded all people who had the minimum possible score or the maximum possible score on either scale at either admission or discharge. This was to ensure that results and inferences were not confounded by floor and ceiling effects. This study was approved by the local ethics committee of the National Hospital for Neurology and Neurosurgery, London, UK. Details of the sample have been reported elsewhere. [START_REF] O'connor | Exploring rating scale responsiveness: Does the total score reflect the sum of its parts?[END_REF][START_REF] Cano | Exploring disability rating scale responsiveness II: Do more response options help?[END_REF]

BI and FIMm

Table 1 shows the BI and FIMm. The BI has 10 items. Two items have two response categories, six items have three response categories, and two items have four response categories. [START_REF] Mahoney | Functional evaluation: the Barthel index[END_REF] The FIMm has 13 items. All items have seven response categories. [START_REF] Granger | Advances in functional assessment for medical rehabilitation[END_REF] The BI and FIMm share eight identical items. The two remaining BI items (dressing, transferring) are represented in the FIMm by five items (dressing upper body, dressing lower body, bed transfer, toilet transfer, bath transfer).

Analysis

Rasch analysis is a method of analysing rating scale data. In brief, the analysis examines the extent to which the data satisfy the requirements of a mathematical model -the Rasch measurement model. [START_REF] Rasch | Probabilistic models for some intelligence and attainment tests[END_REF][START_REF] Andrich | Rasch models for measurement[END_REF][START_REF] Wright | Solving measurement problems with the Rasch model[END_REF] This model articulates a theory of how rating scales must perform if the values they generate are to be considered scientific measurements. [START_REF] Hobart | Rating scales as outcome measures for clinical trials in neurology: problems, solutions, and recommendations[END_REF] Thus, when the data fit the requirements of the Rasch model, within reason, there is evidence that scales (here the BI and FIMm) are measurement instruments. Under these circumstances the analysis is able to transform scale scores for people, which are by necessity ordinal, into interval level measurements. These estimates, termed "person locations" to distinguish them from ordinal scale scores, are in log-odds units (logits). For each individual person's location the analysis also generates a bespoke standard error.

Rasch analysis is explained elsewhere. [START_REF] Hobart | Rating scales as outcome measures for clinical trials in neurology: problems, solutions, and recommendations[END_REF][START_REF] Andrich | Rasch models for measurement[END_REF][START_REF] Wright | Best test design: Rasch measurement[END_REF][START_REF] Massof | The measurement of vision disability[END_REF][START_REF] Hobart | Rating scales for neurologists[END_REF][START_REF] Andrich | Controversy and the Rasch model: a characteristic of incompatible paradigms?[END_REF] Rasch analyses were performed using RUMM2020. [19] We analysed BI and FIMm data together as a co-calibrated pool of items, organised in a racked (by scale) and stacked (by time point) format. We compared the responsiveness of the BI and FIMm scale at both group and individual person-level.

Group-level comparison

The relative responsiveness of the BI and FIMm was examined at the group-level by comparing admission and discharge person locations using four standard indicators: two effect size calculations (Kazis' effect size-ES, [START_REF] Kazis | Effect sizes for interpreting changes in health status[END_REF] standardized response mean -SRM [START_REF] Liang | Comparisons of five health status instruments for orthopedic evaluation[END_REF]), relative efficiency (RE; pairwise squared t-values from paired samples t-tests [START_REF] Liang | Comparative measurement efficiency and sensitivity of five health status instruments for arthritis research[END_REF]), relative precision (RP; ratio of pair-wise Fvalues from one-way ANOVA). [START_REF] Hobart | How responsive is the Multiple Sclerosis Impact Scale (MSIS-29)? A comparison with other self-report scales[END_REF] We compared the results of these analyses, which are derived from person locations and are interval level measurements, with the results of the same analyses undertaken on BI and FIMm total scores, which are generated by summing item scores and are ordinal level data. This was to determine if estimates of responsiveness based on interval level measurements differed, in magnitude or inference, from those based on ordinal level scores.

Individual person-level comparison

The relative responsiveness of the BI and FIMm was compared at the individual person level. This

RESULTS

Sample

Data were available for 1495 people. Complete data at admission and discharge were available for 1396 (93% of sample), of which n= 976 (70%) did not score at either the floor or ceiling of either scale at both time points. In the total sample, at both admission and discharge, total score floor and ceiling effects were lower for the FIMm than the BI. This indicates that the FIMm provides an extended range of measurement. * As predicted for a sample of people undergoing an intervention aimed to improve function, the floor effects were smaller on discharge than admission, and the ceiling effects were larger on discharge than admission. These values were: FIMm admission (floor = 0.8%, ceiling = 0.3%), discharge (floor = 0.2%, ceiling = 1.7); BI admission (floor = 1.1%, ceiling = 5.3%), discharge (floor = 0.1%, ceiling = 27.9%). Overall 519 people were at either the floor or the ceiling, on either scale. Of these, only 30 people (5.9%) scored were at the floor or ceiling on both scales.

The mean age and length of rehabilitation were 49 years (sd: 15) and 36 days (sd: 26) respectively, and 56% of the cohort was female. The main diagnostic groups were multiple sclerosis (46%), Stroke (18%), spinal cord syndromes (17%). Fuller details of the samples have been reported before. [START_REF] Cano | Exploring disability rating scale responsiveness II: Do more response options help?[END_REF]

Group-level comparison of BI and FIMm relative responsiveness

The responsiveness data (Table 2) generated by the analysis of both interval measurements (BI and FIMm person locations) and ordinal scores (BI and FIMm scale scores) shows that both scales quantified significant changes at the group level, and that both scales had near identical similar responsiveness according to the four analyses. Conclusions reached about the relative responsiveness of the BI and FIMm were essentially the same for both interval measurements and ordinal scores.

* At admission and discharge, total score ceiling effects were lower for the FIMm than the BI. Thus, a significant proportion of patients who scored at the maximum of the BI were within the floor and ceiling of the FIM, implying that latter does in fact have an extended range of measurement and thus a better potential to detect change.

Individual person-level comparison of BI and FIMm relative responsiveness

Tables 2a and2b show that the FIMm detected significant improvements in activity limitation in nearly 200 more people than the BI (50%, n=486 versus 31%, n = 298), and also recorded less people as unchanged on discharge (FIM= 4% n=38; BI= 12% n=115). Importantly, these analyses cannot be undertaken legitimately on ordinal rating scale data.

DISCUSSION

The aim of this study was to explore the consistent [START_REF] Cano | Exploring disability rating scale responsiveness II: Do more response options help?[END_REF][START_REF] Van Der Putten | Measuring change in rehabilitation: comparison of the responsiveness of the Barthel Index and the Functional Independence Measure[END_REF][START_REF] Wallace | Comparison of the responsiveness of the Barthel Index and the Motor Component of the Functional Independence Measure in stroke: The impact of using different methods for measuring responsiveness[END_REF][START_REF] Hsueh | Comparison of the psychometric characteristics of the functional independence measure, 5 item Barthel index, and 10 item Barthel index in patients with stroke[END_REF][START_REF] Houlden | Use of the Barthel Index and the Functional Independence Measure during inpatient rehabilitation after single brain injury[END_REF] This study had three major findings. The first is that the FIMm was more responsive than the BI. It detected significant improvements in very many more people (n=486 v 298), and detected change in 67% of those considered unchanged by the BI. However, this clear demonstration of the FIMm's superiority was only possible through individual person-level analyses. These are only legitimately achieved using sophisticated methods, such as Rasch analysis. [START_REF] Hobart | Rating scales as outcome measures for clinical trials in neurology: problems, solutions, and recommendations[END_REF][START_REF] Rasch | Probabilistic models for some intelligence and attainment tests[END_REF][START_REF] Andrich | Rasch models for measurement[END_REF][START_REF] Massof | The measurement of vision disability[END_REF] The explanation for the different responsiveness of the FIMm and BI can be seen by plotting the standard error of measurement (y-axis) for every level of activity limitation defined by the FIMm and BI (x-axis; see Figure ). At every activity limitation level, the standard error associated with a FIMm measurement is smaller than the standard error associated with the corresponding BI measurement. This is mainly because the FIMm has more item response categories. As a consequence, measurements made by the FIMm have narrower confidence intervals than those made by the BI. Thus, statistical significance is achieved with smaller changes in the FIMm than the BI.

The second important finding from this study is that the group-level indicators of responsiveness (ES, SRM, RE, RP) did not detect the superiority of the FIMm, even when the analyses were conducted on interval measurements derived from the BI and FIMm. This finding provides further support for our suggestion [START_REF] Cano | Exploring disability rating scale responsiveness II: Do more response options help?[END_REF] that standard group-level indicators of rating scale responsiveness are limited and may be positively misleading.

The third important finding of this study concerns the similarity of measurements generated by the BI and FIMm. One feature of Rasch analysis is that it enables rating scales measuring the same construct to be equated on an identical metric. A close look at the results in Table 2 shows three things: on admission, the mean FIMm location is higher than the mean BI location; at discharge, the mean FIMm location is lower than the mean BI location; and the mean change measured by the FIMm (0.915 logits) is less than that measured by the BI (1.238 logits).* These three findings raise two questions. Why do the FIMm and BI produce different measurements of the same people on admission and discharge? Why does the FIMm register less mean change than the BI given that it has the greater capacity to detect change? There are a number * These inferences are legitimate because Rasch analysis enables scales measuring the same construct to be equated on a common interval level metric.

of possible explanations. First, these could occur if the FIMm and BI measured somewhat different constructs. This is unlikely as the FIMm was developed, in part, to improve on the limitations of the BI, [START_REF] Granger | Advances in functional assessment for medical rehabilitation[END_REF] all items are common, and Rasch analysis supports them as measures of the same construct.

A second explanation is that inherent psychometric limitations in each scale account for the findings. This is possible as Rasch analysis identifies limitations in both scales (misfitting items, disordered thresholds). A summary of the results of the co-calibrated data analysis (essentially the 10 item BI and 13 item FIMm analysed as if they were a single 23 item scale) are shown in the supplementary Appendix. This table shows that 10 items have disordered thresholds, most items have statistically significant misfit (examination of the item characteristic curves confirmed this misfit, revealing over and under discrimination for the items with highest negative and positive fit, respectively), 14 items demonstrate statistically significant DIF (a combination of items with uniform and non-uniform DIF). When taken together the items with most concerning psychometric properties were the BI and FIMm Bowels and Bladder, Stairs, and FIMm Feeding.

Thus, at face value, the requirements of the Rasch model are not well met by the cocalibrated data, which reflect and build on the findings of others [START_REF] Linacre | The structure and stability of the Functional Independence Measure[END_REF][START_REF] Tennant | The Barthel Index: an ordinal score or interval measure[END_REF][START_REF] Lawton | Cross-cultural validity of FIM in spinal cord injury[END_REF] who have demonstrated a range of psychometric problems including misfit for the BI and FIMm (largely because of the mixing of clinically different constructs, eg activities, mobility, sphincter function), and DIF for the FIMm. However, we explored the impact of the psychometric problems by modifying the data (albeit post hoc) to overcome the weaknesses (focusing specifically on items with disordered thresholds and exclusion of the items with poorest fit), and repeating our analyses of relative responsiveness. The same conclusion was reached; that effect sizes appear to be misleading when seeking to understand the relative ability of scales to detect change (data available on request).

A third explanation is that the results were biased by the therapists who rated the patients. It is conceivable that the clinically crude response categories of the BI might encourage therapists to overestimate people's activity limitation on admission and their activity limitation change at discharge. Our data do not allow us to investigate this further. Another explanation is that our findings reflect the different precisions of the two scales. If this is the case, measures of the same construct, but with different precisions, may come to different conclusions about change associated with an intervention. This warrants further interrogation not possible within our BI and FIMm data.

Although the psychometric concerns outlined above are important considerations, we believe our examination of the two scales in this study bring to the fore some key clinical issues.

We had the rare opportunity to directly compare two firmly established, widely used, highly clinically related instruments, one of which (the FIMm) was developed to improve upon the perceived insensitivity of the other (BI). Clinical experience suggests that the FIMm is more responsive than the BI. Thus, we would expect our study to find the FIMm better able to measure change. However, inferences based on the widely used traditional responsiveness indicators would lead us to believe the FIMm and BI are equally responsive. What we hope we have achieved here is that we have shown that using the more sophisticated analysis techniques (afforded by Rasch measurement methods) indicated that the FIMm is indeed more sensitive to change than the BI. This is in line with clinical expectation, and has important ramifications for the use of the tools in clinical research and trials, and the methods we use to determine and compare scale responsiveness.

At present we do not have a full explanation for our findings. However, from a clinical perspective we would expect the FIMm to be more sensitive to change than the BI. So, we believe that our inference that group-based statistics are misleading has credence. Despite this, at the current time, we cannot square the circle of the issues identified by this study. There is a clear need for further work using scales that better fit the We chose Rasch measurement rather than IRT for these very specific reasons.

We feel that it is vital that neurologists are aware of the key issues surrounding the use and analysis of rating scale data because rating scales have an increasingly crucial role in the determination of patient care, the guidance of clinical research directions, the evaluation of advances in basic science, and the evaluation of clinician professionalism. [START_REF] Richards | A multi-method analysis of free-text comments from the UK General Medical Council Colleague Questionnaires[END_REF][START_REF] Roland | Reliability of patient responses in pay for performance schemes: analysis of national General Practitioner Patient Survey data in England[END_REF] Each of these eventually impacts on patients, clinical practice, and clinicians.

One limitation of this study is that responsiveness of the BI and FIMm was evaluated in a sample of neuro-rehabilitation patients, which included a large sub-group of people with MS, from one tertiary referral hospital in the south-east region of the UK. Importantly, when we analysed the main clinical sub-groups within our sample, the findings remained the same (data available from authors). However, to examine generalisability, it is important that others seek to replicate our analyses.

* In contrast the aim of an IRT analysis is to determine the extent to which the measurement models fit the rating scale data. This fundamental different is poorly appreciated.

Our findings have three important implications for clinicians, clinical practice and clinical trials. First, they demonstrate that group based statistics can be misleading, not of their own volition, when representing the ability, and relative ability, of scales to detect change. As such, they demonstrate the added value of using Rasch analysis and indicate that group based analyses should be complemented by legitimate analyses at the individual person level. The second implication, a consequence of the first, is that clinical investigators need to become familiar with, and apply, modern psychometric methods that enable legitimate comparisons at the individual person level.

Traditional psychometric analyses, using raw scores, are not suitable for that purpose. Fourth, although Rasch analysis does not confirm clinical change, it helps to take us further than existing approaches, because the information provides us with a firm quantitative base upon which qualitative explorations of the differences between those people who report change and those who do not. We believe it is these sorts of explorations can move us towards a better understanding the nuances of what constitutes clinical change. When considered together, the findings demonstrate the added value that Rasch analysis brings to examining and understanding measuring change in activity limitation.

  was achieved by computing, for each and every person, the significance of their own change in activity limitation measurement ('Sig Change'). First, we computed the size of the change score for each individual person (discharge location -admission location). Second, we computed that size of the error associated with their change (standard error of the difference) for each individual person as the square root, of the sum, of the squared standard error values at admission and discharge. Third, we computed the significance of the change for each individual by dividing their change score by their standard error of the difference (ie how big is their change in standard error units). Finally, we categorised the significance of each person's change into one of 5 groups according to the size and direction of the significance of change value. The formulae are as follows: Significance of change (Sig Change) = Discharge location -Admission location Standard error of the difference (SEdiff) Where, SEdiff for a person = √{(SE admission location) 2 + (SE discharge location) 2 . Significance of change values obtained from this formula were categorized into 5 groups: Significant improvement = Sig Change > +1.96; Non significant improvement = 0 < Sig Change < +1.95; No change = Sig Change = 0; Non significant worsening = -1.95 < Sig Change < 0; Significant worsening = Sig Change < -1.96. Now, we can simply count the numbers of people achieving each level of significance of change, and compare the distributions for the FIMm and BI using a Chi-square test and relative risk statistics.

Table 1 : The items and item response categories of the BI and FIMm Barthel Index FIMm motor scale

 1

	Item	No. response categories	Item	No. response categories
	Feeding	3	Feeding	7
	Grooming	2	Grooming	7
	Bathing	2	Bathing	7
	Dressing	3	Dressing upper body	7
	-		Dressing lower body	7
	Toileting	3	Toileting	7
	Bladder	3	Bladder	7
	Bowels	3	Bowels	7
	Transfer	4	Bed transfer	7
	-		Toilet transfer	7
	-		Bath transfer	7
	Mobility	4	Walk/wheelchair use	7
	Stairs	3	Stairs	7

Table 2a : BI with FIMm: admission, discharge, change and relative responsiveness (n=976)

 2a

		Ordinal Scores	Interval Measurements
				(locations)
		BI	FIMm	BI	FIMm
	Possible range
		0 -20	13 -91	-3.15 to +2.86* -2.63 to +4.98*
	Admission
	mean	10.5	53.5	-0.086	0.017
	sd	4.9	18.2	1.372	0.930
	Discharge
	mean	14.5	67.7	1.152	0.932
	sd	4.7	17.3	1.455	1.154
	Change
	Mean	-4.0	-14.1	-1.238	-0.915
	sd	3.7	13.5	1.109	0.868
	Indicators of group-level responsiveness
	t-test
	t	-33.52	-32.76	-34.877	-32.932
	p	<0.000	<0.000	<0.000	<0.000
	RE	100%	96%	100%	89%
	1-way ANOVA
	F	337.3	308.8	373.50	372.11
	P	<0.000	<0.000	<0.000	<0.000
	RP	100%	91%	100%	99%
	Effect size
	Kazis	-0.82	-0.77	-0.90	-0.98
	SRM	-1.08	-1.04	-1.12	-1.05

*These range from 'minus' to 'plus' values as the person locations are transformed log-odds units (logits) centered around a mean of zero. These possible range values presented here are extrapolated estimates as extreme locations cannot be accurately estimated. RE = relative efficiency = (t-scale) 2 / (t-BI) 2 RP = relative measurement precision = (F-scale) / (F-BI) (RE and RP use "best" scale as arbitrary denominator) Kazis effect size = mean change / sd admission SRM = standardized response mean = mean change / sd change

Table 2b : BI with FIMm: relative responsiveness, indicators of individual person-level responsiveness, Chi-ssquare and relative risk statistics (n=976) BI FIM Significance of change

 2b

		%(n)	%(n)	RR (upper, lower 95% CI, p-value)*
	Significant improvement	30.5 (298)	49.8 (486)	1.6 (1.5, 1.8 p<0.000)
	Non-significantly improvement	52.7 (514)	39.8 (389)	0.8 (0.7, 0.8 p<0.000)
	No change	11.8 (115)	3.9 (38)	0.3 (0.2, 0.5 p<0.000)
	Non-significant worsening	4.8 (47)	5.8 (57)	1.2 (0.8, 1.8 p<0.314)
	Significant worsening	0.2 (2)	0.7 (6)	3.0 (0.6, 14.8 p<0.178)
	Chi square (p)	273.81 (p<0.000)

Significance of change (Sig Change) = Discharge location -Admission location / Standard error of the difference (SEdiff) -= Calculation not possible *RR, relative risk, is a ratio of the probability of an event occurring in one group versus another group (eg RR=pA/pB, where p=probability of event, A = Sample A; B = Sample B). In terms of comparing the statistical significance of significance of change comparing the FIMm and BI, RR = (FIMm p/N) / (BI p/N), where p=proportion, N=total sample. Also shown are associated lower and upper bound confidence intervals and p-values. The RR highlights the statistically significant difference between the BI and FIMm detecting individual-level significant improvement post-rehabilitation in this sample.

  Rasch model requirements, to elaborate upon whatwe have uncovered here and to ultimately pin down its root cause Rasch measurement is not the only psychometric method available to analysing change in individual person level data. The other main new method is called Item Response Theory (IRT),[START_REF] Lord | Statistical theories of mental test scores[END_REF] which in contrast to Rasch measurement, takes into account other sample related parameters such as item discrimination. Despite being mathematically similar, Rasch measurement and IRT have different research agendas.[START_REF] Andrich | Controversy and the Rasch model: a characteristic of incompatible paradigms?[END_REF] In essence, albeit a simplification, IRT models are statistical models used to explain data. When the observed data do not fit the chosen IRT model another model is sought to better explain the data (ie taking into account other sample dependent parameters as described above). In contrast, Rasch analysis provides a mathematical model for guiding the construction of stable linear measures from rating scale data.The aim of a Rasch measurement analysis is to determine the extent to which observed rating scale data satisfy (fit) the measurement model. * This is vital for measuring change as the most important measurement axiom is the ability to test for invariance (stability).[START_REF] Andrich | Rasch models for measurement[END_REF] This is achievable with Rasch Measurement, but not with IRT models as the presence of other parameters renders the estimates sample dependent.[START_REF] Hobart | Rating scales as outcome measures for clinical trials in neurology: problems, solutions, and recommendations[END_REF][START_REF] Massof | The measurement of vision disability[END_REF][START_REF] Andrich | Controversy and the Rasch model: a characteristic of incompatible paradigms?[END_REF] It follows that Rasch measurement enabled us to obtain interval level activity limitation measurements to be estimated from ordinal BI and FIMm scores, legitimately examinechange at the individual person-level rather than just the group comparison level, and direct comparison of the BI and FIMm on the same activity limitation metric.

ACKNOWLEDGEMENTS

We thank Professor David Andrich (University of Western Australia, Perth, Western Australia) and Dr Barry Sheridan (RUMM Laboratory, Perth, Western Australia) for their contributions towards this work, and staff at the Neurorehabilitation Unit, National Hospital for Neurology and Neurosurgery London UK who routinely collect audit data.

COPYRIGHT LICENCE STATEMENT

The Corresponding Author has the right to grant on behalf of all authors and does grant on behalf of all authors, an exclusive licence (or non-exclusive for government employees) on a worldwide basis to the BMJ Publishing Group Ltd, and its Licensees to permit this article (if accepted) to be published in Journal of Neurology, Neurosurgery, and Psychiatry and any other BMJPGL products and to exploit all subsidiary rights, as set out in our licence.

COMPETING INTERESTS

There are no competing interests.

FUNDING

Figure: BI vs FIMm -Comparison of standard errors across locations

This Figure shows that the standard errors for all FIMm measurements (person locations) are lower than for all BI locations on the activity limitation continuum. Thus, for a location of '0', the standard error for the FIMm is 0.4, and the BI is 0.6. This equates to 95% confidence intervals of 0.8 for the FIMm and 1.2 for the BI, showing how statistical significance is achieved with smaller changes in the FIMm


Benedetta Bodini

MD Marco Battaglini

Nicola De Stefano

PhD Zhaleh Khaleeli

Frederik Barkhof

PhD Declan Chard

PhD Massimo Filippi

MD Xavier Montalban

Chris Polman

PhD Marco Rovaris

MD A Rovira

MD R Samson

PhD D Miller

FRCP A Thompson

O Frcp

Ciccarelli

Dr Olga Ciccarelli
email: o.ciccarelli@ion.ucl.ac.uk

T2 lesion location really matters: a 10-year follow-up study in primary-progressive MS

Keywords: primary-progressive multiple sclerosis, time-to-event analysis, lesion probability mapping

à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

Prediction of long-term clinical outcome in patients with primary-progressive multiple sclerosis (PPMS) has important clinical implications, in both the design of treatment trials and in providing prognostic advice to individual patients. Whole brain T2 and T1 lesion loads, which are objective measures of visible tissue damage, have been studied as potential predictors of long-term clinical outcome in a large cohort of PPMS patients, who were followed-up for 10 years in five European MAGNIMS (Magnetic Resonance Imaging in MS) centres [START_REF] Khaleeli | Predicting progression in primary progressive multiple sclerosis: a 10-year multicenter study[END_REF] . Neither T2 nor T1 lesion load at study entry predicted clinical deterioration over 10 years [START_REF] Khaleeli | Predicting progression in primary progressive multiple sclerosis: a 10-year multicenter study[END_REF] . This is surprising, since T2 lesion volume has been shown to predict disability after twenty years in patients who presented with clinical isolated syndrome [START_REF] Fisniku | Disability and T2 MRI lesions: a 20-year follow-up of patients with relapse onset of multiple sclerosis[END_REF] , and suggests differences in the mechanisms of tissue damage between relapsingremitting and PPMS.

In searching for other features of T2 and T1 brain lesions which could predict long-term outcome in PPMS, in the same paper we examined the short-term increase in T2 and T1 loads over two years. We found that this also failed to predict progression over 10 years [START_REF] Khaleeli | Predicting progression in primary progressive multiple sclerosis: a 10-year multicenter study[END_REF] , though was more promising over five years 3;4 . This suggests that accumulation of visible lesions over a short time showed no advantage over total lesion load at study entry when it came to predicting progression at 10 years. Thus, predictors which are promising in short-term follow-up studies may not be as compelling in long-term studies.

Another, mostly unexplored, feature of lesions which may be relevant to predicting progression is their topographic distribution. Therefore we aimed to investigate whether the location of T2 and T1 lesions at baseline predicts progression over 10 years, performing a retrospective study in the same cohort of PPMS patients 1 . To explore lesion location, we employed the Lesion Probability Mapping (LPM) approach, that provides a voxel-wise, quantitative description of the topographic distribution of brain lesions [START_REF] Di | Voxel-based assessment of differences in damage and distribution of white matter lesions between patients with primary progressive and relapsing-remitting multiple sclerosis[END_REF] . Since a possible limitation of previous studies which explored predictors of progression, is the use of step changes of Expanded Disability Status Scale (EDSS) as the outcome measure [START_REF] Kragt | Responsiveness and predictive value of EDSS and MSFC in primary progressive MS[END_REF] , we defined the time (in years) taken to require bilateral support in order to walk as a measure of the individual progression rate. We then performed a time-to-event (TTE) analysis and combined it with the LPM. Although cervical cord cross-sectional area at study entry did not predict clinical outcome at 10 years in this cohort of PPMS patients 1 , spinal cord damage is thought to play an important role in determining disability in PPMS [START_REF] Nijeholt | Brain and spinal cord abnormalities in multiple sclerosis. Correlation between MRI parameters, clinical subtypes and symptoms[END_REF][START_REF] Rovaris | In vivo assessment of the brain and cervical cord pathology of patients with primary progressive multiple sclerosis[END_REF][START_REF] Bieniek | Cord atrophy separates early primary progressive and relapsing remitting multiple sclerosis[END_REF] ; therefore, we corrected our analysis for baseline cord cross-sectional area. We also tested whether regional T2 and T1 lesion loads located in specific, clinically strategic areas, were significant predictors of the TTE.

Methods

Patients

Of the 101 patients with PPMS who were followed-up for 10 years in five European centres (Amsterdam, Barcelona, Bordeaux, London and Milan) [START_REF] Khaleeli | Predicting progression in primary progressive multiple sclerosis: a 10-year multicenter study[END_REF] , 80 participants, whose baseline imaging data were available, were included in this study. All these patients were clinically assessed on the Expanded Disability Status Scale (EDSS) [START_REF] Kurtzke | Rating neurologic impairment in multiple sclerosis: an expanded disability status scale (EDSS)[END_REF] at baseline (see Table 1 for baseline demographic, clinical and radiological characteristics), 76 after one year, 59 after two years, 75 after five years, and 79 after ten years. Of the 79 patients assessed after ten years, 36 were scored using the EDSS telephone interview [START_REF] Lechner-Scott | Can the Expanded Disability Status Scale be assessed by telephone?[END_REF] . Four patients scored 10 on the EDSS, since their death was due to MS; one death occurred between years two and five, and three between years five and ten.

All subjects gave informed, written consent before the study, which was approved by the local Ethics Committees in each one of the participating centres.

MRI Acquisition

Patients underwent the same imaging protocol in each centre at study entry. In London, data were acquired using a 1.5T GE Signa scanner, whereas at the other four sites a 1.5T Siemens scanner was used. The protocol included T1 and dual echo (T2 and proton-density (PD) weighted spin echo) imaging of the brain (See Supplemental Table 1 for the acquisition parameters in each centre).

Volumetric images of the spinal cord were also acquired, as previously described 12 .

MRI processing Lesion Probability Map (LPM)

For the following steps, we used the imaging tools available in the Functional Magnetic Resonance Imaging of the Brain's Software Library (FSL, www.fmrib.ox.ac.uk/fsl ).

Lesion masks

A single observer (V.S.), who was blinded to the clinical details, contoured the PD hyperintense and T1 hypointense lesions, with reference to the co-registered T2-weighted images [START_REF] Plummer | DispImage: A display and analysis tool for medical images[END_REF] . Binary lesion masks were then produced and individual lesion loads calculated.

Template construction

Each patient's T1 scan was registered to the Montreal Neurological Institute (MNI152) template, using a fully affine transformation (12 parameters) (FLIRT -FMRIB's Linear Image Registration Tool), as per previous work in patient groups with a variety of neurological conditions [START_REF] Ginestroni | Early structural changes in individuals at risk of familial Alzheimer's disease: a volumetry and magnetization transfer MR imaging study[END_REF][START_REF] Vellinga | Clinical correlations of brain lesion distribution in multiple sclerosis[END_REF][START_REF] Kendi | Altered diffusion in the frontal lobe in Parkinson disease[END_REF] . All the resulting transformed images were averaged to obtain the internal T1 template.

Transformation of individual scans and lesion masks into T1 template

Each patient's PD-weighted scan was registered to the corresponding T1 scan in native space using a rigid body transformation and trilinear interpolation (with FLIRT). The transformation parameters were then applied to the T2 lesion masks, bringing them into alignment with the individual T1 scans. Each patient's T1 scan was then registered to the T1 template, using non-linear registration (FNIRT -FMRIB's Non Linear Image Registration Tool), and the resulting transformation parameters were applied to the T1 and T2 lesion masks, which had been previously registered onto the individual T1, using trilinear interpolation.

In order to maintain the volume of the transformed lesion masks as close as possible to those in the native brain images, after the trilinear interpolation, the lesion masks were thresholded using a value of 0.5. Two observers independently checked all the co-registered lesions masks (B.B., M.B.).

T2 and T1 LPMs

T2 and T1 LPMs were generated by averaging the T2 and T1 lesion masks, at each voxel, in standard space. For each map, the resulting voxel intensity indicates how frequently the voxel in question is within a lesion across all patients, i.e., the probability of that voxel being lesional. Regions containing the peaks of lesion probability were localised using the Johns Hopkins University (JHU) white-matter tractography atlas, provided by FSL [START_REF] Mori | MRI Atlas of Human White Matter[END_REF] .

Since male gender has been associated, in this patient cohort, with a worse longterm outcome 1 , the differences in the probability of each voxel being lesional between male and female patients were assessed using an unpaired t-test and corrected for multiple comparisons at cluster level (t 2, P<0.05) using permutation-based inference [START_REF] Nichols | Nonparametric permutation tests for functional neuroimaging: a primer with examples[END_REF] .

Cord cross-sectional area

Cord cross-sectional area at the C2-3 level was measured using a semiautomated technique previously described [START_REF] Losseff | Spinal cord atrophy and disability in multiple sclerosis. A new reproducible and sensitive MRI method with potential to monitor disease progression[END_REF] . Previous work has suggested that C2-3 level offers several anatomical advantages for measuring cord crosssectional area, with high cord to cerebrospinal fluid contrast, limited inter-subject variability in cord cross-sectional area at this level, and a low probability of disc protrusions.

Statistical analysis

Correlation between lesion location and time-to-event

For each patient, the time (in years) taken from disease onset to reach the level of disability that requires a constant bilateral support to walk (i.e., EDSS=6.5)

was defined as the TTE, and was used as a measure of progression rate. An EDSS of 6.5 was chosen as it easy to recognize and represents a clinically meaningful stage in the progression of disability. It was also appropriate for our cohort, whose median EDSS at study entry was 5.7 (range 2-8.5). In cases where the EDSS was scored as equal or greater than 6.5 at a follow-up visit for the first time, it was assumed that the patient reached this score midway between the date of the visit and the preceding assessment.

Patients were divided into three groups of similar size on the basis of their TTE:

(1) patients who reached EDSS 6.5 before entering the study (n=23); (2) patients who reached EDSS 6.5 between the baseline assessment and year five (n=23);

(3) patients who reached EDSS 6.5 between year five and year ten (n=13) and those who had not reached the event at the end of the study (n=21).

To identify regions where the probability of each voxel being lesional in T2-LPM and T1-LPM correlated with TTE, an ordinal multiple regression was used, with an indicator of the three TTE categories as regressor, and age, gender, disease duration at baseline, centre and cord cross-sectional area as additional covariates. Corrections for multiple comparisons at cluster level (t 2, P<0.05)

were performed using permutation-based inference [START_REF] Nichols | Nonparametric permutation tests for functional neuroimaging: a primer with examples[END_REF] .

Predictors of progression rate

and the number of lesional voxels (or regional lesion loads) extracted from tracts that encompassed regions showing significant associations in the previous analysis, as independent variables. T2 and T1 total lesion loads, cord crosssectional area, gender, age, disease duration, and centre, were entered as additional, independent variables. In particular, each independent variable was entered individually into the model. Subsequently, significant and borderline predictors were modeled together to determine the best, independent predictor. This analysis was performed using Stata 9.2 (www.stata.com) (Stata-Corp, College Station, Texas); results with P<0.05 were considered significant.

Results

T2 and T1 Lesion Probability Maps (LPMs)

The brain locations that showed the highest probability of detecting T2 hyperintense and T1 hypointense lesions were the superior and posterior regions of the corona radiata (Figure 1). The maximum local probability was higher in T2-LPM compared with T1-LPM (42% vs. 26%). No significant differences in the probability of a voxel being lesional in T2-LPM and T1-LPM between men and women were found.

Correlation between lesion location and time-to-event

Of the 80 patients included in the study, 59 (73.7%) reached the event (EDSS= 6.5) either before entering the study (23 patients, 28.7%) or during the course of the study (36 patients, 45%), while 21 (26.3%) had not reached the event when the study terminated. The median of the TTE, calculated on those who reached EDSS 6.5, was 11.5 years (range 3-29 years).

There was a significant correlation between a higher probability of a voxel being lesional on T2-LPM and a shorter TTE (P<0.05) in the three following regions:

the bilateral CST (from the cortex to the corona radiata), the bilateral SLF, and the right IFOF (Figure 2). Conversely, there was no correlation between lesion probability on T1-LPMs and TTE, although a statistical trend towards a significant association was found in a region in the right posterior corona radiata, which included the CST and the SLF (P<0.08).

Predictors of progression rate

The number of lesional voxels and corresponding regional lesion loads extracted from tracts containing regions that showed an association between lesion location and TTE are summarised in Table 2.

When predictors were entered individually into the Cox regression model, we found that a greater number of T2 lesional voxels extracted from the right IFOF was associated with a shorter TTE (p=0.01, Hazard Ratio (HR) 1.00689, 95%Confidence Interval (CI) 1.00158, 1.01225); for every extra ml of lesions within the right IFOF, the time taken to reach EDSS 6.5 was halved. The number of T2 lesional voxels extracted from the bilateral CST showed a trend towards a correlation with TTE (p=0.07, HR 1.002, 95%CI 0.99981, 1.00442); for every extra ml of lesions localised within the CSTs, the time taken to reach the event was reduced by 30%. The number of lesional voxels in the bilateral SLF did not predict TTE.

Among the other variables tested individually in the Cox regression, we found that: (i) The whole brain T1 hypointense lesion load was significantly associated with TTE (p=0.04, HR 1.00004, 95%CI 1.000001, 1.00007); for every extra ml in total T1 lesion load, the time taken to reach the event was reduced by 0.5%; (ii)

Discussion

We found that the location of T2 lesions at baseline is associated with the rate of progression in PPMS over a 10-year period. In particular, a relationship between a higher probability of a voxel being lesional and a faster progression rate was found in the motor tract (i.e., the bilateral CST) and two associative tracts (i.e., the bilateral SLF and the right IFOF). These results extend the findings of previous cross-sectional studies that have found correlations between mobility and regional MRI abnormalities localised within the motor tract 20;21 . Further, the SLF and the IFOF are two associative tracts, which are part of the longitudinal association fibre system that connects each frontal lobe with other regions in the same hemisphere [START_REF] Catani | Virtual in vivo interactive dissection of white matter fasciculi in the human brain[END_REF] . They seem to be involved with high order aspects of motor function, as shown in primate studies [START_REF] Rizzolatti | Two different streams form the dorsal visual system: anatomy and functions[END_REF] . Therefore, a possible interpretation of our findings is that a disruption of long associative tracts, due to the presence of lesions, causes a disconnection syndrome that may contribute to a more rapid deterioration of mobility [START_REF] He | Impaired small-world efficiency in structural cortical networks in multiple sclerosis associated with white matter lesion load[END_REF] . In fact, it is known that disrupted white matter pathways, which play a crucial role in the coordination of information flow between different grey matter regions, are likely to lead to an altered functional connectivity between regions and, ultimately, to clinical impairment [START_REF] Rocca | Cortical adaptation in patients with MS: a cross-sectional functional MRI study of disease phenotypes[END_REF] . Further, damage in these tracts may reduce the cortical reorganisation that originates from activation of regions connected through these pathways, thus resulting in a reduced ability of the brain to limit the clinical impact of structural damage. We found that the relationship between lesion location in the IFOF and TTE was statistically significant on the right side only. This is in agreement with the reports of asymmetry in MS brain pathology [START_REF] Reich | Multiparametric magnetic resonance imaging analysis of the corticospinal tract in multiple sclerosis[END_REF] . In future, the contribution of handedness to these results needs to be explored.

The association between a higher probability of a voxel being lesional and a faster progression rate, was independent of spinal cord atrophy (the mean value of cord cross-sectional area in patients is similar to that previously reported in PPMS [START_REF] Rovaris | In vivo assessment of the brain and cervical cord pathology of patients with primary progressive multiple sclerosis[END_REF] . Interestingly, when the correlation between lesion location and TTE was investigated using both EDSS equal to 6 and EDSS equal to 7 as events, the same motor and associative tracts showed significance (results not shown),

suggesting that the location of T2 brain lesions in these tracts is an important factor in contributing to disability progression in PPMS.

When we investigated the best predictors of TTE, we found that the regional T2 lesion volume measured in the right IFOF was the best, independent predictor of progression. Conversely, the T2 lesion load in the bilateral CST became a borderline predictor, and that in the SLF became non-significant, suggesting a preferential role of the right IFOF, perhaps through the infero-and dorso-lateral frontal, and occipital cortices that it connects [START_REF] Catani | Virtual in vivo interactive dissection of white matter fasciculi in the human brain[END_REF] . Indeed, the IFOF constitutes the inferior part of the fronto-occipital fasciculus, that is thought to be involved in the use of visual information for the purpose of guiding movements and controlling motor actions [START_REF] Schmahmann | The complex history of the fronto-occipital fasciculus[END_REF] .

As previously demonstrated, the whole brain T2 lesion load did not correlate significantly with the progression rate, supporting the concept that in MS a proportion of the total lesion load is clinically silent 15;28-30 , and is not as relevant in determining progression as other lesions localised in strategic areas.

In contrast to our data about T2 lesions, we did not find any significant association between the T1 hypointense lesion probability and progression rate.

A possible interpretation for this finding is that the T1 lesion load is considerably smaller than the T2 lesion load, and, therefore, tracts are less likely to contain T1 compared with T2 lesions. This, in turn, means that the range of lesions loads within a given region is smaller for T1 compared with T2 lesions, reducing the possibility of a correlation with clinical outcomes. Interestingly, we found that whole brain T1 lesion load was a borderline independent predictor of progression rate. This is not surprising, since T1 hypointense lesions are known to be histopathologically associated with severe tissue destruction [START_REF] Van Walderveen | Histopathologic correlate of hypointense lesions on T1-weighted spin-echo MRI in multiple sclerosis[END_REF] and T1 total lesion load correlates with clinical disability in MS more strongly than T2 lesion load 32;33 .

We found that the areas most likely to contain lesions were similar in the T2-and T1 LPMs, and included the superior and posterior regions of the corona radiata. This is interesting, since a large number of axonal projections that contribute to the generation of the CST converge in this region [START_REF] Wakana | Fiber tractbased atlas of human white matter anatomy[END_REF] , and PPMS is typically characterized by a progressive decline in motor function. A previous study reported that patients with PPMS had a greater probability of a lesion occurring in these regions, compared with relapsing-remitting patients [START_REF] Di | Voxel-based assessment of differences in damage and distribution of white matter lesions between patients with primary progressive and relapsing-remitting multiple sclerosis[END_REF] .

An interesting aspect of this study is that we performed a TTE analysis, which, in MS, has been limited to clinical trials 35;36 . In fact, the TTE analysis can be easily adapted to different events, as long as the event in question is temporally defined and clinically meaningful [START_REF] Allen | Time-to-event analysis, or who gets better sooner? An emerging concept in headache study methodology[END_REF] . When the event cannot be specifically dated, it is acceptable to use the mid-point between the visit at which the event is noted and the previous one, as the time of the event [START_REF] Law | Effects of mid-point imputation on the analysis of doubly censored data[END_REF] . However, due to our study design, the interval between visits varied between 1 and 5 years, and 13 patients (16%) reached the event during the 5 years which elapsed between the last two timepoints. When we repeated the analysis without these patients, we found that the results were very similar to those presented here (results not shown), and so concluded that the variability in follow-up intervals has not significantly distorted the results. Further, the progression rate that we observed in our population was similar to the one previously reported in epidemiological studies in PPMS patients [START_REF] Cottrell | The natural history of multiple sclerosis: a geographically based study. 5. The clinical features and natural history of primary progressive multiple sclerosis[END_REF] , suggesting that despite the retrospective design of the study and the suboptimal frequency of assessments, the definition of the clinical outcome was appropriate.

We have studied a large number of PPMS patients over a very long follow-up period, recruited in five European centres. Another potential limitation of this study is that, with the available data, we were not able to look for grey matter

LegendFigure 1 .

 1 Figure 1. T2-LPM (top row) and T1-LPM (bottom row); the colour code indicates

Figure 2 .

 2 Figure 2. Red-orange voxels show the regions on the regression maps where a

Table 1 .

 1 Demographic, clinical and radiological characteristics of patients studied in each centre, at study entry.

	Characteristics Amsterdam Barcelona	Bordeaux	London	Milan
	Patients, n	20	22	4	22	12
	Gender, F/M	10/10	9/13	3/1	11/11	2/10
	Mean age, years (range)	53.5 (76-34)	45.7 (31-64)	48.7 (43-59) 52.7 (29-74) 45.1 (32-50)
	Mean disease
	duration, years	11.7 (2-32)	8.8 (3-24)	9.0 (2-11)	10.3 (1-24)	6.8 (2-17)
	(range)
	Median EDSS score (years)	6.0 (2-8)	5.5 (2-7.5)	5.0 (4-6)	6.0 (2.5-8.5) 4.0 (2-8.5)
	T2 lesion load,
	ml	8.4 (11.0)	15.8 (16.0) 12.6 (17.7)	15.3 (17.5)	7.7 (5.7)
	(SD)
	T1 lesion load,
	ml	3.4 (4.9)	5.6 (7.2)	6.0 (7.7)	4.7 (7.7)	3.8 (6.6)
	(SD)
	Spinal cord area, mm 2 (SD)	72.3 (10.1)	77.9 (8.2)	-	70.3 (9.4)	77.4 (8.1)

Table 2 .

 2 Number of lesional voxels and corresponding regional lesion loads extracted from the three tracts containing regions that showed an association between lesion location and TTE.

	Measures of
	regional lesional	Bilateral CST	Bilateral SLF	Right IFOF
	damage
	Mean number of
		37.6 (58.8)	38.4 (76.1)	30.1 (47.4)
	lesional voxels (SD)
	Mean lesion load
		0.3 (0.5)	0.3 (0.6)	0.2 (0.4)
	(ml) (SD)
	CST= cortico-spinal tract; SLF=superior longitudinal fasciculus; IFOF= inferior
	fronto-occipital fasciculus.

Acknowledgments

B. Bodini is funded by FISM (Fondazione Italiana Sclerosi Multipla -2007/B/01). O. Ciccarelli is a Wellcome Trust Advanced Fellow. The Authors are very grateful to Dr Valerie Stevenson for her analysis of the baseline scans, and to all the patients for participating in the study. This work was undertaken at UCLH/UCL who received a proportion of funding from the Department of Health's NIHR Biomedical Research Centres funding scheme.

There was a borderline significant association between gender and TTE (p=0.062, HR 1.62, 95%CI 0.96, 2.73); the time taken for men to reach the event was reduced by 60%. When all the significant and borderline predictors were entered together into the Cox regression model, the number of T2 lesional voxels in the right IFOF was the best independent predictor of TTE (p=0.016, HR 1.00652, 95%CI 1.00121, 1.01186). The number of T2 lesional voxels in the bilateral CST (p=0.07, HR 1.0022, 95%CI 0.99979, 1.00461) and the whole brain T1 hypointense lesion load (p=0.07, HR 1.00003, 95%CI 0.99999, 1.00006) became borderline, independent predictors, whilst gender became non-significant. lesions 40;41 that may be very relevant in contributing to long-term clinical disability. Another possible limitation of this study is the potential for registration errors and consequent lesion location inaccuracies related to the LPM technique. Two observers, rather than one 5;15 inspected registered lesion masks and were in agreement in all cases. While it is to be expected that there will be some residual misalignments, in the context of a lesion probability analysis, the effects of this will be negligible.

In conclusion, this study has provided insights into the mechanisms of progression in PPMS, reporting that T2 lesion location at study entry is a major determinant of clinical progression independent of spinal cord atrophy. Further, we confirm that the time-to-bilateral-support is a useful approach to identify those who progress more rapidly.