Correlation Between Disability Measure and Black Hole (T1 Hypointense Lesion) Lesion Load in Brain Magnetic Resonance Imaging (MRI) of Patients With Multiple Sclerosis (MS): Protocol for a Systematic Review and Meta-analysis

Introduction: As the role of neurodegeneration in the pathophysiology of multiple sclerosis (MS) has become more prominent, the formation and evolution of chronic or persistent T1-hypointense lesions (Black Holes) have been used as markers of axonal loss and neuronal destruction to measure disease activity. However, ndings regarding this subject are controversial. In this study we aim to clarify the level of importance of T1 hypointense lesions for estimating the prognosis of patients. Methods: We will search MEDLINE (through PubMed), Embase and Web of Science for relevant studies. We will extract the Spearman's rank correlation coecient (SRCC) between the T1 hypointense lesion volume and Extended Disability Status Scale (EDSS) in participants. All included studies will be evaluated for the risk of bias. We will also perform a meta-analysis on the data. The risk of publication bias will be evaluated using Funnel plots. Finally, we will assess the condence in cumulative evidence using an adapted version of GRADE. attrition, prognostic factor measurement, outcome measurement, study confounding,


Rationale
Multiple sclerosis (MS) is a chronic, disabling disease that targets patients in work productive years.
Prognostic factors play important roles in evaluating the cost of the disease on both individual patients and the social economy. Since there is no exact method to predict MS progression con dently, four general MS phenotypes have been de ned: clinically isolated syndrome (CIS), relapsing-remitting MS (PRMS), secondary progressive (SPMS), and primary progressive (PPMS) [2,3]. RRMS is the most common MS phenotype. It is characterized by noticeable exacerbation or initiation of neurologic symptoms. These attacks, also called relapses are followed by periods of recovery, called remissions. Some patients who are diagnosed with RRMS will gradually convert to a secondary progressive course which is a progressive deterioration of neurologic function over time. The other two types are not mentioned in this study.
Magnetic resonance imaging (MRI) is a sensitive para clinical test for diagnosis and assessment of disease progression in MS and is often used to evaluate therapeutic e cacy. As the role of neurodegeneration in the pathophysiology of MS has become more prominent, the formation and evolution of chronic or persistent T1-hypointense lesions (black holes) have been used as markers of axonal loss and neuronal destruction to measure disease activity. On the one hand, T1 lesion load showed greater cross-sectional [4] and longitudinal [4,5] correlations with Expanded Disability Status Scale (EDSS [6]) scores (a scale extensively used in studies for the assessment of disability for patients with MS) for patients with RRMS or SPMS than did T2 lesion load. On the other hand, several other studies reported an absence of correlations between T1 hypointensity and EDSS in patients with SPMS [7][8][9][10][11] and RRMS [9,12].
In this study, we aim to review all the relevant studies, to evaluate the correlation between T1 hypointensities (Black holes) on brain MRI with disability level of the patients with MS. Our main goal is to clarify if this possible prognostic factor can be used to help to determine the prognosis of patients.

Objectives
To evaluate the correlation between T1 hypointensities (Black holes) lesion load (lesion mean volume) on brain MRI with disability level of patients with RRMS or SPMS.

Methods
Design and methods used for this protocol comply with Centre for Reviews and Dissemination (CRD's) Guidance For Undertaking Reviews in Healthcare [13] and is reported in line with Preferred Reporting Items for Systematic Reviews and Meta-Analyses Protocols (PRISMA-P) [14]. Eligibility criteria were informed using the PICOTS [15]  Two attacks or symptom are-ups (lasting at least 24 hours with 30 days between attacks), plus two lesions.
Two attacks, one lesion, and evidence of dissemination in space (or a different attack in a different part of the nervous system).
One attack, two lesions, and evidence of dissemination in time (or nding a new lesion -in the same location -since the previous scan, or presence of immunoglobulin, called oligoclonal bands in the spinal uid).
One attack, one lesion, and evidence of dissemination in space and time.
Worsening of symptoms or lesions and dissemination in space found in two of the following: MRI of the brain, MRI of the spine, and spinal uid.
Relapsing-Remitting course: A multiple sclerosis course characterized by relapses with stable neurological disability between episodes.
Progressive course: A multiple sclerosis course characterized by steadily increasing objectively documented neurological disability independent of relapses. Fluctuations, periods of stability, and superimposed relapses might occur. Primary progressive multiple sclerosis (a progressive course from disease onset) and secondary progressive multiple sclerosis (a progressive course following an initial relapsing-remitting course) are distinguished. The search will employ sensitive topic-based strategies designed for each database with no time frame limitations. There will be no language or geographical restrictions either. We will perform our search on the 10 th of February, 2021.

Study records
Data management Records will be managed through EndNote version X9 [19]; speci c software for managing bibliographies.

Selection process
Two reviewers (AV and MM) will independently screen the title and abstract of identi ed studies for inclusion. We will link publications from the same study to avoid including data from the same study more than once. If any study cannot be clearly excluded based on its title and abstract, its full text will be reviewed. A study will be included when both reviewers independently assess it as satisfying the inclusion criteria from the full text. A third reviewer (MF) will act as arbitrator in the event of disagreement following discussion. We will prepare a ow diagram of the number of studies identi ed and excluded at each stage in accordance with the PRISMA ow diagram of study selection [1].

Data collection process
Using a standardized form, two reviewers (AV and MM) will extract the data independently. We will resolve any disagreements by discussion or, if required, by consultation with a third review author (MF).
We will attempt to extract data presented only in graphs and gures whenever possible but will include such data only if two reviewers independently obtain the same result. If studies are multi-center, then where possible we will extract data relevant to each. In the case of missingness of data, if possible, we will try to contact the original investigators to request missing information. In case that was unsuccessful, we will only analyze the available information and will not try to impute any missing data.

Data items
Data extracted will include the following summary data: sample characteristics, sample size, study methods, inclusion and exclusion criteria, MRI settings used, founding sources, declarations of interests, and results.

Outcomes and prioritization
Our main outcome of interest is the relationship between participants' EDSS score and T1 hypointense lesion mean volume.

Risk of bias in individual studies
Two review authors (AV and MM) will assess the risk of bias of each included study. We will resolve any disagreements by consensus, or by consultation with a third review author (MF). Because at the moment there is no standard tool for assessing the risk of bias in overall prognosis studies, we will use a tailored version of the Quality In Prognosis Studies (QUIPS) tool for assessing the risk of bias in studies [20], presented in Appendix B. Our tailored version of the tool consists of six risks of bias domains: study participation, study attrition, prognostic factor measurement, outcome measurement, study confounding, and statistical analysis and reporting. The study participation domain consists of ve items: an adequate description of the source population or population of interest, adequate description of the baseline study sample, adequate description of the sampling frame and recruitment, adequate description of the period and place of recruitment, and adequate description of inclusion and exclusion criteria. The study attrition domain consists of four items: description of attempts to collect information on participants who dropped out, reasons for loss to follow-up provided, an adequate description of participants lost to followup, and no important differences between participants who completed the study and those who did not.

Data synthesis
We will use R version 4 [21] as the software for our data synthesis. We expect correlation coe cients (r) to be our primary outcome measure. Most meta-analysts do not perform syntheses on the correlation coe cient itself because the variance depends strongly on the correlation. Rather, the correlation is converted to the Fisher's z scale and all analyses are performed using the transformed values [22]. For the meta-analysis of correlation data, we rst convert the correlation coe cients to Fisher's z scale. Then we will calculate the variance and standard error of the Fisher's z. We will perform a meta-analysis on those values based on the random-effects model. Finally, we will convert back Fisher's z to correlation coe cient (r) for the sake of presentation.

Assessment of heterogeneity
We expect some heterogeneity between studies because of ethnicity and methodological diversity. We will report the range of the effects of the random-effects meta-analyses using prediction intervals. In a random-effects meta-analysis, the prediction interval re ects the whole distribution of effects across study populations, including the effect expected in a future study [23,24].

Subgroup analysis
If at least 5 studies are available for each classi cation of MS in our study (RRMS and SPMS), we will perform a subgroup analysis for each of them.

Sensitivity analysis
We plan to perform sensitivity analyses to explore the in uence of the following factors: Studies at high or unclear risk of bias Very long or large studies to establish the extent to which they dominate the results.

Meta-bias
To evaluate the risk of reporting bias across studies, a test for funnel plot asymmetry will be conducted. This test examines whether the relationship between estimated effect size and study size is greater than chance [25]. Funnel plots will be generated for visual inspection of potential publication bias. In the presence of publication bias, the plot will be symmetrical at the top, and data points will increasingly be missing from the middle to the bottom parts of the plot [26].

Con dence in cumulative evidence
The strength of the overall body of evidence will be assessed using an adapted version of the Grading of Recommendations, Assessment, Development and Evaluation (GRADE) framework for prognostic factor research [27], which takes into account seven criteria: study limitations, inconsistency, indirectness, imprecision, publication bias, moderate/large effect size, and dose effect. Two review authors (AV and MM) rate the certainty of the evidence for the outcome as 'high', 'moderate', 'low', or 'very low'. We resolve any discrepancies by consensus, or, if needed, by arbitration by a third review author (MF