Correlation between disability measure and Black Hole (T1 hypointense lesion) lesion load in brain magnetic resonance imaging of patients with multiple sclerosis: Protocol for a systematic review and meta-analysis

Abstract

Rationale: 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, findings 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.Objectives: To evaluate the correlation between T1 hypointensities (Black holes) lesion load (lesion mean volume) on brain MRI with disability level of patients with Relapsing-Remitting Multiple Sclerosis (RRMS) or Secondary-Progressive Multiple Sclerosis (SPMS).Data sources: We will search MEDLINE (through PubMed), Embase, CENTRAL, Science Citation Index – Expanded (Web of Science), and Conference Proceedings Citation Index – Science (Web of Science). We won’t consider any timeframe, language, or geographical restrictions.Methods: Standard systematic review protocol methodology is employed. Eligibility criteria is reported in line with PICOTS system. Population is limited to adult patients diagnosed with RRMS or SPMS, based on the McDonald criteria. Index (prognostic factor) of interest will be T1 hypointense (black hole) lesion mean volume (lesion load) on brain Magnetic Resonance Imaging (MRI). There will be no comparators. Outcome of interest will be the disability measure using Expanded Disability Status Scale (EDSS). For the timing domain, we will include studies only if the outcome was measured at the same time MRI was performed (or with a very close time interval between). Inpatient and outpatient settings will both be included. All included studies will be assessed for the risk of bias using a tailored version of the Quality In Prognosis Studies (QUIPS) tool. Extracted correlation coefficients will be converted to the Fisher’s z scale and a meta-analysis will be performed on the results. We will then convert back the results to correlation coefficients again for the sake of presentation. For the purpose of assessing heterogeneity we will use prediction intervals. If feasible, we will also try to perform subgroup and sensitivity analyses. We will also evaluate the publication bias using Funnel plots and assess the confidence in cumulative evidence using an adapted version of the GRADE for prognostic factor research.