Details

Autor(en) / Beteiligte

• De Stefano, Nicola
• Stromillo, Maria Laura
• Giorgio, Antonio
• Bartolozzi, Maria Letizia
• Battaglini, Marco
• Baldini, Mariella
• Portaccio, Emilio
• Amato, Maria Pia
• Sormani, Maria Pia

Titel

Establishing pathological cut-offs of brain atrophy rates in multiple sclerosis

Ist Teil von

• Journal of neurology, neurosurgery and psychiatry, 2016-01, Vol.87 (1), p.93-99

Ort / Verlag

England: BMJ Publishing Group LTD

Erscheinungsjahr

2016

Quelle

BMJ Journals Archiv - DFG Nationallizenzen

Beschreibungen/Notizen

• ObjectiveTo assess whether it is feasible to establish specific cut-off values able to discriminate ‘physiological’ or ‘pathological’ brain volume rates in patients with multiple sclerosis (MS).MethodsThe study was based on the analysis of longitudinal MRI data sets of patients with MS (n=206, 87% relapsing–remitting, 7% secondary progressive and 6% primary progressive) and healthy controls (HC; n=35). Brain atrophy rates were computed over a mean follow-up of 7.5 years (range 1–12) for patients with MS and 6.3 years (range 1–12.5) for HC with the SIENA software and expressed as annualised per cent brain volume change (PBVC/y). A weighted (on the follow-up length) receiver operating characteristic analysis and the area under the curve (AUC) were used for statistics.ResultsThe weighted PBVC/y was −0.51±0.27% in patients with MS and −0.27±0.15% in HC (p<0.0001). There was a significant age-related difference in PBVC/y between HC older and younger than 35 years of age (p=0.02), but not in patients with MS (p=0.8). The cut-off of PBVC/y, as measured by SIENA that could maximise the accuracy in discriminating patients with MS from HC, was −0.37%, with 67% sensitivity and 80% specificity. According to the observed distribution, values of PBVC/y as measured by SIENA that could define a pathological range were above −0.52% with 95% specificity, above −0.46% with 90% specificity and above −0.40% with 80% specificity.ConclusionsOur evidence-based criteria provide values able to discriminate the presence or absence of ‘pathological’ brain volume loss in MS with high specificity. Such results could be of great value in a clinical setting, particularly in assessing treatment efficacy in MS.