Short-term prediction of secondary progression in a sliding window: A test of a predicting algorithm in a validation cohort.

INTRODUCTION: The Multiple Sclerosis Prediction Score (MSPS, www.msprediction.com) estimates, for any month during the course of relapsing-remitting multiple sclerosis (MS), the individual risk of transition to secondary progression (SP) during the following year. OBJECTIVE: Internal verification of the MSPS algorithm in a derivation cohort, the Gothenburg Incidence Cohort (GIC, n = 144) and external verification in the Uppsala MS cohort (UMS, n = 145). METHODS: Starting from their second relapse, patients were included and followed for 25 years. A matrix of MSPS values was created. From this matrix, a goodness-of-fit test and suitable diagnostic plots were derived to compare MSPS-calculated and observed outcomes (i.e. transition to SP). RESULTS: The median time to SP was slightly longer in the UMS than in the GIC, 15 vs. 11.5 years (p = 0.19). The MSPS was calibrated with multiplicative factors: 0.599 for the UMS and 0.829 for the GIC; the calibrated MSPS provided a good fit between expected and observed outcomes (chi-square p = 0.61 for the UMS), which indicated the model was not rejected. CONCLUSION: The results suggest that the MSPS has clinically relevant generalizability in new cohorts, provided that the MSPS was calibrated to the actual overall SP incidence in the cohort.

Diffusion tensor imaging in multiple sclerosis at different final outcomes.

OBJECTIVES: Methods to evaluate the relative contributions of demyelination vs axonal degeneration over the long-term course of MS are urgently needed. We used magnetic resonance diffusion tensor imaging (DTI) to estimate degrees of demyelination and axonal degeneration in the corpus callosum (CC) in cases of MS with different final outcomes. MATERIALS AND METHODS: We determined DTI measures mean diffusivity (MD), fractional anisotropy (FA), and axial (AD) and radial (RD) diffusivities in the CC of 31 MS patients, of whom 13 presented a secondary progressive course, 11 a non-progressive course, and seven a monophasic course. The study participants were survivors from an incidence cohort of 254 attack-onset MS patients with 50 years of longitudinal follow-up. As reference, we included five healthy individuals without significant morbidity. RESULTS: In patients with secondary progression, compared to all other groups, the corpus callosum showed increased RD and reduced FA, but no change in AD. None of the parameters exhibited differences among non-progressive and monophasic course groups and controls. CONCLUSION: Increased RD was observed in secondary progressive MS, indicating significant myelin loss. Normal RD values observed in the clinically isolated syndrome and non-progressive groups confirm their benign nature. AD was not a characterizing parameter for long-term outcome. Demyelination revealed by increased RD is a distinguishing trait for secondary progression.

Single particle raster image analysis of diffusion for particle mixtures.

Recently we complemented the raster image correlation spectroscopy (RICS) method of analysing raster images via estimation of the image correlation function with the method single particle raster image analysis (SPRIA). In SPRIA, individual particles are identified and the diffusion coefficient of each particle is estimated by a maximum likelihood method. In this paper, we extend the SPRIA method to analyse mixtures of particles with a finite set of diffusion coefficients in a homogeneous medium. In examples with simulated and experimental data with two and three different diffusion coefficients, we show that SPRIA gives accurate estimates of the diffusion coefficients and their proportions. A simple technique for finding the number of different diffusion coefficients is also suggested. Further, we study the use of RICS for mixtures with two different diffusion coefficents and investigate, by plotting level curves of the correlation function, how large the quotient between diffusion coefficients needs to be in order to allow discrimination between models with one and two diffusion coefficients. We also describe a minor correction (compared to published papers) of the RICS autocorrelation function.

Single particle raster image analysis of diffusion.

As a complement to the standard RICS method of analysing Raster Image Correlation Spectroscopy images with estimation of the image correlation function, we introduce the method SPRIA, Single Particle Raster Image Analysis. Here, we start by identifying individual particles and estimate the diffusion coefficient for each particle by a maximum likelihood method. Averaging over the particles gives a diffusion coefficient estimate for the whole image. In examples both with simulated and experimental data, we show that the new method gives accurate estimates. It also gives directly standard error estimates. The method should be possible to extend to study heterogeneous materials and systems of particles with varying diffusion coefficient, as demonstrated in a simple simulation example. A requirement for applying the SPRIA method is that the particle concentration is low enough so that we can identify the individual particles. We also describe a bootstrap method for estimating the standard error of standard RICS.