A novel prognostic score to assess the risk of progression in relapsing-remitting multiple sclerosis patients

Abstract

Background: At the patient level, the prognostic value of several features that are known to be associated with an increased risk of converting from relapsing-remitting (RR) to secondary phase (SP) multiple sclerosis (MS) remains limited.

Methods: Among 262 RRMS patients followed up for 10 years, we assessed the probability of developing the SP course based on clinical and conventional and non-conventional magnetic resonance imaging (MRI) parameters at diagnosis and after 2 years. We used a machine learning method, the random survival forests, to identify, according to their minimal depth (MD), the most predictive factors associated with the risk of SP conversion, which were then combined to compute the secondary progressive risk score (SP-RiSc).

Results: During the observation period, 69 (26%) patients converted to SPMS. The number of cortical lesions (MD = 2.47) and age (MD = 3.30) at diagnosis, the global cortical thinning (MD = 1.65), the cerebellar cortical volume loss (MD = 2.15) and the cortical lesion load increase (MD = 3.15) over the first 2 years exerted the greatest predictive effect. Three patients' risk groups were identified; in the high-risk group, 85% (46/55) of patients entered the SP phase in 7 median years. The SP-RiSc optimal cut-off estimated was 17.7 showing specificity and sensitivity of 87% and 92%, respectively, and overall accuracy of 88%.

Conclusions: The SP-RiSc yielded a high performance in identifying MS patients with high probability to develop SPMS, which can help improve management strategies. These findings are the premise of further larger prospective studies to assess its use in clinical settings.

Keywords: demyelinating diseases; multiple sclerosis; neurological disorders; risk factors.

FIGURE 1

The overall study design. (a) Data split: The entire cohort was randomly split into training and testing set. (b) Model design: Random survival forest (RSF) modelling was performed on the training set. (c) Results: (1) The seven most predictive variables were selected, based on their minimal depth. (2) Risk groups were identified by ensemble mortality. (3) Receiver operating characteristic (ROC) analysis was used to identify the best score cut‐off. (d) Secondary progressive risk score (SP‐RiSc) design: (1) The Sp‐RiSc tool was developed. (2) The Sp‐RiSc performance specificity, sensitivity and overall accuracy were assessed on the testing set. [Colour figure can be viewed at wileyonlinelibrary.com]

FIGURE 2

Secondary progressive risk score (SP‐RiSc) design. (a) Variables are listed based on their minimal depth (MD) values; lower values indicate higher predictive accuracy. Predictive variables with MD lower than the estimated threshold (Thr) (4.21) are highlighted. (b) Discretization steps: Continuous and discrete variables were discretized and weighed for MD measure to be combined to build the SP‐RiSc tool. [Colour figure can be viewed at wileyonlinelibrary.com]

FIGURE 3

Secondary progressive risk score (SP‐RiSc) visualization and receiver operating characteristic (ROC) analysis. (a) SP‐RiSc tool visualization: The seven selected predictors are shown with different colours; the size of the predictive power on SP conversion is reflected by the size of the corresponding shape. Different white and black patterns for each significant variable were reported in the deepest circle. (b) ROC curve analysis: Detection of the optimal SP‐RiSc cut‐off on the training set. [Colour figure can be viewed at wileyonlinelibrary.com]