BACKGROUND: At 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), remain limited.METHODS: Among 262 RRMS patients followed up for ten 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 two 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 two years, exerted the greatest predictive effect. Three patients' risk-groups were identified; in the high-risk group, 85% (46 out of 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.

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

Pisani, Anna Isabella;Crescenzo, Francesco;Calabrese, Massimiliano
2021-01-01

Abstract

BACKGROUND: At 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), remain limited.METHODS: Among 262 RRMS patients followed up for ten 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 two 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 two years, exerted the greatest predictive effect. Three patients' risk-groups were identified; in the high-risk group, 85% (46 out of 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.
2021
relapsing-remitting multiple sclerosis
disease progression
predictive factors
secondary phase multiple sclerosis
risk assessment
Secondary Progressive Risk Score (SP-RiSc)
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11562/1042028
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