Pisani Anna Isabella, Scalfari Antonio, Crescenzo Francesco, Romualdi Chiara, Calabrese Massimiliano
Department of Neurological and Movement Sciences, University of Verona, Verona, Italy.
Brain Division, Imperial College London, London, UK.
Eur J Neurol. 2021 Aug;28(8):2503-2512. doi: 10.1111/ene.14859. Epub 2021 May 5.
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.
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).
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%.
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.
在患者层面，已知与复发缓解型（RR）多发性硬化症（MS）转变为继发进展型（SP）风险增加相关的若干特征的预后价值仍然有限。
在262例接受了10年随访的RRMS患者中，我们根据诊断时及2年后的临床、传统和非传统磁共振成像（MRI）参数评估了发展为SP病程的概率。我们使用一种机器学习方法——随机生存森林，根据其最小深度（MD）识别与SP转化风险相关的最具预测性的因素，然后将这些因素结合起来计算继发进展风险评分（SP-RiSc）。
在观察期内，69例（26%）患者转变为SPMS。诊断时的皮质病变数量（MD = 2.47）和年龄（MD = 3.30）、前2年的整体皮质变薄（MD = 1.65）、小脑皮质体积减少（MD = 2.15）以及皮质病变负荷增加（MD = 3.15）具有最大的预测作用。确定了三个患者风险组；在高风险组中，85%（46/55）的患者在7年中位数时间内进入SP期。估计的SP-RiSc最佳截断值为17.7，特异性和敏感性分别为87%和92%，总体准确率为88%。
SP-RiSc在识别极有可能发展为SPMS的MS患者方面表现出色，这有助于改进管理策略。这些发现是进一步开展更大规模前瞻性研究以评估其在临床环境中应用的前提。
