Herman Stephanie, Arvidsson McShane Staffan, Zjukovskaja Christina, Khoonsari Payam Emami, Svenningsson Anders, Burman Joachim, Spjuth Ola, Kultima Kim
Department of Medical Sciences, Clinical Chemistry, Uppsala University, Uppsala, Sweden.
Department of Pharmaceutical Biosciences, Uppsala University, Uppsala, Sweden.
iScience. 2023 May 19;26(6):106906. doi: 10.1016/j.isci.2023.106906. eCollection 2023 Jun 16.
Progressive multiple sclerosis (PMS) is currently diagnosed retrospectively. Here, we work toward a set of biomarkers that could assist in early diagnosis of PMS. A selection of cerebrospinal fluid metabolites (n = 15) was shown to differentiate between PMS and its preceding phenotype in an independent cohort (AUC = 0.93). Complementing the classifier with conformal prediction showed that highly confident predictions could be made, and that three out of eight patients developing PMS within three years of sample collection were predicted as PMS at that time point. Finally, this methodology was applied to PMS patients as part of a clinical trial for intrathecal treatment with rituximab. The methodology showed that 68% of the patients decreased their similarity to the PMS phenotype one year after treatment. In conclusion, the inclusion of confidence predictors contributes with more information compared to traditional machine learning, and this information is relevant for disease monitoring.
进行性多发性硬化症（PMS）目前是通过回顾性诊断的。在此，我们致力于寻找一组可辅助PMS早期诊断的生物标志物。在一个独立队列中，一组脑脊液代谢物（n = 15）被证明能够区分PMS及其先前的表型（曲线下面积[AUC] = 0.93）。用共形预测对分类器进行补充表明，可以做出高度可靠的预测，并且在样本采集后三年内发展为PMS的八名患者中有三名在该时间点被预测为PMS。最后，作为利妥昔单抗鞘内治疗临床试验的一部分，该方法被应用于PMS患者。该方法表明，68%的患者在治疗一年后与PMS表型的相似性降低。总之，与传统机器学习相比，纳入置信度预测器可提供更多信息，且该信息与疾病监测相关。
