PDF(Pubmed)
Abstract：
UNASSIGNED: With increasing availability of disease-modifying therapies (DMTs), treatment decisions in relapsing-remitting multiple sclerosis (RRMS) have become complex. Data-driven algorithms based on real-world outcomes may help clinicians optimize control of disease activity in routine praxis.
UNASSIGNED: We previously introduced the PHREND® (Predictive-Healthcare-with-Real-World-Evidence-for-Neurological-Disorders) algorithm based on data from 2018 and now follow up on its robustness and utility to predict freedom of relapse and 3-months confirmed disability progression (3mCDP) during 1.5 years of clinical practice.
UNASSIGNED: The impact of quarterly data updates on model robustness was investigated based on the model\'s C-index and credible intervals for coefficients. Model predictions were compared with results from randomized clinical trials (RCTs). Clinical relevance was evaluated by comparing outcomes of patients for whom model recommendations were followed with those choosing other treatments.
UNASSIGNED: Model robustness improved with the addition of 1.5 years of data. Comparison with RCTs revealed differences <10% of the model-based predictions in almost all trials. Treatment with the highest-ranked (by PHREND®) or the first-or-second-highest ranked DMT led to significantly fewer relapses (p < 0.001 and p < 0.001, respectively) and 3mCDP events (p = 0.007 and p = 0.035, respectively) compared to non-recommended DMTs.
UNASSIGNED: These results further support usefulness of PHREND® in a shared treatment-decision process between physicians and patients.
UNASSIGNED: We previously introduced the PHREND® (Predictive-Healthcare-with-Real-World-Evidence-for-Neurological-Disorders) algorithm based on data from 2018 and now follow up on its robustness and utility to predict freedom of relapse and 3-months confirmed disability progression (3mCDP) during 1.5 years of clinical practice.
UNASSIGNED: The impact of quarterly data updates on model robustness was investigated based on the model\'s C-index and credible intervals for coefficients. Model predictions were compared with results from randomized clinical trials (RCTs). Clinical relevance was evaluated by comparing outcomes of patients for whom model recommendations were followed with those choosing other treatments.
UNASSIGNED: Model robustness improved with the addition of 1.5 years of data. Comparison with RCTs revealed differences <10% of the model-based predictions in almost all trials. Treatment with the highest-ranked (by PHREND®) or the first-or-second-highest ranked DMT led to significantly fewer relapses (p < 0.001 and p < 0.001, respectively) and 3mCDP events (p = 0.007 and p = 0.035, respectively) compared to non-recommended DMTs.
UNASSIGNED: These results further support usefulness of PHREND® in a shared treatment-decision process between physicians and patients.
摘要：
未经证实：随着疾病改善疗法（DMT）的日益普及，复发缓解型多发性硬化症(RRMS)的治疗决策变得复杂.基于现实世界结果的数据驱动算法可以帮助临床医生优化常规实践中疾病活动的控制。
UNASSIGNED：我们先前基于2018年的数据引入了PHREND®（预测医疗与现实世界的神经系统疾病证据）算法，现在在1.5年的临床实践中，其鲁棒性和实用性可预测复发的自由和3个月确认的残疾进展（3mCDP）。
UNASSIGNED：根据模型的C指数和系数的可信区间，研究了季度数据更新对模型稳健性的影响。将模型预测与随机临床试验（RCT）的结果进行比较。通过比较遵循模型建议的患者与选择其他治疗方法的患者的结果来评估临床相关性。
UNASSIGNED：模型稳健性随着1.5年数据的增加而提高。与随机对照试验的比较显示，在几乎所有试验中，基于模型的预测差异<10%。与未推荐的DMT相比,用最高等级(通过PHREND®)或第一或第二最高等级的DMT治疗导致显著更少的复发(分别为p<0.001和p<0.001)和3mCDP事件(分别为p=0.007和p=0.035)。
UNASSIGNED：这些结果进一步支持PHREND®在医生和患者之间共享治疗决策过程中的有用性。
UNASSIGNED：我们先前基于2018年的数据引入了PHREND®（预测医疗与现实世界的神经系统疾病证据）算法，现在在1.5年的临床实践中，其鲁棒性和实用性可预测复发的自由和3个月确认的残疾进展（3mCDP）。
UNASSIGNED：根据模型的C指数和系数的可信区间，研究了季度数据更新对模型稳健性的影响。将模型预测与随机临床试验（RCT）的结果进行比较。通过比较遵循模型建议的患者与选择其他治疗方法的患者的结果来评估临床相关性。
UNASSIGNED：模型稳健性随着1.5年数据的增加而提高。与随机对照试验的比较显示，在几乎所有试验中，基于模型的预测差异<10%。与未推荐的DMT相比,用最高等级(通过PHREND®)或第一或第二最高等级的DMT治疗导致显著更少的复发(分别为p<0.001和p<0.001)和3mCDP事件(分别为p=0.007和p=0.035)。
UNASSIGNED：这些结果进一步支持PHREND®在医生和患者之间共享治疗决策过程中的有用性。
