Prognostic models in multiple sclerosis: progress and challenges in clinical integration.

Joachim Havla, Kelly Reeve, Begum Irmak On, Ulrich Mansmann, Ulrike Held
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引用次数: 0

Abstract

As a chronic inflammatory disease of the central nervous system, multiple sclerosis (MS) is of great individual health and socio-economic significance. To date, there is no prognostic model that is used in routine clinical care to predict the very heterogeneous course of the disease. Despite several research groups working on different prognostic models using traditional statistics, machine learning and/or artificial intelligence approaches, the use of published models in clinical decision making is limited because of poor model performance, lack of transferability and/or lack of validated models. To provide a systematic overview, we conducted a "Cochrane review" that assessed 75 published prediction models using relevant checklists (CHARMS, PROBAST, TRIPOD). We have summarized the relevant points from this analysis here so that the use of prognostic models for therapy decisions in clinical routine can be successful in the future.

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多发性硬化症的预后模型:临床整合的进展与挑战。
多发性硬化症(MS)是中枢神经系统的一种慢性炎症性疾病,对个人健康和社会经济意义重大。迄今为止,还没有一个可用于常规临床治疗的预后模型来预测该疾病的不同病程。尽管有多个研究小组使用传统统计学、机器学习和/或人工智能方法研究不同的预后模型,但由于模型性能不佳、缺乏可移植性和/或缺乏经过验证的模型,已发表的模型在临床决策中的应用受到了限制。为了提供系统性概述,我们进行了一项 "Cochrane 回顾",使用相关核对表(CHARMS、PROBAST、TRIPOD)评估了 75 个已发表的预测模型。我们在此总结了这一分析的相关要点,以便今后在临床常规治疗决策中成功使用预后模型。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
7.40
自引率
0.00%
发文量
0
审稿时长
14 weeks
期刊最新文献
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