Medialis Ltd, 3 Warren Yard, Wolverton Mill, Milton Keynes, MK12 5NW, UK.
Centre for Pharmaceutical Medicine Research, King's College University, London, UK.
BMC Med Res Methodol. 2023 May 20;23(1):121. doi: 10.1186/s12874-023-01947-z.
There is a pressing need to improve the accuracy of rare disease clinical study endpoints. Neutral theory, first described here, can be used to assess the accuracy of endpoints and improve their selection in rare disease clinical studies, reducing the risk of patient misclassification.
Neutral theory was used to assess the accuracy of rare disease clinical study endpoints and the resulting probability of false positive and false negative classifications at different disease prevalence rates. Search strings were extracted from the Orphanet Register of Rare Diseases using a proprietary algorithm to conduct a systematic review of studies published until January 2021. Overall, 11 rare diseases with one disease-specific disease severity scale (133 studies) and 12 rare diseases with more than one disease-specific disease severity scale (483 studies) were included. All indicators from clinical studies were extracted, and Neutral theory was used to calculate their match to disease-specific disease severity scales, which were used as surrogates for the disease phenotype. For those with more than one disease-severity scale, endpoints were compared with the first disease-specific disease severity scale and a composite of all later scales. A Neutrality score of > 1.50 was considered acceptable.
Around half the clinical studies for half the rare diseases with one disease-specific disease severity score (palmoplantar psoriasis, achalasia, systemic lupus erythematosus, systemic sclerosis and Fournier's gangrene) met the threshold for an acceptable match to the disease phenotype, one rare disease (Guillain-Barré syndrome) had one study with an acceptable match, and four diseases (Behcet's syndrome, Creutzfeldt-Jakob disease, atypical hemolytic uremic syndrome and Prader-Willi syndrome) had no studies. Clinical study endpoints in almost half the rare diseases with more than one disease-specific DSS (acromegaly, amyotrophic lateral sclerosis, cystic fibrosis, Fabry disease and juvenile rheumatoid arthritis) were a better match to the composite, while endpoints in the remaining rare diseases (Charcot Marie Tooth disease, Gaucher disease Type I, Huntington's disease, Sjogren's syndrome and Tourette syndrome) were a worse match. Misclassifications varied with increasing disease prevalence.
Neutral theory confirmed that disease-severity measurement needs improvement in rare disease clinical studies, especially for some diseases, and suggested that the potential for accuracy increases as the body of knowledge on a disease increases. Using Neutral theory to benchmark disease-severity measurement in rare disease clinical studies may reduce the risk of misclassification, ensuring that recruitment and treatment effect assessment optimise medicine adoption and benefit patients.
提高罕见病临床研究终点的准确性迫在眉睫。中性理论首次在这里描述，可以用于评估终点的准确性，并在罕见病临床研究中改进其选择，降低患者分类错误的风险。
使用中性理论评估罕见病临床研究终点的准确性，并在不同疾病流行率下评估假阳性和假阴性分类的概率。使用专有的算法从孤儿疾病登记处提取搜索字符串，对截至 2021 年 1 月发表的研究进行系统评价。总共纳入了 11 种罕见病（133 项研究），每种疾病都有一个疾病特异性疾病严重程度量表，12 种罕见病（483 项研究）有多个疾病特异性疾病严重程度量表。提取了所有临床研究的指标，并使用中性理论计算它们与疾病特异性疾病严重程度量表的匹配程度，这些量表被用作疾病表型的替代指标。对于有多个疾病严重程度量表的情况，将终点与第一个疾病特异性疾病严重程度量表和所有后续量表的综合进行比较。中性评分>1.50 被认为是可以接受的。
大约一半的罕见病（掌跖银屑病、贲门失弛缓症、系统性红斑狼疮、系统性硬化症和 Fournier 坏疽）的单一疾病特异性疾病严重程度量表的临床研究达到了与疾病表型匹配的可接受标准，一种罕见病（格林-巴利综合征）有一项研究符合要求，四种疾病（贝切特综合征、克雅氏病、非典型溶血尿毒综合征和普拉德-威利综合征）没有研究符合要求。超过一种疾病特异性 DSS 的近一半罕见病（肢端肥大症、肌萎缩侧索硬化症、囊性纤维化、法布雷病和幼年特发性关节炎）的临床研究终点与综合指标的匹配度更好，而其余罕见病（Charcot-Marie-Tooth 病、戈谢病 I 型、亨廷顿病、干燥综合征和妥瑞氏综合征）的匹配度更差。随着疾病流行率的增加，分类错误也有所变化。
中性理论证实，罕见病临床研究中需要改进疾病严重程度的测量，特别是对于某些疾病，并表明随着对疾病知识的增加，准确性的潜力也会增加。使用中性理论来基准罕见病临床研究中的疾病严重程度测量，可能会降低分类错误的风险，确保招募和治疗效果评估优化药物的应用，并使患者受益。
