Beckwith Martha A, Danis Daniel, Bridges Yasemin, Jacobsen Julius O B, Smedley Damian, Robinson Peter N
The Jackson Laboratory for Genomic Medicine, Farmington, CT.
William Harvey Research Institute, Queen Mary University of London, London, United Kingdom.
Genet Med. 2025 Jan;27(1):101292. doi: 10.1016/j.gim.2024.101292. Epub 2024 Oct 10.
Clinical intuition is commonly incorporated into the differential diagnosis as an assessment of the likelihood of candidate diagnoses based either on the patient population being seen in a specific clinic or on the signs and symptoms of the initial presentation. Algorithms to support diagnostic sequencing in individuals with a suspected rare genetic disease do not yet incorporate intuition and instead assume that each Mendelian disease has an equal pretest probability.
The LIkelihood Ratio Interpretation of Clinical AbnormaLities (LIRICAL) algorithm calculates the likelihood ratio of clinical manifestations represented by Human Phenotype Ontology terms to rank candidate diagnoses. The initial version of LIRICAL assumed an equal pretest probability for each disease in its calculation of the posttest probability (where the test is diagnostic exome or genome sequencing). We introduce Clinical Intuition for Likelihood Ratios (ClintLR), an extension of the LIRICAL algorithm that boosts the pretest probability of groups of related diseases deemed to be more likely.
The average rank of the correct diagnosis in simulations using ClintLR showed a statistically significant improvement over a range of adjustment factors.
ClintLR successfully encodes clinical intuition to improve ranking of rare diseases in diagnostic sequencing. ClintLR is freely available at https://github.com/TheJacksonLaboratory/ClintLR.
临床直觉通常被纳入鉴别诊断，作为基于特定诊所所见患者群体或初始表现的体征和症状对候选诊断可能性的评估。用于支持疑似罕见遗传病个体诊断排序的算法尚未纳入直觉，而是假定每种孟德尔疾病具有相等的检验前概率。
临床异常的似然比解释（LIRICAL）算法计算由人类表型本体术语表示的临床表现的似然比，以对候选诊断进行排序。LIRICAL的初始版本在计算检验后概率（其中检验为诊断性外显子组或基因组测序）时假定每种疾病具有相等的检验前概率。我们引入了似然比的临床直觉（ClintLR），这是LIRICAL算法的扩展，可提高被认为更有可能的相关疾病组的检验前概率。
在一系列调整因素下，使用ClintLR进行模拟时正确诊断的平均排名显示出统计学上的显著改善。
ClintLR成功编码临床直觉以改善诊断排序中罕见病的排名。ClintLR可在https://github.com/TheJacksonLaboratory/ClintLR上免费获取。
