Department of Bioelectrics and Biomedical Engineering, School of Advanced Technologies in Medicine, Isfahan University of Medical Sciences, Isfahan, Iran.
Department of Bioinformatics, School of Advanced Technologies in Medicine, Isfahan University of Medical Sciences, Hezar Jerib Street, Po Box 8174673461, Isfahan, Iran.
BMC Med Inform Decis Mak. 2023 Aug 26;23(1):167. doi: 10.1186/s12911-023-02270-9.
Functional gastrointestinal disorders (FGIDs), as a group of syndromes with no identified structural or pathophysiological biomarkers, are currently classified by Rome criteria based on gastrointestinal symptoms (GI). However, the high overlap among FGIDs in patients makes treatment and identifying underlying mechanisms challenging. Furthermore, disregarding psychological factors in the current classification, despite their approved relationship with GI symptoms, underlines the necessity of more investigation into grouping FGID patients. We aimed to provide more homogenous and well-separated clusters based on both GI and psychological characteristics for patients with FGIDs using an unsupervised machine learning algorithm.
Based on a cross-sectional study, 3765 (79%) patients with at least one FGID were included in the current study. In the first step, the clustering utilizing a machine learning algorithm was merely executed based on GI symptoms. In the second step, considering the previous step's results and focusing on the clusters with a diverse combination of GI symptoms, the clustering was re-conducted based on both GI symptoms and psychological factors.
The first phase clustering of all participants based on GI symptoms resulted in the formation of pure and non-pure clusters. Pure clusters exactly illustrated the properties of most pure Rome syndromes. Re-clustering the members of the non-pure clusters based on GI and psychological factors (i.e., the second clustering step) resulted in eight new clusters, indicating the dominance of multiple factors but well-discriminated from other clusters. The results of the second step especially highlight the impact of psychological factors in grouping FGIDs.
In the current study, the existence of Rome disorders, which were previously defined by expert opinion-based consensus, was approved, and, eight new clusters with multiple dominant symptoms based on GI and psychological factors were also introduced. The more homogeneous clusters of patients could lead to the design of more precise clinical experiments and further targeted patient care.
功能性胃肠病（FGIDs）作为一组没有确定结构或病理生理生物标志物的综合征，目前根据罗马标准基于胃肠道症状（GI）进行分类。然而，患者中 FGIDs 之间的高度重叠使得治疗和确定潜在机制具有挑战性。此外，尽管心理因素已被证实与 GI 症状有关，但在当前分类中忽略这些因素，突显了对 FGID 患者进行更深入分组研究的必要性。我们旨在使用无监督机器学习算法，根据 GI 和心理特征为 FGID 患者提供更同质和更好分离的聚类。
基于横断面研究，纳入了 3765 名（79%）至少患有一种 FGID 的患者。在第一步中，仅基于 GI 症状使用机器学习算法进行聚类。在第二步中，考虑到前一步的结果，并侧重于 GI 症状组合多样化的聚类，根据 GI 症状和心理因素重新进行聚类。
基于 GI 症状对所有参与者进行的第一阶段聚类导致形成了纯聚类和非纯聚类。纯聚类准确地说明了大多数纯罗马综合征的特性。根据 GI 和心理因素（即第二步聚类）重新对非纯聚类的成员进行聚类，产生了八个新的聚类，表明多种因素的主导地位，但与其他聚类明显区分。第二步的结果尤其突出了心理因素在 FGIDs 分组中的作用。
在当前研究中，证实了以前基于专家意见共识定义的罗马疾病的存在，并介绍了八个新的基于 GI 和心理因素的具有多种主导症状的聚类。患者更同质的聚类可以导致更精确的临床试验设计和更有针对性的患者护理。
