Centre for Neuroscience, Surgery and Trauma, Blizard Institute, Wingate Institute of Neurogastroenterology, Barts and the London School of Medicine & Dentistry, Queen Mary University of London, London, United Kingdom.
Department of Radiology, University College London Hospital NHS Foundation Trust, London, United Kingdom.
Am J Gastroenterol. 2021 Jan 1;116(1):142-151. doi: 10.14309/ajg.0000000000000816.
Chronic constipation is classified into 2 main syndromes, irritable bowel syndrome with constipation (IBS-C) and functional constipation (FC), on the assumption that they differ along multiple clinical characteristics and are plausibly of distinct pathophysiology. Our aim was to test this assumption by applying machine learning to a large prospective cohort of comprehensively phenotyped patients with constipation.
Demographics, validated symptom and quality of life questionnaires, clinical examination findings, stool transit, and diagnosis were collected in 768 patients with chronic constipation from a tertiary center. We used machine learning to compare the accuracy of diagnostic models for IBS-C and FC based on single differentiating features such as abdominal pain (a "unisymptomatic" model) vs multiple features encompassing a range of symptoms, examination findings and investigations (a "syndromic" model) to assess the grounds for the syndromic segregation of IBS-C and FC in a statistically formalized way.
Unisymptomatic models of abdominal pain distinguished between IBS-C and FC cohorts near perfectly (area under the curve 0.97). Syndromic models did not significantly increase diagnostic accuracy (P > 0.15). Furthermore, syndromic models from which abdominal pain was omitted performed at chance-level (area under the curve 0.56). Statistical clustering of clinical characteristics showed no structure relatable to diagnosis, but a syndromic segregation of 18 features differentiating patients by impact of constipation on daily life.
IBS-C and FC differ only about the presence of abdominal pain, arguably a self-fulfilling difference given that abdominal pain inherently distinguishes the 2 in current diagnostic criteria. This suggests that they are not distinct syndromes but a single syndrome varying along one clinical dimension. An alternative syndromic segregation is identified, which needs evaluation in community-based cohorts. These results have implications for patient recruitment into clinical trials, future disease classifications, and management guidelines.
慢性便秘分为 2 种主要综合征，即伴有便秘的肠易激综合征（IBS-C）和功能性便秘（FC），假设它们在多个临床特征上存在差异，并且可能具有不同的病理生理学机制。我们的目的是通过应用机器学习对一组来自三级中心的大量经过全面表型分析的便秘患者进行研究，来验证这一假设。
在来自三级中心的 768 例慢性便秘患者中收集了人口统计学资料、经过验证的症状和生活质量问卷、临床检查结果、粪便转运以及诊断等信息。我们使用机器学习来比较基于单一鉴别特征（如腹痛，即“单一症状”模型）与包含一系列症状、检查结果和检查的多个特征（即“综合征”模型）的 IBS-C 和 FC 诊断模型的准确性，以从统计学上正式评估 IBS-C 和 FC 的综合征分离的依据。
腹痛的单一症状模型几乎可以完美地区分 IBS-C 和 FC 队列（曲线下面积 0.97）。综合征模型并未显著提高诊断准确性（P > 0.15）。此外，删除腹痛后的综合征模型表现为机会水平（曲线下面积 0.56）。临床特征的统计聚类没有显示出与诊断相关的结构，但有 18 个特征的综合征分离可以根据便秘对日常生活的影响来区分患者。
IBS-C 和 FC 仅在腹痛的存在方面存在差异，可以说由于腹痛本身将这两种疾病在当前的诊断标准中区分开来，这是一种自我实现的差异。这表明它们不是不同的综合征，而是一种单一的综合征，沿着一个临床维度变化。还确定了另一种综合征分离，需要在社区为基础的队列中进行评估。这些结果对临床试验的患者招募、未来的疾病分类和管理指南都具有影响。
