@文章{info:doi/10.2196/25124，作者="Ferreira-Santos, Daniela和Rodrigues, Pedro Pereira"，标题="通过疾病表型筛选增强阻塞性睡眠呼吸暂停诊断:算法开发和验证"，期刊="JMIR Med Inform"，年="2021"，月="6"，日="22"，卷="9"，数="6"，页="e25124"，关键词="阻塞性睡眠呼吸暂停;筛选;风险因素;表型;背景:美国睡眠医学会指南建议，临床预测算法可用于阻塞性睡眠呼吸暂停(OSA)患者，而无需取代多导睡眠描记术，这是金标准。目的:本研究旨在根据OSA的标准定义(呼吸暂停-低通气指数+症状)，根据风险和诊断因素，开发OSA诊断的临床决策支持系统，识别出高前测概率的个体。方法:从接受多导睡眠描记术的患者队列中提取47个预测变量。总共14个单变量显著的变量被用来计算OSA患者之间的距离，定义一个层次聚类结构，从中推导和描述患者的表型。随后计算OSA表型风险个体的亲和度，并将聚类成员作为贝叶斯网络分类器(模型B)的附加预测因子。结果:共纳入318名OSA风险患者，其中207(65.1{\%})人被诊断为OSA(111, 53.6{\%}，轻度;50, 24.2{\%}中等; and 46, 22.2{\%} with severe). On the basis of predictive variables, 3 phenotypes were defined (74/207, 35.7{\%} low; 104/207, 50.2{\%} medium; and 29/207, 14.1{\%} high), with an increasing prevalence of symptoms and comorbidities, the latter describing older and obese patients, and a substantial increase in some comorbidities, suggesting their beneficial use as combined predictors (median apnea-hypopnea indices of 10, 14, and 31, respectively). Cross-validation results demonstrated that the inclusion of OSA phenotypes as an adjusting predictor in a Bayesian classifier improved screening specificity (26{\%}, 95{\%} CI 24-29, to 38{\%}, 95{\%} CI 35-40) while maintaining a high sensitivity (93{\%}, 95{\%} CI 91-95), with model B doubling the diagnostic model effectiveness (diagnostic odds ratio of 8.14). Conclusions: Defined OSA phenotypes are a sensitive tool that enhances our understanding of the disease and allows the derivation of a predictive algorithm that can clearly outperform symptom-based guideline recommendations as a rule-out approach for screening. ", issn="2291-9694", doi="10.2196/25124", url="https://medinform.www.mybigtv.com/2021/6/e25124", url="https://doi.org/10.2196/25124", url="http://www.ncbi.nlm.nih.gov/pubmed/34156340" }