Rush Kathy L, Seaton Cherisse L, O'Connor Brian P, Andrade Jason G, Loewen Peter, Corman Kendra, Burton Lindsay, Smith Mindy A, Moroz Lana
School of Nursing, University of British Columbia-Okanagan, Kelowna, British Columbia, Canada.
Department of Psychology, University of British Columbia-Okanagan, Kelowna, British Columbia, Canada.
CJC Open. 2023 Aug 20;5(11):833-845. doi: 10.1016/j.cjco.2023.08.005. eCollection 2023 Nov.
Examining characteristics of patients with atrial fibrillation (AF) has the potential to help in identifying groups of patients who might benefit from different management approaches.
Secondary analysis of online survey data was combined with clinic referral data abstraction from 196 patients with AF attending an AF specialty clinic. Cluster analyses were performed to identify distinct, homogeneous clusters of AF patients defined by 11 relevant variables: CHADS-VASc score, age, AF symptoms, overall health, mental health, AF knowledge, perceived stress, household and recreation activity, overall AF quality of life, and AF symptom treatment satisfaction. Follow-up analyses examined differences between the cluster groups in additional clinical variables.
Evidence emerged for both 2- and 4-cluster solutions. The 2-cluster solution involved a contrast between patients who were doing well on all variables (n = 129; 66%) vs those doing less well (n = 67; 34%). The 4-cluster solution provided a closer-up view of the data, showing that the group doing less well was split into 3 meaningfully different subgroups of patients who were managing in different ways. The final 4 clusters produced were as follows: (i) doing well; (ii) stressed and discontented; (iii) struggling and dissatisfied; and (iv) satisfied and complacent.
Patients with AF can be accurately classified into distinct, natural groupings that vary in clinically important ways. Among the patients who were not managing well with AF, we found 3 distinct subgroups of patients who may benefit from tailored approaches to AF management and support. The tailoring of treatment approaches to specific personal and/or behavioural patterns, alongside clinical patterns, holds potential to improve patient outcomes (eg, treatment satisfaction).
研究心房颤动（AF）患者的特征有助于识别可能从不同管理方法中受益的患者群体。
对在线调查数据进行二次分析，并结合从196名就诊于AF专科诊所的AF患者的临床转诊数据摘要。进行聚类分析以识别由11个相关变量定义的不同的、同质的AF患者聚类：CHADS-VASc评分、年龄、AF症状、总体健康状况、心理健康状况、AF知识、感知压力、家庭和娱乐活动、总体AF生活质量以及AF症状治疗满意度。后续分析检查了聚类组在其他临床变量上的差异。
出现了支持2聚类和4聚类解决方案的证据。2聚类解决方案涉及在所有变量上表现良好的患者（n = 129；66%）与表现较差的患者（n = 67；34%）之间的对比。4聚类解决方案提供了对数据更详细的视图，表明表现较差的组被分为3个在管理方式上有显著差异的有意义的亚组。最终产生的4个聚类如下：（i）表现良好；（ii）压力大且不满；（iii）挣扎且不满意；（iv）满意且自满。
AF患者可以被准确地分类为不同的、自然的分组，这些分组在临床上具有重要差异。在AF管理不善的患者中，我们发现了3个不同的亚组患者，他们可能从针对AF管理和支持的定制方法中受益。根据特定的个人和/或行为模式以及临床模式量身定制治疗方法，有可能改善患者的治疗效果（如治疗满意度）。
