Data Science Institute, Columbia University (C.D.), New York, New York, USA; School of Nursing, University of Rochester (C.D.), Rochester, New York, USA.
School of Nursing, University of Pittsburgh (S.G., A.C., T.K.), Pittsburgh, Pennsylvania, USA.
J Pain Symptom Manage. 2022 Dec;64(6):555-566. doi: 10.1016/j.jpainsymman.2022.08.018. Epub 2022 Sep 9.
Over half of American adults are diagnosed with a chronic condition, with an increasing prevalence being diagnosed with multiple chronic conditions. These adults are at higher risk for having unrelieved, co-occurring symptoms, known as symptom clusters.
To identify symptom phenotypes of patients diagnosed with four common chronic conditions, specifically, cancer, chronic obstructive pulmonary disease, heart failure, and/or type 2 diabetes mellitus, and to understand factors that predict membership in symptomatic phenotypes.
We conducted a retrospective, cross-sectional analysis using participant responses (N=14,127) to All of Us Research Program, a National Institutes of Health biomedical database, survey questions. We performed hierarchical clustering to generate symptom phenotypes of fatigue, emotional distress, and pain and used multinomial regression to determine if demographic, healthcare access and utilization, and health-related variables predict symptom phenotype.
Four phenotypes, one asymptomatic or mildly symptomatic and three highly symptomatic (characterized by severe symptoms, severe pain, and severe emotional distress), were identified. The percentage of participants belonging to the severe symptoms phenotype increased with the number of chronic conditions. Most notably, foregoing or delaying medical care and rating mental health as poor or fair increased the odds of belonging to a highly symptomatic phenotype.
We found meaningful relationships between demographic, healthcare access and utilization, and health-related factors and symptom phenotypes. With the increasing trends of American adults with one or more chronic conditions and a demand to individualize care in the precision health era, it is critical to understand the factors that lead to unrelieved symptoms.
超过一半的美国成年人被诊断患有慢性疾病,且越来越多的人被诊断患有多种慢性疾病。这些成年人有更高的风险出现未缓解的、同时发生的症状,这些症状被称为症状群。
确定患有四种常见慢性疾病(即癌症、慢性阻塞性肺疾病、心力衰竭和/或 2 型糖尿病)的患者的症状表型,并了解预测症状表型的因素。
我们使用美国国立卫生研究院生物医学数据库 All of Us Research Program 中参与者的回答(N=14127)进行了回顾性、横断面分析,该数据库包含调查问题。我们进行了层次聚类以生成疲劳、情绪困扰和疼痛的症状表型,并使用多项回归来确定人口统计学、医疗保健获取和利用以及与健康相关的变量是否预测症状表型。
确定了四种表型,一种无症状或轻度症状,三种高度症状(表现为严重症状、严重疼痛和严重情绪困扰)。患有严重症状表型的参与者比例随着慢性疾病数量的增加而增加。最值得注意的是,放弃或延迟医疗护理以及将心理健康评为差或一般会增加属于高度症状表型的几率。
我们发现人口统计学、医疗保健获取和利用以及与健康相关的因素与症状表型之间存在有意义的关系。随着美国成年人患有一种或多种慢性疾病的趋势不断增加,以及在精准医疗时代个性化护理的需求不断增加,了解导致症状未缓解的因素至关重要。