Center for Health Measurement, Department of Population Health Sciences, Duke University School of Medicine, 215 Morris Street; Suite 230, DUMC 104023, Durham, NC, 27701, USA.
Department of Psychiatry and Behavioral Sciences, Duke University School of Medicine, Durham, NC, USA.
Qual Life Res. 2023 Nov;32(11):3171-3183. doi: 10.1007/s11136-023-03463-5. Epub 2023 Jun 20.
To assess health-related quality of life (HRQOL) among adolescents and young adults (AYAs) with chronic conditions.
AYAs (N = 872) aged 14-20 years completed NIH's Patient-Reported Outcomes Measurement Information System (PROMIS) measures of physical function, pain interference, fatigue, social health, depression, anxiety, and anger. Latent profile analysis (LPA) was used to group AYAs into HRQOL profiles using PROMIS T-scores. The optimal number of profiles was determined by model fit statistics, likelihood ratio test, and entropy. Multinomial logistic regression models were used to examine how LPA's HRQOL profile membership was associated with patient demographic and chronic conditions. The model prediction accuracy on profile membership was evaluated using Huberty's I index with a threshold of 0.35 for good effect.
A 4-profile LPA model was selected. A total of 161 (18.5%), 256 (29.4%), 364 (41.7%), and 91 (10.4%) AYAs were classified into Minimal, Mild, Moderate, and Severe HRQOL Impact profiles. AYAs in each profile had distinctive mean scores with over a half standard deviation (5-points in PROMIS T-scores) of difference between profiles across most HRQOL domains. AYAs who were female or had conditions such as mental health condition, hypertension, and self-reported chronic pain were more likely to be in the Severe HRQOL Impact profile. The Huberty's I index was 0.36.
Approximately half of AYAs with a chronic condition experience moderate to severe HRQOL impact. The availability of risk prediction models for HRQOL impact will help to identify AYAs who are in greatest need of closer clinical care follow-up.
评估患有慢性病的青少年和年轻人（AYAs）的健康相关生活质量（HRQOL）。
年龄在 14-20 岁的 AYAs（N=872）完成了 NIH 的患者报告的结果测量信息系统（PROMIS）对身体功能、疼痛干扰、疲劳、社会健康、抑郁、焦虑和愤怒的测量。使用潜在剖面分析（LPA）根据 PROMIS T 分数将 AYAs 分为 HRQOL 剖面。通过模型拟合统计、似然比检验和熵来确定最佳剖面数。使用多项逻辑回归模型检查 LPA 的 HRQOL 剖面成员资格如何与患者的人口统计学和慢性疾病相关。使用 Huberty 的 I 指数评估模型对剖面成员资格的预测准确性，阈值为 0.35 表示效果良好。
选择了一个 4 剖面 LPA 模型。共有 161 名（18.5%）、256 名（29.4%）、364 名（41.7%）和 91 名（10.4%）AYAs 被分类为最小、轻度、中度和严重 HRQOL 影响剖面。在大多数 HRQOL 领域，每个剖面的 AYAs 都有独特的平均分数，在剖面之间有超过一半的标准差（PROMIS T 分数为 5 分）的差异。女性或患有心理健康状况、高血压和自我报告的慢性疼痛等疾病的 AYAs 更有可能处于严重 HRQOL 影响剖面。Huberty 的 I 指数为 0.36。
大约一半患有慢性病的 AYAs 经历中度至严重的 HRQOL 影响。用于 HRQOL 影响的风险预测模型的可用性将有助于识别最需要密切临床护理随访的 AYAs。
