Sukumaran Luxsena, Winston Alan, Anderson Jane, Boffito Marta, Post Frank A, Sachikonye Memory, Mallon Patrick W G, Waters Laura, Vera Jaime, Burns Fiona, Sabin Caroline A
Institute for Global Health, University College London, London, UK.
National Institute for Health and Care Research (NIHR) Health Protection Research Unit (HPRU) in Blood-borne and Sexually Transmitted Infections at University College London, London, UK.
J Multimorb Comorb. 2025 Apr 4;15:26335565251331732. doi: 10.1177/26335565251331732. eCollection 2025 Jan-Dec.
There is no consensus definition for multimorbidity. We explored how different frameworks affect multimorbidity patterns and their associations with patient-reported outcomes using the prospective, observational Pharmacokinetic and clinical Observations in PeoPle over fiftY (POPPY) Study. Sixty-four conditions were classified into three frameworks: Framework-D (diseases), Framework-DCI (diseases and clinical indicators) and Framework-DCIS (diseases, clinical indicators and symptoms). Principal component analysis (PCA) identified five comparable patterns: , , , , and . A sixth pattern was identified using Framework-D ( and Framework-DCI/DCIS (. Using PCA loadings, burden z-scores were calculated for each individual/pattern, and their associations with functional impairment (Lawton Instrumental Activities of Daily Living <8), hospitalisation and SF-36 physical and mental health scores were assessed using logistic or linear regression. The analyses included 1073 people with HIV (median [interquartile range; IQR] age 52 [47 - 59] years; 85% male; 97% on ART). Clinical indicators and symptoms were correlated with the , and patterns. While differences were marginal, Framework-DCI showed slightly stronger relationships between and functional impairment, hospitalisation and physical health compared to Framework-D. Similarly, Framework-DCIS demonstrated somewhat stronger associations between and patterns with certain outcomes. The inclusion of clinical indicators and symptoms were associated with some changes in the strength of associations between certain multimorbidity patterns and outcomes. Our findings suggest that their inclusion in multimorbidity frameworks should be guided by the specific research context and question, rather than solely by effect size on patient-important outcomes.
对于共病，目前尚无共识性定义。我们利用针对50岁以上人群的前瞻性观察性药代动力学和临床观察（POPPY）研究，探讨了不同框架如何影响共病模式及其与患者报告结局的关联。64种疾病被分为三个框架：框架-D（疾病）、框架-DCI（疾病和临床指标）和框架-DCIS（疾病、临床指标和症状）。主成分分析（PCA）确定了五种可比模式： 、 、 、 和 。使用框架-D（ 和框架-DCI/DCIS（ ）确定了第六种模式。利用PCA负荷，为每个个体/模式计算负担z分数，并使用逻辑回归或线性回归评估它们与功能损害（Lawton日常生活能力量表<8）、住院以及SF-36身心健康评分的关联。分析纳入了1073名艾滋病毒感染者（年龄中位数[四分位间距；IQR]为52[47 - 59]岁；85%为男性；97%接受抗逆转录病毒治疗）。临床指标和症状与 、 和 模式相关。虽然差异不大，但与框架-D相比，框架-DCI显示出 模式与功能损害、住院和身体健康之间的关系略强。同样，框架-DCIS显示 和 模式与某些结局之间的关联更强。临床指标和症状的纳入与某些共病模式和结局之间关联强度的一些变化有关。我们的研究结果表明，将它们纳入共病框架应以具体的研究背景和问题为指导，而不是仅仅依据对患者重要结局的效应大小。
