Multimorbidity frameworks impact the composition of patterns and their associations with patient-reported outcomes...

Objectives: 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. Methods: 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: Cardiovascular disease (CVD), Sexually transmitted diseases, Metabolic/AIDS-related, Mental health/Other, and Cancer. A sixth pattern was identified using Framework-D (Infections/Skin) and Framework-DCI/DCIS (Cardiometabolic). 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. Results: 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 CVD, Cardiometabolic and Mental health/Other patterns. While differences were marginal, Framework-DCI showed slightly stronger relationships between CVD and functional impairment, hospitalisation and physical health compared to Framework-D. Similarly, Framework-DCIS demonstrated somewhat stronger associations between Metabolic/AIDS-related and Mental health/Other patterns with certain outcomes. Conclusions: 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.