Helga Radner1, Kazuki Yoshida2, Maria Dahl Mjaavatten3, Daniel Aletaha4, Michelle Frits5, Bing Lu5, Christine Iannaccone5, Nancy Shadick5, Michael Weinblatt5, Ihsane Hmamouchi6, M Dougados7, Josef S Smolen4, Daniel H Solomon5. 1. Division of Rheumatology, Immunology and Allergy, Brigham and Women's Hospital, 75 Francis St, Boston MA, 02115; Division of Rheumatology, Department of Internal Medicine III, Medical University Vienna, Vienna, Austria. Electronic address: hradner@partners.org. 2. Division of Rheumatology, Immunology and Allergy, Brigham and Women's Hospital, 75 Francis St, Boston MA, 02115; Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, MA. 3. Division of Rheumatology, Immunology and Allergy, Brigham and Women's Hospital, 75 Francis St, Boston MA, 02115; Department of Rheumatology, Diakonhjemmet Hospital, Oslo, Norway. 4. Division of Rheumatology, Department of Internal Medicine III, Medical University Vienna, Vienna, Austria. 5. Division of Rheumatology, Immunology and Allergy, Brigham and Women's Hospital, 75 Francis St, Boston MA, 02115. 6. Faculty of Medicine, Laboratory of Biostatistics, Clinical Research and Epidemiology (LBRCE), Mohammed V Souissi University, Rabat, Morocco. 7. Department of Rheumatology, Paris Descartes University-Hôpital Cochin, Assistance Publique-Hôpitaux de Paris-EULAR center of excellence, INSERM (U1153) Clinical epidemiology and biostatistics, PRES Sorbonne Paris-Cité, Paris, France.
Abstract
OBJECTIVE: To develop a multimorbidity index (MMI) based on health-related quality of life (HRQol). METHODS: The index was developed in an observational RA cohort. In all, 40 morbidities recommended as core were identified using ICD-9 codes. MMIs of two types were calculated: one by enumerating morbidities (MMI.count) and the other by weighting morbidities based on their association with HRQol as assessed by EQ-5D in multiple linear regression analysis (using β-coefficients; MMI.weight). MMIs were compared to the Charlson comorbidity index (CCI) and externally validated in an international RA cohort (COMORA Study). RESULTS: In all, 544 out of 876 patients were multimorbid. MMI.count was in the range 1-16 (median = 2) and MMI.weight in the range 0-38 (median = 1). Both indices were more strongly associated with EQ-5D than CCI (Spearman: MMI.count = -0.20, MMI.weight = -0.26, and CCI = -0.10; p < 0.01). R(2) obtained by linear regression using EQ-5D as a dependent variable and various indices as independent variables, adjusted for age and gender, was the highest for MMI (R(2): MMI.count = 0.05, MMI.weight = 0.11, and CCI = 0.02). When accounting for clinical disease activity index (CDAI) R(2) increased: MMI.count = 0.18, MMI.weight = 0.22, and CCI = 0.17, still showing higher values of MMI compared with CCI. External validation in different RA cohorts (COMORA, n = 3864) showed good performance of both indices (linear regression including age, gender, and disease activity R(2) = 0.30 for both MMIs). CONCLUSION: In our cohort, MMI based on EQ-5D performed better than did CCI. Findings were reproducible in another large RA cohort. Not much improvement was gained by weighting; therefore a simple counted index could be useful to control for the effect of multimorbidity on patient's overall well-being.
OBJECTIVE: To develop a multimorbidity index (MMI) based on health-related quality of life (HRQol). METHODS: The index was developed in an observational RA cohort. In all, 40 morbidities recommended as core were identified using ICD-9 codes. MMIs of two types were calculated: one by enumerating morbidities (MMI.count) and the other by weighting morbidities based on their association with HRQol as assessed by EQ-5D in multiple linear regression analysis (using β-coefficients; MMI.weight). MMIs were compared to the Charlson comorbidity index (CCI) and externally validated in an international RA cohort (COMORA Study). RESULTS: In all, 544 out of 876 patients were multimorbid. MMI.count was in the range 1-16 (median = 2) and MMI.weight in the range 0-38 (median = 1). Both indices were more strongly associated with EQ-5D than CCI (Spearman: MMI.count = -0.20, MMI.weight = -0.26, and CCI = -0.10; p < 0.01). R(2) obtained by linear regression using EQ-5D as a dependent variable and various indices as independent variables, adjusted for age and gender, was the highest for MMI (R(2): MMI.count = 0.05, MMI.weight = 0.11, and CCI = 0.02). When accounting for clinical disease activity index (CDAI) R(2) increased: MMI.count = 0.18, MMI.weight = 0.22, and CCI = 0.17, still showing higher values of MMI compared with CCI. External validation in different RA cohorts (COMORA, n = 3864) showed good performance of both indices (linear regression including age, gender, and disease activity R(2) = 0.30 for both MMIs). CONCLUSION: In our cohort, MMI based on EQ-5D performed better than did CCI. Findings were reproducible in another large RA cohort. Not much improvement was gained by weighting; therefore a simple counted index could be useful to control for the effect of multimorbidity on patient's overall well-being.
Authors: Sizheng Steven Zhao; Helga Radner; Stefan Siebert; Stephen J Duffield; Daniel Thong; David M Hughes; Robert J Moots; Daniel H Solomon; Nicola J Goodson Journal: Rheumatology (Oxford) Date: 2019-10-01 Impact factor: 7.580
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