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Semin Arthritis Rheum. 2015 Oct;45(2):167-73. doi: 10.1016/j.semarthrit.2015.06.010. Epub 2015 Jun 19.

Development of a multimorbidity index: Impact on quality of life using a rheumatoid arthritis cohort.

Author information

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.

KEYWORDS:

Comorbidity; Health-related quality of life; Multimorbidity; Rheumatoid arthritis

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