Predictors of health-related quality of life in musculoskeletal disease patients: a longitudinal analysis

Author:

Madrid-García Alfredo12ORCID,León-Mateos Leticia1,Pato Esperanza3,Jover Juan A3,Fernández-Gutiérrez Benjamín3ORCID,Abasolo Lydia1,Menasalvas Ernestina4,Rodríguez-Rodríguez Luis5ORCID

Affiliation:

1. Grupo de Patología Musculoesquelética, Hospital Clínico San Carlos, IdISSC, Madrid, Spain

2. Escuela Técnica Superior de Ingenieros de Telecomunicación (ETSIT), Universidad Politécnica de Madrid, Madrid, Spain

3. Rheumatology Service, Hospital Clínico San Carlos, IdISSC, Madrid, Spain

4. Centro de Tecnología Biomédica, Universidad Politécnica de Madrid, Parque Científico y Tecnológico de la UPM, Madrid, Spain

5. Grupo de Patología Musculoesquelética, Hospital Clínico San Carlos, c\ Prof. Martin Lagos s/n, Madrid 28040, Spain

Abstract

Introduction: Rheumatic and musculoskeletal diseases (RMDs) have a significant impact on patients’ health-related quality of life (HRQoL) exacerbating disability, reducing independence and work capacity, among others. Predictors’ identification affecting HRQoL could help to place efforts that minimize the deleterious impact of these conditions on patients’ wellbeing. This study evaluates the influence of demographic and clinical predictors on the HRQoL of a cohort of RMD patients, measured using the Rosser classification index (RCI). Methods: We included patients attending the Hospital Clínico San Carlos (HCSC) rheumatology outpatient clinic from 1 April 2007 to 30 November 2017. The primary outcome was the HRQoL assessed in each of the patient’s visits using the RCI. Demographic and clinical variables extracted from a departmental electronic health record (EHR) were used as predictors: RMD diagnoses, treatments, comorbidities, and averaged HRQoL values from previous periods (for this last variable, values were imputed if no information was available). Association between predictors and HRQoL was analyzed using penalized generalized estimating equations (PGEEs). To account for imputation bias, the PGEE model was repeated excluding averaged HRQoL predictors, and common predictors were considered. Discussion: A total of 18,187 outpatients with 95,960 visits were included. From 410 initial predictors, 19 were independently associated with patients’ HRQoL in both PGEE models. Chronic kidney disease (CKD), an episode of prescription of third level analgesics, monoarthritis, and fibromyalgia diagnoses were associated with worse HRQoL. Conversely, the prescription in the previous visit of acid-lowering medication, colchicine, and third level analgesics was associated with better HRQoL. Conclusion: We have identified several diagnoses, treatments, and comorbidities independently associated with HRQoL in a cohort of outpatients attending a rheumatology clinic.

Funder

instituto de salud carlos iii

Publisher

SAGE Publications

Subject

Orthopedics and Sports Medicine,Rheumatology

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